![]() ![]() Past results exist for grid partitioning (into subdomains) and mapping to parallel and distributed systems. Some of the important research issues arise such as (i) Problem partitioning and virtual interconnection topology mapping (ii) Execution scheduling and load balancing. This has motivated research toward using a network of computers, interconnected via a fast network (cluster system) or a simple Local Area Network (LAN) (distributed system), for high performance concurrent computations. The aggregate peak rate of such systems often matches or exceeds the peak rate offered by the fastest parallel computers. This trend is expected to continue in the near future. The cost/performance ratio of networks of workstations has been constantly improving. It is clear that such power and performance optimization is important for a cloud computing provider to efficiently utilize all the available resources. ![]() Our research problems in large-scale data centers are well-defined multivariable optimization problems, which explore the power-performance tradeoff by fixing one factor and minimizing the other, from the perspective of optimal load distribution. Our strategy is to formulate optimal power allocation and load distribution for multiple servers in a cloud of clouds as optimization problems, i.e., power constrained performance optimization and performance constrained power optimization. In particular, we address the problem of optimal power allocation and load distribution for multiple heterogeneous multi server processors across clouds and data centers. ![]() The present paper aims to develop power and performance constrained load distribution methods for cloud computing in current and future large-scale data centers. The multi service processor technology provides new levels of performance and energy efficiency. Energy efficiency is one of the most important issues for large-scale server systems in current and future data centers. Performance of the cloud of can be optimized by load distribution and balancing. ![]()
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