Generating Adaptation Policies for Multi-tier Applications in Consolidated Server Environments
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Mistral: Dynamically Managing Power, Performance, and Adaptation Cost in Cloud Infrastructures
ICDCS '10 Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems
A Power-Aware Cloud Architecture with Smart Metering
ICPPW '10 Proceedings of the 2010 39th International Conference on Parallel Processing Workshops
Green Task Scheduling Algorithms with Speeds Optimization on Heterogeneous Cloud Servers
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
NBIS '11 Proceedings of the 2011 14th International Conference on Network-Based Information Systems
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Due to the explosive increase in the amount of information in computer systems, we need a system that can process large amounts of data efficiently. Cloud computing system is an effective means to achieve this capacity and has spread throughout the world. In our research, we focus on hybrid cloud environments, and we propose a method for efficiently processing large amounts of data while responding flexibly to needs related to performance and costs. We have developed this method as middleware. For data-intensive jobs using this system, we have created a benchmark that can determine the saturation of the system resources deterministically. Using this benchmark, we can determine the parameters in this middleware. This middleware can provide Pareto optimal cost load balancing based on the needs of the user. The results of the evaluation indicate the success of the system. We then compare the processing time when these jobs are processed sequentially and the processing time using this measurement.