Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature

Vasilios Patsias, Petros Amanatidis, Dimitris Karampatzakis (), Thomas Lagkas, Kalliopi Michalakopoulou and Alexandros Nikitas
Additional contact information
Vasilios Patsias: Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
Petros Amanatidis: Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
Dimitris Karampatzakis: Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
Thomas Lagkas: Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
Kalliopi Michalakopoulou: Department of Logistics, Marketing, Hospitality and Analytics, Huddersfield Business School, University of Huddersfield, Huddersfield HD1 3DH, UK
Alexandros Nikitas: Department of Logistics, Marketing, Hospitality and Analytics, Huddersfield Business School, University of Huddersfield, Huddersfield HD1 3DH, UK

Future Internet, 2023, vol. 15, issue 8, 1-30

Abstract: Task allocation in edge computing refers to the process of distributing tasks among the various nodes in an edge computing network. The main challenges in task allocation include determining the optimal location for each task based on the requirements such as processing power, storage, and network bandwidth, and adapting to the dynamic nature of the network. Different approaches for task allocation include centralized, decentralized, hybrid, and machine learning algorithms. Each approach has its strengths and weaknesses and the choice of approach will depend on the specific requirements of the application. In more detail, the selection of the most optimal task allocation methods depends on the edge computing architecture and configuration type, like mobile edge computing (MEC), cloud-edge, fog computing, peer-to-peer edge computing, etc. Thus, task allocation in edge computing is a complex, diverse, and challenging problem that requires a balance of trade-offs between multiple conflicting objectives such as energy efficiency, data privacy, security, latency, and quality of service (QoS). Recently, an increased number of research studies have emerged regarding the performance evaluation and optimization of task allocation on edge devices. While several survey articles have described the current state-of-the-art task allocation methods, this work focuses on comparing and contrasting different task allocation methods, optimization algorithms, as well as the network types that are most frequently used in edge computing systems.

Keywords: task offloading; edge computing; task allocation; optimization algorithms (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1999-5903/15/8/254/pdf (application/pdf)
https://www.mdpi.com/1999-5903/15/8/254/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:15:y:2023:i:8:p:254-:d:1205578

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().