Task Allocation Optimization Scheme Based on Queuing Theory for Mobile Edge Computing in 5G Heterogeneous Networks

Xue, Jiang; Wang, Zesen; Zhang, Yonggang; Wang, Lu

doi:10.1155/2020/1501403

Cited by 11 publications

(7 citation statements)

References 25 publications

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“…Context-aware task allocation [52] DFD DAG [69] GT IoT, 5G [78] LPA MEC Energy-efficient task allocation [68] MAPE-K IoT [11] Lyapunov MEC [19] Bi-Level MEC [25] DRL IoV [30] QT 5G [43] PSO IoT [47] ILP MEC [71] JTORA MEC [73] hybrid RF-FSO Industrial IoT Dynamic task allocation [13] MPSO VVECNs, VANETs [79] MH IoV [20] AA Fog [23] DP MEC [41] DRL IoT [44] ECTA EC [46] AA Fog [49] ILP [56] GA, GEN IoT [60] PSO EC [64] BPSO Table 1. Cont.…”

Section: Collaborative Task Allocationmentioning

confidence: 99%

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

Patsias,

Amanatidis,

Karampatzakis

et al. 2023

Future Internet

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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.

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Section: Collaborative Task Allocationmentioning

confidence: 99%

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

Patsias,

Amanatidis,

Karampatzakis

et al. 2023

Future Internet

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“…Heterogeneity: it is typical of scenarios where cooperating devices have very different characteristics (e.g. available resources, functionalities, communication protocols) [8][9] [10]. The higher the heterogeneity, the higher the number of elements that need to be considered simultaneously by the TA strategy, as it needs to encompass different device capabilities, characteristics, and functionalities, often at the same time (i.e.…”

Section: Factors Impacting On Task Allocationmentioning

confidence: 99%

Task Allocation Among Connected Devices: Requirements, Approaches, and Challenges

Pilloni

Ning

Atzori

2022

IEEE Internet Things J.

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Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to the best devices, according to the context and task requirements. The main purpose of this paper is to study the different connotations of the concept of TA efficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. The paper first analyzes the domains of connected devices where TA has an important role, which brings to this classification: Internet of Things (IoT), Sensor and Actuator Networks (SAN), Multi-Robot Systems (MRS), Mobile Crowdsensing (MCS), and Unmanned Aerial Vehicles (UAV). The paper then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-ofthe-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. The paper also discusses the issues that need to be addressed by future research activities, i.e.: allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; extending the list of tasks performed by objects; designing TA strategies where network service providers have a role in TA functionalities' provisioning.

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“…In the MEC service system, the average delay for users to obtain content is an important performance indicator to measure the quality of user experience. The smaller the average content delivery delay, the more user requests can be satisfied by the local MEC, and the higher the quality of user experience [27,28].…”

Section: Average Content Transmission Delay (Adl)mentioning

confidence: 99%

Application of Mobile Edge Computing Technology in Civil Aviation Express Marketing

2021

Wireless Communications and Mobile Computing

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With the popularization of mobile terminals and the rapid development of mobile communication technology, many PC-based services have placed high demands on data processing and storage functions. Cloud laptops that transfer data processing tasks to the cloud cannot meet the needs of users due to low latency and high-quality services. In view of this, some researchers have proposed the concept of mobile edge computing. Mobile edge computing (MEC) is based on the 5G evolution architecture. By deploying multiple service servers on the base station side near the edge of the user’s mobile core network, it provides nearby computing and processing services for user business. This article is aimed at studying the use of caching and MEC processing functions to design an effective caching and distribution mechanism across the network edge and apply it to civil aviation express marketing. This paper proposes to focus on mobile edge computing technology, combining it with data warehouse technology, clustering algorithm, and other methods to build an experimental model of MEC-based caching mechanism applied to civil aviation express marketing. The experimental results in this paper show that when the cache space and the number of service contents are constant, the LECC mechanism among the five cache mechanisms is more effective than LENC, LRU, and RR in cache hit rate, average content transmission delay, and transmission overhead. For example, with the same cache space, ATC under the LECC mechanism is about 4%~9%, 8%~13%, and 18%~22% lower than that of LENC, LRU, and RR, respectively.

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Task Allocation Optimization Scheme Based on Queuing Theory for Mobile Edge Computing in 5G Heterogeneous Networks

Cited by 11 publications

References 25 publications

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

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

Task Allocation Among Connected Devices: Requirements, Approaches, and Challenges

Application of Mobile Edge Computing Technology in Civil Aviation Express Marketing

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