Deadline-constrained multi-resource task mapping and allocation for edge-cloud systems
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...
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sg-ntu-dr.10356-1612602023-01-21T23:33:02Z Deadline-constrained multi-resource task mapping and allocation for edge-cloud systems Gao, Chuanchao Shaan, Aryaman Easwaran, Arvind Interdisciplinary Graduate School (IGS) School of Computer Science and Engineering GLOBECOM 2022 - 2022 IEEE Global Communications Conference Energy Research Institute @ NTU (ERI@N) Engineering::Computer science and engineering Multi-Resource Mapping and Allocation Deadline Requirements Edge-Cloud Computing 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. Ministry of Education (MOE) Submitted/Accepted version This work was supported in part by the MoE Tier-2 grant MOET2EP20221-0006. 2023-01-18T04:07:56Z 2023-01-18T04:07:56Z 2022 Conference Paper 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 978-1-6654-3540-6 https://hdl.handle.net/10356/161260 10.1109/GLOBECOM48099.2022.10001137 5037 5043 en MOET2EP20221-0006 10.21979/N9/5D1FBL © 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. application/pdf |
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Engineering::Computer science and engineering Multi-Resource Mapping and Allocation Deadline Requirements Edge-Cloud Computing Gao, Chuanchao Shaan, Aryaman Easwaran, Arvind Deadline-constrained multi-resource task mapping and allocation for edge-cloud systems |
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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. |
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Interdisciplinary Graduate School (IGS) |
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Interdisciplinary Graduate School (IGS) Gao, Chuanchao Shaan, Aryaman Easwaran, Arvind |
format |
Conference or Workshop Item |
author |
Gao, Chuanchao Shaan, Aryaman Easwaran, Arvind |
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Gao, Chuanchao |
title |
Deadline-constrained multi-resource task mapping and allocation for edge-cloud systems |
title_short |
Deadline-constrained multi-resource task mapping and allocation for edge-cloud systems |
title_full |
Deadline-constrained multi-resource task mapping and allocation for edge-cloud systems |
title_fullStr |
Deadline-constrained multi-resource task mapping and allocation for edge-cloud systems |
title_full_unstemmed |
Deadline-constrained multi-resource task mapping and allocation for edge-cloud systems |
title_sort |
deadline-constrained multi-resource task mapping and allocation for edge-cloud systems |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/161260 |
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1756370573568507904 |