Providing Geo-Elasticity in Geographically Distributed Clouds

Author:

Guo Tian1,Shenoy Prashant2

Affiliation:

1. Worcester Polytechnic Institute

2. University of Massachusetts Amherst

Abstract

Geographically 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 adequate for delivering the best possible performance for modern web applications that observe both temporal and spatial workload fluctuations. We propose GeoScale, a system that provides geo-elasticity by combining model-driven proactive and agile reactive provisioning approaches. GeoScale can dynamically provision server capacity at any location based on workload dynamics. We conduct a detailed evaluation of GeoScale on Amazon’s geo-distributed cloud and show up to 40% improvement in the 95th percentile response time when compared to traditional elasticity techniques.

Funder

NSF

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications

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