Journal
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 21, Issue 9, Pages 7682-7695Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2022.3160493
Keywords
Resource management; Wireless communication; Task analysis; Power demand; Processor scheduling; Optimization; Distributed computing; Difference of convex functions; power allocation; wireless distributed computing; workload scheduling; Hadoop
Funding
- National Key Research and Development Program of China [2018YFB1801103]
- National Natural Science Foundation of China [61901110]
- Jiangsu Province Basic Research Project [SBK2019050020]
- Natural Science Foundation of Jiangsu Province [BK20190334]
- Huawei Cooperation Project
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This paper investigates a novel resource allocation problem in wireless distributed computing systems, focusing on workload scheduling and power allocation optimization for multifunctional nodes. The proposed algorithm, through relaxation of integer constraints and utilization of concave-convex procedure, achieves efficient computational speed.
Distributed computing systems, such as Hadoop, have been widely studied and used for executing and analyzing large data. In this paper, we investigate an emerging resource allocation problem for wireless distributed computing systems consisting of multifunctional nodes in charge of both numerical computation and wireless communication with master nodes. We focus on a computation power consumption model based on CMOS devices and a communication power consumption model involving multiple antenna transceivers against mutual interference. We present a joint optimization problem for workload scheduling and power allocation for achieving maximum computational speed under total power constraint. We simplify the joint optimization into two sub-problems. For workload scheduling as an integer programming sub-problem, we relax the integer constraint and establish the equivalence between relaxed and original problems. For the power allocation sub-problem, we maximize a difference of convex functions by utilizing the concave-convex procedure. We prove our proposed algorithm to converge to a stationary point of the original program. Simulation results confirm the efficiency and near-optimal performance of our proposed algorithms.
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