Paper Info
Title
Optimal CPU Scheduling in Data Centers via a Finite-Time Distributed Quantized Coordination Mechanism.
Abstract
In this paper we analyze the problem of optimal task scheduling for data centers. Given the available resources and tasks, we propose a fast distributed iterative algorithm which operates over a large scale network of nodes and allows each of the interconnected nodes to reach agreement to an optimal solution in a finite number of time steps. More specifically, the algorithm (i) is guaranteed to converge to the exact optimal scheduling plan in a finite number of time steps and, (ii) once the goal of task scheduling is achieved, it exhibits distributed stopping capabilities (i.e., it allows the nodes to distributely determine whether they can terminate the operation of the algorithm). Furthermore, the proposed algorithm operates exclusively with quantized values (i.e., the information stored, processed and exchanged between neighboring agents is subject to deterministic uniform quantization) and relies on event-driven updates (e.g., to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). We also provide examples to illustrate the operation, performance, and potential advantages of the proposed algorithm. Finally, by using extensive empirical evaluations through simulations we show that the proposed algorithm exhibits state-of-the-art performance.
Year
DOI
Venue
2021
10.1109/CDC45484.2021.9683763
CDC
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Apostolos I. Rikos100.68
Andreas Grammenos200.68
Evangelia Kalyvianaki3161.69
Christoforos N. Hadjicostis41425127.48
Themistoklis Charalambous521.74
Karl H. Johansson601.01