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首页> 外文期刊>Parallel and Distributed Systems, IEEE Transactions on >Makespan and Energy Robust Stochastic Static Resource Allocation of a Bag-of-Tasks to a Heterogeneous Computing System
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Makespan and Energy Robust Stochastic Static Resource Allocation of a Bag-of-Tasks to a Heterogeneous Computing System

机译：将任务袋的Makespan和能量鲁棒的随机静态资源分配给异构计算系统

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摘要

Today’s data centers face the issue of balancing electricity use and completion times of their workloads. Rising electricity costs are forcing data center operators to either operate within an electricity budget or to reduce electricity use as much as possible while still maintaining service agreements. Energy-aware resource allocation is one technique a system administrator can employ to address both problems: optimizing the workload completion time (makespan) when given an energy budget, or to minimize energy consumption subject to service guarantees (such as adhering to deadlines). In this paper, we study the problem of energy-aware static resource allocation in an environment where a collection of independent (non-communicating) tasks (“bag-of-tasks”) is assigned to a heterogeneous computing system. Computing systems often operate in environments where task execution times vary (e.g., due to cache misses or data dependent execution times). We model these execution times stochastically, using probability density functions. We want our resource allocations to be robust against these variations, where we define as the probability that the energy budget is not violated, and as the probability a makespan deadline is not violated. We develop and analyze several heuristics for energy-aware resource allocation for both energy-constrained and deadline-constrained problems.
机译：当今的数据中心面临着平衡用电量和完成工作时间的问题。电力成本的上涨迫使数据中心运营商要么在电力预算范围内运行，要么在保持服务协议的前提下尽可能减少用电量。能源意识的资源分配是系统管理员可以用来解决这两个问题的一种技术：在获得能源预算时优化工作负载完成时间（makespan），或根据服务保证（例如遵守期限）使能耗最小化。在本文中，我们研究了在将独立（非通信）任务（“任务包”）的集合分配给异构计算系统的环境中的能源感知静态资源分配问题。计算系统通常在任务执行时间变化的环境中运行（例如，由于高速缓存未命中或数据相关的执行时间）。我们使用概率密度函数随机地对这些执行时间进行建模。我们希望我们的资源分配对这些变化具有鲁棒性，在这里我们将定义为不违反能源预算的概率以及不违反延期期限的概率。我们针对能源受限和截止日期受限的问题开发并分析了几种启发式的能源感知资源分配方法。

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