SPEEDING UP THE WEB CRAWLING PROCESS ON A MULTI-CORE PROCESSOR USING VIRTUALIZATION

International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
DOI : 10.5121/ijwsc.2013.4102 19
SPEEDING UP THE WEB CRAWLING PROCESS ON A
MULTI-CORE PROCESSOR USING VIRTUALIZATION
Hussein Al-Bahadili1
, Hamzah Qtishat2
, Reyadh S. Naoum2
1
Faculty of Information Technology, Petra University, Amman, Jordan
hbahadili@uop.edu.jo
2
Faculty of Information Technology, Middle East University, Amman, Jordan
hamzah.qtishat@yahoo.com,rnaoum@meu.edu.jo
ABSTRACT
A Web crawler is an important component of the Web search engine. It demands large amount of hardware
resources (CPU and memory) to crawl data from the rapidly growing and changing Web. So that the
crawling process should be a continuous process performed from time-to-time to maintain up-to-date
crawled data. This paper develops and investigates the performance of a new approach to speed up the
crawling process on a multi-core processor through virtualization. In this approach, the multi-core
processor is divided into a number of virtual-machines (VMs) that can run in parallel (concurrently)
performing different crawling tasks on different data. It presents a description, implementation, and
evaluation of a VM-based distributed Web crawler. In order to estimate the speedup factor achieved by the
VM-based crawler over a non-virtualization crawler, extensive crawling experiments were carried-out to
estimate the crawling times for various numbers of documents. Furthermore, the average crawling rate in
documents per unit time is computed, and the effect of the number of VMs on the speedup factor is
investigated. For example, on an Intel® Core™ i5-2300 CPU @2.80 GHz and 8 GB memory, a speedup
factor of ~1.48 is achieved when crawling 70000 documents on 3 and 4 VMs.
KEYWORDS
Web search engine; Web crawler; virtualization; virtual machines; distributed crawling; multi-core
processor; distribution methodologies; processor-farm methodology.
1. INTRODUCTION
A Web search engine is designed to help finding and retrieving information stored on the Web,
where it enables users to search the Web storage for specific content in a form of text meeting
certain criteria and retrieving a list of files that meet those criteria [1, 2].The size of the Web is
currently witnessing an exponential growth, having over billions of pages, and it continues to
grow rapidly at millions of pages per day. Such growth poses a huge challenge for today’s generic
Web search engines like Google and Yahoo[3].
Web search engine consists of three main components; these are: (1) Web crawler, (2) document
analyzer and indexer, and (3) search processor [1]. When a user enters a query into a search
engine (known as keywords), the Web search engine processes the query, then examines its index
and provides a listing of best-matching Web pages according to its criteria, usually with a short
summary containing the document's title and sometimes parts of the text [4].
The Web crawler is one of the most important and time consuming component of the Web search
engine [5, 6]. It demands large amount of hardware resources (CPU, memory, and disk storage) to

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crawl data from the rapidly growing and changing Web. The crawling process should be
performed continuously in as short as possible time to maintain highest updatability of it search
outcomes. Despite the fact that powerful computers and efficient crawling software are currently
in use by Web crawlers, the largest crawls cover only 30–40% of the Web, and refreshment of the
Web takes weeks to a month [3].
In order to speed up the crawling process, there are two main approaches. The first approach
makes use of high-performance computers or distributed crawling on multiple computers
interconnected in local or wide area networks or combination of both [7-12]. The second
approach makes use of fast and efficient crawling computational architecture, algorithms, and
models, and recognizes and crawl relevant sites and pages for a limited time [13]. This way, the
performance bottlenecks on CPU and memory can be relaxed and fairly good speedups can be
achieved.
Fortunately, there is an impressive gain in processor performance, due to advances in hardware
technologies and also to innovation in processor architecture (i.e., how the processor is designed
and organized to perform its computational tasks) [14]. One distinguish development is the
introduction of parallelism in the architecture of the processors in the form of pipelining,
multitasking, and multithreading leading to significant performance enhancement [15]. As a result
of that a new type of relatively low-cost and powerful multi-core processor is emerged and
widely-used in current computers. A multi-core processor has two or more microprocessors
(cores) each with its own memory cache on a single chip [16]. The cores can operate in parallel
and run programs much faster than a traditional single-core chip with a comparable processing
power. Most manufacturers, such as Intel, offer now up to seven-core processor, which is known
as i7-core processors; and as technology advances, the number of cores will continue to increase.
Another major component of a computer is the software, which is categorized into operating
system (OS) and application software. Current computer software is significantly improved to
efficiently utilize the processing power of the emerged multi-core processors [16].
Due to its high speed, multi-core processors can play a big role in speeding up the crawling
process for both standard and special Web search engines, and can be used as a major building
block in constructing cost-effective high speed crawling system. This paperdevelopsand evaluates
the performance of a new approach to improve the multi-core processor utilization and
consequently speeding up the crawling process on such processors. The new approach utilizes the
concept of virtualization, in which the multi-core processor is decomposed into a number of
virtual machines (VMs) that can operate in parallel performing different crawling tasks on
different data (Webpages or Websites).
In order to evaluate the performance of the new approach, two types of crawlers are developed.
The first one runs on a multi–core processor with no virtualization (i.e., no VMs are installed on),
therefore it is called no virtualization crawler (NOVCrawler). The second one runs on a multi-
core processor with a number of VMs installed on each performing part of the crawling process;
therefore, it is called distributed VM-based crawler (DVMCrawler). The parallel (distributed)
programming methodology that is used in porting NOVCrawler to run efficiently on the VMs is
the processor-farm methodology. Extensive crawling experiments were carried-out to estimate the
speedup factor achieved by DVMCrawler over NOVCrawler for various numbers of crawled
documents. Furthermore, the average crawling rate in documents per unit time is computed, and
the effect of the number of VMs on the speedup factor is investigated.
This paper is divided into eight sections, where this section provides an introduction to the main
theme of the paper, and the rest is organized as follows. Section 2 provides the reader with some
background on two main topics covered in this paper, namely, Web search engine and
virtualization. Review of some of the most recent and related work is presented in Section 3. The

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developed VM-based distributed crawling system is described in Section 4. The main tools used
in this work and the implementations of NOVCrawler and DVMCrawler are described in Section
5 and 6. Section 7 evaluates the performance of the developed VM-based distributed crawling
system through a number of extensive crawling experiments. The obtained results are thoroughly
discussed in Section 7. Finally, in Section 8, based on an in-depth study and analysis of the results
some conclusions are drawn and a number of recommendations for future work are pointed-out.
2. BACKGROUND
This section provides a brief background on two of the main topics underlined in this paper,
namely, the Web search engine and the virtualization.
2.1. Web Search Engine
Web search engines are an information retrieval systems designed to help finding information
stored on the Web quickly [1]. These Web search engines execute user search queries at lightning
speed and the search results are relevant most of the times, especially, if the user frame his search
queries right. Figure 1 outlines the architecture and main components of a standard Web search
engine model [17].
Figure 1. Main components of standard search engine model.
Web search engines can be classified into generic and specialized Web search engines. Example
of generic Web search engines include: MSN, Yahoo, Google, etc. Examples of specialized
search engines include: Truveo and Blinkx TV for finding video content from all video sharing
Websites, Omgili for searching in discussions happening on public Internet forums and online
mailing lists, Pipl for extracting personal information about any person from the Web, Ask Blog
for searching content published on blogs (and other sites that offer RSS feeds). Another Web
search engines with special features also include: TinEye, Wolfram Alpha, Alexa, Agame,
BTJunkie, ChaCha, Crunch Base, Scour, Spokeo, Tweepz, and Address Search.
Standard Web search engine consists of Web crawler, document analyzer and indexer, and
searching process [6, 17]. Consequently, Web search process can be broken up into three main
sub processes as shown in Figure 2; these are:
Back-end Processor
Front-end Processor
Web
Crawler
Indexer
Index
Request
WWW Page WWW page
Ranking
Query
Parser
WebSEARCH
Engine
Interface
Web Search
Engine User
Query
Results

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• Crawling process (CP), which runs on the crawling processor (CPR) (CP→CPR).
• Analysis and indexing process (AIP), which runs on the analysis and indexing processor
(AIPR) (AIP→AIPR).
• Searching process (SP), which runs on the search processor (SPR) (SP→SPR).
Since, we concern with the Web crawling process, in what follows, we shall briefly describe the
Web crawler, its minimum requirements, and the general Web crawling strategies.
Figure 2.Web search process model.
2.1.1. Web Crawler
The Web crawler is an important component of the search engine [6]. It is a program that is used
to read the Meta tags specified by the Website creator, find, download, parse content, and store
pages in an index file. A crawler usually needs initial Web addresses as a starting point in order to
index the information about these Websites. These addresses are the Websites Uniform Resource
Locators (URLs), and they are called the seed URLs. After specifying these URLs, the crawler
finds the hyperlink text and Meta tags in all pages of the Website until the text finishes [5]. Given
a set of seed URLs, the crawler repeatedly removes one URL from the seeds, downloads the
corresponding page, extracts all the URLs contained in it, and adds any previously unknown
URLs to the seeds [2].It is important to know at this stage that most of the Websites are very large
so it can take long time to crawl and index all the data. Furthermore, Websites change their
contents frequently, so it is necessary to carefully consider this frequent change as when to revisit
the page again in order to keep the index updatability. Further requirements for any crawling
system may include: flexibility, high performance, fault tolerance, maintainability,
configurability, etc [5].
Web crawlers are classified into specific crawler architecture and general-purpose (open source)
crawlers. Examples of specific crawler architecture include: Googlebot is the Google crawler,
Yahoo! Slurp is the Yahoo crawler, Bingbot is the Microsoft's Bing webcrawler (It is developed
as a replacement to MSNbot), FAST crawler is a distributed crawler developed for Fast Search &
Transfer, PolyBot is a distributed crawler, RBSE, WebCrawler, World Wide Web Worm,
WebFountain, and WebRACE. Examples of general-purpose (open-source) crawlers, such as:
Aspseek, GNU Wget, Datapark Search, GRUB, ICDL, Heritrix, HTTrack, PHP-Crawler,
mnoGoSearch, Nutch, Open Search Server, tkWWW, YaCy, Seeks, and Larbin.
2.1.2. General Crawling Strategies
There are many crawling strategies that have been developed since the engagement of the
Internet, such as [13]:
Searching Process (SP→SPR)
Analysis & Indexing Process
(AIP→AIPR)
Crawling Process (CP→CPR)

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• Breadth-first crawling: a crawl is carried out from a list of Web pages (initial URLs or
seeds)to build a wide Web archive similar to that of the Internet Archive. A breadth-first
exploration is started by following hypertext links leading to those pages directly connected
with these initial URLs. In fact, it is not as easy as it is explained above, Websites are not
really browsed breadth-first and various restrictions are actually applied, e.g., limiting
crawling processes to within particular sites, or downloading the pages believed most
interesting first.
• Repetitive crawling: once pages have been crawled, some systems continue to perform the
process periodically so that indexes remain updated. This may basically be achieved by
launching a second crawl concurrently. Various heuristics exist to overcome this problem,
such as: re-launching the crawling process of pages frequently, domains or sites considered
important to the detriment of others. However, it can be easily recognized that a good
crawling strategy is crucial for maintaining a constantly updated index list.
• Targeted crawling: specialized search engines use advanced crawling process heuristics in
order to target a particular type of page, e.g., pages on a specific topic/language, images,
audio files, video files or scientific papers. In addition to these advanced heuristics, more
generic approaches have been proposed. They are based on the analysis of the structures of
hypertext links and learning techniques: the objective here being to retrieve the greatest
number of pages relating to a particular subject while using minimum bandwidth.
• Random walks and sampling: some researches have focused on the effect of random walks
on Web graphs or modified versions of these graphs through sampling in order to estimate
the size of documents on line.
• Deep Web crawling: a lot of data accessible through the Web are currently enclosed in
databases and may only be downloaded through appropriate requests or forms. Lately, this
often neglected. The Deep Web is a name given to a Web that contains such datbase.
2.2. Virtualization
Virtualization refers to hiding the physical characteristics of computing resources to streamline
the way in which applications, users, or other systems interact with those computing resources
[18]. It allows a single physical resource (e.g., servers or storage devices) appears as multiple
logical resources; or makes multiple physical resources appears as a single logical resource.
Virtualization also can be defined as the process of decomposing the computer hardware
recourses into a number of VMs. A VM is a software implementation of computing hardware
resources on which an OS or program can be installed and run. The VM typically emulates a
computing hardware resources by formation of a virtualization layer, which translates requests to
the underlying physical hardware, manages requests for processor, memory, hard disk, network
and other hardware resources.
There are different forms of virtualization that have been developed throughout the years as
shown in Figure 3; these are [19]:
• Guest OSbased virtualization. Under this virtualization the physical host system runs a
standard unmodified OS such as Windows, Linux, UNIX, or Mac OS. Running on this OS is
a virtualization application (VA) which is executed as any application would run on the
system. It is within this VA that one or more VMs are created to run the guest OSs on the
host system. The VA is accountable for starting, stopping and managing the VM(s) and
essentially controlling access to physical resources on behalf of the individual VM(s). Some
examples of guest OS virtualization technologies include VMware Server and VirtualBox.
Figure 3a provides an illustration of guest OS based virtualization.
• Shared kernel virtualization. Also known as system level or OS virtualization. Under this
form of virtualization, each virtual guest system has its own root file system and all guest

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systems share the kernel of the host OS. Examples of shared kernel virtualization include:
Solaris Zones and Containers, Linux V Server, Open VZ and Free VPS. This structure is
illustrated in the architectural diagram in Figure 3b.
• Kernel-level virtualization. Under which the host OS runs on a specially modified kernel
which contains extensions designed to manage and control multiple VMs each containing a
guest OS. Examples of this virtualization approach include: Kernel-based Virtual Machine
and User Mode Linux. Figure 3c provides an overview of the kernel level virtualization
architecture.
• Hypervisor virtualization. The x86 family of CPUs provide a variety of protection levels that
are known as rings in which code can execute. The highest level is Ring 0, where the OS
kernel normally runs. Code running in Ring 0 is said to be running in supervisor mode,
kernel mode, or system space. All other codes such as applications running on the OS runs
in less privileged rings, typically Ring 3.In hypervisor virtualization, a program known as a
hypervisor runs directly on the host system in Ring 0to handle resource and memory
allocation for the VMs in addition to providing interfaces for higher level administration and
monitoring tools. Figure 3d illustrates the hypervisor approach to virtualization.
Clearly, with the hypervisor running on Ring 0, the kernels for any guest OSs running on the
system must run in less privileged Rings. Unfortunately, most OS kernels are written
explicitly to run in Ring 0 for the simple reason that they need to perform tasks that are only
available in that Ring, such as the ability to execute privileged instructions and directly
manipulate memory. A number of different solutions to this problem have been proposed,
such as: para virtualization, full virtualization, and hardware virtualization [20].
Hypervisor virtualization solutions include Xen, VMware ESX Server and Microsoft's
Hyper-V technology. In this paper, the Xen hypervisor is used as a virtualization platform
and it will be discussed in more details later on.
Figure 3. Block diagrams of various virtualization structures.
VMs can provide many advantages over directly installing OS's and software on physical
hardware. Isolating applications ensure that applications and services that run within a VM cannot
interfere with the host OS or other VMs. VMs can also be easily manipulated, e.g., moved,
Administrative
Environment
Virtual
Machine
Guest OS
Hypervisor
Host Hardware
(d) Hypervisor virtualization.
Host OS
Guest
OS and
Root
File
System
Host Hardware
Shared Kernel
Guest
OS and
Root
File
System
Guest
OS and
Root
File
System
(b) Share kernel virtualization.
Host OS
Guest
OS and
Root
File
System
Host Hardware
Shared Kernel
Guest
OS and
Root
File
System
Guest
OS and
Root
File
System
(c) Kernel-level virtualization.
Virtualization Application
Guest
OS
Guest
OS
Guest
OS
Host OS
Host Hardware
(a) Guest OS based virtualization.

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copied, and reassigned between host servers to optimize hardware resource utilization.
Administrators can take advantage of virtual environments to simply backups, disaster recovery,
new deployments and basic system administration tasks. VMs also comes with several important
management considerations, many of which can be addressed through general systems
administration best practices and tools that are designed to manage VMs [18].
3. LITERATURE REVIEW
This section presents a review on most recent work related to reducing the crawling processing
time. The reviewed work is presented in chronological order from the old to the most recent.
Choet al. [21] studied the sequence in which the crawler should visit the URLs it has seen, in
order to obtain important pages first. Obtaining important pages quickly can be very useful when
a crawler cannot visit the entire Web in a reasonable amount of time. They defined several
importance metrics, ordering schemes, and performance evaluation measures for this problem.
They also experimentally evaluated the ordering schemes on the Stanford University Web. Their
results showed that a good ordering scheme can help crawler obtains important pages
significantly faster than one without.
Chakrabartiet al. [22] described a hypertext resource discovery system called a Focused Crawler
(FC), which aims to selectively seek out pages that are relevant to a pre-defined set of topics. The
topics are specified using exemplary documents not using keywords. Rather than collecting and
indexing all accessible Web documents to be able to answer all possible ad-hoc queries, a FC
analyzes its crawl boundary to find the links that are likely to be most relevant for the crawl, and
avoids irrelevant regions of the Web. This leads to significant savings in hardware and network
resources, and helps keeping updated crawler.
Chung and Clarke [23] proposed a topic-oriented approach, which partitions the Web into general
subject areas with a crawler assigned to each subject area to minimize the overlap between the
activities of individual partitions. They designed and examined the creation of a Web page
classifier approach for use in this context; which is compared with a hash-based partitioning. The
comparison demonstrated the feasibility of the topic-oriented approach, addressing issues of
communication overhead, duplicate content detection, and page quality assessment.
Yanet al. [24] proposed an architectural design and evaluation result of an efficient Web-crawling
system. Their design involves a fully distributed architecture, a URL allocating algorithm, and a
method to assure system scalability and dynamic configurability. The results showed that load
balance, scalability and efficiency can be achieved in the system. Their distributed Web-crawling
system successfully integrated with Web Gather, a well-known Chinese and English Web search
engine. They also suggested that their design can also be useful in other context such as digital
library.
Shkapenyuk and Suel[12] described the design and implementation of a distributed Web crawler
that runs on a network of workstations. The crawler scales to (at least) several hundred pages per
second, is resilient against system crashes and other events, and can be adapted to various
crawling applications. They presented the software architecture of the system, discussed the
performance bottlenecks, and described efficient techniques for achieving high performance.
They also reported preliminary experimental results based on a crawl of million pages on million
hosts.
Takahashiet al.[25] described a scalable Web crawler architecture that uses distributed resources.
The architecture allows using loosely managed computing nodes (PCs connected to the Internet),
and may save communication bandwidth significantly. They demonstrated the importance of such

26
architecture, point-out difficulties in designing such architecture, and described their design. They
also reported experimental results to support the potential of their crawler design.
Looet al. [26]developed a distributed Web crawler that harnesses the excess bandwidth and
computing resources of clients to crawl the Web. Nodes participating in the crawl use a
Distributed Hash Table (DHT) to coordinate and distribute search. They investigated different
crawl distribution strategies and estimated the trade-offs in communication overheads, crawl
throughput, balancing load on the crawlers as well as crawl targets, and the ability to exploit
network proximity. They described the implementation of the distributed crawler using PIER, a
relational query processor that runs over the Bamboo DHT, and compared different crawl
strategies on Planet-Lab querying live Web sources.
Hafriand Djeraba[27] developed a real-time distributed system of Web crawling running on a
cluster of machines. The system is platform independent and is able to adapt transparently to a
wide range of configurations without incurring additional hardware expenditure, it includes a
high-performance fault manager and it can crawl several thousands of pages every second. They
provided details of the system architecture and described the technical choices for very high
performance crawling.
Expostoet al. [28] evaluated scalable distributed crawling by means of the geographical partition
of the Web. The approach is based on the existence of multiple distributed crawlers each one
responsible for the pages belonging to one or more previously identified geographical zones. For
the initial assignment of a page to a partition they used a simple heuristic that marks a page within
the same scope of the hosting Web server geographical location. During download, if the analysis
of a page contents recommends a different geographical scope, the page is forwarded to the best-
located Web server. A sample of the Portuguese Web pages, extracted during the year 2005, was
used to evaluate page download communication times and overhead of pages exchange among
servers. Evaluation results permit to compare their approach to conventional hash partitioning
strategies.
Chau et al. [29] developed a framework of parallel crawlers for online social networks, utilizing a
centralized queue. The crawlers work independently, therefore, the failing of one crawler does not
affect the others at all. The framework ensures that no redundant crawling would occur. The
crawlers were used to visit approximately 11 million auction users, and about 66,000 of which
were completely crawled.
Ibrahim et al.[30] demonstrated the applicability of Map Reduce on virtualized data center
through conducted a number of experiments to measure and analyze the performance of Hadoop
on VMs. The experiments outlined several issues that will need to be considered when
implementing Map Reduce to fit completely in a virtual environment.
Hsieh et al. [31] introduced extensible crawler, which is a service that crawls the Web on behalf
of its many client applications. Clients inject filters into the extensible crawler; the crawler
evaluates all received filters against each Web page, notifying clients of matches. As a result,
crawling the Web is decoupled from determining whether a page is of interest, shielding client
applications from the burden of crawling the Web them self’s. They focused on the challenges
and trade-offs in the system, such as the design of a filter language that is simultaneously
expressive and efficient to execute, the use of filter indexing to cheaply match a page against
millions of filters, and the use of document and filter partitioning to scale their prototype
implementation to high document throughput and large numbers of filters. They claimed that the
low-latency, high selectivity, and scalable nature of their system makes it a promising platform
for taking advantage of emerging real-time streams of data, such as Facebook or Twitter feeds.

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Anbukodi and Manickam[32] addressed problems of crawling Internet traffic, I/O performance,
network resources management, and OS limitations and proposed a system based on mobile
crawlers using mobile agent. The approach employs mobile agents to crawl the pages by
identifying the modified pages at the remote site without downloading them; instead it downloads
those pages which have actually been modified since last crawl. Hence it will reduce the Internet
traffic and load on the remote site considerably. The proposed system can be implemented using
Java aglets.
4. THE VM-BASED DISTRIBUTED WEB CRAWLING MODEL
This section describes the VM-based distributed Web crawling model. This model assumes that a
multi-core processor is used as the main computing platform. The multi-core processor is divided
into a number of VMs each acts as crawling processor. In particular, in this model, the crawling
processor (CPR) is split into a number virtual crawling processors (vc), one of them acts a master
crawling VM (MCVM), and the rest acts as slave crawling VMs (SCVMs), each of the SCVMs
access the Internet independently retrieving HTML pages and passes them to the MCVM. In this
model, the SCVMs can communicate with the MCVM and also with each other under the control
of the MCVM. Figure 4 shows the architecture of the VM-based distributed crawling model.
With proper resources configuration and job distribution, the model can ensure high resource
utilization result in faster crawling process, and increase scalability, availability, and reliability.
The new model presumes to speed up the crawling process leading to significant increase in
crawling rate (pages per unit time) meeting applications and users growing needs.
Figure 4.The architecture of the VM-based distributed Web crawling model.
5. TOOLS
This section presents a description of the implementation and evaluation of the proposed model.
But, first, a brief introduction is provided for some of the tools and technologies that are used in
this research, such as: Xen hypervisor, Nutch crawler, and Hadoop.
Searching process (SP→SPR)
Analysis & indexing
process(AIP→AIPR)
Master crawling virtual
machine(CP→MCVM)
SCVM1CP→S
CVM1
SCVM2
CP→SCVM2
SCVMvcCP→S
CVMvc

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5.1. Xen Hypervisor Virtualization
Xen hypervisor is an open source standard for virtualization that offers a powerful, efficient, and
secure features for virtualization of x86, x86_64, IA64, ARM, and other CPU architectures[19,
20]. It supports a wide range of guest OSs including Windows®, Linux®, Solaris®, and various
versions of the BSD operating systems. The Xen hypervisor is a layer of software running
directly on computer hardware replacing the OS thus allowing the computer hardware to run
multiple guests OSs concurrently. The Xen hypervisor is developed and maintained by the Xen
community as a free solution licensed under the GNU General Public License [33].
With Xen virtualization, a thin software layer, namely, the Xen hypervisor is inserted between the
server's hardware and the OS. This provides an abstraction layer that allows each physical server
to run one or more virtual servers, effectively decoupling the OS and its applications from the
underlying physical server. In Xen virtualization, the same process occurs, with entire OSs taking
the place of tasks. The scheduling aspect is handled by the Xen kernel, which runs on a level
superior to the supervising guest OSs, and which is thus called the hypervisor. Xen hypervisor is
not quite so simple OS, even the new version that has been modified to be Xen-friendly. It uses a
different set of assumptions than applications, and switching between the traditional and friendly
versions usually involves more complexity [33].
5.2. The Nutch Crawler
Nutch is a framework for building scalable Internet crawlers and search engines. Figure 5
illustrates the major parts as well as the workflow of a Nutch crawl [34].
Figure 5. The workflow of the Nutch process [34].
The workflow of the Nutch crawler can be explained as follows:
1. The injector takes all the URLs of the nutch.txt file and adds them to the Nutch crawled
database (CrawlDB). As a central part of Nutch, the CrawlDB maintains information on all
known URLs (fetch schedule, fetch status, metadata, …).
2. Based on the data of CrawlDB, the generator creates a fetch list (FetchList) and places it in a
newly created segment directory.
3. Next, the fetcher gets the content of the URLs on the FetchList and writes it back to the
segment directory. This step usually is the most time-consuming one.
4. Now the parser processes the content of each Webpage and for example omits all HTML
tags. If the crawl functions as an update or an extension to an already existing one (e.g.
depth of 3), the updater would add the new data to the CrawlDB as a next step.
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5. Before indexing, all the links need to be inverted, which takes into account that not the
number of outgoing links of a Webpage is of interest, but rather the number of inbound
links. This is quite similar to how Google PageRank works and is important for the scoring
function. The inverted links are saved in the link database (LinkDB).
6. and (7). Using data from all possible sources (CrawlDB, LinkDB and segments), the indexer
creates an index and saves it within the Solr directory. For indexing, the popular Lucene
library is used. Now, the user can search for information regarding the crawled Web pages
via Solr.
In addition, filters, no rmalizers and plugins allow Nutch to be highly modular, flexible and very
customizable throughout the whole process. This aspect is also pointed out in the Figure 5 [34].
5.3. Hadoop
Apache Hadoop is a framework for running applications on large cluster of computing hardware
[35]. The Hadoop framework transparently provides applications both reliability and data motion.
Hadoop implements a computational paradigm named Map Reduce, in which the application is
divided into many small computational fragments, each of which may be executed or re-executed
on any node in the cluster. MapReduce works by breaking the process into two phases: the Map
phase and the Reduce phase. Each phase has key-value pairs as input and output, the types of
which may be chosen by the programmer. The programmer also specifies two functions: the Map
function and the Reduce function. Figure 6 shows the dataflow in Map Reduce with multiple
tasks [35].
Figure 6.MapReduce dataflow with multiple tasks.
When a dataset get too large for the storage capacity of a single physical machine, it becomes
necessary to partition it across a number of separate machines. The file systems that manage the
storage across a network of machines are called Distributed File Systems (DFSs). Because
DFSsare required to communicate, all the complications of network programming take effect,
which make networked DFSs more complex than regular disk file systems. One of the main
challenges is making the file system allows for node failure without suffering extensive data loss.
Hadoop comes with a DFS called Hadoop DFS (HDFS) that stores data on the computing nodes,
providing very high aggregate bandwidth across the cluster. Both HDFS and Map Reduce are
designed so that the framework automatically handles nodes failures [35]. More details on using
Hadoop with Nutch and some relevant details on how the Map Reduce paradigm is applied to a
Split 0 Map
Sort
Split 0 Map
Sort
Split 0 Map
Sort
Reduce Part 0
Merge
Reduce Part 0
Merge
Input HDFS
Output HDFS
HDFS
Replication
HDFS
Replication

30
data processing task in Nutch (e.g., link inversion, generation of fetch lists, invert, partition by
host, sort randomly, and multithreading) can be found in [36].
Hadoop is meant to work with cluster of machines (at least two machines), and for the purpose of
our work it should work on single machine. Fortunately, using the virtualization to create VMs
within a single machine will give the Hadoop the illusion that its working on more than one
machine (according to the number of VMs that have been created) and the virtualization isolation,
managing resources features will support that, since every VM will work in isolation of any other
VM.
6. IMPLEMENTATION
This section describes the two crawlers developed in this work, which are:
1. NOVCrawler. A non-virtualization crawler that runs on a computer comprises a multi-
core processor with no VMs installed on (no virtualization).
2. DVMCrawler. A distributed VM-based crawler that runs on a computer comprises a
multi-core processor with a number of VMs installed on performing distributed crawling.
6.1 Implementation of the NOVCrawler
The Web crawling process that is considered in this research and implemented in NOVCrawler
can be summarized as follows:
1. Inject URLs to the Nutch crawled database (CrawlDB). This could be any number of
URLs, which are injected as a seed for the crawling process. The injection process is
performed only once during the whole crawling process.
2. Generate a Nutch segment by copying the top ranked URLs in the Nutch CrawlDB into the
Nutch segment. The number of URLs in the segment must be limited to a specific number
of URLs. The first segment will contain only the seed URLs, the ones that are injected in
Step 1.
3. Fetch the Nutch segment from the Web using the Nutch Fetcher, which fetches the specific
number of URLs, except the first time it only fetches the seed URLs only.
4. Parse the Nutch segment for the sake of updating the rank and the amount of URLs in the
CrawlDB.
5. Update the CrawlDB by adding the new URLs and updating the ranking and the status of
URLs (fetched or unfetched).
6. Read the CrawlDB to determine number of crawled documents (fetched and unfetched).
7. Repeat Steps 2 to 6 until a pre-specified number of documents is crawled from the Web.
The fetching phase (Step 3) is the only phase that uses parallel processing through threading to
fetch the segment URLs from the Web, also in this phase; Nutch is using the file system of the
machine directly to save data or to get the input data for the crawling processes.
We modify the default configuration of Nutch to tune and improve the performance as follows:
1. Enable redirection: Nutch by default don’t follow redirection that is if the URL stored in the
CrawlDB is redirected to another domain or URL, Nutch will not follow the redirection to
fetch the new URL. However, we enable redirecting by re-configuring Nutch to follow new
URLs.

31
2. Limit the number of threads in the fetching phase to a certain number: The default number
of threads is 10 for each fetching phase, to better utilize this feature, in our case, we
changed it to 30 threads per fetch, which is a number estimated through trial and error.
6.2 Implementation of the DVMCrawler
The distributed programming methodology used in porting NOVCrawler to run on a distributed
environment, VM-based system, is the processor-farm methodology [37]. In the processor-farm
methodology, the VMs are configured in master-slave architecture, where one of the VMs is run
as a master (MCVM), while the other machines are run as slaves (SCVM). The program running
on the master VM is called the master program, while the program running on the slave is called
the slave program.
The standard relationship between the master and the slaves programs can be summarized as
follows:
1. The master program reads-in the input data (URLs) and perform any require preliminary
computations.
2. Send part of the URLs to the appropriate slave program (that may include transferring data
(URLs) from one slave to another).
3. After completion of the fetching process, the slave program sends the output results back to
the master program.
4. The master program performs any post-processing computations and then presents the final
outputs.
It is clear from the above relationship that the processor-farm involves no inter-processor
communications other than in forwarding data/results between the master and the slaves and the
slaves between each other to make the data required for computation available in the selected
slave for the task, once for all at the beginning and the end of the computation. Furthermore,
slaves are only allowed to communicate if the master orders them to move data between them as
required to utilize idle processors or handling failures.
In this methodology, the URL space is divided into disjoint sets (i.e., subsets), each of which is
handled by a separate crawler. Each crawler parses and logs only the URLs that lie in its URL
space subset, and forwards the rest of the URLs to the corresponding crawler entity. Each
crawling node will have a pre-knowledge of the look up table relating each URL subset to
[IP:PORT] combination that identifies all the crawler threads. This methodology also helps with
avoiding the important issue of scalability as we can create VMs as many as we need.
The main challenge while using this methodology is to efficiently distribute the computation tasks
avoiding overheads for synchronization, maintenance of consistency, and load balancing. Load
balancing needs to be carefully considered to avoid keeping some VMs overloaded doing a lot of
work while other VMs idle while waiting.
7. RESULTS AND DISCUSSIONS
In order to evaluate the performance of the VM-based distributed crawling model, a number of
performance measures are computed, such as: the speed up factor (S) achieved due to the
introduction of virtualization, i.e., installation of different number of VMs on the same multi-core
processor. In this work, S is calculated as the ratio between the CPU time required to perform a
specific crawling computation on a multi-core processor with no VMs installed on (i.e., no

32
virtualization) (Ts) and the CPU time required to perform the equivalent computation concurrently
on the same processor with a number of VMs installed on (Td). So that S can be expressed as [38]:
= (1)
The speedup depends largely on the relative time spent on computation and communication.
Computation is distributed between processors and communication takes place between processor
and depends on how the whole computation is distributed. Thus, to obtain ultimate performance
three points must be considered:
1. Load balancing. It is necessary to ensure that as many of the VMs as possible are doing
useful work for as much of the time as possible.
2. Data transfer (communication time). It must be reduced to its lowest levels.
3. System topology. The way in which the various VMs are linked together can strongly affect
the speedup. In theory, the hardware configuration should map the software configuration
in well design system. In practice, the limitations imposed by hardware and software can
make the situation rather different.
It is well recognized that the document crawling time depends on the documents content and
network performance, and it is increases with increasing number of crawled documents.
Therefore, we have found it is necessary to estimate the average document crawling time (tavg),
which is the average time required to crawl one document, and it can be expressed as:
= (2)
Where Wais the actual number of crawled documents, and Txis the crawling time. Tx is taken as Ts
for no VM-based crawling and Td for VM-based crawling. Furthermore, we define a another
parameter, namely the average crawling rate (Ravg), which represents the average number of
documents crawled per second (dps), and it is calculated by dividing the actual number of
crawled document during a specific crawling time by the crawling time. In other words, Ravg
represents the reciprocal of tavg, which can be expressed as:
= = (3)
The computer used in this work comprises a high-speed single multi-core processor. The
specifications of the computer are given in Table 1.
Table 1. Specification of the computing system
# Components Specifications
1 Processor Intel® Core™ i5-2300 CPU @ 2.80 GHz
2 Memory 8 GB RAM
3 Hard-disk 1.5 TB Hard Drive
4
Operating System
(OS)
Debian Squeeze (open source Linux-based OS for server and
desktop computers
In order to compute S, NOVCrawler and DVMCrawler are used to crawling documents from 30
well-known Websites of different contents and capacities. A list of the Uniform Resource
Locators (URLs) of these Websites is given in Table 2.

33
Table 2. List of initially visited Websites
# Websites # Websites
1 www.aljazeera.net 16 www.en.wna-news.com
2 www.bbc.co.uk 17 www.visitabudhabi.ae
3 www.tradearabia.com 18 www.interactive.aljazeera.net
4 www.krg.com 19 www.bbcworld.mh.bbc.co.uk
5 www.iranpressnews.com 20 www.bahrainnews.com
6 www.bbc.co.uk/blogs 21 www.khilafah.com
7 www.labs.aljazeera.net 22 www.alarabiya.net
8 www.terrorism-info.org.il 23 www.blogs.aljazeera.net
9 www.live.bbc.co.uk 24 www.ameinfo.com
10
www.alarabiya.net/index/videos/defa
ult
25 www.dubai.ae
11 www.alsumaria.tv 26 www.dubaicityguide.com
12 www.pukmedia.co 27 www.dmi.ae/dubaitv
13 www.alhurriatv.com 28 www.adtv.ae
14 www.tacme.com 29 www.iraq.net
15 www.azzaman.com 30 www.efa.com.eg
Furthermore, for fare evaluation and comparison, the crawling process is perform to retrieve
various number of documents ranged from 10000 to 70000 documents. The results which follow
are presented with the aim of investigating the variation of S against the number of VMs, and the
number of crawled documents.
7.1. NOVCrawler Performance
The crawling process is carries-out by running NOVCrawler, on the computer described in Table
1, to estimate Ts for crawling different pre-specified number of Web documents (Wo) ranging
from 10000 to 70000 in step of 10000. The crawling process starts with 30 URLs given in Table
2, all will be fetched sequentially (one-by-one). For each fetched URL, a number of Web
documents will be retrieved. Since each Web document may contain a number of URLs. These
URLs are extracted, parsed and added to CrawlDB, i.e. updating CrawlDB. NOVCrawler keeps
records of both the actual number of crawled documents (Wa) and the actual number of fetched
URLs (Ua).
The crawling process is designed to continue until Wo documents are fetched (regardless of Ua).
Since, the number of documents in the any fetched URL cannot be predicted; in practice, the
crawling process is terminated when Wa reaches any value just above Wo (i.e., Wa≥Wo).
NOVCrawler is also developed to estimate the CPU time required to crawl the last 1000
documents (Tl). Furthermore, the average CPU time spent to crawling 1000 documents (Tavg) is
calculated as Tavg=1000×tavg=(1000×Ts)/Wa. The values of Ts, Tl, Tavg, and Ravg are shown in
Figures 7 to 10, respectively.
7.2. DVMCrawler Performance
The crawling process is carries-out using DVMCrawler on the same computer described in Table
2 with different number of VMs installed on, to estimate Td for crawling different pre-specified
number of Web documents (Wo) ranging from 10000 to 70000 in step of 10000. In particular, two
different VM-based distributed crawling systems are configured. The first system encompasses 3
VMs, while the second system encompasses 4 VMs. The VMs are configured in master-slave
architecture, where one of the VMs is run as a master, while the other machines are run as slaves.

34
The crawling process starts with the same list of URLs given in Table 2, which will be fetched
concurrently using the installed VMs, each VM starts with 30/(n-1) URLs. However, as we
discussed above that the crawling process is terminated after we reach a number of documents
just above Wo, which we referred to as Wa. The values of Td,Tl,Tavg, andRavg for various crawled
documents (Wa) for 3 and 4 VMs, are shown in Figures 7 to 10, respectively.
Figure 7. Variation of Ts and Td against Wo. Figure 8. Variation of Tl against Wo.
Figure 9. Variation Tavg against Wo. Figure 10. Variation of Ravg against Wo.
To perform a fare comparison, S must be calculated as a ratio between the crawling times Ts and
Td require to crawling exactly the same number of documents. Therefore, the crawling times Ts
and Td are approximated to represent the crawling time of the equivalent base Wo documents. The
approximated time is calculated as (Wo×Ts/d)/Wa. The estimated S factors for the various numbers
of documents are given in Figure 11.
Figure 11. Variation of S against Wo.
1
1.1
1.2
1.3
1.4
1.5
1.6
0 10000 20000 30000 40000 50000 60000 70000 80000
Speedupfactor(S)
Number of Cralwed Documents (Wo)
3 VMs
4 VMs

35
It can be seen in Figure 7 that the crawling time Td for the VM-based configurations is less than Ts
for the no VM-based configuration. Furthermore, the difference between Ts and Td increases as
Wo increases. This demonstrates the potential of the concept of this work which provides better
processor utilization through virtualization. However, the crawling time Td for 3 and 4 VMs are
almost the same. This can be due to two main reasons:
1. As the number of VMs increases, the hardware resources allocated to each VM is reduced,
because the hardware resources of the system processor are distributed between more VMs.
2. For the same value of Wo, when the number of VMs increases, the amount of computation
(CPU crawling time) assigns to each VM is reduced, and, on the other hand, the amount of
communication (communication time) is increased. As a result of that the total crawling
time is almost unchanged.
Figure 11 shows the speedup factor S achieved by the VM-based crawling system, which almost
increases with increasing Wo. The maximum (ideal) value for S is equivalent to the number of
VMs. However, we expect to reach a steady state (saturated value) below the ideal value due to
communication overhead. It is can be easily seen in Figures 8 to 10 that the VM-based distributed
crawling configurations require less crawling time for the last 1000 documents and less average
crawling time per 1000 documents, and at the same time provides higher crawling rate. Once
again, the results for 3 and 4 VMs are almost the same due to the reasons discussed above.
8. CONCLUSIONS
The main conclusions of this work can be summarized as follows:
1. For equivalent crawling task, a VM-based Web crawling system performs faster than the
same system with no VMs installed on, i.e., reduces crawling CPU time and increases
processor utilization.
2. The speedup factor, and the average crawling rate increase as the number of crawled
documents increases. For example, Figure 7 shows that S achieved by DVMCrawler running
on 3 VMs, increases from 1.01 for 10000 documents to 1.48 for 70000 documents. This
demonstrates that for 70000 documents, the crawling CPU time is reduced by nearly 32%.
This expects to further increase as the number of targeted crawled documents increases.
3. A VM-based system crawling system shows smaller growth in the crawling CPU time as the
data grows in size as compared to using the same system as a single processor (no
virtualization).
4. Our investigations on 3 and 4 VMs demonstrate that for this limited range of crawling
computations (crawl a maximum of 70000 documents); the number of VMs has insignificant
effect on S and Ravg, which is discussed in Section 7.
5. Creation of VMs adds an isolation layer between those VMs and the real processor
hardware, which is considered as an overhead. However, when dealing with big data this
overhead layer hides the OS limitations and contributes to a better utilization to the
processor resources (computing powers and memory).
6. The high-performance multi-core processors with VMs installed on can play a big role in
developing cost-effective Web crawlers or can be used to enhance the performance of
current Web crawlers at Google, Yahoo, and other Web search engines.
Due to resources cost and time limit, in this paper, only relatively small numbers of URLs will be
crawled to prove the research concept, therefore, there are a number of recommendations that can
be suggested as future work; these recommendations may include:

36
1. Modify the distributed tool (DVMCrawler) as to assign crawling task to the master VM
instead of being idle while waiting other slave VMs completing their crawling tasks, and
then evaluate the performance of the modified tool and estimate the performance (speedup,
efficiency, average crawling rate) enhancement.
a. Extend the investigation and the performance evaluation for higher number of targeted
crawled documents (Wo>70000), and higher number of VMs installed on the same
multi-core processor (VMs>4).
2. Investigate the effect of the number of initial URLs on the performance of the VM-based
crawling system.
3. Evaluate the performance of the distributed model on various multi-core processors of
different specifications (speed and memory).
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Authors
Hussein Al-Bahadili (hbahadili@uop.edu.jo) is an associate professor at Petra University. He received his
PhD and M.Sc degrees from University of London (Queen Mary College) in 1991 and 1988. He received
his B.Sc in Engineering from the University of Baghdad in 1986. He has published many papers in different
fields of science and engineering in numerous leading scholarly and practitioner journals, and presented at
leading world-level scholarly conferences. His research interests include computer networks design and
architecture, routing protocols optimizations, parallel and distributed computing, cryptography and network
security, data compression, software and Web engineering.
HamzaAlqtaishat (hamzah.qtishat@yahoo.com) is a senior developer at Yahoo Arabia,
Amman, Jordan. He received his B.Sc degree from Al-Balqa Applied University, Jordan
in 2006 and his M.Sc degree in Computer Information Systems from the Middle-East
University, Amman, Jordan in 2012 under the supervision of Dr. Hussein Al-Bahadili. His
current research interests are Web Search Engine Architecture and techniques, Virtual
Machines, Parallel and Distributed Processing and Methodologies, Websites
Development, Web Services, Network Scalability, and Social Networks.
ReyadhS. Naoum (rnaoum@Meu.edu.jo ) is a professor at Middle East University,
Amman, Jordan. He received his PhD and M.Sc degree from University of Manchester in
1976 and 1973.He received his B.Sc degree in Mathematics from University of Basra in
1969. He published more than one hundred papers in the field of Computer Science and
Mathematicshigh standard journals, and well-recognized international conferences. His
current interest includes Neural Computing and Genetic Algorithms, Image Processing,
Cloud Computing, Computer Security and Instruction Detection Systems, Numerical Analysis Computation
and Software.

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