Relaxed resource advance reservation policy in grid computing

doi:10.1016/S1005-8885(08)60213-7

中国邮电高校学报(英文) ›› 2009, Vol. 16 ›› Issue (2): 108-113.doi: 10.1016/S1005-8885(08)60213-7

• Distributed Network Computing • 上一篇下一篇

Relaxed resource advance reservation policy in grid computing

肖鹏,胡志刚

School of Information Science and Engineering, Central South University, Changsha 410083, China

收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-04-30
通讯作者: 肖鹏

Relaxed resource advance reservation policy in grid computing

XIAO Peng, HU Zhi-gang

School of Information Science and Engineering, Central South University, Changsha 410083, China

Received:1900-01-01 Revised:1900-01-01 Online:2009-04-30
Contact: XIAO Peng

摘要/Abstract

摘要：

The advance reservation technique has been widely applied in many grid systems to provide end-to-end quality of service (QoS). However, it will result in low resource utilization rate and high rejection rate when the reservation rate is high. To mitigate these negative effects brought about by advance reservation, a relaxed advance reservation policy is proposed, which allows accepting new reservation requests that overlap the existing reservations under certain conditions. Both the benefits and the risks of the proposed policy are presented theoretically. The experimental results show that the policy can achieve a higher resource utilization rate and lower rejection rate compared to the conventional reservation policy and backfilling technique. In addition, the policy shows better adaptation when the grid systems are in the presence of a high reservation rate.

关键词:

grid;computing,;advance;reservation,;reservation;violation,;computing;economy

Abstract:

Key words:

grid computing;advance reservation;reservation violation;computing economy

XIAO Peng, HU Zhi-gang. Relaxed resource advance reservation policy in grid computing[J]. Acta Metallurgica Sinica(English letters), 2009, 16(2): 108-113.

参考文献

1. Foster I, Kesselman C. The grid: blueprint for a new computing infrastructure. 2nd ed. Singapore: Elsevier Inc, 2004

2. Foster I, Kesselman C, Lee C, et al. A distributed resource management architecture that supports advance reservation and co-allocation. Proceedings of the 7th International Workshop on Quality of Service (IWQoS’99), Jun 1-4, 1999, London, UK. Los Alamitos, CA, USA: IEEE Computer Society, 1999: 27-36

3. Foster I, Roy A, Sander V. A quality of service architecture that combines resource reservation and application adaptation. Proceedings of the 8th International Workshop on Quality of Service (IWQoS’00), Jun 5-7, 2000, Pittsburgh, PA, USA. Los Alamitos, CA, USA: IEEE Computer Society, 2000: 181-188

4. Smith W, Foster I, Taylor V. Scheduling with advanced reservations. Proceedings of the 14th International Parallel and Distributed Processing Symposium (IPDPS’00), May 1-5, 2000, Cancun, Mexico. Los Alamitos, CA, USA: IEEE Computer Society, 2000: 127-132

5. Cao J, Zimmermann F. Queue scheduling and advance reservations with COSY. Proceedings of the 18th International Parallel and Distributed Processing Symposium (IPDPS’04), Apr 26-30, 2004, Santa Fe, NM, USA. Los Alamitos, CA, USA: IEEE Computer Society, 2004: 63

6. Sulistio A, Buyya R. A grid simulation infrastructure supporting advance reservation. Proceedings of the 16th International Conference on Parallel and Distributed Computing and Systems (PDCS’04), Nov 9-11, 2004, Boston, MA, USA. Anaheim, CA, USA: ACTA Press, 2004

7. Sodan A C, Doshi C, Barsanti L, et al. Gang scheduling and adaptive resource allocation to mitigate advance reservation impact. Proceedings of International Symposium on Cluster Computing and the Grid (CCGRID’06): Vol 1, May 16-19, 2006, Singapore. Los Alamitos, CA, USA: IEEE Computer Society, 2006: 649-653

8. Wu M, Sun X H, Chen Y. QoS oriented resource reservation in shared environments. Proceedings of International Symposium on Cluster Computing and the Grid (CCGRID’06): Vol 1, May 16-19, 2006, Signapore. Los Alamitos, CA, USA: IEEE Computer Society, 2006: 601-608

9. Snell Q, Clement M, Jackson D, et al. The performance impact of advance reservation metascheduling. Proceedings of Job Scheduling Strategies for Parallel Processing (JSSPP’00), May 1-5, 2000, Cancun, Mexico. Los Alamitos, CA, USA: IEEE Computer Society, 2000: 137-153

10. Buyya R, Abramson D, Venugopal S. The grid economy. Proceeding of the IEEE, 2005, 93(3): 698-714

11. Roy A, Sander V. Advance reservation API. Technical Report GFD-E.5, Scheduling Working Group, Global Grid Forum, 2002

12. Czajkowski K, Foster I, Kesselman C. Resource co-allocation in computational grids. Proceedings of the 8th International Symposium on High Performance Distributed Computing, Aug 3-6, 1999, Redondo Beach, CA, USA. Los Alamitos, CA, USA: IEEE Computer Society, 1999: 219-228

13. Mu’alem A W, Feitelson D G. Utilization, predictability, workloads, and user runtime estimates in scheduling the IBM SP2 with backfilling. IEEE Transactions on Parallel and Distributed Systems, 2001, 12(6): 529-543

14. Sulistio A, Schiffmann W, Buyya R. Advanced reservation-based scheduling of task graphs on clusters. Proceedings of International Conference on High Performance Computing (HiPC’06), Dec 18-21, 2006, Bangalore, India. Los Alamitos, CA, USA: IEEE Computer Society, 2006: 60-71

15. Kaushik N R, Figueira S M, Chiappari S A. Flexible time-windows for advance reservation scheduling. Proceedings of International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS’06), Sep 11-14, 2006, Monterey, CA, USA. Los Alamitos, CA, USA: IEEE Computer Society, 2006: 218-225

16. Wu L B, Xing J B, Wu C L, et al. An adaptive advance reservation mechanism for grid computing. Proceedings of International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT’05), Dec 5-8, 2005, Dalian, China. Los Alamitos, CA, USA: IEEE Computer Society, 2005: 400-403

17. Alsuwaiyel M H. Algorithms: design techniques and analysis. River Edge, NJ, USA: World Scientific Publishing Co, 1999

18. Buyya R, Murshed M. GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concurrency and Computation: Practice and Experience, 2002, 14(13/14/15): 1175-1220

19. Lublin U, Feitelson D G. The workload on parallel supercomputers: modeling the characteristics of rigid jobs. Journal of Parallel and Distributed Computing, 2003, 63(11): 1105-1122

[1]	Li Linpei, Zhao Chuan, Su Yu, Huo Jiahao, Huang Yao, Li Haojin. Energy-efficient computation offloading assisted by RIS-based UAV[J]. 中国邮电高校学报(英文版), 2024, 31(1): 37-48.
[2]	Zhang Luying, Liu Xiaokai, Li Zhao, Xu Fangmin, Zhao Chenglin. Prediction based dynamic resource allocation method for edge computing first networking [J]. 中国邮电高校学报(英文版), 2023, 30(3): 78-87.
[3]	Wu Hongxin, Lin Zhijian, Chen Pingping, Chen Feng. Joint partial computation offloading and resource allocation in MEC-enable networks[J]. 中国邮电高校学报(英文版), 2023, 30(1): 80-86.
[4]	Zhu Ruijie, Li Gong, Wang Peisen, Zhang Wenchao. Reinforced virtual optical network embedding algorithm in EONs for edge computing [J]. 中国邮电高校学报(英文版), 2022, 29(6): 18-29.
[5]	Yang Chao, Li Yimin, Li Tong, Xu Siya, Qi Jun, Zhang Yu. Intelligent Service Function Chain Mapping Framework for Cloud-and-Edge-Collaborative IoT[J]. 中国邮电高校学报(英文版), 2022, 29(3): 54-68.
[6]	陈佳舟 Amal Ahmed Hasan Mohammed 黄可妤缪永伟. Surveys on line drawing simplification for Chinese cultural computing[J]. 中国邮电高校学报(英文版), 2022, 29(2): 33-42.
[7]	Mao Sun, Zhang Yan. Aerial edge computing for 6G[J]. 中国邮电高校学报(英文版), 2022, 29(1): 50-63.
[8]	Zhu Xiaorong, Jing Chuanfang, Shi Jindou, Wang Yong, Ho Chifong. Data-flow in mobile edge computing networks: end-to-end performance analysis using stochastic network calculus[J]. 中国邮电高校学报(英文版), 2022, 29(1): 81-92.
[9]	Tao Yunting, Kong Fanyu, Yu Jia. EPMDA: an efficient privacy-preserving multi-dimensional data aggregation scheme for edge computing-based IoT system [J]. 中国邮电高校学报(英文版), 2021, 28(6): 26-35.
[10]	刘巧唐碧华 Chen Xue Fan Wu 范文浩. Multi-level sharded blockchain system for edge computing [J]. 中国邮电高校学报(英文版), 2021, 28(5): 46-58.
[11]	郑颖孙司远魏翼飞宋梅. User association and resource allocation in green mobile edge networks using deep reinforcement learning[J]. 中国邮电高校学报(英文版), 2021, 28(3): 1-10.
[12]	Xue Chenzi, Wei Yifei, Zhang Yong. Performance optimization for smart grid blockchain integrated with fog computing using DDQN[J]. 中国邮电高校学报(英文版), 2021, 28(2): 68-78.
[13]	Xie Renchao, Liu Xu, Duan Xuefei, Tang Qinqin, Yu Fei Richard, Huang Tao. Dynamic computation offloading in time-varying environment for ultra-dense networks: a stochastic game approach[J]. 中国邮电高校学报(英文版), 2021, 28(2): 24-37.
[14]	陈彦萍靳晓东夏虹王忠民. Incremental QR-based tensor-train decomposition for industrial big data [J]. 中国邮电高校学报(英文版), 2021, 28(1): 10-23.
[15]	费炜刘聪胡胜. Research on swarm intelligence optimization algorithm[J]. 中国邮电高校学报(英文版), 2020, 27(3): 1-20.

Relaxed resource advance reservation policy in grid computing

Relaxed resource advance reservation policy in grid computing

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价