Performance evaluation of a discovery and scheduling protocol for multihop ad hoc mobile grids

ISSN 0104-6500Journal of the Brazilian Computer Society, 2009; 15(4):15-29.
*e-mail: atagomes@lncc.br
Performance evaluation of a discovery and scheduling
protocol for multihop ad hoc mobile grids
Antônio Tadeu Azevedo Gomes1
*, Artur Ziviani1
, Luciana dos Santos Lima2
, Markus Endler3
1
National Laboratory for Scientific Computing – LNCC,
Av. Getúlio Vargas, 333, 25651-075, Petrópolis, RJ, Brazil
2
Nokia Siemens Networks (NSN)
Av. das Américas, 3434 - Bloco 7- 3o.
andar, 22640-102, Rio de Janeiro, RJ, Brazil
3
Pontifical Catholic University of Rio de Janeiro (PUC-Rio)
Rua Marquês de São Vicente, 225, 22453-900, Rio de Janeiro, RJ, Brazil
Received: July 10, 2009; Accepted: December 3, 2009
Abstract: Despite the many research efforts addressing the integration of mobile nodes into grids, only a few of them have
considered the establishment of mobile grids over wireless ad hoc networks (hereafter, mobile ad hoc grids). Clearly, such grids
need specialized resource discovery and scheduling mechanisms. To the best of our knowledge, though, the research on
these mechanisms for mobile ad hoc grids is still preliminary. Besides, and more importantly, it has approached discovery
and scheduling as separate mechanisms, which, we argue, is not suitable for mobile ad hoc grids. In this paper, we propose
the integration of resource discovery and scheduling for mobile ad hoc grids into a single protocol called DICHOTOMY
(DIscovery and sCHeduling prOTOcol for MobilitY). This protocol allows computational tasks to be distributed appropriately
in a mobile ad hoc grid, while mitigating the overhead of discovery messages exchanged among the nodes. Our experiments
show that the protocol: (i) does proper scheduling, allowing an efficient load balancing among the nodes and helping with
lowering the average completion time of tasks; (ii) keeps the discovery efficiency at acceptable levels in mobility scenarios and
(iii) scales very well with respect to an increasing number of nodes, both in the total amount of energy savings due to packet
transmissions and the distribution of such savings among the nodes.
Keywords: mobile grids, resource management, self-organizing networks.
1. Introduction
There has been an increasing amount of research over the
past few years on wireless grids36
. These extend traditional
grids2
by integrating mobile devices in a wireless infrastruc-
tured network as either resource consumers or providers in
the computing infrastructure. Only a few efforts31,45
, however,
have addressed the more challenging issue of dynamically
establishing spontaneous, purely mobile ad hoc grids i.e.
grids established over mobile ad hoc networks, and even
these have achieved only preliminary results. Further devel-
opment in mobile ad hoc grids can be justified by some
applications that demand high computational power but at
the same time have to be used at places or conditions where
network infrastructure may be or may suddenly become
unavailable21
. In particular, we focus on application scenarios
in which the mobility of devices is constrained to pedestrian
(walking) speeds. Examples of such scenarios include:
• Emergency response systems for disaster handling and
crisis management situations. For instance, consider
a large rescue and medical team working in a natural
disaster scenario, such as the one caused by the Indian
Ocean tsunami in Dec. 2004 or the hurricane Katrina
that hit New Orleans in Aug. 2005. In such scenarios,
the seamless integration of computational resources
from on-site mobile nodes can be crucial for rapidly
achieving advanced forms of collaborative work, as
for example to collect and automatically process infor-
mation about groups of injured people (e.g. for triage)
and thus better allocate rescue teams and medical
resources13
. In this context, mobile ad hoc grids can
be regarded as specializations of the more general
concept of hastily formed networks9
.
• Field research systems dynamically deployed on isolated
areas. For instance, consider a team of hydrogeolo-
gists throughout a large semi-arid region, such as the
Northeastern Brazil. Such a team can exchange data
about underground water resources (e.g. hydraulic
head) being collected by their mobile nodes, and
may use the computational resources of such nodes
for in-loco, preliminary numerical analysis on the
collected data, so as to simulate and predict some
aquifer condition of interest (e.g. the effect of irriga-
tion developments).
Both aforementioned application scenarios demand the
formation of multihop mobile ad hoc networks (hereafter,

Journal of the Brazilian Computer Society16 Gomes ATA et al.
MANETs). MANETs allow mobile nodes to self-organize
into arbitrary and temporary topologies that expand the
basic communication range of–and consequently the service
offering to these nodes. In the case of mobile ad hoc grids, the
basic service provided by the nodes is the capability to allo-
cate computational resources to execute tasks. The expanded
range offered by MANETs permits that more nodes share
computational resources, but for this to be feasible special-
ized resource discovery and scheduling mechanisms are
needed. In the context of this paper, resource discovery
comprises gathering information about resources that mobile
nodes make available in the MANET, whereas resource
scheduling comprises selecting the most appropriate nodes
(from the point of view of resource provisioning) to execute
a particular set of tasks. We highlight that our focus in this
paper is on the resource discovery and the initial scheduling
of tasks to declared available resources. Hence, we do not
consider an eventual unavailability of a resource after it has
been discovered, selected, and is already running a submitted
task. Dealing with this particular issue is actually related to
techniques such as task migration24,39
, redundant submis-
sions for increased reliability30
, and so on. Such techniques
constitute another line of work on their own and are thus out
of scope of this paper.
In this paper, we propose the integration of resource
discovery and scheduling for mobile ad hoc grids into a single
protocol called DICHOTOMY (DIscovery and sCHeduling
prOTOcol for MobilitY). This protocol allows the computa-
tional tasks that comprise an application to be distributed
among the most resourceful nodes in a MANET. Such nodes
are selected automatically by the protocol, based on some
suitability criteria defined by the inquiring application. The
suitability of a node is determined in terms of the resources
the application is interested in and the relative importance
among them from the viewpoint of the application. By
always selecting the most resourceful nodes and making
advance reservations of the resources, our proposed protocol
provides an implicit scheduling mechanism that balances the
load among resource providers in the mobile ad hoc grid. For
computationally-intensive applications, such balance also
helps with lowering the average completion time of tasks in
the mobile ad hoc grid.
The DICHOTOMY protocol works in a peer-to-peer
fashion, regardless of underlying routing protocols. The
protocol can be classified as a purely query-based discovery
protocol–requests are broadcast over the network on demand
and providers reply to these requests accordingly. Query-
based protocols are known to cause waste of resources if
consumers naively broadcast requests by flooding (also
referred to as the broadcast storm problem37
) or if providers
naively reply to such requests (a.k.a. the reply implosion
problem11,8
). To mitigate broadcast storms, a simple mecha-
nism that limits the range of flooding is used. To avoid reply
implosions, an in-network filtering algorithm is employed,
which allows the more suitable replies to suppress unneces-
sary replies from other nodes alongside the paths used for
forwarding the more suitable replies back to the inquiring
node.
In our previous publications29,16,15
we gave an overview
of the architecture in which the DICHOTOMY protocol is
employed and of preliminary versions of the protocol and its
mechanisms. The present paper builds upon these, presenting
an extended version of the work that focuses specially on the
details of the protocol implementation and its performance
evaluation. In particular, besides providing further details
of the protocol implementation and design choices, we also
present a novel performance evaluation study that thor-
oughly analyzes the DICHOTOMY protocol covering key
aspects such as scheduling assessment, discovery efficiency
under mobility, and scalability analysis. Our prototypical
implementation–deployed both in an experimental ad hoc
grid testbed and in the NCTUns simulation and emulation
platform46
was focused on showing the feasibility of our
approach under operating conditions. We also devised a
simpler simulation model over the ns-2 simulator33
, to eval-
uate the scalability of our approach.
The remainder of the paper is structured as follows.
Section 2 provides some background on mobile ad hoc grids.
Section 3 introduces the DICHOTOMY protocol as well as
its main features and mechanisms. We present some relevant
implementation details in Section 4. We evaluate the perform-
ance of our proposed protocol in Section 5. Section 6 discusses
related work in discovery and scheduling in wireless and
mobile ad hoc grids. Finally, in Section 7 we summarize the
contributions of our proposed protocol and discuss possible
future work.
2. Background
In this section, we discuss why the discovery and sched-
uling mechanisms have to be different for MANETs and
present the basic ideas behind our proposed DICHOTOMY
protocol that impact discovery and scheduling procedures.
We also introduce the MoGrid architecture29
, our underlying
mobile ad hoc grid middleware.
2.1. Resource discovery and scheduling in mobile
ad hoc grids
To the best of our knowledge, the research on combined
resource discovery and scheduling for mobile ad hoc grids
is still rather preliminary. The mechanisms for resource
discovery and scheduling in such grids must be fundamen-
tally different from those used in wired grids as the former
are much more sensitive to network behavior than the latter.
This is due to the very dynamic nature of MANETs for
instance, nodes may move, or the QoS properties of the wire-
less medium can vary over short periods of time. In MANETs,
there are two ways for devices to exchange resource informa-
tion14
: queries and announcements. Queries involve some form
of devices sending requests throughout the network and
making the devices providing the required resource infor-
mation reply to these requests. Announcements permit that

17Performance evaluation of a discovery and scheduling protocol for multihop ad hoc mobile grids2009; 15(4)
devices advertise their provided resources in the network
to interested devices. Most MANET applications actually
employ a combination of the two techniques. The scope of
diffusion of queries and announcements as well as the perio-
dicity and caching policies of announcements are important
parameters that determine the efficiency and accuracy of
resource discovery in such networks.
In the particular case of mobile ad hoc grids, announce-
ments are inadequate for offering resource information.
Besides the declaration of the availability of a resource
becoming time-sensitive due to node mobility, the availability
of highly dynamic resources, such as CPU load and available
memory, may vary considerably in very short periods of
time10,3
. Therefore, we adopt a purely query-based approach
in our proposed DICHOTOMY protocol (further details in
Section 3). Moreover, we argue that discovery and sched-
uling must be approached in an integrated way in mobile ad
hoc grids, in particular for multihop scenarios. This is due to
two main reasons:
1. The discovery of highly dynamic resources should
be associated with some form of advance reserva-
tion42,48
, so as to ensure that these resources are indeed
available at the resource provider when the resource
consumer submits the intended computational task.
2. The messaging overhead and the information required
for discovering available (and reachable) resource
providers in the mobile ad hoc grid can be used as
well for automatically selecting the set of most appro-
priate providers. The automatic selection of resource
providers bears resemblance to metascheduling
techniques in traditional grids7
, and is particularly
important for applications targeted at solving compu-
tationally-intensive problems.
As shown in Section 3, our proposed DICHOTOMY
protocol implements advance reservation and an in-network
filtering algorithm that allows the automatic selection of the
most suitable resource providers.
2.2. MoGrid architecture
To support the establishment of mobile ad hoc grids, we
proposed in Lima et al.29
a middleware architecture, called
MoGrid, which is depicted in Figure 1. In our work, we refer
to a mobile ad hoc grid as a set of (possibly heterogeneous)
mobile nodes in a MANET that manage resource usage
and provisioning for applications in a decentralized way.
Moreover, in our architecture resourceful nodes (e.g. laptops)
may provide resources to multiple application tasks simulta-
neously, while other, resource-constrained nodes (e.g. mobile
phones) may not provide resources at all in these cases, such
nodes may only ask for resources from remote nodes. We
further discuss the issue of node heterogeneity in Section 7.
In the remainder of this section we provide some
background on this architecture, so as to identify some
requirements on the design of the DICHOTOMY protocol.
The MoGrid architecture supports applications in two
phases. First, nodes acting as resource consumers discover
other nodes acting as resource providers by means of serv-
ices that the discovery layer offers. The DICHOTOMY
protocol is used for implementing these services. Second,
resource consumers submit application tasks to selected
resource providers according to the resources they make
available by means of services the submission layer offers.
Protocols within this layer are out of the scope of this paper;
nevertheless, we assume a resource usage model in which a
resource consumer has a group of independent tasks (e.g. bag
of tasks) which it needs to submit for execution on resource
providers. The adopted resource usage model implies the
use of a mechanism during the discovery phase that selects
multiple resource providers simultaneously. This is a major
shortcoming of current discovery approaches to MANETs, as
discussed in Section 6.
Each kind of application may have its specific resource
requirements on the mobile ad hoc grid. As an example, for
long-lived CPU-intensive applications battery power is more
important than highly available bandwidth when submit-
ting their tasks for execution, whereas storage capacity and
connection stability have greater importance to data replica-
tion applications. Therefore, the selection mechanism must
take into account the current level of resource usage asso-
ciated with a given provider and the relative importance
between its resources from the application’s point of view.
The MoGrid architecture supports applications imple-
mented directly on top of the discovery layer, or on top of an
application-specific layer (see Figure 1). One of the purposes
of the application-specific layers is to implement different
resource weightings according to different application types,
as for instance computationally-intensive, communication-
intensive, or data intensive applications (Section 5 provides
an example of implementation of an application-specific
layer).
The focus of the MoGrid architecture on resource
discovery instead of service discovery also implies the use of
a query-based approach to resource discovery, as opposed
to announcement-based approaches (see discussion on
Section 6). This is because the highly-variable availability
of resources such as CPU load and available memory may
demand a higher announcement rate, which in turn may
lead to an increased consumption of other resources in the
MANET, such as network bandwidth and energy.
Appl. Appl.
Appl. Appl. Appl. Appl.
Application-specific
layer
Application-specific
layer
Submission
layer
...
... ...
Discovery layer
(DICHOTOMY Protocol)
Figure 1. ModGrid arquitecture.

3. The DICHOTOMY Protocol
In this section, we present the DICHOTOMY protocol for
integrated discovery and scheduling in mobile ad hoc grids.
First, we discuss the basic operation of the proposed protocol
(Section 3.1). Next (Section 3.2), we describe the operation
of the adopted mechanism for determining the suitability
of nodes (acting as resource providers) to answer particular
requests from inquiring nodes (acting as resource consumers).
Finally (Section 3.3), we describe how the protocol avoids the
reply implosion problem.
3.1. Basic operation
The DICHOTOMY protocol defines two main messages:
INITIATORREQUEST (IREQ) and COLLABORATORREPLY
(CRep). An IReq message conveys:
i) A unique request identifier used to match requests to
replies (REQID);
ii) The maximum reply delay that the inquiring node
is willing to tolerate (MAXREPLYDELAY) c.f.
Section 3.2;
iii) Thenumberofresourceproviderstowhichtheinquiring
node wishes to submit tasks (NUMMAXREPLIES);
iv) Information about the resources the application is
interested in and the relative importance among them
we call this the contextual information (ctxtInfo) of the
request;
v) The current and maximum diameter (in number of
hops) of the request propagation (NUMHOPS and
maxHops, respectively); and
vi) A unique identification of the last node (e.g. its MAC
address) sending out the message (hopID). The CRep
message will be explained later in this section.
Figure 2 illustrates the basic operation and involved
message exchange within DICHOTOMY. An inquiring node
sends out IREQ messages to the other nodes in the mobile
ad hoc grid to ask them for resource provisioning. The IREQ
message is replicated at each intermediate node to form a
controlled flood limited by the maxHops parameter. This is
shown in Figure 2 as node i, the inquiring node, sends an
IREQ message to node t, an ordinary intermediate node, that
replicates the IREQ message to all its neighbors; the process
is repeated until the maxHops value is achieved, making the
IREQ message reach for instance nodes y and z.
Upon reception of an IREQ message, a node records it as
a pending request in a local data structure (pendingList). In
addition to REQID, numMaxReplies, and hopID, which are
obtained from the IREQ message, each entry of pendingList
has a numReplies field (initially set to 0) and two associated
timers (replyDelay and cleanUp). replyDelay is described
in Section 3.2; numReplies and cleanUp are described in
Section 3.3.
After updating PENDINGLIST, the receiver node replies
to an IREQ message depending on its willingness to collab-
orate as a resource provider, as described in Section 3.2. In
addition to replying to requests, nodes may also forward
requests to other nodes farther away from the inquiring node
in the mobile ad hoc grid, if numHops < maxHops. Such
forwarding is done through simple flooding to neighboring
nodes a scheme for inhibiting redundant rebroadcasts37
should be employed in this case. Before being forwarded,
a request has its numHops field incremented and its hopID
field updated with the identification of the current forwarding
node. Forwarding requests with updated hopIDs allows
nodes farther away from the inquiring node to keep track (in
their local pendingList structures) of the path traversed by
the request, which will be used for determining the return
path of the corresponding replies.
Nodes willing to collaborate as resource providers (here-
after, collaborating nodes) send CRep messages to inquiring
nodes in response to IREQ messages. In Figure 2, it is
assumed that all nodes in the mobile ad hoc grid collaborate
by replying to the inquiring node. In particular, nodes t, y,
and z send CRep messages as well as possibly other collab-
orating nodes whose CRep messages are represented by
dots in Figure 2. A CRep message informs the inquiring
node about the collaborating node address (identified by a
collAddr field) as well as its resource availability according
to the contextual information of interest indicated in the
Inquiring
Node
Collaboring Nodes
IReqhopID = i, numHops = 0
IReqhopID = t, numHops = 1
IReqhopID = ..., numHops = maxHops
IReqhopID = ..., numHops = maxHopsIReqhopID = t, numHops = 1
CReqcollAddr = z, retPath = i
CReqcollAddr = z, retPath = t
CReqcollAddr = y, retPath = t
CReqcollAddr = z, retPath = ...
CReqcollAddr = y, retPath = ...
CReqcollAddr = y, retPath = i
CReqcollAddr = t, retPath = i
i
...
...
...
...
t
z
y
Figure 2. Example of DICHOTOMY messages.

corresponding request (resInfo field). Besides the collAddr
and resInfo fields, a CRep message also conveys:
i) The REQID matching that of the corresponding IREQ
message; and
ii) The identification of the node from which the corre-
sponding request was received (RETPATH)–the
replying node obtains such an identification from the
HOPID field in the corresponding IREQ message in
the PENDINGLIST.
A collaborating node sends a CRep message towards
the inquiring node through an application-level forwarding
mechanism that uses the already known path traversed by
the request. When a node receives a CRep message, it is proc-
essed according to Algorithm 1. If the reply corresponds to a
request the node has previously originated, the node proc-
esses the message and prevents this message to be further
forwarded in the network (lines 2 and 3). If the reply is instead
addressed to an inquiring node other than the receiver, the
latter first checks whether there is an entry for the corre-
sponding request in its pendingList structure (lines 6 and 7).
If not, the reply is silently discarded.I
Otherwise, the receiving
node determines whether this reply must be forwarded to the
inquiring node (line 8), according to the algorithm described
in Section 3.3. If this is the case, the node updates the reply’s
RETPATH field with the value of the hopID field stored in the
corresponding entry of pendingList and forwards the reply
(lines 9 and 10). This allows a neighbor node along the return
path to also forward such a reply to the inquiring node.
The application-level forwarding mechanism for reply
messages avoids the use of network-level routing protocols
that would generate additional network load on the MANET
during the discovery phase. Besides, with small modifications
I. If maxHops = ∞, this condition should not happen because every node in
the mobile ad hoc grid would in principle receive all IReq messages. Never-
theless, when a scheme for inhibiting redundant rebroadcasts is used, it is not
always guaranteed that an IReq message will reach all nodes in the network
even for maxHops = ∞.
in the hopID and retPath fields–instead of storing the last hop
in the path, they should in this case store the whole sequence
of traversed nodes, inquiring and collaborating nodes could
also learn the entire path between each other, and then use
source routing–a fairly common approach used in some
routing protocols for MANETs, such as DSR23
at the begin-
ning of the task submission phase. Details on the mapping
of the application-level forwarding mechanism for reply
messages onto the link level are discussed in Section 4.3.
3.2. Determining suitability
In the DICHOTOMY protocol, every node willing
to collaborate with the provision of a specific resource
delays the transmission of its CRep message according to
the REPLYDELAY timer in the corresponding entry of its
PENDINGLIST. Such a timer is set so that more resourceful
nodes reply earlier. This way, a node willing to collaborate
can detect, before sending its own CRep message, whether
other, more suitable nodes have already replied to the corre-
sponding request. If the total number of replies generated
in the mobile ad hoc grid is larger than the value NM
of the
numMaxReplies field in the corresponding IReq message,
and if the inquiring node chooses the NM
first received replies,
the protocol guarantees that the inquiring node discovers the
NM
most suitable nodes for providing the resource. Moreover,
when used together with the algorithm presented in Section
3.3, the strategy of delaying replies helps in reducing the total
number of replies being conveyed in the mobile ad hoc grid
by eliminating unnecessary additional replies alongside the
return path from the replying node to the inquiring one.
It should be remarked that the determination of reply
delays is flexible with regard to the resources to be taken into
account e.g. connectivity status, CPU load, available memory,
remaining battery power and the relative importance among
them. Information about resources to be considered when
computing the suitability of a node is conveyed by IREQ
messages in the CTXTINFO field. When a node receives a
request, it gathers its current state in terms of the resources of
interest to compute the reply delay. For this algorithm to work
correctly, all nodes in the mobile ad hoc grid must use the
same criterion for this computation. In our implementation
(c.f. Section 4), a collaborating node sets the REPLYDELAY
timer in the corresponding entry of its PENDINGLIST to
t units of time, as given by Equation 1:
t ω
α
= 1 2
=1 =1
−














−( )∑
∑i
N
i i
jj
N
P
P
D HSmax (1)
where 0 ≤ α ≤ 1 and 0 ≤ ω ≤ 1. N represents the number of
different resource types the collaborating node should take
into account. Pi
is the weight that describes the relative
importance of each resource type i from the viewpoint of the
application on the inquiring node, 1 ≤ i ≤ N. Both N and Pi
are
described as part of the ctxtInfo field in the request. Dmax
is the maximum reply delay, which is also obtained from the
Algorithm 1 CREP message main processing
Input: msg/*Received CREP*/
1: if FIRSTCOPY (msg) then
2: if MYREPLY (msg) then
3: PROCESS (msg)
4: return
5: end if
6: entry ← pendingList[msg.reqID]
7: if entry ≠ NULL then
8: if CANFORWARD (entry, msg) then
9: msg.retPath ← entry.hopID
10: FORWARD (entry.hopID, msg)
11: end if
12: return
13: end if
14: DISCARD (msg)
15: else
16: .../* Deal with duplicate replies */
17: end if

request (maxReplyDelay field). αi
is the normalized level of
current availability (in the interval [0,1]) of resource type i at
the collaborating node. ω indicates the willingness (also in the
interval [0,1]) of the collaborating node to participate in the
resource provisioning. t is undefined for ω = 0; such a value
means that no replies will be sent by the collaborating node
Section 4.1 provides an example of ω usage in our implemen-
tation. Finally, H and S are used for considering the transfer
delays that IREQ and CREP messages may experience. H is
the distance in hops between the collaborating node and the
inquiring node, and S is a tuning parameter representing the
mean transfer delay at each hop.
3.3. Avoiding the reply implosion problem
According to Algorithm 2, CREP messages are selectively
forwarded towards the inquiring node.
Whenever the function CANFORWARD() is called, it
increments the value NR
of the numReplies field in the entry
of pendingList corresponding to the received CREP message
(line 1), regardless of the receiving node being on the return path of
the reply. The node’s identification is then compared with the
value of the retPath field in the reply (line 2). If the values are
equal, it means the receiver is in the return path of the reply.
The receiving node, however, will only be able to forward
the reply to the inquiring node if NR
≤ NM
, where NM
is the
value of the numMaxReplies field in the corresponding entry
of its pendingList (line 3). If NR
> NM
, the node suppresses
the reply. Note that each intermediate node in the return
path implicitly informs its own vicinity due to the applica-
tion-level forwarding mechanism about requests that have
already been replied, thus allowing for further suppressions
at nodes nearby the reply’s return path.
To allow further suppressions, an entry in pendingList is
kept alive until its cleanUp timer expires. In our implementa-
tion, a node sets cleanUp for entries in pendingList connected
with replies not originated from this node to τmax
= Dmax
– 2HS
units of time. For entries associated with replies originated
from the node (i.e. its resource offerings), cleanUp is set to
2τmax
, to take account of advance reservations (c.f. Section 4.1).
Figure 3 illustrates an example of the operation of
Algorithm 2. In the initial configuration (Figure 3a), only
nodes w and z are within y’s transmission range. y receives
a reply to a request with reqID = 1000, and increments the
value NR
of the numReplies field in the corresponding entry
of its pendingList (Figure 3b). Since y is in the return path
of the reply (Figure 3c), y forwards such a message towards
w, which is y’s next hop in the return path. z overhears this
transmission due to the characteristics of the mapping of the
application-level forwarding mechanism onto the link level
one (discussed in Section 4.3) and then increments the value
NR
of the numReplies field in the corresponding entry of
its pendingList, but does not forward that reply because it
is not in the reply’s return path. z receives another reply to
the request with REQID = 1000 (Figure 3d), but although it
is in this reply’s return path, it does not forward the message
because the numMaxReplies field in the corresponding
entry of its pendingList indicates that it has already either
forwarded or overheard NM
messages.
4. Implementation
We have implemented the DICHOTOMY protocol in
Java, as part of the implementation of our MoGrid middle-
ware architecture29,II
The implementation is done in J2SE
according to the restrictions of the CDC (Connected Device
Configuration) profile of the Java ME. CDC is a stand-
ards-based framework for building and delivering mobile
applications that can be shared across a range of network-
connected personal mobile devices, such as grid services as
seen in mobile ad hoc grids discussed in this paper. Typically,
these devices include a 32-bit microprocessor/controller and
require about 2 MB of RAM and 2.5 MB of ROM for the Java
application environment. Our implementation of the protocol
uses a monitoring service available as part of the MoCA
architecture. This service is responsible for gathering infor-
mation about the current state of a mobile node, including
connectivity, CPU load, available energy and memory, and
disk storage space.
4.1. Setup of parameters
Parameters S and ω in Equation (1) are configurable in
our implementation. For our experiments (c.f. Section 5) we
set S = 10 ms which is of the same order of magnitude as
100 m one-trip packet delays for 1 Mbps transmission rate
and 1500-byte packets (disregarding delay variations due to
queuing and medium access contention). ω is computed as
ωusr/βL, where ωusr
describes the user’s level of interest (in
the interval [0,1]) in allowing its device to collaborate with
others on the mobile ad hoc grid, L is the number of entries
in a node’s pendingList corresponding to this node’s current
resource offerings, and β is an scalar factor. The denominator
βL implements a simple mechanism for advance reservation
by decreasing the node’s willingness to collaborate when the
number of pending requests at this node increases; β in this
case controls how fast the influence of a user’s willingness is
lowered as its pendingList grows. We set β = 2 arbitrarily in
our implementation. Finetuning this factor was left for future
work.
II The implementation is available for download at http://martin.lncc.br.
Algorithm 2 Function CANFORWARD ().
Input: entry/*Entry in PENDINGLIST */ and
msg/*Received CREP */
Output: boolean /* CREP is to be forwarded? */
1: entry.NR
← entry NR
+ 1
2: if msg.retPath = localID then
3: if entry.NR
≤ entry.NM
then
4: return true
5: end if
6: end if
7: return false

4.2. Resource description and matching
In the implementation of the DICHOTOMY protocol, the
contextual information description conveyed by a request
message (CTXTINFO field) and the matching of such descrip-
tion to the resource availability on the collaborating nodes are
both based on simple attributes (key-value pairs). Semantic
description languages e.g. ontology languages32,34
could be
also employed. Such languages support queries with more
expressiveness and inference power, thereby allowing richer
matching options (e.g. partial matchings). Such expressive-
ness, however, incurs some additional computational cost in
terms of both processing time and memory footprint, which
may be undesirable in scenarios involving resource-poor
devices. The trade-off analysis of attribute and ontology-
based resource description and matching for mobile ad hoc
grids is out of the scope of this paper.
4.3. Application-level forwarding of reply messages
CRep messages are sent towards the inquiring node
through an application-level forwarding mechanism. There
are two alternative mappings of this scheme onto the link
level: using unicast or broadcast/multicast transmissions.
For link-level unicast mappings, the RETPATH value
associated with replies could be inferred from the destination
address field in the encapsulating packets (e.g. the destination
MAC address in IEEE 802.11 packets). This address field could
convey the value of the HOPID field in the corresponding
entry of PENDINGLIST which indicates the link-level
address of the next node in the return path–as part of func-
tion forward() (line 1 in Algorithm 1). By adopting such
mappings, the retPath field carrying the link-level address of
the next node in the return path could be omitted from the
CRep messages, and the statement msg.retPath←entry.hopID
(line 1) in Algorithm 1 could be removed. For a participating
node to overhear replies from its neighbors, however, its
network interface would have to work in promiscuous mode.
Besides the security issues involved, this alternative has the
drawback that, in promiscuous mode, the node must process
the payload of all packets (not only those pertaining to the
DICHOTOMY protocol) at the higher levels, which results
in a waste of resources (CPU, memory and energy) that are
crucial to computational tasks.
reqID NR
NM
hopID τ τmax
1000 3 4 x
...
reqID NR
NM
hopID τ τmax
1000 1 4 w
...
reqID NR
NM
hopID τ τmax
1000 4 4 x
...
reqID NR
NM
hopID τ τmax
1000 2 4 w
...
reqID NR
NM
hopID τ τmax
1000 3 4 x
...
reqID NR
NM
hopID τ τmax
1000 2 4 w
...
reqID NR
NM
hopID τ τmax
1000 4 4 x
...
reqID NR
NM
hopID τ τmax
1000 2 4 w
...
x
w
y
z
y’s pendingList
z’s pendingList z’s pendingList
y’s pendingList
z’s pendingList
y’s pendingList
supressed by z
NR
= NM
z’s pendingList
w
y
x
z
CRepreqID = 1000, retPath = w
CRepreqID = 1000, retPath = y
x
w z
y
x
w
y
z
(a) Initial configuration
(c) y rebroadcasts the reply towards w. (d) z receives another reply to the request
(b) y receives a reply to a request
Figure 3. Scenario illustrating the suppression of CREP messages by Algorithm 2.

For link-level broadcast/multicast mappings, nodes do
not need to set their network interfaces to work in promis-
cuous mode; however, the destination link address field in
packets encapsulating reply messages do not specify a single
recipient. Thus, the RETPATH field is necessary in such
messages and line 1 in Algorithm 1 must be kept. Even so, we
argue that the additional computational overhead of promis-
cuous mode operation introduced by unicast mappings can
do more harm for applications targeted at solving computa-
tionally-intensive problems than the additional transmission
overhead introduced by broadcast mappings. Therefore, we
have adopted link-level broadcast mappings for the applica-
tion-level forwarding mechanism in our implementation of
the DICHOTOMY protocol.
It is worth noting that for MANETs in which the media
access control is based on CSMA/CA (Carrier Sense Multiple
Acess/ Collision Avoidance), broadcast transmissions are
more unreliable and prone to collisions in comparison with
unicast transmissions37
. This is mainly due to the lack of
acknowledgments, of RTS/CTS (Request/Clear to Send)
dialogues, and of a mechanism for collision detection. The
problem of collisions in link-level broadcast transmissions
may be rather alleviated by the DICHOTOMY protocol, since
replies from different resource providers are time-shifted, as
discussed in Section 3.2. Regarding the lack of acknowledg-
ments, an implicit acknowledgment mechanism for broadcast
transmissions could be built upon the application-level
forwarding mechanism. To understand this, consider again
the example of Figure 3. When w receives the reply message
from y (Figure 3c), w will forward the message because it
is in the return path. Such a transmission will be overheard
by y (since it is within w’s range); y could then regard this
transmission as a higher-level acknowledgement from w.
Nonetheless, many subtle issues arise if a retransmission
policy based on such implicit acknowledgments is devised
to improve the reliability of the protocol. We argue that such
additionalcomplexityisnotworthwhile,sinceCRepmessages
are always subject to suppression along the remaining path
towards an inquiring node. Moreover, retransmissions add
delays that may render the retransmitted CRep message
useless at the inquiring node if it arrives after the predefined
maxReplyDelay. In fact, the experimental results presented
in Section 5.2 demonstrate that the DICHOTOMY protocol is
efficient even without such a retransmission policy.
5. Performance Evaluation
We carried out our experiments in testbed and simula-
tion environments, each of them to evaluate a different aspect
of the DICHOTOMY protocol. These are described in the
following subsections.
5.1. Scheduling Assessment
To assess the scheduling properties of the DICHOTOMY
protocol under an increasing volume of requests, we set up
an experimental ad hoc grid testbed. This testbed comprises 6
fixed, homogeneous nodes running Linux. These nodes have
beenarrangedinavarietyofmultihopscenarios,withonenode
in each of such scenarios being used as the resource consumer
and the remaining nodes as resource providers. Figure 4 illus-
trates the scenario from which the results presented in this
section have been obtained. Other scenarios provided similar
results, which have been omitted for brevity.
For our experiments, we implemented a simple master-
worker matrix-matrix multiplication application on top of
the testbed. To easily split the application into independent
tasks, we employed a very simple distributed multiplica-
tion algorithm: given matrices Am × n
and An × p
, a master node
computes Cm × p
= AB by selecting p worker nodes with the
DICHOTOMY protocol and sending to each worker node i
(1 ≤ i ≤ p) a copy of matrix A along with bn
i
×1 , i.e. the trans-
posed vector whose elements are those of the i-th column
of B. Each worker node i runs a task that computes matrix
c Abn
i
n
i
× ×1 1= and returns it to the master node, which then
builds the i-th column of C from cn
i
×1 . The selection of the
worker nodes in the experimental ad hoc grid that will run
the tasks is made by only considering those nodes with the
most available CPU and memory resources. We implemented
an application-specific layer for this purpose (c.f. Section 2.2),
with N = 2, PCPU
= 4, and Pmem
= 1 in Equation 1.
For each testbed scenario, we ran a total of 30 experi-
ments. In each experiment, the resource consumer sent a
total of 30 IREQ messages at regular intervals of 30 s and
set MAXREPLYDELAY = 10 s. Each resource provider s
selected by a specific IREQ message was sent a task to
compute cn
s
×1. Matrices Am×n
and Bn×p
were dimensioned in
such a way that such computation could generate a cumu-
lative load on the resource providers. Figure 5 shows the
cumulative load share for each node during one of these
experiments. The other experiments on the testbed scenario
depicted in Figure 4 provided similar results. We observe
in the figure that the slave nodes have their loads balanced
after the transient state (first 10 requests). This shows that our
protocol performed an efficient and dynamic load balancing
between resource providers under an increasing volume of
requests.
Provider
A
Provider
D
Provider
E
Provider
B
Provider
C
Consumer
Figure 4. Testbed scenario.

We also used the testbed experiments described above
to analyze the scheduling quality that our protocol provides
to applications. We calculated the cumulative wall clock
time (the time difference between the start and finish of a
task) of all tasks generated in the testbed application after
the 30 experiments with our protocol. We then compared it
with the cumulative wall clock time achieved after 30 experi-
ments by a scheduler that selects worker nodes randomly. As
can be seen in Figure 6, our protocol schedules tasks among
worker nodes in such a way that the cumulative wall clock
time is approximately 36.5% smaller than that achieved by
a random scheduler. Further, the time overhead implied by
our protocol for discovery and scheduling, which is upper
bounded by the MAXREPLYDELAY parameter (10 s in our
simulation set up), is negligible with regard to the obtained
cumulative wall clock time of the tasks, which is in the order
of hundreds of thousands of seconds. These results indicate
that our protocol also has a positive impact on the applica-
tion efficiency.
5.2. Discovery efficiency under mobility
The simulation scenarios consisted of 40 nodes placed in an
obstacle-free, 500 × 500 m area. The initial position of each node
in a specific simulation was set randomly, with the constraints
that at the beginning of the simulation the nodes formed a
connected topology, and the distance among the nodes was
set to between 50 and 90% of the transmission range.
The first scenario consisted of a stationary topology. In
the remaining scenarios, the movement of nodes followed
the random walk model4
. In such a model, each node moves
in a random direction for some seconds–in a speed that is
uniformlydistributedintherange]0,Smax
]×thenchoosesanew
random direction, with no pauses in between such changes of
direction. Table 1 summarizes the parameters adopted in the
scenarios simulated with the NCTUns platform.
The discovery efficiency for each simulation scenario was
measured as a sample proportion calculated over 100 runs.
Each run consisted of a single resource consumer issuing
a single IReq message throughout the simulated MANET.
The sample proportion indicates the percentage of runs in
which the protocol delivered at least R replies from resource
providers to the resource consumer, as determined by the
NUMMAXREPLIES field in the IREQ message. The number
of collaborating nodes at each run was fixed at 10, which
corresponds to 25% of the nodes in the simulated scenarios.
Such a percentage was chosen based on the study by Hughes
et al.19
, which states that in Gnutella–a famous P2P, collabora-
tion-based file-sharing system–this percentage of participants
is responsible for 98% of all service provisions.
Figure 7 presents the discovery efficiency of the
DICHOTOMY protocol as a function of the maximum node
0
10
20
30
40
50
0 5 10 15 20 25 30
Loadshare(%)
Request number
A
B
C
D
E
Figure 5. Cumulative load of slave nodes.
0
50000
100000
150000
200000
DICHOTOMY RANDOM
Seconds
Figure 6. Cumulative wall clock time of tasks.
Table 1. Parameters for the NCTUns simulations.
Parameter Value
Number of nodes (N) 40
Number of resource providers 10
Maximum number of replies (R) 4
Transmission range 100 m
Maximum node speed (Smax
) 0 to 5 m/s
0
20
40
60
80
100
0 1 2 3 4 5
Discoveryefficiency(%)
Node speed (m/s)
Figure 7. Discovery efficiency in a mobile scenario.

speed (Smax
). The vertical error bars correspond to the 95%
confidence intervals for each sample proportion. The results
show that the protocol behaves well under situations of
human mobility (from 0.8 to 1.2 m/s, which corresponds to a
typical range for pedestrian walking speeds40
).
As can be observed in Figure 7, even for the stationary
scenario (Smax
= 0) the protocol does not reach 100% effi-
ciency–the sample proportion is 92%, with ±4.13 confidence
intervals. This is due to the drawbacks stated in Section 4.3
as regards the application-level forwarding mechanism for
reply messages being mapped onto link-level broadcast
transmissions in CSMA/CA enabled nodes.
5.3. Scalability Analysis
In principle, we could use our implementation of the
DICHOTOMY protocol on the NCTUns platform to evaluate
its scalability in scenarios with an increasing number of nodes.
Nevertheless, any simulation of the DICHOTOMY protocol
on the NCTUns platform was restricted to a maximum of
50 nodes; above these values the simulations generated a
load beyond the capacity of our NCTUns simulation infra-
structure. To allow a more extensive analysis of the scalability
of the DICHOTOMY protocol, we devised a simpler simu-
lation model over the ns-2 simulator33
. Our experiments in
such simulator considered fixed nodes forming topologies
with a constant number of nodes within the same transmis-
sion range (node density), so that the impact of increasing
the number of nodes in the ad hoc grid could be properly
evaluated. The results presented in this section correspond to
the average of 100 sample runs per simulated scenario with
a 95% confidence level. This analysis was mainly focused on
the evaluation of two metrics: the network load in the ad hoc
grid due to reply messages, and the suppression diameter of
these messages–the distance (in number of hops) between
the inquiring node and the nodes where these messages were
suppressed. Table 2 presents the parameters adopted in the
scenarios simulated by ns-2.
The average network load in the ad hoc grid due to
reply messages was computed using, for each scenario,
the mean number of packets involving these messages.
0
50
100
150
200
250
300
350
400
50 100 150 200
Numberoftransmissions
Number of nodes
Percentage of replying nodes (p) = 20%
0
100
200
300
400
500
600
700
800
20 40 60 80 100 120 140 160 180 200 220 240
Number of nodes
0
150
300
450
600
750
900
1050
1200
20 40 60 80 100 120 140 160 180 200 220 240
Number of nodes
0
200
400
600
800
1000
1200
1400
1600
20 40 60 80 100 120 140 160 180 200 220 240
Number of nodes
DICHOTOMY (R = 1)
DICHOTOMY (R = 2)
DICHOTOMY (R = 4)
DICHOTOMY (R = 6)
DICHOTOMY (R = 8)
DICHOTOMY (R = 10)
UCast
Figure 8. Network load in the mobile ad hoc grid due to reply messages.

Importantly, this metric also allow us to imply whether there
is a significant reduction in energy consumption of devices
in a mobile ad hoc grid due to the suppression of replies,
given that transmissions are known to be responsible for a
high energy consumption. Using this metric, we compared
the DICHOTOMY protocol with a hypothetical query-
based discovery protocol in which service replies are sent
by unicast directly to inquiring nodes (we call it `ÙCast’’)
in both protocols the inquiring nodes broadcast requests by
flooding, and no service announcements are employed. In our
belief, a comparison with advertisement- and hybrid-based
approaches would not make sense for mobile ad hoc grids
because the typical resources involved (e.g. CPU, memory)
are highly dynamic (c.f. Section 2).
Figure 8 presents the network load due to reply messages
as a function of the number of nodes for different percent-
ages of nodes willing to collaborate as resource providers.
The vertical error bars indicate the confidence intervals.
The results show that the adoption of the DICHOTOMY
protocol allows for an increasing reduction–with respect to
the `ÙCast’’ protocol–in the total number of transmissions,
as the number of devices in the mobile ad hoc grid increases.
We also observe an even higher level of suppressions when
there is a larger percentage of nodes (p) in the mobile ad hoc
grid with interest in collaborating on resource provisioning.
These results suggest the scalability of our approach.
The suppression diameter of reply messages allow us to
evaluate the degree of distribution of the mitigation of the
forwarding burden provided by the DICHOTOMY protocol
among the nodes in a mobile ad hoc grid, and consequently
the distribution of energy savings among such nodes due to
the reduction in the amount of transmissions. Figures 9 and
10 present the distribution of suppressions as a cumulative
distribution function (CDF) for different numbers of nodes
and percentages of replying nodes. To better illustrate the
distribution of suppressions through the network, the results
presented in these figures are contrasted with a uniform CDF
(represented by the straight line in the figures).
Comparing all graphs in Figures 9 and 10, we observe a
better distribution of suppressions as the number of nodes
and the percentage of replying nodes (p) increase. Again, this
suggests the scalability of our proposed approach.
Comparing the curves of each individual graph, it is also
possible to verify that the distribution of the suppression
diameter becomes less uniform as the maximum number of
Table 2. Parameters for ns-2 simulations.
Parameter Value
Number of nodes (N) 10 to 240
Percentage of resource providers (p) 20% to 80%
Maximum number of replies (R) 1 to 10
Node density 5
Transmission range 12.5 m
Distance between nodes 10 m
replies (R) increases, with a tendency to suppressions being
concentrated closer to the inquiring node. This is an expected
behavior, since less suppressions occur at nodes farther away
from the inquiring node and replies follow convergent paths
towards such node.
6. Related Work
In spite of some research efforts over the past few years
on wireless and mobile ad hoc grids, none of it as far as we
can see has addressed the issues related to the interplay
of resource discovery and scheduling in such grids. In the
following we survey relevant research related to these two
areas.
6.1. Resource discovery
Although some papers point out the need for resource
discovery protocols in wireless grids31,35
, no work has explic-
itly addressed this area for multihop mobile ad hoc grids.
A close subject–service discovery protocols (SDPs)–has
been a hot topic in the area of MANET research44
and could
arguably be extended or adapted for resource discovery
purposes. Nonetheless, most of the SDPs for MANETs are
based on announcements, allowing nodes to advertise serv-
ices in the network; nodes interested in such services cache
the related advertisements. Clearly, such protocols are inad-
equate for offering computational services that depend on
highly dynamic resources, such as CPU load and available
memory. The availability of such resources may vary consid-
erably in short periods of time10,3
, thus demanding frequent
announcements, which in turn may lead to an increased
consumption of other resources in the MANET, such as
network bandwidth and energy. In this context, some pieces
of work propose improvements to the broadcasting of service
requests in multihop MANETs, so as to reduce the amount
of packet transmissions related to such requests. Examples
include Konark27
, Group-based Service Discovery (GSD)5
,
and Field Theoretic Approach (FTA)28
.
Konark introduces the concept of service gossiping, in
which a node can selectively forward both service requests
and replies based on cached announcements from other
nodes. The efficiency of the Konark approach, however, is
highly dependent on caching of service information, thus
being inadequate for grid-like computational services. The
GSD architecture controls request broadcasts based on the
semantic grouping of services as ontology classes, but its effi-
ciency is also dependent on the advertisement and caching
of such classes. When compared with the two previous
approaches, FTA further reduces the amount of transmis-
sions related to request forwarding by adopting an analogy
of electrostatic fields. In the FTA approach, an inquiring node
sends out a service request (a negative test charge), which
is `àttracted’’ by the most appropriate service instance (the
positive charge that creates the field with highest potential).
This way, FTA allows the automatic selection of a single node
as the most suitable provider of a particular service, with

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7
SuppressionCDF
Suppression diameter
Number of nodes = 60
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7 8 9 10
SuppressionCDF
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
SuppressionCDF
Uniform CDF
DICHOTOMY (R = 1)
DICHOTOMY (R = 2)
DICHOTOMY (R = 4)
DICHOTOMY (R = 6)
DICHOTOMY (R = 8)
DICHOTOMY (R = 10)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7 8 9 10
SuppressionCDF
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
SuppressionCDF
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7
SuppressionCDF
Uniform CDF
DICHOTOMY (R = 1)
DICHOTOMY (R = 2)
DICHOTOMY (R = 4)
DICHOTOMY (R = 6)
DICHOTOMY (R = 8)
DICHOTOMY (R = 10)
Figure 10. Distribution of reply suppressions (60% of replying nodes
in the mobile ad hoc grid).
Figure 9. Distribution of reply suppressions (20% of replying nodes
in the mobile ad hoc grid).

smaller overhead on the MANET as compared with Konark
and GSD. Nevertheless, this approach disregards the auto-
matic selection of multiple providers, and its efficiency is also
highly dependent on the spread of potentials, which is based
on periodic announcements.
Other researchers have focused on extending routing
protocols to accomodate service discovery. One example
comes from Varshavsky et al.43
and their cross-layer approach
to integrating service discovery functionality within the
DSR22
(query-based) and DSDV38
(announcement-based)
routing protocols. This approach and others similar to it26,6
is also inappropriate for mobile ad hoc grids, since either
its discovery efficiency is again dependent on caching of
announced service information, or all replies from service
providers matching the service description of a client’s query
arrive at the inquiring node for local selection by the client,
thus wasting communication and energy resources.
6.2. Resource Scheduling
As far as traditional grids are concerned, many different
scheduling strategies have been developed along the
years1,12,47
. Only very recently, however, have specific sched-
uling strategies for wireless and mobile ad hoc grids emerged
in the literature17,25,30,20,49
. Most of them employ task replication
to optimize some objective function related to the inherent
limitations in processing, memory, battery power, and wire-
less communications capabilities of mobile devices.
Huang et al.17
propose a two-level scheduling model
suitable for wireless grids. The first level of their model is
responsible for mapping tasks to fixed grid nodes; some of
these nodes act as proxies between a wireless domain (e.g.
an area covered by an access point) and the fixed grid. The
second level conducts scheduling within each proxy-centric
wireless domain. The proxy runs a revised Min-Min heuristic
algorithm, which aims at minimizing energy consumption at
the mobile nodes. A similar approach based on hierarchical
scheduling is proposed by Katzaros and Polyzos25
, with the
difference that task replication is employed to treat discon-
nection events. Both approaches are inadequate for mobile
ad hoc grids due to the need for a fixed node acting as the
scheduler.
Litke et al.30
focus on shortening the overall system
response time with a scheme for task replication based on
the knapsack problem formulation. Their strategy aims
at maximizing the utilization of computational resources
provided by the mobile nodes. A probability function is
used for computing the availability of each node based on
its current failure rate (e.g. number of times it was unreach-
able in the network), so that a node only receives tasks that
may be completed before its mean time to failure (MTTF).
Nevertheless, Litke et al. disregard energy consumption,
which–in the case of multihop mobile ad hoc grids–may limit
the operational life time of the whole system.
Zong et al.49
aim at reducing schedule lengths of prece-
dence-constrained parallel tasks while conserving energy at
the mobile nodes. Their strategy relies on judiciously repli-
cating tasks so that energy consumption related to inter-task
communication is diminished; however, the process of node
selection, as described in Wolf et al.49
, implies a centralized
discovery service.
Hummel and Jelleschitz20
propose a decentralized sched-
uler, thus bearing more resemblance to our approach; each
mobile node decides autonomously whether to process a
submitted task depending on its current and estimated near
future capabilities. Their main focus, however, is on providing
fault tolerance through task replication, which is coordinated
by means of task queues managed in a distributed virtual
shared memory. Crucially, their solution does not take into
account the waste of communication and energy resources
due to this coordination.
7. Summary and Outlook
In this paper, we have presented the specification, imple-
mentation, and performance evaluation of a novel protocol
(DICHOTOMY) for integrated resource discovery and
scheduling on multihop mobile ad hoc grids. Overall, our
experimental results show that:
• Our protocol does appropriate resource scheduling,
allowing an efficient load balancing among resource
providers and having a positive impact on the appli-
cation efficiency under an increasing volume of
discovery requests;
• The discovery efficiency–i.e. the percentage of success-
fully answered requests–is kept at acceptable levels in
the mobile application scenarios we are interested in,
which involves pedestrian (walking) mobility;
• The proposed protocol scales very well with respect
to an increasing number of nodes in comparison
with the traditional query-based solutions for service
discovery, since it increases the total amount of energy
savings due to packet transmissions;
• Thesuppressionofrepliesperformedbyourin-network
filtering algorithm is not concentrated at specific points
of a MANET–instead, the filtering is distributed among
the nodes. As a consequence of the distributed reduc-
tion in the amount of transmissions, the energy savings
due to nodes having fewer replies to retransmit are also
distributed throughout the network.
During the development of this work, some aspects have
been identified for future investigation:
• A more elaborated mechanism for advance reserva
tions may be devised, including the treatment of
resource ``underbookings’’ a situation that might
happen if the collaborating node reserves resources
that are not actually used because its reply is
suppressed before arriving at the inquiring node.
Although such a reservation is temporary (as defined
by the CLEANUP timer), it may affect concurrent
requests.
• The MAXREPLYDELAY parameter has an impor-
tant impact on the efficiency of the DICHOTOMY
protocol. Finetuning this parameter e.g. as a func-

tion of the transmission delay of messages is essential
to increase the discovery efficiency under mobility
(since messages typically take no more than a few
hundred milliseconds for delivery), and also to reduce
the discovery time without increasing the number of
reply collisions (which is achieved through the asyn-
chronism in the transmission of these messages).
• The investigation of heterogeneity-aware suitability
criteria is an interesting open issue. In this context,
the theoretical and simulation study by Huang et al.18
has recently brought interesting insights into how
to handle nodes with heterogeneous computational
power.
• Finally, in our current implementation the suitability
of a resource provider is only computed by its own
context (i.e. the state of its own resources) and not
the context of intermediate nodes in its path to the
inquiring node. This did not affect our results in the
testbed because it formed topologies with just few
hops. We are currently evaluating to what extent
disregarding the context of intermediate nodes may
affect a mobile ad hoc grid and investigating alterna-
tive solutions.
Acknowledgements
This work has been partially funded by the Brazilian
Ministry of Science and Tecnology (MCT), CNPq, and
FAPERJ.
References
1. Abramson D, Sosic R, Giddy J and Hall B. Nimrod: a tool
for performing parametised simulations using distributed
workstations. In: Proceedings of the 4 IEEE Symposium on High
Performance Distributed Computing; Washington, DC, USA.
New York, NY, USA: IEEE Press; 1995.
2. Berman F, Fox G and Hey AJG. Grid computing: making the
global infrastructure a reality. New York: John Wiley & Sons;
2003.
3. Bolosky WJ, Douceur JR, Ely D and Theimer M. Feasibility of
a serverless distributed file system deployed on an existing
set of desktop PCs. SIGMETRICS Performance Evaluation
Review 2000; 28(1):34‑43.
4. Camp T, Boleng J and Davies V. A survey of mobility models
foradhocnetworkresearch. WirelessCommunications&Mobile
Computing (WCMC): Special issue on Mobile Ad Hoc Networking:
Research, Trends and Applications 2002; 2(5):483‑502.
5. Chakraborty D, Joshi A, Yesha Y and Finin T. Toward
distributed service discovery in pervasive computing
environments. IEEE Transactions on Mobile Computing 2006;
5(2):97‑112.
6. Cheng L and Marsic I. Service discovery and invocation for
mobile ad hoc networked appliances. In: Proceedings of the 2
International Workshop on Networked Appliances; 2000; New
Brunswick. USA; 2000.
7. Czajkowski K, Foster I and Kesselman C. Resource and
service management. In: Foster I and Kesselman C. (Eds.).
The grid 2: blueprint for a new computing infrastructure. 2 ed.
New York: Morgan Kaufmann Publishers; 2003. p. 259‑284.
(cap. 18)
8. DabrowskiC,MillsKLandQuirolgicoS.Amodel‑basedanalysis
of first‑generation service discovery systems. Gaithersburg, MD,
USA: National Institute of Standards and Technology; 2005.
(Technical report, NIST special publication 500‑260)
9. Denning PJ. Hastily formed networks. Communications of the
ACM 2006; 49(4):15‑ 20.
10. Dinda PA. The statistical properties of host load. Scientific
Programming 1999; 7(3‑4):211‑229.
11. Erramilli Aand Singh RP. Areliable and efficient multicast for
broadband broadcast networks. ACM SIGCOMM Computer
Communication Review 1987; 17(5):343‑352.
12. Frey J, Tannenbaum T, Foster I, Livny M and Tuecke
S. Condor‑G: a computation management agent for
multi‑institutional grids. Cluster Computing 2002;
5(3):237‑246.
13. Gaynor M, Moulton SL, Welsh M, LaCombe E, Rowan A and
Wynne J. Integratng wireless sensor networks with the grid.
IEEE Internet Computing 2004; 8(4):32‑39.
14. Gomes ATA, Ziviani A, Lima LS and Endler M. Service
discovery approaches to mobile peer‑to‑peer computing.
In: Seet BC. (Ed.). Mobile peer‑to‑peer computing for next
generation distributed environments: advancing conceptual and
algorithmic applications. Hershey, PA, USA: IGI Gobal; 2009.
p. 437‑459. (cap. 20)
15. Gomes ATA, Ziviani A, Lima LS, Endler M and Chelius
G. Mitigating reply implosions in query‑based service
discovery protocols for mobile wireless ad hoc networks. In:
Proceedings of the 7 International Conference on Ad‑hoc, Mobile &
Wireless Networks; 2008; SophiaAntipolis, France. Heidelberg,
Germany: Springer; 2008. p. 29‑42.
16. GomesATA,ZivianiA,LimaLSandEndlerM.DICHOTOMY:
A resource discovery and scheduling protocol for
multihop ad hoc mobile grids. In: Proceedings of the 1 IEEE
InternationalWorkshop on Context Awareness and Mobility for
Grid Computing; 2007; Rio de Janeiro, RJ, Brazil.
17. Huang CQ, Zhu ZT, Wu YH and Xiao ZH. Power‑aware
hierarchical scheduling with respect to resource intermittence
in wireless grids. In: Proceedings of the 5 IEEE International
Conference on Machine Learning and Cybernetics; 2006; Dalian,
China. New York, NY, USA: IEEE Press; 2006. p. 693‑698.
18. Huang J and Lee SY. A heterogeneityaware approach to
load balancing of computational tasks: a theoretical and
simulation study. Cluster Computing 2007; 11(2):133‑149.
19. Hughes D, Coulson G and Walkerdine J. Free riding on
gnutella revisited: the bell tolls? Distributed Systems Online
2005; 6(6).
20. Hummel KA and Jelleschitz G. A robust decentralized job
scheduling approach for mobile peers in ad‑hoc grids. In:
Proceedings of the 7 IEEE International Symposium on Cluster
Computing and the Grid; 2007; Rio de Janeiro, RJ, Brazil. New
York, NY, USA: IEEE Press; 2007. p. 461‑471.

21. Hwang J and Aravamudham P. Middleware services for
P2P computing in wireless grid networks. IEEE Internet
Computing 2004; 8(4):40‑46.
22. Johnson D, Maltz D and Broch J. DSR: the dynamic source
routing protocol for multihop wireless ad hoc networks. In:
Perkins CE. (Ed.). Ad Hoc Networking. Upper Saddle River,
NJ, USA: Addison‑Wesley; 2001. p. 139‑172. (cap. 5)
23. Johnson D, Maltz D and Hu YC. The Dynamic Source Routing
Protocol (DSR) for mobile ad hoc networks for ipv4. California:
IETF; 2007. (RFC 4728, experimental)
24. Kagawa K,Yamada K, Kamiya T and Nagata M. Fault tolerant
grid migration using network storage. In: Proceedings of
Parallel and Distributed Processing Techniques and Applications;
2006; Las Vegas, NV, USA. Bogart, GA, USA: CSREA Press;
2006. p. 241‑245.
25. Katsaros K and Polyzos GC. Optimizing operation of a
hierarchical campus‑widemobile grid for intermittent
wireless connectivity. In: Proceedings of the 15 IEEE Workshop
on Local and Metropolitan Area Networks; 2007; Princeton, NJ,
USA. New York, NY, USA: IEEE Press; 2007. p. 111‑116.
26. Koodli R and Perkins C. Service discovery in ondemand ad hoc
networks. California: IETF; 2000. (Expired)
27. Lee C, Helal A, Desai N, Verma V and Arslan B. Konark: a
system and protocols for device independent, peer‑to‑peer
discovery and delivery of mobile services. IEEE Transactions
on Systems, Man and Cybernetics 2003; 33(6):682‑696.
28. Lenders V, May M and Plattner B. Service discovery in
mobile ad hoc networks: A field theoretic approach. Pervasive
and Mobile Computing 2005; 1(3):343‑370.
29. Lima LS, Gomes ATA, Ziviani A, Endler M, Soares LFG and
Schulze BR. Peer‑to‑peer resource discovery in mobile grids.
In: Proceedings of the 3 International Workshop on Middleware for
Grid Computing; 2005; Grenoble, France.
30. Litke A, Skoutas D, Tserpes K and Varvarigou T. Efficient
task replication and management for adaptive fault tolerance
in mobile grid environments. Future Generation Computer
Systems 2007; 23(2):163‑178.
31. LitkeA, Skoutas D and Varvarigou T. Mobile grid computing:
changes and challenges of resource management in a mobile
grid environment. In: Proceedings of International Conference
on Practical Aspects of Knowledge Management; 2004; Viena,
Austria.
32. Defense Advanced Research Projects Agency ‑ DARPA.
DAML services. Arlington: DARPA; 2006.
33. Information Sciences Institute ‑ ISI. The network simulator
ns‑2. California: ISI; 1995.
34. World Wide Web Consortium ‑ W3C. Ontology Web Language
reference. Cambridge, MA, USA: W3C; 2004.
35. McKnight LW and Howison J. Towards a sharing protocol
for wireless grids. In: Proceedings of International Conference
on Computing, Communication and Control Technologies; 2003;
Orlando, FL, USA.
36. McKnight LW, Howison J and Bradner S. Wireless grids:
distribute resource sharing by mobile, nomadic, and fixed
devices. IEEE Internet Computing 2004; 8(4):24‑31.
37. Ni SY, Tseng YC, Chen YS and Sheu JP. The broadcast
storm problem in a mobile ad‑hoc network. In: Proceedings
of the 5 Annual ACM/IEEE International Conference on Mobile
Computing and Networking; 1999; Seattle, WA, USA. New
38. Perkins CE and Bhagwat P. Highly dynamic
destination‑sequenced distance‑vector routing (DSDV) for
mobile computers. ACM SIGCOMM Computer Communication
Review 1994; 24(4):234‑244.
39. Priya SB, Prakash M and Dhawan KK. Fault tolerance‑genetic
algorithm for grid task scheduling using check point.
In: Proceedings of the 6 International Conference on Grid and
Cooperative Computing; 2007; Washington. Washington: IEEE
Computer Society; 2007. p. 676‑680.
40. Rouphail N, Hummer J, Milazzo J and Allen P. Literature
review for Chapter 13, Pedestrians, of the Highway Capacity
Manual. Washington: Federal HighwayAdministration; 1998.
(Technical report)
41. SacramentoV,EndlerM,RubinsztejnHK,LimaLS,Goncalves
K, Nascimento FN et al. MoCA: a middleware for developing
collaborative applications for mobile users. IEEE Distributed
Systems Online 2004; 5(10).
42. Smith W, Foster I and Taylor V. Scheduling with advance
reservations. In: Proceedings of the 14 International Parallel
and Distributed Processing Symposium; 2000; Cancun, Mexico.
New York, NY, USA: IEEE Press; 2000. p. 127‑132.
43. Varshavsky A, Reid B and Lara E. A crosslayer approach to
service discovery and selection in MANETs. In: Proceedings
of IEEE International Conference on Mobile Adhoc and Sensor
Systems; 2005; Washington, DC, USA. New York, NY, USA:
IEEE Press; 2005. p. 8.
44. Ververidis CN and Polyzos GC. Service discovery for
mobile ad hoc networks: A survey of issues and techniques.
Communications Surveys & Tutorials 2008; 10(3):30‑45.
45. Waldburger M and Stiller B. Toward the mobile grid: Service
provisioning in a mobile dynamic virtual organization. In:
Proceedings of IEEE International Conference on Computer
Systems and Applications; 2006; Dubai/Sharjah, UAE. New
46. Wang SY, Chou CL, Huang CH, Hwang CC, Yang ZM, Chiou
CC et al. The design and implementation of the NCTUns 1.0
network simulator. Computer Networks 2003; 42(2):175‑197.
47. Weng C and Lu X. Heuristic scheduling for bag of tasks
applications in combinationwith QoS in the computational
grid. Future Generation Computing Systems 2005;
21(2):271‑280.
48. Wolf L, Delgrossi L, Steinmetz R, Schaller S and Wittig H.
Issues of reserving resources in advance. In: Proceedings of
ACM International Workshop on Network and Operating System
Support for Digital Audio and Video; 1995; Durham, NH, USA.
New York, NY, USA: ACM Press; 1995. p. 151‑162.
49. Zong Z, Nijim M, Manzanares and Qin X. Energy efficient
scheduling for parallel applications on mobile clusters.
Cluster Computing 2008; 11(1):91‑113.

Performance evaluation of a discovery and scheduling protocol for multihop ad hoc mobile grids

Performance evaluation of a discovery and scheduling protocol for multihop ad hoc mobile grids

More Related Content

What's hot (20)

Viewers also liked (20)

Similar to Performance evaluation of a discovery and scheduling protocol for multihop ad hoc mobile grids (20)

Recently uploaded (20)

Performance evaluation of a discovery and scheduling protocol for multihop ad hoc mobile grids