2016 IEEE International Conference on Cloud Engineering (IC2E) (2016)
April 4, 2016 to April 8, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IC2E.2016.40
Distributed cloud platforms are well suited for serving a geographically diverse user base. However traditional cloud provisioning mechanisms that make local scaling decisions are not well suited for temporal and spatial workload fluctuations seen by modern web applications. In this paper, we argue the need of geo-elasticity and present GeoScale, a system to provide geo-elasticity in distributed clouds. We describe GeoScale's model-driven proactive provisioning approach and conduct an initial evaluation of GeoScale on Amazon's distributed EC2 cloud. Our results show up to 31% improvement in the 95th percentile response time when compared to traditional elasticity techniques.
Servers, Cloud computing, Time factors, Elasticity, Monitoring, Prediction algorithms, Forecasting
T. Guo, P. Shenoy and H. H. Hacigumus, "GeoScale: Providing Geo-Elasticity in Distributed Clouds," 2016 IEEE International Conference on Cloud Engineering (IC2E), Berlin, Germany, 2016, pp. 123-126.