IEEE-International Conference on Recent Trends in Information Technology, ICRTIT 2011 MIT, Anna University, Chennai. June 3-5, 2011
Improving scheduling of backfill algorithms using balanced spiral method for cloud metascheduler Suresh.A#1, Vijayakarthick.P#2 #
Information Technology, Kalasalingam University Krishnankoil, Srivilliputtur, India 1 2
[email protected] [email protected]
Abstract— Job scheduling problem is a core and challenging issue in cloud computing. It is impossible to predict the job execution time in cloud environment. Hence the scheduler must be dynamic. Parallel job scheduling strategies like EASY, conservative backfill algorithms are failed to fill the resource gap fully. In combinational backfill algorithm (CBA) small jobs are getting high priority. To achieve QOS in cloud environment; propose an improved backfill algorithm using balanced spiral (BS) method. In this paper analyzed the various parallel job scheduling algorithms like EASY, conservative and CBA. Analysis and number results show that improved backfill algorithm (IBA) not only provides dynamic scheduler but also guarantee the QOS in cloud environment. Keywords— cloud computing, balanced spiral, metascheduler, QOS.
I. INTRODUCTION Cloud computing [1] is a future technology that won’t need to compute on local computers, but on centralized facilities operated by third-party compute and storage utilities. Job scheduling (JS) system is one of the core and challenging issues in a Cloud Computing system. The aim of JS systems in Cloud or Grid computing mainly considers how to meet the QoS requirements. In general two schedulers [2] are available in cloud one is global or metascheduler and another is local scheduler. Local scheduling discipline determines how the processes resident on a single CPU are allocated and executed. Users submits their jobs to Metascheduler, it uses information about the system to allocate processes among the clusters. It is impossible to predict the job execution time in cloud, hence scheduler must be dynamic. This paper addresses the problem of making dynamic metascheduler in cloud environment. The availability of idle cloud resources are utilize as maximum as possible and evaluation based on parallel job scheduling strategies to provide required QoS .In this paper ,it is attempted to employ improve backfill algorithm using balanced spiral (BS) method[3] implementation in cloud metascheduler to solve the cloud scheduling problem with multiple objectives. The multiple objectives are maximizing the resource utilization and minimize the resource gap of idle resources. The evaluations of improved backfill algorithm (IBA) with other algorithm such as EASY, conservative, combinational backfill (CBA) are made. For convention this paper mentioned both
978-1-4577-0590-8/11/$26.00 ©2011 IEEE
EASY and conservative backfill algorithm as traditional backfill algorithm. The rest of the paper is organized as follows: the next section discusses the related works. Section 3 presents an overview of cloud metascheduler architecture where improved CBA is implemented. Section 4 describes improved CBA. Results of the performance and parameter study are reported in section 5. II. RELATED WORKS Recent years have seen many efforts focused on the efficient utilization of cloud resources by cloud metascheduler that lead satisfaction to both cloud service provider and service users. Cloudsim [4] allows modeling and simulations of entities in parallel and distributed computing systems. Aneka[5] form enterprise grid and cloud platform provide following services as task scheduler service for the task programming model, thread scheduler services, for the thread programming model, storage service for file store for applications. Hadoop a popular open-source implementation of the Google’s Map Reduce model is primarily developed by Yahoo. The work done by [6], [7] considers Hadoop scheduler can cause severe performance degradation in heterogeneous environments and provide a new scheduling algorithm, Longest Approximate Time to End (LATE) for concurrent jobs in heterogeneous environments. But LATE doesn’t always improve the performance. The work related to [8] considers self adaptive backfill policy for parallel systems using multiqueue. The paper [9] evaluates traditional backfill algorithm to show that System generated predictions is better than user runtime estimation. And IBM in paper [10] proves the effectiveness of backfill algorithms for parallel systems. The work done by [11] focuses on optimizing the system throughput by maximizing the overall resource utilization and guaranteeing increased performance of the applications. Here an optimal solution for cloud job scheduling is made only better than the traditional First Come First Serve (FCFS), Round robin and failed to fill the resource gap completely. The work related to [12], consider the commonly used method of job scheduling FCFS, along with Backfilling method EASY and CONSERVATIVE algorithms where small jobs are moved ahead in the schedule can fill the resources gap that is generated by FCFS. However, existing FCFS, backfilling scheduling algorithms are available for a queued job
624
IEEE-ICRTIT 2011 backfilled to schedule. The resources gap can't be fully utilized. III. PROBLEM DESCRIPTION Fig 1. Shows the cloud datacenter, in general cloud computing environment consists of multiple datacenter. Datacenter is a collection of enormous scalable resources. Resources may be hardware, software, infrastructure and platform etc..Each datacenter include one or more virtual machines (VM’s). User jobs are mapped between those VM by metascheduler. Each VM has one or more processing elements (PE’s) and jobs are scheduled to processing elements by local scheduler. Fig 2. Shows cloud metascheduler architecture. Three entities are involved in this architecture • The cloud users submit their request for job completion to the metascheduler • All the tasks received by metascheduler and decision for scheduling is made by querying the cloud cluster about the availability of free nodes. Only the metascheduler responsible for mapping the jobs between cloud users and cloud clusters. • Jobs are executed at the cloud cluster by local scheduler. The goal of the metascheduler is to receive the request from cloud users to provide dynamic scheduling to maximize the resource utilization using parallel job scheduling strategies. Cloudsim is the cloud computing simulator allows modeling and simulations of entities in parallel and distributed computing systems and is used to show the performance of IBA to the other parallel scheduling algorithms by varying the simulation of the datacenters to enable allocation policies for migration of VM’s. The EASY backfilling [13] allows short jobs to skip ahead provided they do not delay the job in queue. Both EASY and conservative provide dynamic scheduling but resources are not fully utilize hence resource gap is present. In CBA multiple small jobs are grouped together and backfill which maximize the resource utilization but longer jobs are failed to backfill and gets starved.
Cloud users
Submit jobs
METASCHEDULER Dispatch jobs to VM
Figure 2.Metascheduler architecture
IV. PROPOSED IMPROVED BACKFILL ALGORITHM FOR CLOUD METASCHEDULER In this study, backfill is improved using BS method to solve the scheduling problem in cloud is discussed. The basic of the method is derived from the Operation Research from management studies and backfill algorithms are evaluated using cloudsim toolkit. A. Limitation of Backfill Algorithms Core concept beyond backfill algorithms has its own limitation. According to backfill algorithm, move forward the smaller jobs until it does not cause the further jobs delayed. Also in backfill jobs are scheduled according to their arrival sequence. This shows that the traditional backfill algorithms give priority to smaller jobs. Here scheduler selects the first possible job to backfill and results in fragmentation.
Data Center
Metascheduler VM
VM
Local schedule
Local schedule
PE
PE
PE
PE
Fig 3.Example of traditional backfill algorithms
Fig 1.Cloud datacenter
625
Improving scheduling of backfill algorithms using balanced spiral method for cloud metascheduler
…
Jn-2
... Jn-3
… …
Jn Jn-1
… Fig 4.”V” shape job arrangement
V. RESULTS AND DISCUSSION In this section, the experimental evaluation for the cloud metascheduler is discussed. The Cloudsim toolkit is used to simulate the algorithm with various experimental setups. The default classes in Cloudsim toolkit are extended to implement the proposed policy and other parallel job scheduling strategies. The experimental setup include by varying jobs arrival time, speed of processing elements, size of cloudlets and also policies. It can be analyzed by experimental results that job completion ratio of improved backfill is higher than other backfill algorithms and shown in Fig 6. The cruciality of the proposed algorithm stands on satisfying the multiple objectives as eliminating the limitation of backfill algorithm along with high resource utilization. Job Completion Ratio 100 80 60 40 20 0
Traditional Backfill
35 00
25 00
Improved Backfill 15 00
50 0
B. Improved Backfill Algorithm(IBA) IBA reorders the job arrival sequence using BS method. In BS method jobs are placed between the left (L) and right(R) side in the job pool. Consider a job set J has the following job sequence {j1, j2, j3, j4, j5} where j1≤j2≤j3≤j4≤j5. Here schedule the same set of jobs to prove that IBA utilize resources better than backfill. Before following backfill algorithm order the queue using BS method. Original jobs arrival sequence is {j1, j2, j3, j4, j5} Jobs sequence in ascending order with respect to processor as {j3, j4, j1, j2, j5}.According to BS method place the largest job j5 in last position and j2 in the last but one position.j1 in the first position L= {j1} R= {j2}.Note that j5 not included in R. remaining jobs in the job pool are {j3, j4}.Now sum_L≤sum_R hence place the large job j4 from the job pool to the left. Here sum_L≥sum_R hence place the remaining job j3 to the right. New job sequence is getting by merging L and R with last position j5 as {j1, j4, j3, j2, j5}. Now job sequence gets changed as shown in Fig 5 .The first job in the queue have enough processors to run hence it is release immediately .The second queued job can be started at time 0, by jumping over first but that would delay the first job. The third queue job has a potential anchor point after first one job terminates. The fourth queued job also started at time 4, by jumping over third and that would delay the job further. Finally last queued job get released. Previously in traditional backfill it is possible to backfill only a single job. Now using IBA, here backfill two jobs that the way achieved high resource utilization.
Fig 5.Example of improved backfill algorithm
Processing Time
An example is given for backfill algorithm in Fig. 3 the first job in the queue have enough processors to run hence it is release immediately .The third job can be started at time 0, by jumping over first but that would delay the first job .The second queue job has a potential anchor point after another as arrival sequence there is no chance of backfilling the job. As a result both response time of J3 and system utilization is improved. IBA eliminates the above stated limitations in backfill algorithms and help to achieve QoS in cloud metascheduler. In BS method the jobs are arranged as ‘V’ shape [14] as shown in Fig 4.
Combination al backfill
Number of Cloudlet
Fig 6.Performance backfill algorithms
VI. CONCLUSIONS AND FUTURE WORKS This study has presented that the dynamic metascheduler which deploy the job using improved backfill for cloud
626
IEEE-ICRTIT 2011 environment. By the way metascheduler achieved the multiple objectives that discussed earlier and stands superior to other parallel job scheduling strategies. Future plan to propose policy for cloud local scheduler along with metascheduler and going to made evaluation on parallel job scheduling strategies with job grouping methods. REFERENCES [1] [2] [3]
[4] [5] [6] [7] [8] [9]
[10]
[11]
[12] [13] [14]
Ian Foster, Yong Zhao, Ioan Raicu, Shiyong Lu, ”Cloud and grid computing 360-degree compared”, December 2009. Mayon L.M. Peixato et al, “A metascheduler architecture to provide qos on the cloud computing”, 17th International Conference on Telecommuniacetion, 2009. Nong Ye, Xueping Li, Tong Fraely, Xiaoyun Xu, “Job scheduling methods for reducing waiting time variance”, Computer & Operation Reasearch 34(2007) pp 3069-3083, Available Online at www.sciencedirect.com. Rajkumar Buyya,Rajiv Ranjan and Rodingo N. Calheiros, “Modeling and simulation of scalable cloud computing environment and the cloudsim toolkit”, 2009 Rajkumar Buyya, “Aneka next generation .net grid/cloud computing company”, 2009. Matei Zaharia, Andy Konwinski, Anthony D. Joseph, Randy Katz, Ion Stoica, “Improving map reduce performance in heterogenous environment”, OSDI 2008. Michael Isard et al, ”Quincy:fair scheduling for distributed comptuing clusters” Microsoft Research ,SOSP 2008. Barry G. Lawson, Evgenia Smirni, Daniela Puiu, ”Self-adapting backfilling scheduling for parallel systems”. Ahuva W. Mu'alem and Dror G. Feitelson,” Utilization, predictability, workloads, and user runtime estimates in scheduling the ibm sp2 with backfilling”, “IEEE transactions on Parallel and Distributed Systems,Vol. 12, no. 6, June 2001. Dan Tsafrir, Yoav Etsion and Dror G. Feitelson, “Backfilling using system-generated predictions rather than user runtime estimates” IEEE transactions on Parallel and Distributed Systems, vol. 18, no. 6, june 2007 , pp.789-803. Sudha Sadhasivam, R. Jeya Rani, N. Nagaveni, R.Vasanth Ram, “Design and implementation of two level scheduler for cloud computing environment”, International Conference on Advance in Recent Technologies in Communication and Computing, 2009. Shenwei Yi et al, “Combinational Backfilling for Parallel Job Scheduling”, 2nd International Conforence on Education Technology and Computer (ICETC), 2010. Bo Li, Dongfeng Zhao,”Performance impact of advance reservations from the grid on backfill algorithms”,Sixth International Conference on Grid and Cooperative Computing (GCC 2007). Xiaoyun Xu and Nong Ye,”Minimization of job waiting time variance on identical parallel machines”, IEEE transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, Vol. 37, no. 5, September 2007.
627
