Paper Info
Title
Sla-Aware Resource Scaling For Energy Efficiency
Abstract
Cloud data centers (CDCs) with abundant resource capacities have prevailed in the past decade. However, these CDCs often struggle to efficiently deal with resource provisioning in terms of performance and energy efficiency. In this paper, we present Energy-Based Auto Scaling (EBAS) as a new resource auto-scaling approach-that takes into account Service Level Agreement (SLA)-for CDCs. EBAS proactively scales resources at the CPU core level in terms of both the number and frequency of cores. It incorporates the dynamic voltage and frequency scaling (DVFS) technique to dynamically adjust CPU frequencies. The proactive decisions on resource scaling are enabled primarily by the CPU usage prediction model and the workload consolidation model of EBAS. The experimental results show that EBAS can save energy on average by 14% compared with the Linux governor. In particular, EBAS contributes to enhancing DVFS by making it aware of SLA conditions, which leads to savings of computing power and in turn energy.
Year
Venue
Keywords
2016
PROCEEDINGS OF 2016 IEEE 18TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS; IEEE 14TH INTERNATIONAL CONFERENCE ON SMART CITY; IEEE 2ND INTERNATIONAL CONFERENCE ON DATA SCIENCE AND SYSTEMS (HPCC/SMARTCITY/DSS)
Auto-Scaling, Energy Efficiency, Cloud Data Centers, Docker Containers, Resource Provisioning
Field
DocType
Citations 
Central processing unit,Computer science,Efficient energy use,Service-level agreement,Provisioning,Real-time computing,Frequency scaling,Energy consumption,Governor,Cloud computing,Distributed computing
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Eidah J. Alzahrani111.04
Zahir Tari22409368.61
Panlop Zeephongsekul314419.11
Young Choon Lee4133673.05
Deafallah Alsadie510.70
Albert Y. Zomaya65709454.84