cloud computing review over various scheduling algorithms

Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426

147 NITTTR, Chandigarh EDIT-2015

Cloud computingReview over various scheduling algorithms

Meenakshi BhagtaniPhD Scholar, University Of Kota, Kota

[email protected]

Abstract: Cloud computing has taken an importantposition in the field of research as well as in thegovernment organisations. Cloud computing uses virtualnetwork technology to provide computer resources tothe end users as well as to the customer’s. Due tocomplex computing environment the use of high logicsand task scheduler algorithms are increase which resultsin costly operation of cloud network. Researchers areattempting to build such kind of job scheduling algorithmsthat are compatible and applicable in cloud computingenvironment.In this paper, we review research work which isrecently proposed by researchers on the base of energy savingscheduling techniques. We also studying various schedulingalgorithms and issues related to them in cloud computing.

Keywords: Cloud computing, virtualization, schedulers

CLOUD COMPUTINGCloud computing will spark a revolution in a wayorganizations provide or consume information andcomputing. Today’s most popular social networking site,e-mail services , document sharing and online gamingsites, are hosted on a cloud network of servers. Whereasthe giants of computer field like Microsoft are also takeninitiative to develop a cloud network for their users acrossthe globe. And to do that, more than half of theirdevelopers and R&D are working on the project.We define cloud computing, based on capabilities, whichare provided “as software”, “as a platform” and “as aninfrastructure” for consumers and enterprise to access ondemand regardless of time and location4.

Three basic services provided by cloud computing are asfollows:Software as a servicePlatform as a serviceInfrastructure as a service

While doing study about cloud computing, found thatscheduling and resource allocation are the important

research topic. A scheduler is required to schedule numberof virtual machine, as virtual machine are used to requestfrom consumer, to save maximum energy and achievegreater degree of load balancing and less resourceutilization from network which makes cloud computingmore responsive.The main objective of scheduling algorithms indistributed systems is to spreading the load onprocessors and maximizing their utilization whileminimizing the total task execution time while performingJob scheduling, one of the most known optimizationproblems, plays an important role for creating a flexibleand reliable systems. The main purpose of using such kindof scheduler is to schedule jobs to the adaptable resourcesin accordance with adaptable time, which involves findingout a proper sequence in which jobs can be executedunder transaction logic constraints.

BackgroundDistributed computing is a field of computer science thatstudies distributed systems. A distributed system consistsof multiple autonomous computers that work together andcommunicate through a computer network.

Types of distributed computing systems:1. Cluster computing systems: It is not a new area ofcomputing. There is an increase usage of it in all areas,where application is traditionally used in parallel ordistributed computing platforms.2. Grid computing systems: Computing becomes pervasiveand individual users have gain access to computingresource as needed with little knowledge of where thoseresources are located or stored, and what the underlyingtechnologies, hardware, operating system and so on are.3. Peer to peer computing: A class of systems andapplications that offers distributed resources to platform afunction in a decentralized manner. The resources areencompasses by computing power data, networkbandwidth, and presence of human, computers or otherresources.4. Cloud computing system: With cloud computing, usersuse a variety of devices, including PC’s, laptops, and smartphones to access program storage and application-development platforms over the internet, via servicesoffered by cloud computing providers.

Cloud computing possess the following keycharacteristics:-1.On-demand self-service: A user having provisioncomputing capabilities, such as server time and networkstorage, works automatically without having humaninteraction with each service provider.2. Broad network access: Cloud computing provide


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resources to the users with various capabilities over thenetwork which is accessed through standard mechanismsthat promote use by heterogeneous thin or thick clientplatforms such as mobile phones, laptops etc.)3.Resource pooling: It provider’s computing resources whichare pooled to serve multiple consumers using a multi-tenantmodel, having different physical and virtual resourceswhich are dynamically assigned and reassigned accordingto consumer demand. Examples of resources includestorage, processing, memory, network bandwidth, and virtualmachines.4. Rapid elasticity: Capabilities of such kind of systems canbe rapidly and elastically provisioned, but in some casesautomatically, to quickly scale out, and rapidly released toquickly scale in to the consumer, the capabilities availablefor provisioning often appear to be unlimited and can bepurchased in any quantity at any time for the use.5. Measured Service: Cloud systems are built in a way thatthey automatically control and optimize the resource use byleveraging a metering capability at some level of abstractionappropriate to the type of service (e.g., storage, processing,bandwidth, and active user accounts). Resource usage canbe monitored, controlled, and reported, for providingtransparency for both the provider and consumer of theutilized service.

Types of Cloud Models:1. Public Cloud: It is developed where severalorganizations have similar needs and they need to seek toshare infrastructure. It helps in allowing freeing use fromperforming important task like installation of resource,their configuration and storage.2. Private Cloud: It enables the remote access ofapplications by smart phones. The cloud-based resourcesare delivering to one platform and can be access from localPC.3. Community Cloud: It is developed to shareinfrastructure between several organizations from aspecific community with common concerns, and can bemanaged internally or by a third party hosted internally orexternally.4. Hybrid Cloud: The cloud infrastructure is acomposition of two or more clouds(private, communityor public) that remain unique entities but are boundtogether by standardized or proprietary technology thatenables data and application portability.

Issues in CloudOne of the major issues in implementing cloud computingis taking virtual machines in use, which contain criticalapplications and sensitive data to public and shared throughcloud environment. The following are certain issues in cloudcomputing.1.Performance: The major problem arises in theperformance can be for some intensive transaction-orientedand other data-intensive applications, in which cloudcomputing may lack adequate performance. Also, the userswho are using the cloud network from a long distance mayexperience high latency and delays.2.Security and Privacy: Customers are worried about theirdata and the vulnerability of attacks, when information andcritical IT resources are outside the range of firewall.3.Control: Some IT departments are concerned because thecloud computing providers have the full control over the

platforms. Cloud computing providers typically do not designplatforms for specific companies and their business practices.4.Bandwidth Costs: With cloud computing, companies cansave money on hardware and software; however they couldhave to pay higher network bandwidth charges. Bandwidthcost may be low for smaller Internet-based applications,which are not data intensive, but could significantly grow fordata intensive applications.5. Reliability: Cloud computing still does not always offerround-the-clock reliability. There were cases where cloudcomputing services suffered few-hours outages.6.Security Policy: It is very difficult to choose whether theuser would have same security policy control over theirapplications and services or the cloud provider will provideits own policies. If so, then the issue of trusting third partyvendor arises.7.Scheduling: Scheduling is the method of time division bywhich threads, processes or data flows are given access tosystem resources (e.g. processor time, communicationsbandwidth). This is usually done to balance the load on asystem effectively or achieve a target quality of service. Theneed for such kind of scheduling algorithm arises becausefrom the requirement for most modern systems to performmultitasking (execute more than one process at a time) andmultiplexing (transmit multiple flows simultaneously).Thescheduler is concerned mainly with the following:Throughput : The total number of processes that completetheir execution per time unit.Latency, specifically, Turnaround time - total time takenbetween submission of a process and its completion.Response time–total duration of time it takes from when arequest was submitted until the first response is produced.

Scheduling for Cloud ComputingMainly there are several type of scheduling techniques areuse by the cloud network. Most of them can be applied inthe cloud environment with suitable verifications. Themain advantage of job scheduling algorithm is toachieve a high performance computing and the bestsystem throughput. Traditional job scheduling algorithmsare not able to provide scheduling in the cloudenvironments.According to a simple classification, job schedulingalgorithms in cloud computing can be categorized intotwo main groups; Batch mode heuristic schedulingalgorithms(BMHA) and online mode heuristic algorithms.In BMHA, Jobs are queued and collected into a set whenthey arrive in the system. The scheduling algorithm willstart after a fixed period of time. The main examples ofBMHA based algorithms are; First Come First Servedscheduling algorithm (FCFS), Round Robin schedulingalgorithm (RR), Min-Min algorithm and Max-Minalgorithm.By On-line mode heuristic scheduling algorithm, Jobs arescheduled when they arrive in the system. Since thecloud environment is a heterogeneous system and the speedof each processor varies quickly, the on-line modeheuristic scheduling algorithms are more appropriate for acloud environment. Most fit task scheduling algorithm(MFTF) is suitable example of On-line mode heuristicscheduling algorithm.



A. First Come First Serve Algorithm: The jobs in a queuewhich arrive first get processed first.B. Round Robin algorithm: In the round robin scheduling,processes are dispatched in a FIFO manner but are given alimited amount of CPU time called a time-slice or aquantum. If a process does not complete before its CPU-time expires, the CPU is pre-emptedand given to the nextprocess waiting in a queue. The pre-empted process isthen placed at the back of the ready list.C. Min-Min algorithm: This algorithm searches for thesmall task among the group of task for processing, which inturn large task delays for long time.D. Max-Min algorithm: This algorithm chooses large tasksto be executed firstly, which in turn small task delays forlong time.E. Most fit task scheduling algorithm: In this algorithmtask which fit best in queue are executed first. Thisalgorithm has high failure ratio.F. Priority scheduling algorithm: The basic idea isstraightforward: each process is assigned apriority, and priority is allowed to run. Equal-Priorityprocesses are scheduled in FCFS order.G. Shortest-Job-First (SJF) algorithm: This is a specialcase of general priority scheduling algorithm. An SJFalgorithm is simply a priority algorithm where thepriority is the inverse of the (predicted) next CPUburst.That is, the longer the CPU burst, the lower the priorityand vice versa. Priority can be defined either internally orexternally. Internally defined priorities use somemeasurable quantities or qualities to compute priority of aprocess.

EXISTING SCHEDULINGALGORITHMSThe following scheduling algorithms are presentlyestablished in the cloud computing environment.

1.Fuzzy-Genetic Algorithm based Task schedulingOptimizationsAn optimized algorithm is proposed based on the Fuzzy-Genetic Algorithm optimization which makes ascheduling policy by evaluating the entire group of task inthe job queues. Fuzzy sets were used to representimprecisescheduling parameters and also to representsatisfaction grades of each objective. Genetic algorithmswith different components were developed on the basedtechnique for task level scheduling in HardtopMap Reduce.To gain a better balanced load across all the nodes in thecloud environment, the scheduler is revised by predictingthe execution time of tasks assigned to certain processorsand making an optimal decision over the entire group oftasks. Although this method meets user’s requirement andgets good resource utilization, the predicted execution timeis a disadvantage of this scheduling method since it is notpossible topredict the execution time of tasks effectivelybefore executing the tasks.

2. The Analytic Hierarchy Process for Task scheduling andresource allocationDaji Ergu et al. presented a model for task-orientedresource allocation in a cloud computing environment .Inthis model computing tasks is collected in the Task Pool.These tasks areranked using the pair wise comparison matrix technique andthe Analytic Hierarchy Process giving the available

resources and user preferences and are submitted tocomputing resourcesdistributed in Cloud Computing Nodes. Thecomputing resources can be allocated in terms of the rankof tasks. When all tasks are ranked according to availableresources this model improves the resource utilization andalso meets user requirements. But here it is not possible toallocate resources dynamically.

3. A Priority based Job Scheduling AlgorithmA new priority based job scheduling algorithm (PJSC) isproposed in cloud computing environment based on multiplecriteria decision making model, using analytical hierarchyprocess. Provided a discussion about some issues related tothe proposed algorithm such as complexity, consistency andfinish time. The proposed algorithm has reasonablecomplexity. But the main disadvantage is that the finish timecannot be calculated and response time is more .Also formore number of jobs allocations it is not suitable sincefinding priority of each job is tedious one.

4. Market Oriented Scheduling PoliciesBy considering the time and cost of resource provisioning,two Market oriented scheduling policies (MOSP) wereproposedthat aim at satisfying the application deadline byextending thecomputational capacity of local resources via hiringresource from Cloud providers. The policies are nothaving any earlier knowledge about the applicationexecution time. The proposed the Cost Optimization andthe Time Optimization scheduling policies increase thecomputational capacity of the local resources byhiringresources from IAAS providers.

5. Online Optimization for SchedulingPreemptable TasksJiayinLi et al. proposed a resourceoptimization mechanism in federated IaaS cloud systemwhich enables preemptable task scheduling. In this model,every data centre has a manager server that knows thecurrent statuses of VMs in its own cloud. And managerservers communicate with each other. When a cloudreceives requests from users, its manager servercommunicate with manager servers of other cloudsand distribute its tasks across thewhole cloud system byassigning them to other clouds or executing them by itself.The proposed algorithms, dynamic cloud list scheduling(DCLS) and dynamic cloud min-min scheduling(DCMMS) adjust the resourceallocation dynamicallyaccording to the updated information of actual taskexecution. Also they have proposed energy aware localmapping mechanism which can reducetheenergyconsumption in federated cloud system.

6. Resource Scheduling Strategy based on GeneticAlgorithmJianhua Gu et al. presented a scheduling strategy on loadbalancing of virtual machine resources usingGenetic Algorithm (RSGA). It uses historical data andcurrent states of VMs. In the proposed method startingfrom the initialization incloud itself they look for the bestscheduling solution by genetic algorithm in every


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scheduling and when there are no VM resources in thewhole system use the algorithm to choose schedulingsolution according to the computed probability. Even thoughthis method can better realize load balancing and properresource utilization, it does not deals with the dynamicbehaviour of resource allocation.7. Job scheduling algorithm based on Berger model

Berger model theory on distributive justice in the field ofsocial distribution is introduced into the job schedulingalgorithm in cloud computing.Job scheduling algorithmbased on Berger model(JSBM) concentrates on the fairnessof theresource allocation.The proposed model agrees withthe Quos parameters like completion time and bandwidth.

COMPARISON OF DIFFERENT SCHEDULING POLICIESTable 1: list out the environment, algorithmand schedulingparameters used in different scheduling policies. Also comparesvarious scheduling policies in terms of their advantages anddisadvantages.

Sl no Paper title/Author

Algorithm/technique used

Schedulingparametersconsidered

Advantage Disadvantage

1 Task schedulingoptimizations forthe cloudcomputing system,Sandeep Tayal

Genetic algorithmbased scheduling

Execution time oftasks

Meet userrequirements andimproved resourceutilization

Execution time ismore

2 The analytichierarchy process:Task schedulingand resourceallocation in cloudcomputingenvironment,DajiErgu, GangKou, YiPeng,YongShi,YuShi

Ranking of tasks isdone by usingreciprocal pair wisecomparison matrixand analyticalhierarchy process

Response time,task expense

Improves resourceutilization

Cannot allocatetasks dynamically

3 A Priority basedjob schedulingalgorithm in cloudcomputing,shamsollahGhanbari,Mohamed Othman

Based on thetheory ofAnalyticalhierarchy process

Make span Since priority isconsideredimportant task willnot be lagged

Increased makespan

4 Adapting marketoriented schedulingpolicies for cloudcomputing,Mohsen AminiSalehi,RajkumarBuyya

Deadline budgetconstraint basedTime and costoptimizationscheduling policy

Response time,execution time,cost

Increase thecomputationalcapacity of thelocal resources byhiring resourcesfrom IaaSproviders

Increasedcompletion time

5 Onlineoptimization forschedulingpreemptable taskson IaaS cloudsystems, JiayinLi,MeikangQiu,ZhongMing,GangQuan,XiaoQin, ZonghuaGu

Based on cloud listscheduling andcloud min-mingreedy algorithmfor scheduling

Arrival time andexecution time

Thedynamic procedureprovidessignificantimprovement in thefierce resourcecontentionsituation.

Preemption leadsto increasedresponse time andoverhead to thecloud providers

6 Evaluation of gangschedulingperformance andcost in cloudcomputing system,Ioannis A,Moschakis,Helen.D, Karatza

Gang schedulingapproach basedshortest queue first,adaptive first comefirst served andlargest job firstalgorithm

Waiting time,response time, cost

Improved resourceutilization

Not considered thepriority among thetasks

7 Anew resourcescheduling strategybased on geneticalgorithm in cloudcomputing

Based on geneticalgorithm andspanning treeprinciple

Number of virtualmachines,execution time

This method canbetter realize loadbalancing andproper resourceutilization

It does not dealwith the dynamicbehaviour ofresource allocation.



environment,Jianhua Gu, JinhuaHu, Tianhai Zhao,Guofei Sun

REVIEW OF RELATED WORKIn this decade we refer many approaches viz. algorithm,methods, paradigms, techniques how to schedulevirtual machines running on physical machines and alsoconcentrate on energy consumption less, optimization ,fully workload distribution , exploitation with physicalmachine rateability.

M. Devare et al,proposed a scheduling policytoimplement Scheduler which assign number of virtualmachine requests coming from consumer to virtualmachines on the base of ‘bully’ and “non-bully” approach.

The solutions in the context of Haizea are shown, throughexperiments. The big improvement in utilization and energyconsumption is found as workloads are running with lowerfrequencies. The coincidence of energy consumption andutilization is improved.

Jiandun Li et al introduce a hybrid energy-efficientscheduling algorithm for private clouds,concentrated on load balancing, Load migration on thebase of state of virtual machines, count response time. Ifresponse time increases then energy also increases. Sothey minimised response time in their algorithm.

Gregor Von Laszewski et al proposed scheduling virtualmachine in a compute cluster to reduce powerconsumption through Dynamic Voltage FrequencyScaling (DVFS),implementation of energy efficientalgorithm to allocate virtual machine.

Bo Li, Jianxin Li et al states Energy aware heuristicalgorithm on base of distributes workload in virtualmachine with minimum number of virtual machines ornodes requiredthat workload. So that workload migration, workloadresizes virtual machine migration these approaches areused in algorithm.

CONCLUSIONS AND FUTURE WORKThis paper is based on cloud computing technology whichhas a very vast potential and is still unexplored. Thecapabilities of cloud computing are endless. Cloudcomputing provides everything to the user as a servicewhich includes platform as a service, application as aservice, infrastructure as a service.One of the major issuesof cloud computing is scheduling mechanism becauseoverloading of a system may lead to poor performancewhich can make the technology unsuccessful. So there isalways a requirement of efficient scheduling algorithm forefficient utilization of resources. Our paper focuses on thevarious scheduling algorithms and their applicability incloud computing environment.

We first categorized the algorithms asBatch mode heuristicscheduling algorithms(BMHA) and online mode heuristicalgorithms. Then we analyzed the various algorithmswhich can be applied in BMHA environments. After thatwe described the various dynamic scheduling mechanismalgorithms. For solving any particular problem some

special conditions need to be applied. So we havediscussed some additional algorithms which can help insolving some sub-problems in scheduling mechanismwhich are applicable to cloud computing. In our futurework we will analyze the algorithms with numericalanalysis and simulation, which are energy efficient, haveless power consumption.

REFERENCES[1] Shailesh S. Deore, Ashok Narayan Patil (2012), “SystematicReview of Energy-Efficient Scheduling Techniques in Cloud Computing,International Journal of Computer Applications (0975 - 8887)”

[2] Pinal Salot, “A Survey of Various Scheduling Algorithm in CloudComputing Environment,ISSN: 2319-1163”[3]M.S.Saleem Basha,Silpa.C.S (2013), “A Comparative Analysis ofScheduling Policies in Cloud Computing Environment, International Journalof Computer Applications (0975 - 8887)”

[4] Dzmitry Kliazovich, Sisay T. Arzo, Fabrizio Granelli, PascalBouvryand Samee Ullah Khan(2013), “e-STAB: Energy-EfficientScheduling for Cloud Computing Applications with Traffic LoadBalancing,IEEE International Conference on Green Computing andCommunications and IEEE Internet ofThings and IEEE Cyber, Physicaland Social Computing”[5]Yogita Chawla1 and Mansi Bhonsle,(2012) “A Study on SchedulingMethods in Cloud Computing, International Journal of Emerging Trends& Technology in Computer Science (IJETTCS)”[6]Shaminder Kaur Amandeep Verma(2012), “An Efficient Approach toGenetic Algorithm for Task Scheduling in Cloud ComputingEnvironment”, I.J. Information Technology and Computer Science[7]A Study of Mobile Cloud Computing And Challenges Pragaladan. R1 ,Leelavathi .M[8] International Journal of Cloud Computing and Services Science (IJ-CLOSER) Vol.2, No.2, April 2013, pp. Cloud Computing : ResearchIssues and Implications M.Rajendra Prasad, R. Lakshman Naik, V.Bapuji[9]Journal of Information Engineering and Applications ISSN 2224-5782(print) ISSN 2225-0506 Vol 2, No.7, 2012Mobile Cloud Computing:Implications and Challenges by M.Rajendra Prasad, Jayadev Gyani,P.R.K.Murti