T-Alloc: A practical energy efficient resource allocation algorithm for traditional data centers

Even if the cloud computing data centers are emerging as new candidates for replacement, traditional data centers are still growing rapidly in both number and capacity to meet the increasing demands for highly responsive computing and massive storage. Making the data center more energy efficient is therefore a necessary task. A traditional data center has many distinguished features with heterogeneous hardware, heterogeneous workload, average load rate focused, intensive time and personal effort for administrative tasks. This paper will propose a way of saving energy for traditional data centers considering all the above features. The basic idea is rearranging the allocation in such a way that energy is saved with suitable human effort. The simulation results show the efficiency of the method.

[1]  Dang Minh Quan.  Mapping Heavy Communication Workflows onto Grid Resources Within an SLA Context , 2006, HPCC.

[2]  Tony Bourke.  Server Load Balancing , 2001 .

[3]  Amin Vahdat,et al.  Managing energy and server resources in hosting centers , 2001, SOSP.

[4]  Rami G. Melhem,et al.  Scheduling with Dynamic Voltage/Speed Adjustment Using Slack Reclamation in Multiprocessor Real-Time Systems , 2003, IEEE Trans. Parallel Distributed Syst..

[5]  Dang Minh Quan,et al.  On architecture for SLA-aware workflows in grid environments , 2005, 19th International Conference on Advanced Information Networking and Applications (AINA'05) Volume 1 (AINA papers).

[6]  F. Petrini,et al.  The Case of the Missing Supercomputer Performance: Achieving Optimal Performance on the 8,192 Processors of ASCI Q , 2003, ACM/IEEE SC 2003 Conference (SC'03).

[7]  Christoforos E. Kozyrakis,et al.  Automatic power management schemes for Internet servers and data centers , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[8]  Anand Sivasubramaniam,et al.  Managing server energy and operational costs in hosting centers , 2005, SIGMETRICS '05.

[9]  Qi Yang,et al.  Energy-aware partitioning for multiprocessor real-time systems , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[10]  Jan Broeckhove,et al.  An Evaluation of the Benefits of Fine-Grained Value-Based Scheduling on General Purpose Clusters , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[11]  Claudio Scordino,et al.  Energy-Efficient Real-Time Heterogeneous Server Clusters , 2006, 12th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'06).

[12]  Rami G. Melhem,et al.  Scheduling with dynamic voltage/speed adjustment using slack reclamation in multi-processor real-time systems , 2001, Proceedings 22nd IEEE Real-Time Systems Symposium (RTSS 2001) (Cat. No.01PR1420).

[13]  Zhiyuan Li,et al.  Energy-Aware Scheduling for Real-Time Multiprocessor Systems with Uncertain Task Execution Time , 2007, 2007 44th ACM/IEEE Design Automation Conference.

[14]  Flavius Gruian.  Hard real-time scheduling for low-energy using stochastic data and DVS processors , 2001, ISLPED '01.

[15]  Henri Casanova,et al.  Resource Allocation Using Virtual Clusters , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[16]  Ricardo Bianchini,et al.  Energy conservation in heterogeneous server clusters , 2005, PPoPP.

[17]  Kevin Skadron,et al.  Optimal procrastinating voltage scheduling for hard real-time systems , 2005, Proceedings. 42nd Design Automation Conference, 2005..

[18]  E. N. Elnozahy,et al.  Energy-Efficient Server Clusters , 2002, PACS.

[19]  Rajarshi Das,et al.  Expressive Power-Based Resource Allocation for Data Centers , 2009, IJCAI.

[20]  Chandra Kopparapu,et al.  Load Balancing Servers, Firewalls, and Caches , 2002 .

[21]  Klara Nahrstedt,et al.  Energy-efficient soft real-time CPU scheduling for mobile multimedia systems , 2003, SOSP '03.

[22]  Georges Da Costa,et al.  2005 IEEE International Symposium on Cluster Computing and the Grid , 2005, CCGRID.

[23]  Laurent Lefèvre,et al.  Multi-facet approach to reduce energy consumption in clouds and grids: the GREEN-NET framework , 2010, e-Energy.

[24]  M. Adachi,et al.  A Study on a Resource Allocation Algorithm for On-demand Data Center Services , 2008, 2008 10th International Conference on Advanced Communication Technology.

[25]  Yung-Hsiang Lu,et al.  Dynamic Voltage Scaling for Multitasking Real-Time Systems With Uncertain Execution Time , 2008, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[26]  Ying Lu,et al.  Efficient Power Management of Heterogeneous Soft Real-Time Clusters , 2008, 2008 Real-Time Systems Symposium.

[27]  Miron Livny,et al.  Condor-a hunter of idle workstations , 1988, [1988] Proceedings. The 8th International Conference on Distributed.

[28]  Hakan Aydin,et al.  Energy-aware task allocation for rate monotonic scheduling , 2005, 11th IEEE Real Time and Embedded Technology and Applications Symposium.

[29]  Frank Bellosa,et al.  Memory-aware Scheduling for Energy Efficiency on Multicore Processors , 2008, HotPower.