A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective

Computer Networks, Journal Year: 2020, Volume and Issue: 182, P. 107496 - 107496

Published: Aug. 22, 2020

Language: Английский

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Survey on computation offloading in UAV-Enabled mobile edge computing

Journal of Network and Computer Applications, Journal Year: 2022, Volume and Issue: 201, P. 103341 - 103341

Published: Feb. 1, 2022

With the increasing growth of internet-of-things (IoT) devices, effective computation performance has become a critical issue. Many services provided by IoT devices (e.g., augmented reality, location-tracking, traffic systems, and autonomous driving) require intensive real-time data processing, which demands powerful computational resources. Mobile edge computing (MEC) been introduced to effectively handle this problem reliably over internet. The inclusion MEC server allows computationally tasks be offloaded from devices. However, communication overhead delays are major drawbacks. advantages high mobility low cost, unmanned aerial vehicles (UAVs) can mitigate issue acting as servers. offloading decisions for such scenarios involve service latency, energy/power consumption, execution delays. For reason, study reviews UAV-enabled solutions in was focus research. We compare algorithms qualitatively assess features performance. Finally, we discuss open issues research challenges terms design implementation.

Language: Английский

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An autonomous computation offloading strategy in Mobile Edge Computing: A deep learning-based hybrid approach

Journal of Network and Computer Applications, Journal Year: 2021, Volume and Issue: 178, P. 102974 - 102974

Published: Jan. 12, 2021

Language: Английский

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RL/DRL Meets Vehicular Task Offloading Using Edge and Vehicular Cloudlet: A Survey

IEEE Internet of Things Journal, Journal Year: 2022, Volume and Issue: 9(11), P. 8315 - 8338

Published: March 1, 2022

The last two decades have seen a clear trend toward crafting intelligent vehicles based on the significant advances in communication and computing paradigms, which provide safer, stress-free, more enjoyable driving experience. Moreover, emerging applications services necessitate massive volumes of data, real-time data processing, ultrareliable low-latency (URLLC). However, capability current is minimal, making it challenging to meet delay-sensitive computation-intensive demand such applications. In this situation, vehicular task/computation offloading edge cloud (EC) cloudlet (VC) seems be promising solution improve network’s performance applications’ Quality Service (QoS). At same time, artificial intelligence (AI) has dramatically changed people’s lives. Especially for task applications, AI achieves state-of-the-art various environments. Motivated by outstanding integrating reinforcement learning (RL)/deep RL (DRL) systems, we present survey RL/DRL techniques applied offloading. Precisely, classify works into main categories: 1) RL/ DRL solutions leveraging EC 2) using VC computing. section-based are further subcategorized multiaccess (MEC) server, nearby vehicles, hybrid MEC (HMEC). To best our knowledge, first cover RL/DRL-based Also, lessons learned open research challenges field discuss possible future research.

Language: Английский

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Solving the Multi-Objective Problem of IoT Service Placement in Fog Computing Using Cuckoo Search Algorithm

Neural Processing Letters, Journal Year: 2022, Volume and Issue: 54(3), P. 1823 - 1854

Published: Jan. 22, 2022

Language: Английский

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Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Mobile Information Systems, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 17

Published: June 28, 2022

Cloud and mobile edge computing (MEC) provides a wide range of services for applications. In particular, enables storage infrastructure provisioned closely to the end-users at cellular network. The small base stations are deployed establish network that can be coined with cloud infrastructure. A large number enterprises individuals rely on offered by clouds meet their computational demands. Based user behavior demand, tasks first offloaded from users then executed one or several specific in MEC architecture has capability handle devices turn generate high volumes traffic. this work, we provide holistic overview MCC/MEC technology includes background evolution remote computation technologies. Then, main part paper surveys up-to-date research concepts offloading mechanisms, granularities, techniques. Furthermore, discuss mechanism static dynamic environment along optimization We further challenges potential future directions research.

Language: Английский

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MAP based modeling method and performance study of a task offloading scheme with time-correlated traffic and VM repair in MEC systems

Wireless Networks, Journal Year: 2022, Volume and Issue: 29(1), P. 47 - 68

Published: Aug. 25, 2022

Language: Английский

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Reinforcement Learning Methods for Computation Offloading: A Systematic Review

ACM Computing Surveys, Journal Year: 2023, Volume and Issue: 56(1), P. 1 - 41

Published: June 9, 2023

Today, cloud computation offloading may not be an appropriate solution for delay-sensitive applications due to the long distance between end-devices and remote datacenters. In addition, a can consume bandwidth dramatically increase costs. However, such as sensors, cameras, smartphones have limited computing storage capacity. Processing tasks on battery-powered energy-constrained devices becomes even more complex. To address these challenges, new paradigm called Edge Computing (EC) emerged nearly decade ago bring resources closer end-devices. Here, edge servers located end-device perform user tasks. Recently, several paradigms Mobile (MEC) Fog (FC) complement Cloud (CC) EC. Although are heterogeneous, they further reduce energy consumption task response time, especially applications. Computation is multi-objective, NP-hard optimization problem. A significant part of previous research in this field devoted Machine Learning (ML) methods. One essential types ML Reinforcement (RL), which agent learns how make best decision using experiences gained from environment. This article provides systematic review widely used RL approaches offloading. It covers complementary mobile computing, fog Internet Things. We explain reasons various methods technical point view. analysis includes both binary partial techniques. For each method, elements characteristics environment discussed regarding most important criteria. Research challenges Future trends also mentioned.

Language: Английский

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Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms

Cluster Computing, Journal Year: 2021, Volume and Issue: 24(3), P. 1825 - 1853

Published: Jan. 11, 2021

Language: Английский

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Autonomous computation offloading and auto-scaling the in the mobile fog computing: a deep reinforcement learning-based approach

Journal of Ambient Intelligence and Humanized Computing, Journal Year: 2020, Volume and Issue: 12(8), P. 8265 - 8284

Published: Sept. 25, 2020

Language: Английский

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