I. Kovacevic, E. Harjula, S. Glisic, B. Lorenzo and M. Ylianttila, "Cloud and Edge Computation Offloading for Latency Limited Services," in IEEE Access, vol. 9, pp. 55764-55776, 2021, doi: 10.1109/ACCESS.2021.3071848
Cloud and edge computation offloading for latency limited services
|Author:||Kovacevic, Ivana1; Harjula, Erkki1; Glisic, Savo2;|
1Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
2Worcester Polytechnic Institute, Worcester, MA 01609, USA
3Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA 01003, USA
|Online Access:||PDF Full Text (PDF, 1.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021041910818
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-04-19
Multi-access Edge Computing (MEC) is recognised as a solution in future networks to offload computation and data storage from mobile and IoT devices to the servers at the edge of mobile networks. It reduces the network traffic and service latency compared to passing all data to cloud data centers while offering greater processing power than handling tasks locally at terminals. Since MEC servers are scattered throughout the radio access network, their computation capacities are modest in comparison to large cloud data centers. Therefore, offloading decision between MEC and cloud server should minimize the usage of the resources while maximizing the number of accepted delay critical requests. In this work we formulate the joint optimization of communication and computation resources allocation for computation offloading (CO) requests with strict latency constraints. We show that the global optimization problem is NP-hard and propose an efficient heuristic solution based on the single user optimal solution. Simulation results are presented to show the effectiveness of the proposed algorithm, compared to optimal and baseline solution where tasks are allocated in the order of arrival, with different system parameters. They show that our algorithm performs close to the optimal in terms of resource utilization and outperforms the baseline algorithm in terms of acceptance rate.
|Pages:||55764 - 55776|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the Academy of Finland 5GEAR project, in part by the Academy of Finland 6Genesis Flagship under Grant 318927, in part by the Academy of Finland Digihealth project, and in part by the NSF SII National Center for Wireless Spectrum Research under Grant NSF 2037782.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© The Authors 2021. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.