Differences and Problems Task Scheduling Algorithm -A Survey

International Journal of Hybrid Information Technology Vol.8, No.6 (2015), pp.145-152 http://dx.doi.org/10.14257/ijhit.2015.8.6.15 Differences and Problems Task Scheduling Algorithm -A Survey Kapil Kumar *, Abhinav Hans, Navdeep Singh and Mohit Birdi CSE Department Guru Nanak Dev University Regional Campus Jalandhar, INDIA [email protected] Abstract Cloud computing is a computing paradigm where applications, resources and services are provided over the internet. Software and hardware can be used to pay as service basis, without buying them. The key role of scheduling is to manage different tasks in different cloud environment. Cloud computing service providers use the available resources efficiently to achieve maximum profit. This makes task scheduling as a challenging issue for cloud service providers. This paper gives an introduction about cloud computing, various existing scheduling algorithms in different task scheduling environments, existing problem and the future suggestions in existing algorithms. Keywords: cloud computing, user level scheduling, dynamic level scheduling, real time scheduling, workflow level scheduling component, IVQ, SLA, LBIMM, PALBIMM, CPROVISIONTQS, DGS, OFDT, HEFT, CSO 1. Introduction Cloud computing provides services, shared resources or common infrastructure on demand through the internet. Service provider provides the facilities to pay per use policy [1]. Customer can use storage space, processing capabilities, servers, operating system and application development environments. The user can scale up and down the resources in an instant (timely) and on-demand manner in the cloud [2]. Service providers schedule tasks by taking care of the different needs of users. Due to the increase in popularity of cloud model, cloud, environment gives access to computing with the appearance of having unlimited resources. Cloud service providers serve the users by giving the permission to use their resources like memory, bandwidth, disk, etc. According to the available resources in cloud environment different tasks with different QoS requirements are scheduled in different environments. Task scheduling in different cloud environment is of many types- static and dynamic scheduling, workflow, scheduling, user level scheduling, and real time scheduling, heuristic scheduling. In this paper, section I give a brief introduction about cloud computing, section II contains various user level scheduling algorithms, section III contain various dynamic level scheduling algorithms, section IV contain various workflow level scheduling algorithms, section V contain various real time scheduling algorithms and section VI concludes the paper. 2. User Level Scheduling Algorithms Cloud consist of many things like User Requirement, Load Balance and other constraints that affects user consumption rate of resource [3]. In this section, various user scheduling algorithms are reviewed. In the table, the existing problems in user level scheduling algorithms, tool used, Parameters considered and future suggestions are summarized. ISSN: 1738-9968 IJHIT Copyright ⓒ 2015 SERSC International Journal of Hybrid Information Technology Vol.8, No.6 (2015) 2.1 IVQ (Intelligent Approach for VM and QoS Provisioning) [4] Amit Kumar Das, Tamal Adhikary and Md. Abdur Razzaque, Choong Seon Hong proposed an adaptive QoS aware VM provisioning approach which gives the efficient utilization of system resources by recycling the virtual machines in proper way. A lot amount of time is required to create and destroy Virtual machines again and again to serve user’s request. By taking into account that virtual machine is not necessary to be created for all jobs, IVQ proves better in term of rejection rate. The goal of this model is achieved by serving more users at same time and ensuring QoS parameters. 2.2 Novel Scheduling Heuristic based on SLA [5] Vincent C. Emeakaroha, Ivona Brandic, Michael Maurer, Ivan Breskovi proposed an approach related to service level agreement(SLA) which is a contract between service provider and user. Many previous algorithms worked on the single SLA parameter but the proposed approach works on multiple SLA parameters like load balancing and resource utilization. In this, author proposed a novel scheduling heuristic approach to schedule user requests on VM’s based on agreed SLA terms and allocate VM’s with available physical resources. Algorithm works on cloudsim simulation tool with custom extension layer. Proposed algorithm is two times better than traditional task scheduler. 2.3 LBIMM (Load Balance Improved Min-Min Scheduling ), Pa-Lbimm(User Priority Aware Load Balance Improved Min-Min Scheduling) [6] The proposed algorithm is based on min-min algorithm [7]. The biggest drawback of traditional min-min algorithm that is load unbalancing is improved in the proposed scheme. Two algorithms are proposed in this. First algorithm named LBIMM is proposed to optimize the load balancing by considering min-min algorithm. Second algorithm named PA-LBIMM is proposed by considering user priority to serve users with better services. Proposed algorithm performs better in case of completion time, load balancing and average resource utilization. 2.4 CPROVISION [8] Sharrukh Zaman, Daniel Grosu proposed an auction based mechanism for dynamic VM provisioning. Proposed algorithm takes user demand into account when taking VM’s allocation decision. Algorithm includes a reserve price concept which is the operating cost of resources. Reserve price means users has to pay some minimum amount to the cloud service provider. When compare with CGREEDY [9] it performs better in case of resource utilization and percentage of served users. In high demands, CPROVISION proves better in term of profit. Table 1. Various User Level Scheduling Algorithms And Future Suggestions Problem find Proposed scheme Large IVQ[4] amount of time required to create and deploy VM’s 146 Tool used Findings Parameters Future suggestions Cloudsim Minimizes the rejection rate Simulation time, rejection rate Resource allocation policy will be included with VM’s. Energy memory mechanism Copyright ⓒ 2015 SERSC International Journal of Hybrid Information Technology Vol.8, No.6 (2015) will also included. Previous proposed algorithm works on only single SLA parameter. Novel Cloudsim scheduling heuristic based on SLA [5] Load imbalances of traditional Min-min LBIMM, PA- MATLAB 1.LBIMM LBIMM [6] is better in case of load balancing 2. PALBIMM proves better in term of completion time. Dynamically CPROVISION provisioning [8] VM instances for higher profit Real workload tracers Achieve load balancing and higher resource utilization. be Resource utilization, load balancing To investigate this approach by considering energy efficiency objectives in utilizing resources. Completion time, load balancing and resource utilization To study PALBIMM by considering tasks as dependent entity. It performs Cost, better in resource case of utilization resource utilization and in term of profit To combine both CPROVISION and CGREEDY and to set a private cloud for implementation 3. Dynamic Level Scheduling Algorithms An internet-based large-scale distributed computing provides dynamically-scalable, efficient and optimized services, platforms and resources , according to the demands of users.[10]. In this section, various dynamic level scheduling algorithms are reviewed. In table, the existing problems in dynamic level scheduling algorithms, tool used, Parameters considered and future suggestions are summarized. 3.1 TQS (Tri Queue Job Scheduling Algorithm) [11] Liang Ma, Yueming Lu, Fangwei Zhang, and Songlin Sun. proposed an algorithm to avoid the fragmentation at time of scheduling. This algorithm gives equal opportunity to small, medium and long job using dynamic quantum time to make efficient use of resources. Starvation problem is removed by TQS. Algorithm divides the jobs into three different queues small, medium and large using queue forming technique and make efficient use of available resources. Copyright ⓒ 2015 SERSC 147 International Journal of Hybrid Information Technology Vol.8, No.6 (2015) 3.2 DGS (Dynamically Allocating VMs and Distributing Tasks by Greed strategy)[12] AV.Karthick, Dr.E.Ramaraj, R.Kannan proposed a DGS [12] algorithm which is feasible and flexible dynamic task scheduling scheme. This scheme dynamically allocates virtual resources to execute tasks by using improved greedy strategy. DGS evaluates the amount of resources required by user’s application and then dynamically adjust the virtual resources for load balancing and to increase resource utilization rate. Greedy strategy is used later to dynamically allocate tasks to computing node to get fastest response time. 3.3 OFDT’s (An Optimally Fair Dynamic Task Scheduling Algorithm) [13] In this, Shilpi Saxena, Satyendra Singh Chouhan proposed a dynamic task scheduling technique OFDT’s because traditional methods tends to overpricing and slow processing rate. This algorithm works on the requirement of each individual task and then allocates the task to most appropriate resource. OFDT’s performs better in term of cost and execution time when compared with other traditional algorithms Table 2. Various Dynamic Level Scheduling Algorithms And Future Suggestion Problem find Proposed scheme Tool used Findings Parameters Fragmentation TQS[11] problem at time of scheduling Cloudsim Better resource utilization Processing time, resource utilization TQS algorithm with reservation category of scheduling. Dynamically DGS[12] allocation of VM’s to achieve load balancing and resources utilization Cloudsim Achieve high load balancing and resource utilization Resource utilization, load balancing Cost of virtual resources will also be included Due to OFDT’s[13] Cloudsim overpricing 2.1.1 and slower processing time in bulk of tasks Performs Cost, better in execution term of time cost and execution time Future suggestions By taking more parameters like bandwidth, energy and latency,algorithm will be enhanced. 4. Workflow Level Scheduling Algorithms It schedules interdependent tasks of workflow application on a available virtual machines to achieve the overall objective of workflow application. In workflow scheduling, various sub tasks of the main task should be executed in particular manner. In this section, various workflow scheduling algorithms are reviewed. In table, the existing problem in workflow level scheduling environment proposed schemes, tool used, Parameters considered and future suggestions are summarized. 148 Copyright ⓒ 2015 SERSC International Journal of Hybrid Information Technology Vol.8, No.6 (2015) 4.1 HEFT (Heterogeneous Earlier Finish Time) [14] Nitish Chopra, Sarbjeet Singh proposed an algorithm which works on the deadline and monetary problems of user’s tasks. HEFT use a new concept of subdeadline for rescheduling and allocate the best resource from public cloud to user. HEFT works on a concept of deadline which is compared with makespan to set the best schedule. At the starting, all tasks are assigned to private cloud. If the allocated resourcees in private cloud meets the deadline than it is a best schedule otherwise some of them are send to public cloud. When HEFT is compared with min-min and greedy approach, it proves better in term of cost and always meets deadlines. 4.2 CSO(Cat Swarm Optimization) [15] Saurabh Bilgaiyan, Santwana Sagnika, Madhabananda Das proposed an heuristic scheduling algorithm with hypothetical workflow. CSO algorithm optimizes the transfer cost between two dependent resources. CSO considers two costs; data transfer cost and other is execution cost. CSO algorithm is inspired by two social behavior of cat, seeking mode and tracing mode. CSO proves better in term of total cost, load balancing and in number of iterations to achieve best solution. 4.3 Critical Greedy Algorithm [16] Xiangyu Lin, Chase Qishi Wu proposed an algorithm to reduce the cost and meet the other performance goals. Author find that an analytical problem called MEDCC(minimum end to end delay under cost constraints) which is NP-complete as well as non-approximable.Then to find a solution of heuristic workflow, end-to-end delay critical greedy algorithm is proposed. When compare with GAIN3, it proves better in case of cost under large budget. Table 3. Various Workflow Level Scheduling Algorithms And Future Suggestions Problem find Resource allocation in hybrid cloud which is a difficult task Data produced by complex problems is always large and the cost to transfer that data is also large Proposed scheme HEFT[14] Tool used Findings Parameters Cloudsim Better in term of cost and always meet deadlines. Cost, deadline, makespan. CSO[15] Benchmarks Better in term of total cost, load balancing and in number of iterations to achieve best solution. Energy consumption, cost , load balancing Copyright ⓒ 2015 SERSC Future suggestions On full workflow application and measure the cost in real time. To include multiple parameters in CSO like execution time, energy efficiency etc. 149 International Journal of Hybrid Information Technology Vol.8, No.6 (2015) MED-CC which is NP complete and nonapproximable Critical greedy algorithm [16] Cloudsim Proves better in case of cost under large budget Cost and end to end delay To achieve higher accuracy in real world cloud environment. 5. Real Time Scheduling Algorithms Real time tasks have to be complete before deadline. There are some real time scheduling algorithms presented in this section. In Table, the existing problem in real time scheduling environment proposed schemes, tool used, Parameters considered and future suggestions are summarized. 5.1 Multiobjective Particle Swarm Optimization [17] Pengju He, Yan Liang, Xingxing Chou proposed an algorithm to achieve real-time task scheduling and to make embedded cloud computing resources. In this resource load balancing degree and task completion time are objective functions. Multiobjective particle swarm optimization is used to achieve task scheduling. Algorithm proves better in case of task processing time and in load balancing degree. 5.2 ECMM( Max-Min Task Scheduling Algorithm For Elastic Cloud) [18] Xiaofang Li1, Yingchi Mao, Xianjian Xiao, Yanbin Zhuang1 proposed an algorithm for load balancing and maintain a real time load status table. It maintains two tables, task status table and virtual machine status Table. By estimating the total execution time and number of tasks in virtual machine, a task is scheduled to virtual machine. ECMM proves better in case of average task pending time when compared with round robin algorithm. Table 4. Various Real Time Scheduling Algorithms and Future Suggestions Problem find Proposed Tool used Findings scheme To solve Multiobjective MATLAB Better in multiobjective particle R2009a case of optimization swarm optim task problem ization[17] processing time and in load balancing degree. Elasticity in ECMM( Max- Cloudsim cloud Min task computing scheduling algorithm for elastic cloud)[18] 150 Parameters Future suggestions Completion To achieve time, more real processing time time,load requirements balancing and practically implement them should be done in future. Better in Task To estimate case of pending load average time, balancing in task response more real pending time, load environment time balancing can be the future task Copyright ⓒ 2015 SERSC International Journal of Hybrid Information Technology Vol.8, No.6 (2015) 6. Conclusion Cloud computing is an emerging technology. Lot of work is going on the task scheduling in different cloud environment. Some algorithms are good in terms of cost and some are in processing time etc. This paper helps in good understanding of task scheduling options in different environments according to user needs. This paper gives a review on various task scheduling algorithms in different environment, problems existing in different environment, findings of algorithms by taking different parameters and future suggestions in existing algorithms. Acknowledgment I would like to thank my teachers, parents and my friends for all their support in this paper. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] S. M. Hashemi and A. Kh. Bardsiri, “Cloud computing vs. grid computing,” ARPN journal of systems and software, vol. 2, no. 5, (2012) May, pp. 188-194. H. Alhakami, H. Aldabbas and T. Alwada, "Comparison between cloud and grid computing : review paper," International journal on cloud computing: services and architecture (IJCCSA), vol. 2, no. 4, (2012) August, pp. 1-21. O. M. Elzeki, M. Z. Reshad and M. A. Elsoud, “Improved Max-Min Algorithm in Cloud Computing”, International Journal of Computer Applications, vol. 50, Issue 12, (2012), pp. 22-27. A. K. Das, T. Adhikary and Md. A. Razzaque and C. S. Hong, “An Intelligent Approach for Virtual Machine and QoS Provisioning in Cloud Computing”, IEEE ICOIN, (2013). V. C. Emeakaroha, I. Brandic, M. Maurer and I. Breskovi, “SLA-Aware Application Deployment and Resource Allocation in Clouds”, 35th IEEE Annual Computer Software and Applications Conference Workshops, (2011). H. Chen, F. Wang, N. Helian and G. Akanmu, “User-Priority Guided Min-Min Scheduling Algorithm For Load Balancing in Cloud Computing”. X. Yu and X. Yu, "A new grid computation-based Min-Min algorithm", Fuzzy Systems and Knowledge Discovery, FSKD'09 Sixth International Conference on IEEE, vol. 1, (2009), pp. 43–45. S. Zaman and D. Grosu, “Combinatorial Auction-Based Dynamic VM Provisioning and Allocation in Clouds”, Third IEEE International Conference on Coud Computing Technology and Science, (2011). S. Zaman and D. Grosu, “Combinatorial auction-based allocation of virtual machine instances in clouds,” in Proc. 2nd IEEE Intl. Conf. on Cloud Computing Technology and Science, (2010), pp. 127–134. M. Choudhary, et al., “A Dynamic Optimization Task Scheduling Algorithm in Cloud Environment”, International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622, www.ijera.com, vol. 2, Issue 3, (2012) May-June, pp. 2564-2568, 2564. L. Ma, Y. Lu, F. Zhang and S. Sun, “Dynamic Task Scheduling in Cloud Computing Based on Greedy Strategy”, Springer-Verlag Berlin Heidelberg, (2013). A. V. Karthick, E. Ramaraj and R. Kannan, “An Efficient Tri Queue Job Scheduling using Dynamic Quantum Time for Cloud Environment”, International Conference on Green Computing, Communication and Conservation of Energy (ICGCE) in IEEE, (2013). S. Saxena and S. S. Chouhan, “OFDTs:-An Optimally Fair Dynamic Task Scheduling Algorithm in Cloud Environment”, 978-1-4799-5173-4/14/$31.00, IEEE, (2014). N. Chopra and S. Singh, “HEFT based Workflow Scheduling Algorithm for Cost Optimization within Deadline in Hybrid Clouds”, 4th ICCCNT, IEEE, vol. 31661, (2013) July 4-6, Tiruchengode, India. S. Bilgaiyan, S. Sagnika and M. Das, “Workflow Scheduling in Cloud Computing Environment Using Cat Swarm Optimization”, 978-1-4799-2572-8/14/$31.00_c,IEEE, (2014). X. Lin and C. Q. Wu, “On Scientific Workflow Scheduling in Clouds under Budget Constraint”, 42nd International Conference on Parallel Processing 0190-3918/13 $26.00 ©,IEEE, (2013). P. He, Y. Liang and X. Chou, “Resource Scheduling Algorithm in Embedded Cloud Computing and Application”, IIAI 3rd International Conference on Advanced Applied Informatics 978-1-4799-41735/14 $31.00 ©,IEEE, (2014). X. Li1, Y. Mao, X. Xiao and Y. Zhuang1, “An Improved Max-Min Task-Scheduling Algorithm for Elastic Cloud”, International Symposium on Computer, Consumer and Control 978-1-4799-5277-9/14 $31.00,IEEE, (2014). Copyright ⓒ 2015 SERSC 151 International Journal of Hybrid Information Technology Vol.8, No.6 (2015) Author Kapil Kumar, Born on March 4 1993 in Nakodar city District Jalandhar Punjab India. Completed his B Tech (CSE) from CTIT Shahpur Jalandhar in year 2013 and Pursuing M.Tech in computer Science and Engineering GNDU Regional Campus Jalandhar, Punjab. Area of interest in technology is Cloud Computing, Traffic Control of Cloud Computing, Sensor Cloud. Many research papers have been published of the author in the different conferences like IEEE, International Conferences and different Journals. Looking forward to go for Doctorate in the same field to continue his research. 152 Copyright ⓒ 2015 SERSC