In Cloud computing systems, resource management is one of the main issues. Indeed, in any time instant resources have to be allocated to handle effectively workload fluctuations, while providing Quality of Service (QoS) guarantees to the end users. In such systems, workload prediction-based autonomic computing techniques have been developed. In this paper we propose capacity allocation techniques able to coordinate multiple distributed resource controllers working in geographically distributed cloud sites. Furthermore, capacity allocation solutions are integrated with a load redirection mechanism which forwards incoming requests between different domains. The overall goal is to minimize the costs of the allocated virtual machine instances, while guaranteeing QoS constraints expressed as a threshold on the average response time. We compare multiple heuristics which integrate workload prediction and distributed non-linear optimization techniques. Experimental results show how our solutions significantly improve other heuristics proposed in the literature (5-35% on average), without introducing significant QoS violations.
['Danilo Ardagna', 'Sara Casolari', 'Barbara Panicucci']
Flexible Distributed Capacity Allocation and Load Redirect Algorithms for Cloud Systems