a hierarchical resource allocation architecture for mobile grid environments

8/8/2019 A Hierarchical Resource Allocation Architecture for Mobile Grid Environments

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A Hierarchical Resource Allocation Architecture for

Mobile Grid Environments

S. Thenmozhi

Assistant Professor, Department of Computer Applications

Chettinad College of Engineering and Technology

Tamilnadu, India.

[email protected]

A.Tamilarasi

Professor, Department of MCA

Kongu Engineering College, Erode

Tamilnadu, India

Abstract— The mobility issue in grid environments has

established new challenges to the research communities

particularly in the areas of scheduling, adaptation, security and

mobility. Especially, the resource allocation becomes more

challenging when mobility is considered in grid environment.

Hence it is necessary to consider the mobility of users along with

the resource availability while scheduling the resources for theexecution of jobs. In this paper, we propose to design a

Hierarchical Resource Allocation Architecture (HRAA) which

includes resource monitoring and scheduling operations for

mobile grid. In this architecture, the Mobile Grid is divided into

clusters. Each cluster has one cluster head (CH). A master server

(MS) controls each local clusters and has frequent updates of all

the CH information. Each CH has a monitoring agent (MA)

which will periodically predict the mobility of the cluster nodes

and monitor the resource availability and update their values.

When the MS forwards the job request of a user to the ideal CH,

the CH schedules the jobs based on the predicted time for

resource availability and sufficiency of the resources. By

simulation results, we show that our proposed architecture

achieves good success ratio and throughput with reduced delay

and energy consumption.

Keywords-Resource Allocation, monitoring agent (MA),

Hierarchical Resource Allocation Architecture (HRAA), cluster

head, master server (MS)

I. INTRODUCTION

A. Mobile Grid

The Grid is a distributed, high performing computing anddata handling infrastructure. It provides common interfaces forall the resources by using standard, open, general-purposeprotocols and interfaces by incorporating the geographicallyand organizationally dispersed, heterogeneous resources. Butit is the basis and the enabling technology for the persistent

and utility computing [1]. Multiple administrative domains,autonomy, heterogeneity, scalability anddynamicity/adaptability are the important features of the Grid.

The mobility issues are handled by enabling both fixed andmobile users, in the mobile grid environment. By using theunderlying technologies transparently and efficiently, theaccess for both fixed and mobile grid resources are provided.Mobile Grid is derived from Grid with the additional supportof mobile users and resources in a seamless, transparent,

secure and efficient way. Moreover, it has the capability toorganize the underlying ad hoc networks. It forms arbitraryand unpredictable topologies by providing a self-configuringgrid system of mobile resources which are connected by thewireless links [1].

The mobile grid uses the advanced capabilities of wirelessnetworks and lightweight thin devices. Though grid computingintegrates geographically dispersed resources and users tocreate a dynamic virtual organization, most of the resourcesare static in nature. The user and the resource participating inproblem solution are the two basic units of the processingenvironment. [2].

For many large scale applications which are dynamic innature and require transparency for users, Mobile Grid isconsidered as the best solution. Grid will increase the jobthroughput and performance of the corresponding applicationsby applying efficient mechanisms for resource management.Moreover, it will enable the advanced forms of cooperativework by allowing the seamless integration of resources, data,

services and ontologies [1]. Some of the applications of themobile grids are scientific, public services and commercialbusinesses. Mobile grids integrate the mobile devices likelaptops, PDAs (Personal Digital Assistants) [2].

B. Resource Allocation (or) Management in Mobile Grid

Resource allocation is a basic issue to achieve highperformance on a grid workflow [3]. Resource allocation canbe classified into resource selection (discovery) and resourcebinding (acquiring). The resource selection is separated fromresource binding based on the common architecture of conventional resource brokering systems. These systemsmainly concentrate on the resource selection for providingcomplex resource specification languages and resource

selection algorithms. On providing a resource specification byusing available resource information, a resource selectionalgorithm first discovers a matching set of resources andnegotiates with an individual local resource manager. Then theapplication attempts to acquire the resources [4].

C. Resource Allocation challenges in Mobile Grid

When mobility is considered, the resource selectionbecomes more challenging. Therefore, it is necessary toconsider the mobility of users with the resources in resource

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 8, No. 5, August 2010

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selection. The mobility issue in grid environments hasestablished new challenges to the research communitiesparticularly in the areas of scheduling, adaptation, security andmobility. Mostly, the behavior of the user and/or mobiledevice is highly unpredictable which produces disconnectionproblems

In grid environments, new challenges are introduced to theresearch communities especially in the fields of mobility,

scheduling, adaptation and security due to issues like mobility,power consumption and size of devices. The secondaryproblems are the small screen size and difficult inputmechanisms. Peer-to-peer computing provides many usefultechnologies and ideas, for developing scalable and reliablemobile grids. In mobile environment, the most challengingproblem is the disconnection problem [2].

The problems of resource allocation are:

1) Identification of an appropriate service and resources.

2) Based on certain criteria, allocating the resources,such as pricing or priority.

3) Dynamically allocating and updating the state of the

resources [5].

A centralized allocation manager is not possible becausethe portions of the Grid may apply different allocationstrategies due to decentralized Grid policies. The lack of accurate resource status information at the global scale is theadditional challenge to the Grid resource allocation. At theirremoval, the knowledge of real time environment has beenlimited by the allocation strategies which are utilized by theusers and brokers. Therefore the possible allocationmechanisms should not depend on the availability of currentglobal knowledge [6].

In this paper, we develop a resource monitoring andscheduling scheme for mobile grid. In this scheme, the usersubmits the job request for a job to be executed to a server.The job request contains job description, number of resourcesrequired, expected job completion time and the quoted priceallotted for it.

II. RELATED WORK

Xiaozhi Wang et al [7], have proposed a layered structureof Grid QoS. Based on the analysis of the content of gridresource allocation management (GRAM) based on QoS, theirwork puts forward the architecture of GRAM based on QoS.Through mapping, converting and negotiating the QoSparameters, it can implant the user's requirement about QoS inthe process of resource allocation management, and connectGrid QoS with GRAM very well. All these provide areasonable consulting model for QoS and resource allocationmanagement in grid. Their work raised the performance of GRA from different aspects. In their process of searching,system may negotiate with the user, then get final result: notbeing able to supply, being able to supply or reducing QoSdemands to supply.

Konstantinos Katsaros et al [8] have discussed a campus-wide hierarchical Mobile Grid system architecture in whichmobile nodes (MNs), willing to offer their computational

resources, move between WLANs. This willingness of themobile node is based on reciprocity criteria. They have alsoconsidered the divisible load applications (DLA) whichdivides the load of computation into parts and made to carryout independently. Moreover, they have described anarchitecture for the realization of a Mobile Grid andinvestigate key design decisions and optimizations.

Lei Zhang et al [9] used the Particle Swarm Optimization

(PSO), the latest evolutionary optimization technique to solvethe task scheduling problem in grid (computational grids)environment. Here each particle is represented as a possiblesolution, and the position vector is transformed from thecontinuous variable to the discrete variable. They have alsoaimed to generate an optimal schedule to get the minimumcompletion time while completing the tasks.

Abdullah M. Elewi et al [10] have addressed the problemof energy efficient real time task scheduling where the tasksare dependent due to exclusive access shared resources.Moreover, they have proposed about the enhancements overthe existing dual speed switching algorithm (DSA) where theirproposed algorithm achieves more energy saving and has the

capability to function with both SRP and DPCP protocols.Homam Reda El-Taj et al [11] have given a survey about

mobile computing, the mobile grid computing, mobile agentsand how to apply mobile agents on mobile grid computing andwhat has been done to solve the issues in these areas of study.

Ming Wu et al [12] have proposed a prototype of GridHarvest Service (GHS) which provide dynamic and self-adaptive task scheduling. Their study is made upon task scheduling of a parallel or distributed application with adivisible workload in a heterogeneous environment. It alsoshows the possibility of integrating the three parts of task scheduling, that is the task allocator, scheduler and predictorinto existing toolkits for better service.

Hesham A. Ali et al [13] have introduced a "self rankingalgorithm", which will be used to build a mobile computingscheduling mechanism to schedule the tasks on the mobiledevices, which will maximize the profit of the mobile deviceswhich are integrated within the grid using their computationalpower as an addition to the system overall power.

Gurleen Kaur et al [14] have addressed the promising andbright side of the grid computing technology. They haveexplored the grid capabilities further by organizing the gridcomputing concept from two broad perspectives such asUser’s Perspective and Administrator’s Perspective.

III. HIERARCHICAL RESOURCE ALLOCATION ARCHITECTURE

A. System Design

In our system design, a set of machines and a cluster head(CH) are included in each local cluster. A master server (MS)controls and groups many local clusters. The MS collectsinformation about the resources in its local clusters. Then itstores the information in its own database.

The proposed scheme divides a given task that is submittedby a user into subtasks. Then it finds spare processors andother critical resources on the network, distributes the



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ISSN 1947-5500



subtasks, monitors the progress of the subtasks, and restartsthe subtasks that fail.

It consists of the following steps:

1) The MS divides a given task into a sequence of subtasks and allocates the subtasks to the local clusterheads (CH).

2) The CH finds the available processing power and

average mobility on the local machines and thendistributes the subtasks over these machines.

3) The CHs gather the completed subtasks from the localmachines and then send the data back to the MS.

4) The MS aggregates the completed subtasks and thenstores the results in its own database, and

5) MS also sends back the results to the application of the respective users.

B. Mobility Metric

User Range is a given fact about the initiator coverage, inwhich user can communicate with mobile devices. Average

Mobility, a derived parameter, represents the average mobilityof a resource and/or user (based on user and resourcemobility). Average mobility is calculated based on two recentcommunications between user/initiator and resource withrespect to the user/initiator.

Average Mobility is calculated as

21 f f Mobility Average −= (1)

Where, 1 f and 2 f are the first history and second history

respectively. The history is simply the distance between userand resource and it can be calculated by finding differencebetween the two recent interactions.

Ptime shows the predicted time for resource availability

within the user’s range and is calculated by the followingequation

Ptime = (User Range – Distance) / Average Mobility (2)

The “Distance” is the net difference between the newlocations of user and resource.

C. Resource Availability Metric

The monitoring agents (MA) estimates the workload of its

grid nodes ),,2,1,( k ini L= present in the cluster CL1 using

the following formula:

i

k

j

ji

iPower

JobsizeCWL

WL

+

=

∑=1

(3)

Where iCWL is the current workload of in , iWL is the

work load of in

iPower is the power of the node in and j Jobsize is the

size of the j Job .

D. Scheduling Strategy

After estimating the Ptime and WL values, the MA sends

these values to its cluster head 1CH . The 1CH then schedules

the jobs if their grid node satisfies the following condition

ThWLPtime If >) / ( (4)

where Th is a threshold value (which can be fixed based

on the job request).

From 4, we can observe that if the Ptime is less and if the

work load is more then the gird nodes are unable to executethe job request. Therefore, the jobs are executed by the nodesonly when Ptime is high and the work load is low. If the CH

is unable to allocate the resources in its cluster, it resubmitsthe job request information to the MS.

E. Functions of HRAA

Figure 1. Functions of HRAA

The sequence of operations in HRAA is shown in Figure. 1. Inthis figure, the arrows represent the communication messagesand the nodes represent the agents/servers. The sequence is asfollows.

1) The MAs of each node in the local cluster send theresource status information to the CHs.

2) The CHs send this information to the MS.

3) The MS then create a database which containsinformation about the status and the price of each

resource.4) A user submits its job details and the resource

requirements to the MS.

5) The MS sends the job request information to the localCH.

6) The local CH allocates the resources in their controldepending on the predicted time, the averageprocessing power and average load of its localmachines




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7) If any CH unable to allocate the resources in itscluster, it resubmits the job request information to theMS.

8) The MS forwards the job request information to otherCH. The process is continued until the job issuccessfully assigned

9) The CHs gather the completed subtasks from the localmachines and then send the data back to the MS.

10) The MS aggregates the completed subtasks and thenstores the results in it’s own database.

11) MS also sends back the results to the application of the respective users.

IV. SIMULATION RESULTS

A. Simulation Model and Parameters

Figure 2. Simulation Setup

In this section, we examine the performance of ourHierarchical Resource Allocation Architecture (HRAA) with

an extensive simulation study based upon the ns-2 network simulator [15]. The simulation topology is given in Figure 2.We compare our results with Agent-based Resource Allocation

Model (ARAM) [5].Various simulation parameters are given intable 1.

TABLE I. SIMULATION SETTINGS

Mobile Nodes 9

Users 3

Clusters 3

Area Size 1000 X 1000

Mac 802.11

Radio Range 250m

Routing Protocol DSDV

Simulation Time 50 sec

Traffic Source CBR

Packet Size 512

Rate 250kb,500kb,….1000kb

No. Of tasks 2,4,6,8 and 10

Speed 5m/s

Transmit Power 0.660 w

Receiving Power 0.395 w

Idle Power 0.335 w

Initial Energy 10.1 J

B. Performance Metrics

In our experiments, we measure the following metrics.

Average Execution Delay: It measures the average delayoccurred while executing a given task.

Average Success Ratio: It is the ratio of the number of tasks executed successfully and the total number of taskssubmitted.

Average Energy: It is the average energy consumption of all nodes in executing the tasks.

Throughput: It is the number of tasks finishedsuccessfully.

C. Results

A. Based on RateIn this experiment, we vary the execution rate as 250Kb,

500Kb, 750Kb and 1000Kb.

Rate Vs Delay

0

2

4

6

8

10

250 500 750 1000

Rate (Kb)

D e l a y ARAM

HRAA

Figure 3. Rate Vs Delay

Rate Vs SuccessRatio

0

0.2

0.4

0.6

0.8

1

250 500 750 1000

Rate (Kb)

S u c c e s s R a t i o

ARAM

HRAA

Figure 4.Rate Vs Success Ratio

Rate Vs Energy

0

2

4

6

8

10

250 500 750 1000

Rate (Kb)

E n e r g y

ARAM

HRAA

Figure 5.Rate Vs Energy




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Rate Vs Throughput

0

500

1000

1500

2000

2500

3000

250 500 750 1000

Rate (Kb)

T h

r o u g h p u t ( p k t s . )

ARAM

HRAA

Figure 6.Rate Vs Throughput

From Figure 3, it is clear that when the execution rate isincreased then the delay also increases. We can see that theaverage execution delay of the proposed HRAA algorithm isless when compared to the ARAM algorithm when the rate isincreased.

Figure 4 shows that when the execution rate is increased

then the success ratio gets decreased. From the figure we cansee that the HRAA achieves good success ratio, compared toARAM.

Figure 5 shows that when the execution rate is increasedthen the energy consumption gets increased slightly. From theresults, we can see that HRAA consumes less energy than theARAM.

Figure 6 shows that when the execution rate is increasedthen the throughput is also increased. As we can see from thefigure, the throughput is more in the case of HRAA whencompared to ARAM.

B. Based on Number of Tasks

In this experiment, we vary the number of tasks to beexecuted as 2, 4….10.

No. Of Tasks Vs Delay

0

2

4

6

8

10

12

14

2 4 6 8 10

Tasks

D e l a y ( s )

ARAM

HRAA

Figure 7. Number of Tasks Vs Delay

No. of Tasks Vs SucessRatio

0

0.2

0.4

0.6

0.8

1

2 4 6 8 10

Tasks

S u c c e s s R a t i o

ARAM

HRAA

Figure 8. Number of Tasks Vs Success Ratio

No. Of Tasks Vs Energy

0

2

4

6

8

10

12

2 4 6 8 10

Tasks

E n e r g y ( J )

ARAM

HRAA

Figure 9. Number of Tasks Vs Energy

No. Of Tasks Vs Throughput

0

500

1000

1500

2000

2500

3000

3500

2 4 6 8 10

Tasks

T h r o u g h p u t ( p k t

s . )

ARAM

HRAA

Figure 10. Number of Tasks Vs Throughput

From Figure 7, it is clear that when the number of tasks isincreased then the delay also gets increased. We can see that

the average execution delay of the proposed HRAA algorithmis less when compared to the ARAM algorithm when thenumber of tasks is increased.

Figure 8 shows that when the number of tasks is increasedthen the success ratio gets decreased. From the figure we cansee that the HRAA achieves good success ratio, compared toARAM.

Figure 9 shows that when the number of tasks is increasedthen the energy consumption gets increased slightly. From the




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results, we can see that HRAA consumes less energy than theARAM.

Figure 10 shows that when the number of tasks isincreased then the throughput is also increased. As we can seefrom the figure, the throughput is more in the case of HRAAwhen compared to ARAM.

V. CONCLUSION

In this paper, we have designed a Hierarchical ResourceAllocation Architecture (HRAA) which includes monitoringand scheduling operations for mobile grid. In this architecture,a set of machines and a cluster head (CH) are included in eachlocal cluster. A master server (MS) controls and groups manylocal clusters. The MS collects the information about theresources in its local clusters and stores it in its own database.Each CH has a monitoring agent (MA) which will periodicallypredict the mobility of the cluster nodes and monitor theresource availability and update their values. The MS forwardsthe job request of a user to the ideal CH. If the CH finds theavailable processing power, workload and predicted time forresource availability on the local machines, then it distributesthe subtasks over these machines. Otherwise the job request is

resubmitted to the MS which again forwards the job request toanother CH. The process is continued until the job issuccessfully assigned. The completed job is returned back tothe MS through the corresponding CH. The MS then returns itto the requested user. By simulation results, we have shownthat our proposed protocol achieves good success ratio andthroughput with the reduced delay and energy consumption.

REFERENCES

[1] Litke, A., Skoutas D. and Varvarigou T., “Mobile Grid Computing:Changes and Challenges of Resource Management in a Μobile GridEnvironment”, presented in Workshop: “Access to Knowledge throughGrid in a Mobile World”, PAKM 2004 Conference, Vienna

[2] Umar Farooq, Saeed Mahfooz and Wajeeha Khalil, “An EfficientResource Prediction Model for Mobile Grid Environments”, 2006- PGNet.

[3] Pengcheng Xiong, Yushun Fan, “Cost-aware Grid Workflow ResourceAllocation”, IEEE Computer Society, 2007.

[4] Yang-Suk Kee, Ken Yocum, Andrew A. Chien, Henri Casanova,“Improving Grid Resource Allocation via Integrated Selection andBinding”, 2006 IEEE

[5] S.S. Manvia, M.N. Birjeb, Bhanu Prasad, “An Agent-based ResourceAllocation Model for computational grids”, IOS Press Amsterdam, TheNetherlands, 2005.

[6] Aram Galstyan, Karl Czajkowski,Kristina Lerman, “Resource Allocationin the Grid Using Reinforcement Learning”, IEEE Computer Society2004.

[7] Xiaozhi Wang, Junzhou Luo, “Architecture of Grid Resource AllocationManagement Based on QoS”, Springerlink – 2004.

[8] Konstantinos Katsaros and George C. Polyzos , “Optimizing OperationOf A Hierarchical Campus-Wide Mobile Grid”, The 18th Annual IEEEInternational Symposium on Personal, Indoor and Mobile RadioCommunications (PIMRC’07)

[9] Lei Zhang, Yuehui Chen, Runyuan Sun, Shan Jing and Bo Yang, “ATask Scheduling Algorithm Based on PSO for Grid Computing ”,International Journal of Computational Intelligence Research. (2008),

[10] Abdullah M. Elewi, Medhat H. A. Awadalla and Mohamed I. Eladawy ,“Energy Efficient Real Time Scheduling Of Dependent Tasks SharingResources” Proceedings of the High Performance Computing &Simulation Conference, 2008.

[11] Homam Reda El-Taj and Chan Huah Yong, “Applying Mobile Agentson Mobile Grid Computing”, Computer Science PostgraduateColloquium (CSPC’07).

[12] Ming Wu and Xian-He Sun, “A General Self-adaptive Task SchedulingSystem for Non-dedicated Heterogeneous Computing”, Proceedings of IEEE International Conference on Cluster Computing, 2003.

[13] Hesham A. Ali and Tamer Ahmed Farrag, “High Performance MobileComputing Algorithm Based On Self-Ranking Algorithm (Sar)”,Department of Computers and Systems, Faculty of Engineering,Mansoura University, Egypt, Department of Computers and Systems,Faculty of Engineering, Mansoura University, Egypt

[14] Gurleen Kaur and Inderpreet Chopra, “Grid Computing – ChallengesConfronted and Opportunities Offered”, Proceedings of Challenges andOpportunities in Information Technology, COIT-2007.

[15] Network Simulator: http:// www.isi.edu/nsnam/ns

S. Thenmozhi received MCA degree from Annamalai

University, India in 2000 and M.Phil degree in computerscience from Bhrathiar University, India in 2003. Currently

she is pursuing the Ph.D from Anna University

Coimbatore, India. Her area of interest in research includes

Grid Computing and Mobile Computing. She has published

6 papers in National/International Conferences/Journals.

Presently, she is working as Assistant Professor inDepartment of Computer Applications, Chettinad College of Engineering and

Technology, Tamilnadu, India.

A.Tamilarasi post graduated from Bharathiar University,India 1986. She obtained her Ph.D from University of

Madras, Chennai in the year 1994. She was awarded JRF by

UGC in the year 1986. She has published more than 40research papers in the reputed national/international

Journals. She is author of 10 books. Her area of interest

includes Semigroup theory, Soft computing, Data Mining.Presently she is working as a Professor Department of

MCA, Kongu Engineering College, Erode, Tamilnadu, India




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a hierarchical resource allocation architecture for mobile grid environments

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