Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control
Proceedings of the 3rd workshop on Scientific Cloud Computing Date
Proceedings of the 9th international conference on Autonomic computing
AutoScale: Dynamic, Robust Capacity Management for Multi-Tier Data Centers
ACM Transactions on Computer Systems (TOCS)
E2DC'12 Proceedings of the First international conference on Energy Efficient Data Centers
Autonomic performance-per-watt management (APM) of cloud resources and services
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
Capacity-Aware Utility Function for SLA Negotiation of Cloud Services
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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This paper presents a novel approach to correctly allocate resources in data centers, such that SLA violations and energy consumption are minimized. Our approach first analyzes historical workload traces to identify long-term patterns that establish a "base" workload. It then employs two techniques to dynamically allocate capacity: predictive provisioning handles the estimated base workload at coarse time scales (e.g., hours or days) and reactive provisioning handles any excess workload at finer time scales (e.g., minutes). The combination of predictive and reactive provisioning achieves a significant improvement in meeting SLAs, conserving energy, and reducing provisioning costs. We implement and evaluate our approach using traces from four production systems. The results show that our approach can provide up to 35% savings in power consumption and reduce SLA violations by as much as 21% compared to existing techniques, while avoiding frequent power cycling of servers.