International Journal of Distributed and Cloud Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Scheduling Work Load Based on Priority in Cloud


Affiliations
1 Department of Computer Science and Engineering, Karpagam University, Coimbatore, Tamil Nadu, India
     

   Subscribe/Renew Journal


Cloud computing offers ability to provide parallel and distributed simulated services remotely to the users through the internet. Services hosted within the "cloud" can potentially incur processing delay due to load sharing among other active services , and can cause active optimistic simulation protocols to perform poorly. Number of complex application runs in remote data centres, parallel processing capabilities often show a increase in utilization of CPU resources as parallelism grows, mainly because of communication and synchronization. To achieve certain level of utilization, Our proposed method partitions a node's computing capacity into the 4-tiers with low CPU priority, medium CPU priority, high CPU priority and very high CPU priority. In large datacenter, processes of a job may need to be allocated to nodes that are close to each other to minimize the communication cost. We provide scheduling algorithms for parallel jobs to make efficient use of the k-tiers VMs to improve the responsiveness of these jobs. We focus on improving resource utilization for datacenters that run parallel jobs; particularly we intend to make use of the remaining computing capacity of datacenter nodes that run parallel processes with low resource utilization to improve the performance of parallel job scheduling. The method is practical and effective for consolidating parallel workload in data centres.

Keywords

Distributed Computing, Parallel Computing, Parallel Simulation, Resource Consolidation, Scheduling, Virtualization.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Delavar, A. G. & Aryan, Y. (2011). A Synthetic heuristic algorithm for independent task scheduling in cloud systems. IJCSI International Journal of Computer Science, November, 8(6), 1694-814.

  • Ingole, A., Chavan, S. & Pawde, U. (2011). An Optimized Algorithm for Task Scheduling based on Activity based Costing in Cloud Computing. 2nd National Conference on Information and Communication Technology (NCICT) 2011 Proceedings published in International Journal of Computer Applications® (IJCA).

  • Rasooli, A. & Down, D. G. (2011). An Adaptive Scheduling Algorithm for Dynamic Heterogeneous Hadoop Systems. Proceeding CASCON '11 Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research on IBM corp.USA.

  • Gupta, B. D. & Palis, M. A. (2001). Online realtime preemptive scheduling of jobs with deadlines. Journal of Scheduling, November/December, 4(6), 297-312.

  • D'Angelo, G. (2011). Parallel and Distributed Simulation from Many Cores to the Public Cloud. Proceedings of International Conference on High Performance Computing and Simulation (HPCS) (pp. 14-23).

  • Ahmad, I, Shamala, S., Othman†, M. & Othman, M. F. (2008). A preemptive utility accrual scheduling algorithm for adaptive real time system. IJCSNS International Journal of Computer Science and Network Security, 8(5), 57-61.

  • Moschakis, I. A. & Karatza, H. D. (2012). Evaluation of gang scheduling performance and cost in a cloud computing system. The Journal of Supercomputing, February, 59(2), 975-992.

  • Hwang, J. & Wood, T. (2012). Adaptive Dynamic Priority Scheduling for Virtual Desktop Infrastructures. Proceedings of the 2012 IEEE 20th International Workshop on Quality of Service.

  • Jann, J., Pattnaik, P., Franke, H., Wang, F., Skovira, J. & Riordan, J. (1997). Modeling of Workload in MPPs. Proceedings of Workshop Job Scheduling Strategies for Parallel Processing (pp. 95-116).

  • Kargahi, M. & Movaghar, A. (2006). A method for performance analysis of earliest-deadline-first scheduling policy. The Journal of Supercomputing, 37(2), 197-222.

  • Paul, M., Samant, D. & Sanyal, G. (2011). Dynamic job scheduling in cloud computing based on horizontal load balancing. International Journal of Computer Technology and Application, 2(5), 1552-1556.

  • Jettee, M. & Feitelson, D. (1997). Improved Utilization and Responsiveness with Gang Scheduling. Proceedings of Workshop Job Scheduling Strategies for Parallel Processing (pp. 238-261).

  • Fujimoto, R., Malik, A. & Park, A. (2010). Parallel and distributed simulation in the cloud. International Simulation Magazine, Society for Modeling and Simulation, 1(3).

  • Fujimoto, R. (1999). Parallel and Distributed Simulation. Proceedings of 31st Conference Winter Simulation: Simulation-A Bridge to the Future (1, pp. 122-131)

  • Fujimoto, R., Malik, A. & Park, A. (2009). Optimistic Synchronization of Parallel Simulations in Cloud Computing Environments. Proceedings of IEEE International Conference on Cloud Computing (pp. 49-56).

  • Tayal, S. (2011). Tasks scheduling optimization for the cloud computing systems. International Journal of Advanced Engineering Sciences and Technologies, 5(2), 111-115.

  • Das, S., Viswanathan, H. & Rittenhouse, G. (2003). Dynamic Load Balancing Through Coordinated Scheduling in Packet Data Systems. 22nd Annual Joint Conference of the IEEE Computer and Communications (pp. 786-796)

  • Etsion, Y. & Tsafrir, D. (2005). A Short Survey of Commercial Cluster Batch Schedulers. Technical Report 2005-13. The Hebrew University of Jerusalem.

  • Lin, Y. (1992). Parallelism Analyzers for Parallel Discrete Event Simulation. ACM Transactions on Modeling and Computer Simulation, 2(3), 239-264.

  • Wiseman, Y. & Feitelson, D. (2003). Paired Gang Scheduling. IEEE Transactions on Parallel and Distributed Systems, 14(6), 581-592.

  • Zhang, Y., Franke, H., Moreira, J. & Sivasubramaniam, A. (2003). An Integrated Approach to Parallel Scheduling Using Gang-Scheduling, Backfilling, and Migration. IEEE Transactions on Parallel and Distributed Systems, 14(3), 236-247.


Abstract Views: 357

PDF Views: 2




  • Scheduling Work Load Based on Priority in Cloud

Abstract Views: 357  |  PDF Views: 2

Authors

R. Sindhuja
Department of Computer Science and Engineering, Karpagam University, Coimbatore, Tamil Nadu, India
R. Santhosh
Department of Computer Science and Engineering, Karpagam University, Coimbatore, Tamil Nadu, India

Abstract


Cloud computing offers ability to provide parallel and distributed simulated services remotely to the users through the internet. Services hosted within the "cloud" can potentially incur processing delay due to load sharing among other active services , and can cause active optimistic simulation protocols to perform poorly. Number of complex application runs in remote data centres, parallel processing capabilities often show a increase in utilization of CPU resources as parallelism grows, mainly because of communication and synchronization. To achieve certain level of utilization, Our proposed method partitions a node's computing capacity into the 4-tiers with low CPU priority, medium CPU priority, high CPU priority and very high CPU priority. In large datacenter, processes of a job may need to be allocated to nodes that are close to each other to minimize the communication cost. We provide scheduling algorithms for parallel jobs to make efficient use of the k-tiers VMs to improve the responsiveness of these jobs. We focus on improving resource utilization for datacenters that run parallel jobs; particularly we intend to make use of the remaining computing capacity of datacenter nodes that run parallel processes with low resource utilization to improve the performance of parallel job scheduling. The method is practical and effective for consolidating parallel workload in data centres.

Keywords


Distributed Computing, Parallel Computing, Parallel Simulation, Resource Consolidation, Scheduling, Virtualization.

References