JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Performance Improvement of an Energy Efficient Cluster Management Based on Autonomous Learning
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Performance Improvement of an Energy Efficient Cluster Management Based on Autonomous Learning
Cho, Sungchul; Chung, Kyusik;
  PDF(new window)
 Abstract
Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(quality of service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to activate only the minimum number of servers needed to handle current user requests. Previous studies on energy aware server cluster put efforts to reduce power consumption or heat dissipation, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management method to improve not only performance per watt but also QoS of the existing server power mode control method based on autonomous learning. Our proposed method is to adjust server power mode based on a hybrid approach of autonomous learning method with multi level thresholds and power consumption prediction method. Autonomous learning method with multi level thresholds is applied under normal load situation whereas power consumption prediction method is applied under abnormal load situation. The decision on whether current load is normal or abnormal depends on the ratio of the number of current user requests over the average number of user requests during recent past few minutes. Also, a dynamic shutdown method is additionally applied to shorten the time delay to make servers off. We performed experiments with a cluster of 16 servers using three different kinds of load patterns. The multi-threshold based learning method with prediction and dynamic shutdown shows the best result in terms of normalized QoS and performance per watt (valid responses). For banking load pattern, real load pattern, and virtual load pattern, the numbers of good response per watt in the proposed method increase by 1.66%, 2.9% and 3.84%, respectively, whereas QoS in the proposed method increase by 0.45%, 1.33% and 8.82%, respectively, compared to those in the existing autonomous learning method with single level threshold.
 Keywords
Power Mode Control;QoS;Power Consumption;Autonomous Learning;Prediction Algorithm;Hybrid;
 Language
Korean
 Cited by
 References
1.
Fanxin Kong and Xue Liu, "A Survey on Green-Energy- Aware Power Management for Datacenters," in ACM Computing Surveys(CSUR), 2014.

2.
Chenguang Liu, Jianzhong Huang, Qiang Cao, Shenggang Wan, and Changsheng Xie, "Evaluating Energy and Performance for Server-Class Hardware Configurations," 6th IEEE International Conference on Networking, Architecture and Storage, 2011.

3.
J. Mair, K. Leung, Z. Huang, "Metrics and task scheduling policies for energy saving in multicore computers," 11th IEEE/ACM International Conference on Grid Computing (GRID), 2010.

4.
G. Chen et. al., "Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services," NSDI'08 Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, 2008.

5.
A Krioukov, et al., "NapSAC: design and implementation of a power-proportional web cluster," ACM SIGCOMM computer communication overview, 2011.

6.
Abdul Hameed, Alireza Khoshkbarforoushha, Rajiv Ranjan, Prem Prakash Jayaraman, Joanna Kolodziej, Pavan Balaji, Sherali Zeadally, Qutaibah Marwan Malluhi, Nikos Tziritas, Abhinav Vishnu, Samee U. Khan, and Albert Zomaya, "A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems," Springer Computing, Jun., 2014.

7.
Anton Beloglazov, Rajkumar Buyya, Young Choon Lee, and Albert Zomaya, "A Taxonomy and Survey of Energy- Efficient Data Centers and Cloud Computing Systems," The University of Melbourne, Australia, The University of Sydney, Australia, 2010.

8.
Sungchul Cho, Hukeun Kwak, and Kyusik Chung, "An Energy Efficient Cluster Management Method based on Autonomous Learning in a Server Cluster Environment," KIPS Transactions on Computer and Communication Systems, Vol.4, No.6, pp.185-196, 2015. crossref(new window)

9.
Taejune Ahn, Sungchul Cho, Seokkoo Kim, Kyongho Chun, and Kyusik Chung, "A Flexible Multi-Threshold Based Control of Server Power Mode for Handling Rapidly Changing Loads in an Energy Aware Server Cluster," KIPS Transactions on Computer and Communication Systems, Vol.3, No.9, pp.279-292, 2014. crossref(new window)

10.
Hoyeon Kim, Chihwan Ham, Hukeun Kwak, and Kyusik Chung, "Dynamic Shutdown of Server Power Mode Control for Saving Energy in a Server Cluster Environment," KIPS Transactions on Computer and Communication Systems, 2013.

11.
LVS(Linux Virtual Server) [Internet], http://www. linuxvirt ualserver.org.

12.
Sungchul Cho, Sanha Kang, Heungsik Moon, Hukeun Kwak, and Kyusik Chung, "Prediction of Power Consumption for Improving QoS in an Energy Saving Server Cluster Environment," KIPS Transactions on Computer and Communication Systems, Vol.4, No.2, pp.47-56, 2015. crossref(new window)

13.
Hoyeon Kim, Chihwan Ham, Hukeun Kwak, Hulung Kwon, Youngjoung Kim, Kyusik Chung, "A Dynamic Server Power Mode Control for Saving Energy in a Server Cluster Environment," The KIPS Transactions: PartC, Vol.19, No.3, pp.135-144, 2012.

14.
SPECweb [Internet], http://www.spec.org/benchmarks.html/.

15.
Apache [Internet], http://www.apache.org/.

16.
InternetTrend [Internet], http://www.internettrend.co.kr.

17.
Direct Routing [Internet], http://www.linuxvirtualserver.org /VS-DRouting.html.

18.
H. Kwak, A. Sohn and K. Chung, "Autonomous Learning of Load and Traffic Patterns to Improve Cluster Utilization," Cluster Computing, Vol.14, Issue 4, Dec., 2011.

19.
Hukeun Kwak, Kyusik Chung, Hyung Won Choi, and Andrew Sohn "Enabling Scalabe Cloud Infrastructure using Autonomous VM Migration," 2012 IEEE 14th International Conference on High Performance Computing and Communications, 2012.