A Survey on Computation Offloading for Mobile Edge Computing Information

Shan, Xiaoyu; Zhi, Hanxiao; Han, Zhijie

doi:10.1109/bds/hpsc/ids18.2018.00060

Cited by 32 publications

(15 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These surveys focused on multiple aspects such as architecture [5]- [7], resource allocation [8], [9], communication [8], [10], [11], caching [10], mobility management [6], [10], [12], integration with wireless, IoT and 5G technologies [5], [8], [13], [14], decision on task offloading [6], [11], application partitioning [12], [15], application models [8], [12], [16], [17], application scenarios [5], [8], [10], [15], [18], [19] and algorithms [11], [12], [17].…”

mentioning

confidence: 99%

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

et al. 2021

View full text Add to dashboard Cite

Next generation communication networks are expected to accommodate a high number of new and resource-voracious applications that can be offered to a large range of end users. Even though end devices are becoming more powerful, the available local resources cannot cope with the requirements of these applications. This has created a new challenge called task offloading, where computation intensive tasks need to be offloaded to more resource powerful remote devices. Naturally, the Cloud Computing is a well-tested infrastructure that can facilitate the task offloading. However, Cloud Computing as a centralized and distant infrastructure creates significant communication delays that cannot satisfy the requirements of the emerging delay-sensitive applications. To this end, the concept of Edge Computing has been proposed, where the Cloud Computing capabilities are repositioned closer to the end devices at the edge of the network. This paper provides a detailed survey of how the Edge and/or Cloud can be combined together to facilitate the task offloading problem. Particular emphasis is given on the mathematical, artificial intelligence and control theory optimization approaches that can be used to satisfy the various objectives, constraints and dynamic conditions of this end-to-end application execution approach. The survey concludes with identifying open challenges and future directions of the problem at hand.

show abstract

mentioning

confidence: 99%

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

et al. 2021

View full text Add to dashboard Cite

show abstract

“…(iii) If the user is not legal, he is not provided with the service of computation offloading [51]. Mostly, the computation offloading algorithm's purpose is to minimize the energy consumed by MDs by migrating tasks to the MEC server while fulfilling the reduction in execution delay accordingly.…”

Section: A Computation Offloading (Co) Processmentioning

confidence: 99%

Energy Efficient UAV-Enabled Mobile Edge Computing for IoT Devices: A Review

Abrar

Ajmal

Almohaimeed³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

With the emergence of computation-intensive and delay-sensitive applications, such as face recognition, virtual reality, augmented reality, and Internet of Things (IoT) devices ; Mobile Edge Computing (MEC) allows the IoT devices to offload their heavy computation tasks to nearby edge cloud network rather than to compute the tasks locally. Therefore, it helps to reduce the energy consumption and execution delay in the ground mobile users. Flying Unmanned Aerial Vehicles (UAVs) integrated with the MEC server play a key role in 5G and future wireless communication networks to provide spatial coverage and further computational services to the small, battery-powered and energy-constrained devices. The UAVenabled MEC (U-MEC) system has flexible mobility and more computational capability compared to the terrestrial MEC network. They support line-of-sight (LoS) links with the users offloading their tasks to the UAVs. Hence, users can transmit more data without interference by mitigating small-scale fading and shadowing effects. UAVs resources and flight time are very limited due to size, weight, and power (SWaP) constraints. Therefore, energy-aware communication and computation resources are allocated in order to minimize energy consumption.In this paper, a brief survey on U-MEC networks is presented. It includes the brief introduction regarding UAVs and MEC technology. The basic terminologies and architectures used in U-MEC networks are also defined. Moreover, mobile edge computation offloading working, different access schemes used during computation offloading technique are explained. Resources that are needed to be optimized in U-MEC systems are depicted with different optimization problem, and solution types. Furthermore, to guide future work in this area of research, future research directions are outlined. At the end, challenges and open issues in this domain are also summarized.

show abstract

“…Owing to such unprecedented amount of data with exponential growth trend [3] and the typical private nature of these data, sending all the data to a remote cloud becomes impracticable, unnecessary, and full of privacy concerns [2]. Therefore, all of these factors have contributed to the emergence of the new mobile edge computing (MEC) paradigm [4][5][6], which exploits the advancement in the storage and computation capacity of modern devices for pushing processing and storing procedures locally on the devices themselves. The MEC approach involves the cooperation of edge-nodes with the remote cloud in order to give rise to a computing system able to support large-scale task processing and managing the environment [1].…”

Section: Introductionmentioning

confidence: 99%

Federated learning framework for mobile edge computing networks

Fantacci

Picano

2020

CAAI trans. intell. technol.

View full text Add to dashboard Cite

The continuous growth of smart devices needing processing has led to moving storage and computation from cloud to the network edges, giving rise to the edge computing paradigm. Owing to the limited capacity of edge computing nodes, the presence of popular applications in the edge nodes results in significant improvements in users’ satisfaction and service accomplishment. However, the high variability in the content requests makes prediction demand not trivial and, typically, the majority of the classical prediction approaches require the gathering of personal users' information at a central unit, giving rise to many users' privacy issues. In this context, federated learning gained attention as a solution to perform learning procedures from data disseminated across multiple users, keeping the sensitive data protected. This study applies federated learning to the demand prediction problem, to accurately forecast the more popular application types in the network. The proposed framework reaches high accuracy levels on the predicted applications demand, aggregating in a global and weighted model the feedback received by users, after their local training. The validity of the proposed approach is verified by performing a virtual machine replica copies and comparison with the alternative forecasting approach based on chaos theory and deep learning.

show abstract

A Survey on Computation Offloading for Mobile Edge Computing Information

Cited by 32 publications

References 11 publications

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

Energy Efficient UAV-Enabled Mobile Edge Computing for IoT Devices: A Review

Federated learning framework for mobile edge computing networks

Contact Info

Product

Resources

About