2023/01/27 by Binquan Guo, Guo, Binquan, Hongyan Li +7
Computer Science · #Age of Information Optimization #Cloud Computing and Resource Management #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #FOS: Electrical engineering #Parallel #Performance (cs.PF) #Systems and Control (eess.SY) #and Cluster Computing (cs.DC) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2301.11831
openalex publication_date 2023/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Emerging smart grid applications analyze large amounts of data collected from millions of meters and systems to facilitate distributed monitoring and real-time control tasks. However, current parallel data processing systems are designed for common applications, unaware of the massive volume of the collected data, causing long data transfer delay during the computation and slow response time of smart grid systems. A promising direction to reduce delay is to jointly schedule computation tasks and data transfers. We identify that the smart grid data analytic jobs require the intermediate data among different computation stages to be transmitted orderly to avoid network congestion. This new feature prevents current scheduling algorithms from being efficient. In this work, an integrated computing and communication task scheduling scheme is proposed. The mathematical formulation of smart grid data analytic jobs scheduling problem is given, which is unsolvable by existing optimization methods due to the strongly coupled constraints. Several techniques are combined to linearize it for adapting the Branch and Cut method. Based on the topological information in the job graph, the Topology Aware Branch and Cut method is further proposed to speed up searching for optimal solutions. Numerical results demonstrate the effectiveness of the proposed method.