Comparative Analysis of Round Robin VM Load Balancing With Modified Round Robin VM Load Balancing Algorithms in Cloud Computing

Transcription

1 Iteratioal Joural of Egieerig, Maagemet & Scieces (IJEMS) Comparative Aalysis of Roud Robi Balacig With Modified Roud Robi Balacig s i Cloud Computig Areeba Samee, D.K Budhwat Abstract Cloud computig is a ew emergig tred i computer techology that has iflueced every other etity i the etire idustry, whether it is i the public sector or private sector with the advace feature of the cloud there are ew possibility opeig up how applicatio ca be built ad how differet services ca be offered to the ed user through Virtualizatio, o the iteret. Cosiderig the growig importace of cloud fidig ew way to improve cloud services is a area of cocer ad research focus. I available Virtual Machie Load Balacig policies limitatio of cloud is that they do t save the state of the previous allocatio of virtual machie to a request from the user ad the Balacig algorithm require executio each time a ew request for VM allocatio received from user. This problem ca be resolve by developig a efficiet VM load balacig algorithm for usig Roud Robi approach. Idex Terms VM load balacer, Cloud Aalyst, Dataceter, Roud Robi Virtual Machie Load Balacig s, Modified Approach to Roud Robi Virtual Machie Load Balacig. I. INTRODUCTION The virtualizatio forms the foudatio of cloud techology where virtualizatio is a emergig techology that separate computig fuctio ad techology implemetatio from physical hardware. Cloud computig is the virtualizatio of computer program through the iteret coectio rather tha istallig applicatio o everywhere. Usig virtualizatio user ca access server or storage without kowig specificatio of storage details. The umber of service providers are growig ad the cost of services are decreasig. Cosiderig the growig importace of cloud, fidig ew ways to improve cloud services is a area of cocer ad research focus. Cloud computig ca be defied as follows: Cloud computig is the delivery of computig as a service rather tha a product, whereby shared resources, software, ad iformatio are provided to computers ad other devices as a utility (like the electricity grid) over a etwork (typically the Iteret) [1]. Mauscript received May 07, Areeba Samee, Departmet of Computer sciece ad Egieerig, Dr.B.A.M Uiversity Jawaharlal Nehru Egieerig college Auragabad, , M.S., Idia D.K Budhwat, Departmet of Computer sciece ad Egieerig, Dr.B.A.M Uiversity Jawaharlal Nehru Egieerig college Auragabad, , M.S., Idia II. RELATED STUDY Studyig ad aalyzig the proposed algorithm o a real cloud sceario is extremely difficult. Also, cosiderig the budget costraits, the most feasible optio is to study ad aalyze the proposed algorithm by usig simulatio. As such, we used CloudAalyst to study ad aalyze the proposed algorithm. CloudAalyst [2] is a cloud simulatio tool that supports visual modelig ad the simulatio of large-scale applicatios that are deployed o Cloud Ifrastructure. CloudAalyst, which is built o CloudSim [3], allows for the descriptio of applicatio workloads, icludig iformatio o the geographic locatio of users geeratig traffic ad the locatio of data ceters, the umber of users ad data ceters, ad the umber of resources i each data ceter. Usig this iformatio, CloudAalyst geerates iformatio about the respose time of requests, the processig time of requests, virtual machie cost, ad total data trasfer cost. It is based o top of mature simulatio frameworks such as SimJava [4,5] ad CloudSim [3]. CloudSim framework [3] is i tur built o top of GridSim framework [6]. The GridSim toolkit is a Java based simulatio toolkit that supports the modelig ad simulatio of heterogeeous Grids. III. CLOUD COMPUTING ENVIRONMENT Cloud computig provides service accordig to several models: Ifrastructure as a Service (IaaS), Platform as a Services (PaaS), Software as a Services (SaaS) Software as a service (SaaS): Saas is a ew model, which tells how software is delivered. I this service model, we access the software by usig web browser ad cost is paid o a subscriptio basis. As i other models, if a user wats to use software, he has to buy the licese of the software. I this way Saas provides pay per use service to the customer. This model is faster ad cost effective. Customers do ot eed to sped moey o hardware, implemetatio or acquisitio to ru the applicatio. It s the resposibility of the service provider to maage ad ru the applicatio with security, reliability ad performace. Saas ca be used by Widows, Liux or Mac users, providig platform idepedece over the iteret. 61

2 Comparative Aalysis of Roud Robi Balacig With Modified Roud Robi Balacig s i Cloud Computig Platform as a Service (PaaS): Paas is oe of the category of Cloud Computig services that facilitate with computig platform ad a solutio stack as a service. Paas providig a service of deploymet of applicatios without the cost ad complexity of buyig ad maagig the uderlyig hardware ad software. Paas providig services to the customer as applicatio desig, applicatio developmet, testig ad deploymet, also facilitate team collaboratio, web service itegratio, security, scalability. Ifrastructure as a Service (IaaS): I case of Iaas, service providers provide computers-physical or virtual machies ad other resources. Iaas also facilitate additioal resources such as virtual-machies desk image library, raw ad file-based storage, firewalls, load balacers, IP addresses ad software budles. Examples of Iaas providers iclude: -Amazo EC2, Azure Services Platform, Google Compute Egie etc. Fig. 2. Load balacig i cloud computig V. CLOUDSIM CloudSim is a framework developed by the GRIDS laboratory of Uiversity of Melboure which eables seamless modelig, simulatio ad experimetig o desigig Cloud computig ifrastructures. CloudSim is a self-cotaied platform which ca be used to model data ceters, service brokers, schedulig ad allocatio policies of a large scaled Cloud platform. It provides a virtualizatio egie with extesive features for modelig the creatio ad life cycle maagemet of virtual egies i a data ceter. CloudSim framework is built o top of GridSim framework also developed by the GRIDS laboratory. VI. MODELLING THE VIRTUAL MACHINE ALLOCATION Fig.1.Cloud Computig Services IV. LOAD BALANCING IN CLOUD COMPUTING Load balacig is the process of improvig the performace of distributed ad parallel computig with the help of distributio of load amog the processors or odes. As the use of the web icreasig day by day, with this there is eed to icrease the requiremet for load balacig. The itroductio of E-Commerce has lead may busiesses to carry out the most of their day-to-day busiess olie. As a result of the icrease i demad of the web, web sites providers wat to esure the availability of access for their users ad make sure that their requests are processed as quickly as possible. Cloud computig ifrastructure differet from a Grid computig is the massive deploymet of virtualizatio techologies ad tools. Hece with Clouds there is a virtualizatio layer that acts as a executio ad hostig eviromet for Cloud-based applicatio services. Hece, traditioal applicatio mappig models that assig idividual applicatio elemets to computig odes do ot accurately represet the computatioal abstractio which is commoly associated with the Clouds. To allow simulatio of differet policies uder varyig levels of performace isolatio, CloudSim supports VM schedulig at two levels: First, at the host level ad secod, at the VM level. At the host level, it is possible to specify how much of the overall processig power of each core i a host will be assiged to each VM. At the VM level, the VMs assig specific amout of the available processig power to the idividual task uits that are hosted withi its executio egie. At each level, CloudSim implemets the time-shared ad space-shared resource allocatio policies. I this paper, we have proposed the Roud Robi Balacig at the VM level where idividuals applicatio services is assiged varyig amout of the available processig power of virtual machie. 62

3 Iteratioal Joural of Egieerig, Maagemet & Scieces (IJEMS) VII. EXISTING ROUND ROBIN LOAD BALANCING ALGORITHM It is oe of the simplest schedulig techiques that utilize the priciple of time slices. Here the time is divided ito multiple slices ad each ode is give a particular time slice or time iterval i.e. it utilizes the priciple of time schedulig [7]. Each ode is give a quatum ad i this quatum the ode will perform its operatios. The resources of the service provider are provided to the requestig cliet o the basis of this time slice. The followig figure shows how roud robi works. The followig figure shows that each user request is served by every processor withi give time quatum. After the time slice is over, the ext queued user request will come for executio. If the user request completes withi time quatum the user should ot wait otherwise user have to wait for its ext slot. A. Advatage The mai advatage of this algorithm is that it utilizes all the resources i a balaced order. A equal umber of VMs are allocated to all the odes which esure fairess. B. Disadvatage I this method it cosiders curret load o each virtual machie. It does ot save the last status of the virtual machie allocatio. It oly cocer with the curret load of virtual machie. C. Schedulig based o RR The schedulig process based o RR experimetatio ad its Pseudo code is represeted i the followig Fig 3 ad Fig 4. Fig.4.Schedulig based o RR VIII. PROPOSED ALGORITHM: MODIFIED ROUND ROBIN VIRTUAL MACHINE ALGORITHM The proposed algorithm is a improvemet over the Roud Robi Balacig.As Roud Robi does ot save the state of the previous allocatio of a VM to a request from give Userbase, while the same state is saved i the proposed algorithm. The Modified Roud Robi VM load balacer maitais two data structures, which are as listed below. 1. Hash map: this stores the etry for the last VM allocated to a request from a give Userbase 2. VM state list: this stores the allocatio status (i.e., Busy/Available) of each VM. Fig.3.Roud Robi Processig 63

4 Comparative Aalysis of Roud Robi Balacig With Modified Roud Robi Balacig s i Cloud Computig Iitialize the VMStatelist to AVAILABLE. No VM is busy iitially. Iitially Hash Map table is empty While (ew request received by Data Ceter Cotroller) Do { Data Ceter Cotroller queries the Modified Roud Robi Balacer for the ext allocatio Modified Roud Robi scas the allocatio table completely till it fouds the VM. If foud The Modified algorithm returs the requested VMid To the dataceter cotroller DataCeter Cotroller seds the request to that VM idetified by id. Modified balacer updates the allocatio table. If VM ot foud Modified algorithm returs -1. DataCeter Cotroller queues the request. with the oe adopted by CloudAalyst [8]. The reaso for usig this is that a social etworkig site ca greatly beefit by movig to cloud ad cosiderig the large umber of users o Facebook the data available will be the right choice for testig the efficiecy of the proposed algorithm. The approximate distributio of the Facebook [9] Userbase aroud the world is give i the Table 1 (o Dec. 31, 2011). TABLE I USERBAE PARAMETER LIST ACROSS THE REGIONS OF THE GLOBE Regio North Americ a South Americ a Re gi o ID Users Europe Asia Africa 4 38 Oceaia 5 13 User-b ase Peak hrs. (GMT ) Olie users durig peak hrs Olie users durig off peak hrs 87,500 8,750 51,500 5,150 97,500 9, , ,650 19,000 1,900 6, } Whe VM fiishes processig DataCeter cotroller otifies Modified VM load balacer for de-allocatio of VM. DataCeter cotroller checks for waitig request if exist the cotiues with step3 Fig.5. MRR Otherwise cotiue with step2. Cosiderig the resource ad budget costraits, we used a subset of the data give i Table 1 for simulatio. We further defied 6 Userbases represetig the 6 regios with the parameters show i Table I. X. SIMULATION CONFIGURATION IX. EXPERIMENTAL SETUP To aalyze ad compare the proposed algorithm with the existig algorithm we have used CloudAalyst as the simulator. The approach adopted by CloudAalyst ivolved simulatig } data of oe of the most promiet ad popular social etworkig websites (i.e., Facebook). To c arry out simulatios ad aalysis of the proposed algorithm the eviromet setup ad the approach adopted were kept i lie For simplicity each user base is cotaied withi a sigle time zoe ad let us assume that most users use the applicatio i the eveigs after work for about 2 hours. Let us also assume that 5% of the registered users will be olie durig the peak time simultaeously ad oly oe teth of that umber durig the off-peak hours. Let us also assume that each user makes a ew request every 5 miutes whe olie. I terms of the cost of hostig, let us assume a pricig pla which closely follows the actual pricig pla of 64

5 Iteratioal Joural of Egieerig, Maagemet & Scieces (IJEMS) Amazo EC2[6], the most popular Cloud Service provider at No. of Avg(ms) Mi(ms) Mi(ms) preset. The assumed pla is: VM s Cost per VM per hour (1024Mb, 100MIPS) =$0.10 Cost per 1Gb of data trasfer (from/to Iteret) =$0.10. The parameters for the Cloud cofiguratio are defied i Table II TABLE II PARAMETER VALUES Parameter Value used VM image size VMMemory 1024 Mb VM Badwidth 1000 DataCeter Architecture DataCeter OS DataCeter VMM X86 Liux Xe DataCeter umber of 20 machie DataCeter Memory per 2048Mb machie DataCeter storage per Mb machie DataCeter Available badwidth per machie Dataceter umber of processor per machie 4 DataCeter processor speed DataCeter VM policy 100 MIPS Time shared Table IV shows the result based o Roud Robi Balacig algorithm for Data Cetre processig time of the cloud. I this mi (ms) time, max(ms) time to differet umber of virtual machies are aalyzed. TABLE IV DATACENTER PROCESSING TIME FOR RR No of Avg(ms) Mi(ms) Max(ms) Vm s User groupig Factor 1000 Request groupig Factor 100 Executable istructio legth 250 The simulatio results that have bee used for comparig the two algorithms are based o the parameters listed below. A. Userbase respose time B. Data ceter processig time XI. RESULT ANALYSIS BASED ON ROUND ROBIN VM LOAD BALANCER The proposed implemeted for simulatio. Java laguage is used for implemetig VM load balacig algorithm. Table III shows the result based o Roud Robi Balacig algorithm for overall respose time of the cloud. I this mi(ms) time, max (ms) time to differet umber of virtual machies are aalyzed. TABLE III RESPONSE TIME OF USERBASES IN RR VM LOADBALANCER. XII. RESULT ANALYSIS BASED ON MRR VM LOAD BALANCING ALGORITHM Table V shows the results based o Modified Roud Robi Balacig for overall respose time of the cloud. I this mi(ms) time, max (ms) time to differet umber of virtual machies are aalyzed. TABLE V RESPONSE TIME OF USERBASES IN MRR VM LOAD BALANCER No. of Avg(ms) Mi(ms) Mi(ms) VM s

6 Comparative Aalysis of Roud Robi Balacig With Modified Roud Robi Balacig s i Cloud Computig Table VI shows the result based o Modified Roud Robi Balacig algorithm for Data Cetre processig time of the cloud. I this mi (ms) time, max(ms) time to differet umber of virtual machies are aalyzed. TABLE VI DATACENTER PROCESSING TIME FOR MRR No of Avg(ms) Mi(ms) Max(ms) VM s VMs VMs VMs VMs The overall average respose time of Userbases ad overall processig time of dataceter processig time is of Roud robi ad Modified Roud Robi is show i the followig graphs. Performace Aalysis Of Overall Respoce Time i RR VM load balacer XIII. COMPARATIVE PERFORMANCE ANALYSIS OF ROUND ROBIN AND MODIFIED ROUND ROBIN VM LOAD BALANCING ALGORITHMS Aalyzed result shows that Roud Robi Load Balacig cosumes less time for overall respose time ad dataceter processig time over Roud Robi method. Whe umbers of virtual machie are icreases the it takes more time for over all respose time ad data cetre processig time. Table VII show the comparative aalysis of RR ad MRR Virtual Machie Load Balacig s. TABLE VII COMPARATIVE ANALYSIS OF ROUND ROBIN AND MODIFIED ROUND ROBIN VM LOAD BALANCING ALGORITHM Scearios Overall Average Userbase Respose Time Roud Robi Balacig Modified Roud Robi Balacig Overall Average Dataceter Processig Time Roud Robi Balacig Modified Roud Robi Balacig Respose Time i ms No of Virtual Machies RR Balacer Fig.6. Performace Aalysis Of Overall Respose Time i RR VM load balacer Respose Time i ms Performace Aalysis Of Overall Respoce Time i MRR VM load balacer No of Virtual Machies MRR VM Load Balacer 5 VMs Fig.7.Performace Aalysis Of Overall Respose Time i MRR VM load balacer 10VMs VMs

7 Iteratioal Joural of Egieerig, Maagemet & Scieces (IJEMS) Performace Time i ms Performace Aalysis Of Overall Dataceter Processig Time i RR VM load balacer No of VirtualMachies RR Balacer Fig 8.Performace Aalysis Of Overall Dataceter Processig time i RR Balacer Performace Time i ms Performace Aalysis Of Overall Dataceter Processig Time i MRR VM load balacer MRR VM Load Balacer ACKNOWLEDGEMENT I would like to express my deepest gratitude to my guide, D.K Budhwat ad coordiator Dr. M.S Joshi for their excellet guidace, carig, patiece, ad providig me with a excellet atmosphere for doig research. My research would ot have bee possible without their help. REFERENCES [1] Foster, I; Yog Zhao; Raicu, I.; Lu, S. Cloud Computig ad Grid Computig 360-Degree Compared, published i Grid Computig Eviromets Workshop, GCE '08 IEEE DOI Nov [2]Bhathiya Wickremasighe, Rodrigo N. Calheiros, ad Rajkumar Buyya CloudAalyst: A CloudSimbased Visual Modeler for Aalyzig Cloud Computig Eviromets ad Applicatios ; Techical Report, CLOUDS-TR , Cloud Computig ad Distributed Systems Laboratory, The Uiversity of Melboure, Australia, Oct. 23, [3] R. Buyya, R. Raja, ad R. N. Calheiros, Modelig ad Simulatio of Scalable Cloud Computig Eviromets ad the CloudSim Toolkit: Challeges ad Opportuities, Proc. of the 7th High Performace Computig ad Simulatio Coferece (HPCS 09), IEEE Computer Society, Jue [4] as o March [5] F. Howell ad R. Macab, SimJava: a discrete evet simulatio library for Java, Proc. of the 1 st Iteratioal Coferece o Web based Modelig ad Simulatio, SCS, Ja [6] R. Buyya, ad M. Murshed, GridSim: a toolkit for the modelig ad simulatio of distributed resource maagemet ad schedulig for Grid computig, Cocurrecy ad Computatio: Practice ad Experiece, vol. 14, Nov. 2002, pp [7] A. Sigh, P. Goyal, S. Batra : A optimized roud robi schedulig algorithm for CPU schedulig, Iteratioal joural of computer ad electrical egieerig (IJCEE), vol. 2, No. 7, pp , December, [8] Bhathiya Wickremasighe CloudAalyst: A CloudSim-based Tool for Modelig ad Aalysis of Large Scale Cloud Computig Eviromets MEDC Project Report. [9] (as o Dec. 2011) No of Virtual Machies Figure 13.5: Performace Aalysis Of Overall Dataceter Processig time i MRR Balacer XIV. CONCLUSION A virtual machie is a virtual form of computer hardware withi software. Virtual machie is a software implemetatio that executes programs as if they were actual physical machies. I this paper, a ew VM load balacig algorithm is proposed which is implemeted i CloudSim, a abstract cloud computig eviromet usig java laguage. The proposed algorithm fids the expected respose time of each resource (VM) ad proved that the proposed algorithm is better tha roud robi virtual machie load balacig algorithm. By usig this experimet, we coclude that if we select the modified approach to the roud robi virtual machie load balacer the it effects the overall performace of the cloud Eviromet ad also decreases the average respose time ad the processig time of dataceter. 67