Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/161260
Title: Deadline-constrained multi-resource task mapping and allocation for edge-cloud systems
Authors: Gao, Chuanchao
Shaan, Aryaman
Easwaran, Arvind
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Gao, C., Shaan, A. & Easwaran, A. (2022). Deadline-constrained multi-resource task mapping and allocation for edge-cloud systems. GLOBECOM 2022 - 2022 IEEE Global Communications Conference, 5037-5043. https://dx.doi.org/10.1109/GLOBECOM48099.2022.10001137
Project: MOET2EP20221-0006 
Conference: GLOBECOM 2022 - 2022 IEEE Global Communications Conference
Abstract: In an edge-cloud system, mobile devices can offload their computation intensive tasks to an edge or cloud server to guarantee the quality of service or satisfy task deadline requirements. However, it is challenging to determine where tasks should be offloaded and processed, and how much network and computation resources should be allocated to them, such that a system with limited resources can obtain a maximum profit while meeting the deadlines. A key challenge in this problem is that the network and computation resources could be allocated on different servers, since the server to which a task is offloaded (e.g., a server with an access point) may be different from the server on which the task is eventually processed. To address this challenge, we first formulate the task mapping and resource allocation problem as a non-convex Mixed-Integer Nonlinear Programming (MINLP) problem, known as NP-hard. We then propose a zero-slack based greedy algorithm (ZSG) and a linear discretization method (LDM) to solve this MINLP problem. Experiment results with various synthetic tasksets show that ZSG has an average of 2.98% worse performance than LDM with a minimum unit of 5 but has an average of 6.88% better performance than LDM with a minimum unit of 15.
URI: https://hdl.handle.net/10356/161260
ISBN: 978-1-6654-3540-6
DOI: 10.1109/GLOBECOM48099.2022.10001137
DOI (Related Dataset): 10.21979/N9/5D1FBL
Schools: Interdisciplinary Graduate School (IGS) 
School of Computer Science and Engineering 
Research Centres: Energy Research Institute @ NTU (ERI@N) 
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/GLOBECOM48099.2022.10001137.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ERI@N Journal Articles
IGS Conference Papers
SCSE Conference Papers

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