HEROS: Energy-Efficient Load Balancing for Heterogeneous Data Centers

Heterogeneous architectures have become more popular and widespread in the recent years with the growing popularity of general-purpose processing on graphics processing units, low-power systems on a chip, multi- and many-core architectures, asymmetric cores, coprocessors, and solid-state drives. The design and management of cloud computing data-centers must adapt to these changes while targeting objectives of improving system performance, energy efficiency and reliability. This paper presents HEROS, a novel load balancing algorithm for energy-efficient resource allocation in heterogeneous systems. HEROS takes into account the heterogeneity of a system during the decision-making process and uses a holistic representation of the system. As a result, servers that contain resources of multiple types (computing, memory, storage and networking) and have varying internal structures of their components can be utilized more efficiently.

[1]  Junaid Shuja,et al.  Data center energy efficient resource scheduling , 2014, Cluster Computing.

[2]  Dzmitry Kliazovich,et al.  DENS: Data Center Energy-Efficient Network-Aware Scheduling , 2010, GreenCom/CPSCom.

[3]  Robert Shorten,et al.  Stratus: Load Balancing the Cloud for Carbon Emissions Control , 2013, IEEE Transactions on Cloud Computing.

[4]  Dzmitry Kliazovich,et al.  GreenCloud: A Packet-Level Simulator of Energy-Aware Cloud Computing Data Centers , 2010, GLOBECOM.

[5]  Albert Y. Zomaya,et al.  CA-DAG: Communication-Aware Directed Acyclic Graphs for Modeling Cloud Computing Applications , 2013, 2013 IEEE Sixth International Conference on Cloud Computing.

[6]  Albert Y. Zomaya,et al.  Performance and Energy Efficiency Metrics for Communication Systems of Cloud Computing Data Centers , 2017, IEEE Transactions on Cloud Computing.

[7]  Giorgio Ventre,et al.  Network Simulator NS2 , 2008 .

[8]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[9]  Pascal Bouvry,et al.  Management of an academic HPC cluster: The UL experience , 2014, 2014 International Conference on High Performance Computing & Simulation (HPCS).

[10]  Ripal Nathuji,et al.  Exploiting Platform Heterogeneity for Power Efficient Data Centers , 2007, Fourth International Conference on Autonomic Computing (ICAC'07).

[11]  Pascal Bouvry,et al.  Multi-objective evolutionary algorithms for energy-aware scheduling on distributed computing systems , 2014, Appl. Soft Comput..

[12]  Xiaoming Fu,et al.  Revisiting the Design of Mega Data Centers: Considering Heterogeneity Among Containers , 2014, IEEE/ACM Transactions on Networking.

[13]  Huaxi Gu,et al.  Distributed Flow Scheduling in Energy-Aware Data Center Networks , 2013, IEEE Communications Letters.

[14]  Wolf-Dietrich Weber,et al.  Power provisioning for a warehouse-sized computer , 2007, ISCA '07.

[15]  Rajkumar Buyya,et al.  Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers , 2011, J. Parallel Distributed Comput..

[16]  Roberto Rojas-Cessa,et al.  Communication-Aware and Energy-Efficient Scheduling for Parallel Applications in Virtualized Data Centers , 2013, 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing.

[17]  Dzmitry Kliazovich,et al.  A Holistic Model for Resource Representation in Virtualized Cloud Computing Data Centers , 2013, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science.

[18]  Yuguang Fang,et al.  Energy and Network Aware Workload Management for Sustainable Data Centers with Thermal Storage , 2014, IEEE Transactions on Parallel and Distributed Systems.

[19]  Christoforos E. Kozyrakis,et al.  A Comparison of High-Level Full-System Power Models , 2008, HotPower.

[20]  Zahir Tari,et al.  ADAPT-POLICY: Task Assignment in Server Farms when the Service Time Distributionof Tasks is Not Known A Priori , 2014, IEEE Transactions on Parallel and Distributed Systems.

[21]  Albert Y. Zomaya,et al.  Energy Conscious Scheduling for Distributed Computing Systems under Different Operating Conditions , 2011, IEEE Transactions on Parallel and Distributed Systems.

[22]  Dzmitry Kliazovich,et al.  e-STAB: Energy-Efficient Scheduling for Cloud Computing Applications with Traffic Load Balancing , 2013, 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing.

[23]  Jie Wu,et al.  Joint power optimization through VM placement and flow scheduling in data centers , 2014, 2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC).

[24]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[25]  Luiz André Barroso,et al.  The Case for Energy-Proportional Computing , 2007, Computer.

[26]  Roberto Rojas-Cessa,et al.  Task Scheduling and Server Provisioning for Energy-Efficient Cloud-Computing Data Centers , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops.

[27]  Pascal Bouvry,et al.  A holistic model of the performance and the energy efficiency of hypervisors in a high‐performance computing environment , 2014, Concurr. Comput. Pract. Exp..

[28]  Jitender S. Deogun,et al.  Energy models driven green routing for data centers , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).