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Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers

机译：跨云和数据中心的多个异构多核服务器处理器的最佳功率分配和负载分配

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摘要

For multiple heterogeneous multicore server processors across clouds and data centers, the aggregated performance of the cloud of clouds can be optimized by load distribution and balancing. Energy efficiency is one of the most important issues for large-scale server systems in current and future data centers. The multicore processor technology provides new levels of performance and energy efficiency. The present paper aims to develop power and performance constrained load distribution methods for cloud computing in current and future large-scale data centers. In particular, we address the problem of optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. 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. 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. It is clear that such power and performance optimization is important for a cloud computing provider to efficiently utilize all the available resources. We model a multicore server processor as a queuing system with multiple servers. Our optimization problems are solved for two different models of core speed, where one model assumes that a core runs at zero speed when it is idle, and the other model assumes that a core runs at a constant speed. Our results in this paper provide new theoretical insights into power management and performance optimization in data centers.
机译：对于跨云和数据中心的多个异构多核服务器处理器，可以通过负载分配和平衡来优化云云的聚合性能。能源效率是当前和未来数据中心中大型服务器系统最重要的问题之一。多核处理器技术提供了更高水平的性能和能效。本文旨在为当前和未来的大型数据中心开发针对云计算的功率和性能受限的负载分配方法。特别是，我们解决了跨云和数据中心的多个异构多核服务器处理器的最佳功率分配和负载分配问题。我们的策略是为云中的多个服务器制定优化的电源分配和负载分配，作为优化问题，即电源受限的性能优化和性能受限的电源优化。我们在大型数据中心的研究问题是定义明确的多变量优化问题，它从最佳负载分配的角度通过固定一个因素并最小化另一个因素来探索功率性能的权衡。显然，这种功能和性能优化对于云计算提供商有效利用所有可用资源很重要。我们将多核服务器处理器建模为具有多个服务器的排队系统。我们针对两种不同的核心速度模型解决了我们的优化问题，其中一个模型假设一个核心在空闲时以零速度运行，另一个模型假设一个核心以恒定速度运行。本文的研究结果为数据中心的电源管理和性能优化提供了新的理论见解。

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