QoS-aware resource allocation in mobile edge computing networks: Using intelligent offloading and caching strategy

Bonab, Mohammad Jalilvand Aghdam; Kandovan, Ramin Shaghaghi

doi:10.1007/s12083-021-01271-7

Cited by 6 publications

(5 citation statements)

References 38 publications

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“… Jalilvand Aghdam Bonab & Shaghaghi Kandovan (2022) presented a novel framework for QoS-aware resource allocation and mobile edge computing (MEC) in multi-access heterogeneous networks, with the objective of maximizing overall system energy efficiency while ensuring user QoS requirements. The framework introduced a customized objective function tailored specifically for the multi-server MEC environment, taking into account computation and communication models to minimize task completion time and enhance energy efficiency within specified delay constraints.…”

Section: Heuristic Approaches For Task Schedulingmentioning

confidence: 99%

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Alsadie

2024

PeerJ Computer Science

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Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely execution of tasks within fog computing environments. This article presents a comprehensive review of the advancements in task scheduling methodologies for fog computing systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid heuristics, and nature-inspired heuristic approaches. Through a systematic analysis of relevant literature, we highlight the strengths and limitations of each approach and identify key challenges facing fog computing task scheduling, including dynamic environments, heterogeneity, scalability, resource constraints, security concerns, and algorithm transparency. Furthermore, we propose future research directions to address these challenges, including the integration of machine learning techniques for real-time adaptation, leveraging federated learning for collaborative scheduling, developing resource-aware and energy-efficient algorithms, incorporating security-aware techniques, and advancing explainable AI methodologies. By addressing these challenges and pursuing these research directions, we aim to facilitate the development of more robust, adaptable, and efficient task-scheduling solutions for fog computing environments, ultimately fostering trust, security, and sustainability in fog computing systems and facilitating their widespread adoption across diverse applications and domains.

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Section: Heuristic Approaches For Task Schedulingmentioning

confidence: 99%

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Alsadie

2024

PeerJ Computer Science

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“…In ref. [129], authors researched the use of intelligent offloading and caching techniques for link control in MEC systems. The goal of this study is to optimize QoS through coordinated resource allocation and MEC in multiaccess heterogeneous networks, aiming to guarantee the users' QoS requirements while also optimizing system energy efficiency.…”

Section: Resource Allocationmentioning

confidence: 99%

A Comprehensive Survey Exploring the Multifaceted Interplay between Mobile Edge Computing and Vehicular Networks

Pashazadeh,

Nardini,

Stea

2023

Future Internet

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In recent years, the need for computation-intensive applications in mobile networks requiring more storage, powerful processors, and real-time responses has risen substantially. Vehicular networks play an important role in this ecosystem, as they must support multiple services, such as traffic monitoring or sharing of data involving different aspects of the vehicular traffic. Moreover, new resource-hungry applications have been envisaged, such as autonomous driving or in-cruise entertainment, hence making the demand for computation and storage resources one of the most important challenges in vehicular networks. In this context, Mobile Edge Computing (MEC) has become the key technology to handle these problems by providing cloud-like capabilities at the edge of mobile networks to support delay-sensitive and computation-intensive tasks. In the meantime, researchers have envisaged use of onboard vehicle resources to extend the computing capabilities of MEC systems. This paper presents a comprehensive review of the most recent works related to MEC-assisted vehicular networks, as well as vehicle-assisted MEC systems. We illustrate the MEC system architecture and discuss its deployment in vehicular environments, as well as the key technologies to realize this integration. After that, we review the recent literature by identifying three different areas, i.e.: (i) MEC providing additional resources to vehicles (e.g., for task offloading); (ii) MEC enabling innovative vehicular applications (e.g., platooning), and (iii) vehicular networks providing additional resources to MEC systems. Finally, we discuss open challenges and future research directions, addressing the possible interplays between MEC systems and vehicular networks.

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“…The results show that the system can effectively achieve the computational offloading task with energy consumption as a constraint. In the literature [29], a joint QoS-aware resource allocation mobile edge computing is proposed based on a multi-access heterogeneous network with the constraint of reducing system energy consumption, i.e., maximizing total system energy efficiency. The original problem is transformed into a mixed integer nonlinear programming by integrating the communication model and the computational model, and a carrier matching algorithm is used to obtain the optimal channel allocation strategy.…”

Section: Multi-user Computing Offload Service (1) Reduce Latencymentioning

confidence: 99%

Research on computational offloading strategies based on mobile edge computing

Wang

2022

Second International Conference on Optics and Communication Technology (ICOCT 2022)

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With the advent and continuous development of the Internet of Things era, a large number of emerging applications, such as driverless, have emerged. Usually such applications need to consume a lot of computing resources and have high demand for low latency and data processing. However, the limited computing power of the devices themselves cannot meet the low latency and other needs of emerging applications, which limits the further development of IoT. Mobile edge computing (MEC) is an emerging computing paradigm where mobile devices interact directly with MEC platforms at the network edge and offload computational tasks to MEC servers to solve the problem of limited device resources. In this paper, we conduct an in-depth study of the computational offloading problem in mobile edge computing, and provide an in-depth and comprehensive summary of the current status and results of the research on MEC-oriented computational offloading strategies. First, the MEC technology and its typical application scenarios are introduced; then, the computation offloading strategy with optimized time, energy consumption, and hybrid goals for system performance gain is introduced from two aspects: single-user computation offloading and multi-user computation offloading.

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QoS-aware resource allocation in mobile edge computing networks: Using intelligent offloading and caching strategy

Cited by 6 publications

References 38 publications

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

A Comprehensive Survey Exploring the Multifaceted Interplay between Mobile Edge Computing and Vehicular Networks

Research on computational offloading strategies based on mobile edge computing

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