A Review of the Current Task Offloading Algorithms, Strategies and Approach in Edge Computing Systems

Acheampong, Abednego; Zhang, Yiwen; Xu, Xueqing; Kumah, Daniel Appiah

doi:10.32604/cmes.2022.021394

Cited by 9 publications

(5 citation statements)

References 133 publications

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“…These papers are categorized based on the title, but three papers are classified [11][12][13] based on the abstract. Figure 2B based on the area of interest such as generic, 4,6-8,10,14-19 energy-efficient, 8,11,12,20 machine learning (ML)/AI and game theory (GT)-based, 9,[21][22][23][24] vehicle communication and computing, [25][26][27][28][29] and optimization techniques and algorithms 3,13,16,30,31 as shown in Figure 3.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 2B illustrates the distribution of papers based on the area of interest. An overview of related works on EC based on CO has been divided into five categories based on the area of interest such as generic, 4,6‐8,10,14‐19 energy‐efficient, 8,11,12,20 machine learning (ML)/AI and game theory (GT)‐based, 9,21‐24 vehicle communication and computing, 25‐29 and optimization techniques and algorithms 3,13,16,30,31 as shown in Figure 3.…”

Section: Introductionmentioning

confidence: 99%

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Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

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SummaryIn today's era, Internet of Things (IoT) devices generate a vast amount of data, which is typically stored in the cloud environment and can be accessed by edge and IoT devices. The data generated by these devices are offloaded through computation offloading (CO) techniques in an edge/cloud computing environment. This paper conducts a systematic literature review (SLR) to review the state‐of‐the‐art CO techniques in edge computing (EC) in the context of energy, Quality of Service (QoS), authentication and traceability. In this SLR, the evolution of offloading techniques is analyzed in detail. A total of 138 articles, spanning from 2016 to 2023 (till date), have been classified into QoS, energy, and authentication and traceability‐based CO techniques. The optimization‐based techniques are the most preferred choices to improve the QoS and reduce energy in the research field of CO in EC. In addition, this paper explores the significant issues and challenges that require further investigation. For future research, energy, QoS, and data provenance for dependent or dynamic task offloading in mobility scenarios can be explored further.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

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“…When discussing the offloading of tasks and the management of computing resources in edge computing, the majority of existing approaches concentrate on methods related to machine learning [3]. However, alternative methods also exist.…”

Section: Related Workmentioning

confidence: 99%

Knapsack-Based Approach for Optimizing Resource Management in Edge Computing

Babaei,

Morshedlou

2024

Preprint

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In the era of rapid internet expansion, Edge Computing has emerged as a critical field in computer science, addressing challenges posed by increasing internet data, bandwidth limitations, and the distance between cloud servers and users. The impact of Edge Computing can be observed in various domains such as IoT networks, 5G cellular networks, healthcare systems, and more. A key focus is the optimal resource management problem within Edge Computing. This problem involves efficiently allocating computational resources from edge and cloud servers to respond to user requests, aiming to minimize reliance on distant cloud servers and bring calculations nearer to the edge for task offloading. This article proposes a knapsack-based approach to optimize resource management in hybrid edge and cloud networks. The Multiple Knapsack Problem (MKP) is employed to model the resource allocation issue, defining servers as knapsacks and tasks as items. The proposed algorithm is defined in two parts: an exact solution for scenarios with a moderate number of servers and tasks, and a greedy solution for situations where the scale is too large for precise calculations within a reasonable time. Application of the proposed algorithm is demonstrated using the ECHOES simulator, designed for testing optimization methods in environments that combine both cloud and edge components. This work focuses on a new approach for offloading tasks to computational resources at edge or cloud.

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“…In cases where the desired content is not found in the local cache, the base station attempts to request the content from neighboring base stations. Only if adjacent base stations cannot provide the requested content does the base station forward the request to the cloud center [22].…”

Section: System Modelmentioning

confidence: 99%

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

Ma,

Zhao,

Guo

et al. 2024

CMES

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Mobile Edge Computing (MEC) is a technology designed for the on-demand provisioning of computing and storage services, strategically positioned close to users. In the MEC environment, frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery, ultimately enhancing the quality of the user experience. However, due to the typical placement of edge devices and nodes at the network's periphery, these components may face various potential fault tolerance challenges, including network instability, device failures, and resource constraints. Considering the dynamic nature of MEC, making high-quality content caching decisions for real-time mobile applications, especially those sensitive to latency, by effectively utilizing mobility information, continues to be a significant challenge. In response to this challenge, this paper introduces FT-MAACC, a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms. This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content. Furthermore, it relies on collaborative caching strategies based on multi-agent deep reinforcement learning models and establishes a fault-tolerance model to ensure the system's reliability, availability, and persistence. Empirical results unequivocally demonstrate that FT-MAACC outperforms its peer methods in cache hit rates and transmission latency.

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A Review of the Current Task Offloading Algorithms, Strategies and Approach in Edge Computing Systems

Cited by 9 publications

References 133 publications

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Knapsack-Based Approach for Optimizing Resource Management in Edge Computing

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

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