地理分布式数据中心中多工作流分配的电费成本最小化
Electricity Cost Minimization for Multi-Workflow Allocation in Geo-Distributed Data Centers
AI总结：
针对地理分布式数据中心中异构资源与动态电价下多工作流调度的电费成本最小化问题，提出基于图嵌入与策略网络求解MDP的电费成本感知多工作流调度算法（ECMWS），在满足截止时间约束下较现有方法提升超15%。
摘要：
在全球范围内，地理分布式数据中心（GDCs）为海量工作流应用提供计算和存储服务，导致高昂的电费成本，且该成本因地理位置和时间而异。如何在满足工作流应用的截止时间约束的同时降低电费成本，在GDCs中非常重要，这取决于服务器的执行时间、功耗和电价。确定具有不同服务器频率的工作流的完成时间可能具有挑战性，尤其是在GDCs中存在异构计算资源的场景下。此外，电价在不同地理位置也有所不同，并且可能会动态变化。为了应对这些挑战，我们开发了一种地理分布式系统架构，并提出了一种电费成本感知的多工作流调度算法（ECMWS），适用于具有固定频率和功率的GDCs服务器。ECMWS包含四个阶段，即工作流排序、截止时间划分、任务排序和资源分配，其中构建了两个图嵌入模型和一个策略网络来求解马尔可夫决策过程（MDP）。在全面的工作流实例集上对参数和算法组件进行统计校准后，所提出的算法与最先进的方法在两类工作流实例上进行了比较。实验结果表明，我们提出的算法显著优于其他算法，实现了超过15%的改进，同时保持了可接受的计算时间。源代码可在 https://gitee.com/public-artifacts/ecmws-experiments 获取。
英文摘要：
Worldwide, Geo-distributed Data Centers (GDCs) provide computing and storage services for massive workflow applications, resulting in high electricity costs that vary depending on geographical locations and time. How to reduce electricity costs while satisfying the deadline constraints of workflow applications is important in GDCs, which is determined by the execution time of servers, power, and electricity price. Determining the completion time of workflows with different server frequencies can be challenging, especially in scenarios with heterogeneous computing resources in GDCs. Moreover, the electricity price is also different in geographical locations and may change dynamically. To address these challenges, we develop a geo-distributed system architecture and propose an Electricity Cost aware Multiple Workflows Scheduling algorithm (ECMWS) for servers of GDCs with fixed frequency and power. ECMWS comprises four stages, namely workflow sequencing, deadline partitioning, task sequencing, and resource allocation where two graph embedding models and a policy network are constructed to solve the Markov Decision Process (MDP). After statistically calibrating parameters and algorithm components over a comprehensive set of workflow instances, the proposed algorithms are compared with the state-of-the-art methods over two types of workflow instances. The experimental results demonstrate that our proposed algorithm significantly outperforms other algorithms, achieving an improvement of over 15\% while maintaining an acceptable computational time. The source codes are available at https://gitee.com/public-artifacts/ecmws-experiments.
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