QoS-Aware Revenue-Cost Optimization for Latency-Sensitive Services in IaaS Clouds
DS-RT '12 Proceedings of the 2012 IEEE/ACM 16th International Symposium on Distributed Simulation and Real Time Applications
A generic framework for service-based business process elasticity in the cloud
BPM'12 Proceedings of the 10th international conference on Business Process Management
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Infrastructure-as-a-Service (IaaS) cloud computing provides the ability to dynamically acquire extra or release existing computing resources on-demand to adapt to dynamic application workloads. In this paper, we propose an extensible framework for on-demand cloud resource provisioning and adaptation. The core of the framework is a set of resource adaptation algorithms that are capable of making informed provisioning decisions to adapt to workload fluctuations. The framework is designed to manage multiple sets of resources acquired from different cloud providers, and to interact with different local resource managers. We have developed a fully functional web-service based prototype of this framework, and used it for performance evaluation of various resource adaptation algorithms under different realistic settings, e.g. when input data such as jobs' wall times are inaccurate. Extensive experiments have been conducted with both synthetic and real workload traces obtained from the Grid Workload Archives, more specifically the traces from the Large Hadron Collider Computing Grid. The results demonstrate the effectiveness and robustness of our proposed algorithms.