A Study on Energy Efficient Server Consolidation Heuristics for Virtualized Cloud Environment

International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 3 Issue 4, April 2014
ISSN: 2278 – 1323 All Rights Reserved © 2014 IJARCET 1175

Abstract— With a growing concern over the energy
consumption costs by data centers, research efforts
are aiming towards energy efficient data centers to
maximize the energy efficiency. Server
virtualization is emerging as a vital approach
towards the consolidation of applications from
multiple applications to one server, with a goal to
save energy usage. Savings can be achieved by
dynamic consolidation with live migration of VMs
depending on the resource utilization, virtual
network topologies and thermal state of the
computing machines. However, little understanding
has been obtained about the potential overhead in
the energy consumption for virtualized servers in
data centers. This paper presents a study of the
research work and innovations done by researchers
and academicians based on energy-aware dynamic
VM consolidation from one host to another in cloud
data centers.
Index Terms— Cloud Computing, Quality of Service (QoS),
Bin Packing Problem, Constraint Programming.
I. INTRODUCTION
In 1969, Leonard Kleinrock [15], one of the chief scientists of
the original Advanced Research Projects Agency Network
(ARPANET) which seeded the Internet, said: “As of now,
computer networks are still in their infancy, but as they grow
up and become sophisticated, we will probably see the spread
of computer utilities‟ which, like present electric and
telephone utilities, will service individual homes and offices
across the country.” This vision of computing utilities based
on a service provisioning model anticipated the massive
transformation of the entire computing industry in the 21st
century whereby computing services will be readily available
on demand, like other utility services available in today‟s
society. Similarly, users need to pay providers only when
they access the computing services. In addition, users no
longer need to invest heavily or encounter difficulties in
building and maintaining complex IT infrastructure.
Manuscript received April, 2014.
Susheel Thakur, Department of Computer Science, Himachal Pradesh
University, Shimla, India,
Arvind Kalia, Department of Computer Science, Himachal Pradesh
Jawahar Thakur, Department of Computer Science, Himachal Pradesh
Cloud Computing is defined by NIST [18] as a model for
enabling convenient, on demand network access to a shared
pool of configurable computing resources that can be rapidly
provisioned and released with minimal management effort or
service provider interaction. For simplicity, a cloud is a pool
of physical computing resources i.e. a set of hardware,
processors, memory, storage, networks, etc. which can be
provisioned on demand into services that can grow or shrink
in real-time scenario [27]. Cloud computing can be classified
as a new paradigm for the dynamic provisioning of
computing services supported by state-of-the-art data centers
that usually employ virtual machines technologies for
consolidation and environment isolation purposes [20].
Modern data centers, operating under the cloud computing
model are hosting a variety of applications ranging from
those that run for a few seconds (e.g. serving requests of web
applications such as e-commerce and social networks portals
with transient workloads) to those that run for longer periods
of time (e.g. simulations or large data set processing) on
shared hardware platforms. The need to manage multiple
applications in a data center creates the challenge of
on-demand resource provisioning and allocation in response
to time-varying workloads. Normally, data center resources
are statically allocated to applications, based on peak load
characteristics, in order to maintain isolation and provide
performance guarantees. Until recently, high performance
has been the sole concern in data center deployments and this
demand has been fulfilled without paying much attention to
energy consumption. The fact that power consumption is set
to rise 76% from 2007 to 2030[28] with data centers
contributing an important portion of this hike emphasize the
importance of reducing energy consumptions in the cloud
data centers. According to the Gartner report, the average
data centers consumes as much as energy as 25,000
households [8]. As energy costs are increasing while
availability dwindles, there is a need to shift focus from
optimizing data center resource management for pure
performance to optimizing for energy efficiency while
maintaining high service level performance.
“The total estimated energy level bill for data centers in 2010
is $11.5 billion and energy costs in a typical data centers
double every five years “, according to Mckinsey report”
[11].
Minimizing the energy consumption or going for Green
computing [17], [24] is a major issue of concern. Energy
consumed by servers and in cooling data centers of the cloud
system is an expensive affair. The United States Environment
Protection Agency (EPA) in its report says that energy
consumption of only federated servers and data centers in this
nation was 100 billion KWh in 2011 and infrastructure and
energy (I&E) cost will be 75 percent of the total operation
A Study on Energy Efficient Server
Consolidation Heuristics for Virtualized Cloud
Environment
Susheel Thakur, Arvind Kalia, Jawahar Thakur

cost in 2014[21]. Energy consumption of data centers has
risen 56 percent from 2005 to 2010 worldwide, and in 2010 it
is accounted to be between 1.1 percent and 1.5 percent of the
total electricity used[12]. Thus, minimizing energy
consumption is important and designing energy efficient data
centers has recently received considerable attention of
research community.
There are numerous technologies, services, and
infrastructure-level configurations that can be used to make
cloud computing energy efficient. Virtualization in cloud
computing provides a mechanism the hardware and the
system resources from a given operating system. This is
typically performed within cloud environment across a large
set of servers using hypervisor or virtual machine monitor
(VMM) that acts as a bridge between the hardware and the
operating system (OS) [20]. The cloud computing
middleware is deployed on the top of the virtualization
technologies to exploit the capability to its maximum
potential while maintaining the Quality of service (QoS) to
clients.
The concept of consolidation of virtual machines (VMs) is
applied to minimize energy consumption as it significantly
reduces the percentage of ideal power in the overall
infrastructure. Such a consolidation is done either statically
or dynamically at run time. In the static approach, the
mapping of the VMs to physical machines cannot be changed
at the runtime. While dynamic consolidation of VMs allows
the reallocation of physical resources at the runtime, when
the load on the virtual machines increases or decreases. When
there is a low load on the VMs, less physical infrastructure
need to be employed to provide certain performance level.
And if the load on virtual machine increases, more physical
machines can be allocated. The VMs can be migrated to
another physical host if the current host gets overloaded. A
dynamic consolidation of virtual machines mandates the
cloud service provider to monitor the resource utilization of
virtual machines, in order to determine how many physical
resources have to be assigned for a particular scenario.
Dynamic VM consolidation consists of two basic processes:
Migrations of VMs from underutilized hosts to minimize the
number of active hosts, and offloading VMs from the
physical hosts when they become overloaded to avoid
performance degradation as experienced by the VMs. The
idle hosts automatically switch to a low-power mode to
eliminate the static power and reduce the overall energy
consumption by the system. When required, the physical
hosts are reactivated to accommodate new VMs or VMs
being migrated.
Another major capability provided by virtualization is live
machine migration in which VMs between physical servers
can be transferred with a close to zero downtime. Using live
machine migration, VMs can be dynamically consolidated to
leverage the fluctuations in the workload and keep the
number of active physical servers at the minimum at all times
[5].
Consolidation will result in reduced power consumption and
thus reducing overall operational costs for data center
administrators. Live migrations can be used to achieve this.
Based on the load conditions, under-utilized machines having
resource usage above a certain threshold are identified and
migrations are triggered to tightly pack VMs to increase
overall resource usage on all PMs and try to minimize the
overall energy consumption as possible [1].
II. ENERGY EFFICIENT SERVER CONSOLIDATION
HEURISTICS
Server consolidation is an approach for the efficient usage of
computer server resources in order to reduce the total number
of servers or server locations that an organization requires.
This approach was developed in response to the problem of
“server sprawl”. In order to reduce server sprawl in the data
centers, server consolidation algorithms are implemented.
These algorithms are VM packing heuristics which try to
pack as many VMs as possible on the physical machine (PM)
so that resource usage is improved and under-utilized
machines can be turned off, aiming to minimize the energy
consumption and enhancing the throughput in the cloud data
centers.
A. Sandpiper: Black box and Gray-box resource
management for Virtual Machines
Sandpiper is a system that automates the task of monitoring
and detecting hotspots, determining a new mapping of
physical resources to virtual resources, by resizing or
migrating VM‟s to eliminate the hotspots. Sandpiper makes
use of automated black-box and gray box strategies for
virtual machine provisioning in cloud data centers.
Specifically the black-box strategy can make decisions by
simply observing each virtual machine from the outside and
without any knowledge of the application resident within
each VM. The authors present a gray-box approach that
assumes access to OS-level statistics in addition to external
observations to better inform the provisioning algorithm.
Sandpiper implements a hotspot detection algorithm that
determines when to resize or migrate virtual machines, and a
hotspot migration algorithm that determines what and where
to migrate and how many resources to allocate. The hotspot
detection component employs a monitoring and profiling
engine that gathers usage statistics on various virtual and
physical servers and constructs profiles of resource usage.
These profiles are used in conjunction with prediction
techniques to detect hotspots in the system. Upon detection,
Sandpiper grants additional resources to overloaded servers if
available. If necessary, Sandpiper‟s migration is invoked for
further hotspot mitigation. The migration manager employs
provisioning techniques to determine the resource needs of
overloaded VMs to underloaded servers.
Sandpiper supports both black-box and gray-box monitoring
techniques that are combined with profile generation tools to
detect hotspots and predict VM Resource requirements.
Hotspots are detected when CPU usage values are violated
with respect to the CPU thresholds set. Physical machines
(PMs) are classified as underloaded or overloaded. The PMs
are sorted based on the descending order of their volume
metric, and VMs are sorted based on the descending order of
their vsr metric, where volume (vol) and vsr are computed as:
(1)
(2)
where cpu, mem and net refers to cpu, memory and n/w
usages of the PMs and VMs respectively and size refers to the
memory footprint of the VM. vsr is the volume-size metric.
To mitigate hotspot on an overloaded PM, the highest vsr VM
is migrated to a least loaded PM amongst the underloaded
ones. If the least loaded PM can‟t house the PM, next PM in
the sorted order is checked. Similarly, if the VM cannot be

housed in any of the underloaded PMs, next VM in the sorted
order is checked. This way sandpiper tries to eliminate
hotspots by remapping VMs on PMs through migration. The
experimental results showed that migration overhead is less
than that of swapping overhead; however, swapping
increases the chances of mitigating hotspots in cluster with
high average utilization [23], [25].
B. EnaCloud: An Energy Saving Application
Bo Li et. al proposed EnaCloud which supports application
scheduling and live migration to minimize the number of
running physical machines in order to save energy. It also
aims to reduce the number of migrations of virtual machines.
In EnaCloud, there is a central global controller which runs
the Concentration Manager and Job Scheduler. The job
scheduler receives the arrival of workload, departure and
resizing events and deliver them to the Concentration
Manager. Then Concentration Manager then generates a
series of insertion and migration operations for application
placement scheme which are passed to the Job Scheduler.
The Job Scheduler then dispatches them to the virtual
machine controller by decomposing the schemes.
Each resource node consists of Virtual Machine Controller,
Resource Provision Manager and Performance Monitor. The
Virtual Machine Controller invokes the hypervisor to execute
the commands such as VM start, stop or migrate. The
Resource Provision Manager does the resizing of VMs based
on the performance statistics collected by the Performance
Monitor.
Figure 2.1 EnaCloud Architecture
Here, two types of nodes are considered: computing nodes
and storage nodes. Storage nodes store the data and files
while the computing nodes are considered homogeneous and
hosts one or more VMs. The application program in the VM
and the underlying operating system together are termed as
workload. A server node running VMs is known as openbox
and idle server node is known as closebox. In EnaCloud, the
workloads are aggregated tightly to minimize the number of
openboxes. The applications have varying resource demands
and so workload resizing is included in it. It finds a solution
for remapping workloads to the resource nodes through
migration whenever a workload arrives, departs or resizes.
The migration here mainly has two aims: first to reduce the
number of openboxes and second is to minimize the
migration time [4].
C. Application Performance Management in Virtualized
Server Environments
Gunjan et al., in [6], [23] proposed Dynamic Management
Algorithm (DMA) that is based on Polynomial-Time
Approximation Scheme (PTAS) heuristic algorithm. The
algorithm operates by maintaining two types of ordering lists,
which are migration cost list and residual capacity list. The
PMs are sorted according to the increasing order of their
residual capacities across any resource dimension like CPU.
The VMs on each PM are sorted according to the increasing
order of their resource utilization like CPU usage. Migration
costs of the VMs are determined based on their resource
usage i.e. high usage implies high costly migration.
Whenever a hotspot is detected on a PM due to violation of
upper threshold, VM with least resource usage is chosen for
migration to target host which has the least residual capacity
to house it. If a PM cannot accommodate the VM, next PM in
the sorted order is checked. Similarly, if the VM cannot be
accommodated by any of the candidate target PMs, next least
usage VM from the sorted order is checked.
Whenever coldspots are detected, the least usage VMs across
all the underloaded PMs is chosen and migrated to a targeted
PM, only if addition of the new VM increases the variance of
residual capacities across all the PMs, else we choose the next
VM in order. If there is no residual space left for the chosen
VM, then the heuristic for coldspot mitigation stops.
Variance is defined as follows:
(3)
(4)
rn= (5)
In above equation 4, mean is defined as the average of
normalized residual capacities across „m‟ different resources
like cpu, memory, networks, etc. rescpu , resmem , resnet …
stands for residual capacities across different resource
dimensions. In equation 5, rn is the magnitude of the vector
which comprises of the individual variances across „n‟
physical machines.
Khanna‟s Algorithm packs the VMs as tightly as possible
trying to minimize the number of PMs by maximizing the
variance across all the PMs. Thus, Khanna‟s algorithm
minimizes power consumption by detecting underutilization
in the managed using Max-Min thresholds selection model.
When the resource usage of a running PM violates a
minimum predefined threshold value, the algorithm tries to
pack the running VMs as close as possible thus trying to
minimize the number of running physical machines.
D. A Load Aggregation Method
One important method for reducing the energy
consumption in data centers is to consolidate the server load

within a few number of physical machines while switching
off the rest of the physical systems. Usually this is achieved
by using virtualization of the systems.
Daniel Versick et. al [6] proposed this load aggregation
method which uses some ideas of K-means partitioning
clustering algorithm that can compute the results quickly.
The K-means chooses cluster centers within an
n-dimensional space randomly and the distances between the
cluster centers and vertices is calculated. The algorithm has
three steps: Initialization, Iteration and Termination. First,
the number of clusters is calculated based on resource needs.
Some of the physical machines are defined as cluster centers.
Each cluster center represents one cluster. A cluster consists
of physical machines hosting a number of virtual machines.
Virtual Machines are added to the cluster VM list of nearest
cluster center that can fulfill necessary requirements. If the
clusters cannot fulfill the requirements, the virtual machine
are added to new cluster with still unused physical server as
cluster center. Every virtual machine is assigned to a cluster
center. A new cluster center for every cluster which is nearest
physical machine is calculated. If the cluster centers gets
changed during last iteration and if the iterations are not at its
maximum, then the empty clusters are used again to add the
virtual machines. Else, the VMs of a cluster are migrated to
the physical machines representing a cluster center. And at
last, the physical which are not cluster centers are turned off.
E. Entropy: A Consolidation Manager for Clusters
Entropy proposes a consolidation algorithm based on
constraint problem solving. The main idea of the constraint
programming based resource manager is to formulate the VM
resource allocation problem as constraint satisfaction
problem, and then applies a constraint solver to solve the
optimization problem. The ability of this solver to find the
global optimum solution is the main motivation to take this
approach. Entropy resource manager utilizes Choco
constraint solver to achieve the objectives of minimizing the
number of the running nodes and minimizing the migration
cost. Entropy iteratively checks optimality constraint i.e. the
current placement uses minimum number of the running
nodes. If Entropy is successful in constructing a new optimal
placement (uses fewer nodes) at VM packing problem
(VMPP) phase, it will activate the re-allocation. Entropy
employs a migration cost model that relates memory and
CPU usage with migration context. High parallelism
migration steps increases the cost. Using constraint
programming techniques facilitates the task of capturing such
context in two phases. In the first phase, Entropy computes a
tentative placement (mapping of VMs to PMs) based on the
current topology and resource usage of PMs and VMs and
reconfiguration plan needed to achieve the placement using
minimum number of PMs required. In the second phase, it
tries to improve the reconfiguration plan by reducing the
number of migrations required. Since obtaining the
placement and reconfiguration may take a considerable
amount of time, the time given to the CSP solver is defined
by the users, exceeding which whatever immediate value the
solver has computed is considered for dynamic placement of
VMs. VMs are classified as active or inactive based on their
usage of CPU with respect to thresholds set. The author
define a viable configuration as one in which every active
VM present in the cluster has access to sufficient cpu and
memory resources on any PM. There can be any number of
inactive VM on the PM satisfying the constraint. The CSP
solver takes this viable condition into account in addition to
the resource constraints, while procuring the final placement
plan. However, considering only viable processing nodes and
Cpu-Memory Resource model is the limitation of the Entropy
model [7], [23].
F. Adaptive Threshold-based Approach
Anton Beloglazov and R. Buyya [2] proposed a novel
technique for dynamic VM Consolidation based on adaptive
utilization thresholds which ensures a high level of service
level agreements (SLA) providing high quality service to
customers and dealing with the energy-performance
trade-offs. Fixed values of thresholds are not suitable in
environments when dealing with dynamic and unpredictable
workloads. The system should be able to automatically adjust
in such a way as to handle the workload pattern exhibited by
the application. So, there is need for auto-adjustment of the
utilization thresholds based on the statistical analysis of the
historical data collected during the VM‟s life time. The CPU
utilization of a host is the summation of the utilizations of the
VMs allocated to that host and can be modelled by
t-distribution. The data of each VM‟s CPU utilization is
collected separately. This, along with the inverse cumulative
probability function for the t-distribution enable to set the
interval of the CPU utilization. The lower threshold is the
same for all the hosts. The complexity of the algorithm is
proportional to the sum of the number of non-over-utilized
hosts plus the product of the number of over-utilized hosts
and the VMs allocated to the over-utilized hosts.
G. Sercon: Server Consolidation Algorithm using live
migration of virtual machines for Green Computing
This server consolidation algorithm aimed at consolidating
the virtual machines (VMs) so that minimum nodes (physical
machines) are used and reducing the number of VM
migrations. Certain constraints are taken into account in
Sercon. They include compatible virtualization software,
comparable CPU‟s types, and similar n/w topology and
shared storage usage on both source and destination nodes,
choosing the right value of CPU threshold to prevent
performance degradation and migration of VM is done if it
results in releasing a node. The algorithm goes like this: The
nodes based on loads by VMs are sorted in decreasing order.
Then, the VMs on the least loaded node in this list are
selected as a candidate for migration and are again sorted
according to their weights. They are allocated to the most
loaded node first and so on, thus trying to compact them and
so can release the least loaded node. By using this method, we
can avoid numerous migrations which might otherwise be
necessary if there are nodes that are still least loaded. These
steps are repeated until no more migrations are possible. CPU
and Memory are considered for representing load in VMs [3],
[23].
Table I: Energy Efficient Server Consolidation Heuristics
Algorithm Goal Platform
Used
Resource
Considered
Sandpiper
[25]
Hotspot
Mitigation
Xen CPU,
memory &
network
EnaCloud
[4]
Minimize
energy
consumption,
Application
Xen
VMM
Memory,
Storage

scheduling
Khanna‟s
Algorithm
[6]
Server
Consolidation
VMware
ESX
CPU,
Memory
A Load
Aggregation
Method [6]
Minimize
energy
consumption,
minimize
running
physical
machines
Not yet
implement
ed
Memory,
Storage
Entropy [7] Server
Consolidation
Xen 3.0.3 CPU,
Memory
Adaptive
Threshold
Based
Approach [2]
Dynamic
Consolidation
of VMs with
minimum
SLA
violations, no.
of migrations
Cloudsim CPU
Sercon [3] Server
Consolidation
Sercon
Software
CPU,
Memory
Energy
Efficient VM
consolidatio
n for Cloud
Computing
[19]
Energy
efficient
storage
migration,
live migration
of VMs
Eucalyptu
s
Storage
MiyakoDori
[22]
Server
Consolidation
Qemu/KV
M
Memory
Memory
Buddies [26]
Server
Consolidation
& Hotspot
Mitigation
VMware
ESX
Memory
H. Energy Efficient VM Consolidation in Eucalyptus
Pablo Graubner et. al [19] discussed this energy efficient
VM Consolidation in Eucalyptus and is based on the storage
synchronization. VM live migration and storage
synchronization phases are introduced explicitly instead of
permanent synchronization of the disk image via network.
This technique leverages the concept of Distributed
Replicated Block Device (DBRD), which is typically used
for high availability data storage in a distributed system. The
DBRD modules works in two modes: Stand-alone and
Synchronized. In stand-alone mode, all the disk accesses are
passed to the underlying disk driver, while in synchronized
mode, disk writes are both passed to underlying disk driver
and sent to a backup machine through a network while the
disk reads are served locally. A multilayered root file system
(MLRFS) is used for the virtual machine‟s root image. The
basic image is cached on a local disk. The local modifications
are stored on a separate layer and then they are overlaid with
the basic image transparently and form a single logical file
system using Copy-on-Write (COW) mechanism. Thus, only
the local modifications are sent during the disk
synchronization phase.
I. MiyakoDori: A Memory Reusing Mechanism for
Dynamic VM Consolidation
MiyakoDori proposes a memory reusing mechanism to
reduce the amount of transferred data in a live migration
process. In the case of dynamic VM consolidation, a VM may
migrate back to the host where it was once executed. The
memory image of the VM is left on the host when it migrates
out from the host and the image will be reused when the VM
migrate back to host later. The few amounts of data
contribute to a shorter migration time and greater
optimization by VM placement algorithms. Evaluations
showed MiyakoDori reduces the amount of transferred
memory and total migration time of a live migration and thus
reduces the energy consumption of a dynamic VM
consolidation system [22], [23].
J. Memory Buddies: Exploiting Page Sharing for Smart
Colocation in Virtualized Data Centers
Memory Buddies is a memory sharing aware placement
system for virtual machines. It is a memory fingerprinting
system to efficiently determine the page sharing potential
among a set of VMs, and compute more efficient placements.
It makes use of live migration to optimize VM placement as
workload changes. Memory buddies detects sharing potential
to realize these benefits. The memory buddies system
consists of a nucleus, which runs on each server, and a control
pane, which runs on distinguished control server. Each
nucleus generates a memory footprint of all memory pages
within the VMs resident on that server. This fingerprint
represents the page-level memory contents of a VM in a way
which allows efficient calculation of the number of pages
with identical content across two VMs. The control plane is
responsible for virtual migration placement and hotspot
mitigation. For placing a virtual machine, it compares the
fingerprint of that VM against server fingerprints in order to
determine a location for it which will maximize sharing
opportunities. It then places the VM on that server, initiating
migrations if necessary. The control plane interacts with the
VMs through a VM management API such as VMware‟s
Virtual Infrastructure or the libvirt API.
Memory Buddies server consolidation algorithm
opportunistically identifies servers that are candidates for
shutting down and attempts to migrate virtual machines to
hosts with high sharing opportunities. In doing so, it attempts
to pack VMs onto servers so as to reduce aggregate memory
footprint and maximize the number of VMs that can be
housed in the data center. Once the migrations are completed
the consolidation candidates can be retired from service or
powered down until new server capacity is required, thereby
saving on operational costs. The consolidation algorithm runs
periodically to check the list of hosts which are candidates for
consolidating if its mean usage remains below a low
threshold for an extended duration. The system considers
only memory usages when identifying consolidation
candidates. Once the consolidation candidates are identified,
the algorithm determines a new physical server to house each
VM. To do so, VMs are arranged according to their
decreasing order of the memory sizes and consider them one
at a time. Firstly for each VM, the algorithm determines the
set of feasible servers in the data centers and then the host
which will provide the greatest level of sharing is selected for
each VM. Once new targets have been determined for each
VM on the consolidation servers, actual migration is
performed using live migration. Live migration ensures
transparency and near-zero down-times for the application
executing inside the migrated VMs. To ensure minimum
impact of network copying triggered by each migration of
application performance, the algorithm places a limit on the
number of the concurrent migrations; once each migration

completes, a pending one is triggered until all VMs have
migrated to their new hosts. The servers are then powered off
and retired or moved to a shutdown so that they can be
reinitialized later if memory requirements increase [23], [26].
III. CONCLUSION
Energy Management is the one of the most challenging
tasks faced by the infrastructure providers in cloud data
centers. Due to subsequent increase in the data and
processing, it is not possible to keep control over the energy
consumption as the performance will be primarily affected.
Some of the existing heuristics for energy efficient VM live
migration were studied. All these heuristics mainly focus on
reducing the energy consumption. Overcoming all the
barriers for energy consumption is not possible as each of the
heuristics through light on different parameters, though with
certain disadvantage of their own.
More Heuristics can be proposed to reduce the energy
consumption and to overcome the energy-performance
trade-offs.
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009.pdf
Susheel Thakur received his B.Tech (I.T)
Degree from University Institute of Information
Technology, Himachal Pradesh University, Shimla,
India and pursuing M.Tech (CS) Degree from
Department of Computer Science, Himachal
Pradesh University, Shimla, India. His field of
Interest are Cloud Computing, Database
Management System and Software Engineering.
Arvind Kalia is currently working as
Professor in Department of Computer
Science, Himachal Pradesh University,
Shimla (H.P) India. He is having 25 years
of teaching and research experience. His
area of interest includes Software
Engineering, Computer Networks, Data
mining and Cloud Computing. He is a
member of different science bodies like
CSI, Indian Science Congress, etc.
Jawahar Thakur is currently working as
Associate Professor in Department of Computer
Science, Himachal Pradesh University, Shimla,
India. He has received his B.Tech (CSE) Degree
from NIT Hamirpur, India and M.Tech (CSE) from
NITTTR, Panjab University Chandigarh. He is
currently pursuing PhD in Computer Networking.

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