Adaptive, Network-Aware Cluster Selection for Cloud Computing in Wireless Networks

Abstract

We describe and demonstrate fully distributed algorithms that enable cloud clients to select among a set of available computing clusters adaptively, based on measurements of cluster computing loads and the relative bandwidths of paths between the client and each cluster. These techniques are particularly important in cases where (1) clients connect to clusters over stressed wireless networks whose characteristics vary considerably over time, and (2) cloud computing resources are physically dispersed over several locations to improve robustness against physical attack, power failure, network failure, or cyber attack. We demonstrate new means of measuring path bandwidth reliably over multi-hop wireless networks. We then show how the resulting network awareness can be combined with available data on cluster computing loads to arrive at favorable cluster selection decisions by cloud clients, without the need for a centralized cloud controller.