Performance evaluation and optimization of a task offloading strategy on the mobile edge computing with edge heterogeneity

Li, Wei; Jin, Shunfu

doi:10.1007/s11227-021-03781-w

Cited by 22 publications

(6 citation statements)

References 31 publications

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“…Liu et al [36] conducted an in-depth study and found the optimal offloading probability and optimal transmission rate based on M/M/1 queueing theory to minimize energy consumption, execution delay, execution delay, and price cost. Li et al [37] developed a system model consisting of M/M/1 and M/M/c queues to capture the task execution process of an IMD, an MEC server, and a remote cloud server, respectively, and solved a joint optimization problem regarding task offloading delay and energy consumption. Chen et al [38] considered the emergent task computation queue idleness in edge servers.…”

Section: Related Workmentioning

confidence: 99%

Minimize average tasks processing time in satellite mobile edge computing systems via a deep reinforcement learning method

Pang,

Zheng,

Wang

et al. 2023

J Cloud Comp

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Recently, the development of Low Earth Orbit (LEO) satellites and the advancement of the Mobile Edge Computing (MEC) paradigm have driven the emergence of the Satellite Mobile Edge Computing (Sat-MEC). Sat-MEC has been developed to support communication and task computation for Internet of Things (IoT) Mobile Devices (IMDs) in the absence of terrestrial networks. However, due to the heterogeneity of tasks and Sat-MEC servers, it is still a great challenge to efficiently schedule tasks in Sat-MEC servers. Here, we propose a scheduling algorithm based on the Deep Reinforcement Learning (DRL) method in the Sat-MEC architecture to minimize the average task processing time. We consider multiple factors, including the cooperation between LEO satellites, the concurrency and heterogeneity of tasks, the dynamics of LEO satellites, the heterogeneity of the computational capacity of Sat-MEC servers, and the heterogeneity of the initial queue for task computation. Further, we use the self-attention mechanism to act as a Q-network to extract high-dimensional dynamic information of tasks and Sat-MEC servers. In this work, we model the Sat-MEC environment simulation at the application level and propose a DRL-based task scheduling algorithm. The simulation results confirm the effectiveness of our proposed scheduling algorithm, which reduces the average task processing time by 22.1$$\%$$ % , 30.6$$\%$$ % , and 41.3$$\%$$ % , compared to the genetic algorithm(GA), the greedy algorithm, and the random algorithm, respectively.

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Section: Related Workmentioning

confidence: 99%

Minimize average tasks processing time in satellite mobile edge computing systems via a deep reinforcement learning method

Pang,

Zheng,

Wang

et al. 2023

J Cloud Comp

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“…Using the method of randomly generating workload, the distribution of task quantity satisfies the constraint condition (6a-6c), and the influence of different users and nodes on communication price is studied in the delay scenes. For further analysis, we compare our proposed PSEC algorithm with MOERA [13] algorithm and MEH [39] algorithm, respectively. The MEH is based on M/M/c queue to capture the execution process of tasks in MEC server with the optimal offloading probability (p = 0.35).…”

Section: Simulated Analysismentioning

confidence: 99%

Distributed Location-Aware Task Offloading in Multi-UAVs Enabled Edge Computing

Liu

et al. 2022

IEEE Access

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Edge computing is a new computing paradigm which distributes tasks to edge networks for processing, it provides effective support for various mobile applications to meet their rapid response requirement. Unmanned Aerial Vehicle (UAV) has been widely used in emergency rescue, mapping, etc., with the advantages of flexible deployment and rapid movement. However, on one hand, mobile applications will be terminated when the battery energy is exhausted. On the other hand, mobile applications will be out of service when mobile devices are out of radio coverage of UAVs. How to achieve low-cost task unloading in the resource limited and location sensitive multi-UAVs edge computing environment is rather challenging. In this paper, we propose a distributed location-aware task offloading scheme, aiming at the above issues. Specifically, we create a nonlinear task allocation problem by combining the limited energy constraints of edge nodes with the random movement of users, where the cost function is divided into static and dynamic costs, respectively. Then, we formulate this problem to a convex optimization one with linear constrains, based on regularization technology. The mathematical proof shows that the scheme can support a parameterized competitive ratio without requiring any prior knowledge of the input task. The simulation results show that the proposed scheme can achieve lower cost edge computing services.INDEX TERMS convex programming,edge computing,task allocation,UAV

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“…Electronics 2024, 13, x FOR PEER REVIEW The demand for smart gadgets, including smartphones, tablets, wearable tech and intelligent sensors, is rising quickly around the world due to recent advanc stemming from the implementation of 5G communication and Internet of Thing technologies [1]. At this stage, the massive usage of mobile devices (MDs) leads network latency and data traffic, affecting real-time communication and computi cesses [2]. Moreover, the large amount of mobile data produces heavy demand fo servers, which degrades systems' consistency [3].…”

Section: Introductionmentioning

confidence: 99%

Secure Task Offloading and Resource Allocation Strategies in Mobile Applications Using Probit Mish-Gated Recurrent Unit and an Enhanced-Searching-Based Serval Optimization Algorithm

Sindi,

Si,

2024

Electronics

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Today, with the presence of 5G communication systems, including Internet of Things (IoT) technology, there is a high demand for mobile devices (especially smartphones, tablets, wearable technology, and so on). Regarding this proliferation and high demand, the massive adoption of mobile devices (MDs) has led to an exponential increase in network latency; the heavy demand for cloud servers causes the degradation of data traffic, which considerably impacts the real-time communication and computing aspects of mobile devices. As a result, mobile edge computing (MEC), an efficient framework capable of enhancing processing, optimizing energy usage, and offloading computation tasks, is considered a promising solution. In current research, numerous models have been implemented to achieve resource allocation and task offloading. However, these techniques are ineffective due to privacy issues and a lack of sufficient resources. Hence, this study proposes secure task offloading and resource allocation strategies in mobile devices using the Probit Mish–Gated Recurrent Unit (PM-GRU) and Entropic Linear Interpolation-Serval Optimization Algorithm (ELI-SOA). Primarily, the tasks to be offloaded and their attributes are gathered from mobile users and passed to a local computing model to identify the edge server. Here, the task attributes and the server attributes are compared with a cache table using the Sorensen–Dice coefficient. If the attributes match, then details about the appropriate edge server are produced. If the attributes do not match, then they are inputted into a global scheme that analyzes the attributes and predicts the edge server based on the Probit Mish-Gated Recurrent Unit (PM-GRU). Then, the server information is preserved and updated in the cache table in the local scheme. Further, the attributes, along with the predicted edge server, are inputted into a system for privacy-preserving smart contract creation by using Exponential Earth Mover’s Distance Matrix-Based K-Anonymity (EEMDM-KA) to develop a secure smart contract. Subsequently, the traffic attributes in the smart contract are extracted, and the request load is balanced by using HCD-KM. Load-balanced requests are assigned to the edge server, and the optimal resources are allocated in the cloud server by using the Entropic Linear Interpolation-Serval Optimization Algorithm (ELI-SOA). Finally, the created smart contract is hashed based on KECCAK-512 and stored in the blockchain. With a high accuracy of 99.84%, the evaluation results showed that the proposed approach framework performed better than those used in previous efforts.

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Performance evaluation and optimization of a task offloading strategy on the mobile edge computing with edge heterogeneity

Cited by 22 publications

References 31 publications

Minimize average tasks processing time in satellite mobile edge computing systems via a deep reinforcement learning method

Minimize average tasks processing time in satellite mobile edge computing systems via a deep reinforcement learning method

Distributed Location-Aware Task Offloading in Multi-UAVs Enabled Edge Computing

Secure Task Offloading and Resource Allocation Strategies in Mobile Applications Using Probit Mish-Gated Recurrent Unit and an Enhanced-Searching-Based Serval Optimization Algorithm

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