A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing

Ali, Zaiwar; Khaf, Sadia; Abba, Ziaul Haq; Abbas, Ghulam; Jiao, Lei; Irshad, Amna; Kwak, Kyung Sup; Bilal, Muhammad

doi:10.32604/cmc.2020.013743

Cited by 8 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the overall performance and task requirements of devices were not considered comprehensively in the allocation process, and the allocation efficiency still needs to be improved. Reference [ 20 ] comprehensively considered factors such as CPU, hard disk space, and required time and distance and proposed a comprehensive utility function for MEC resource allocation to achieve the optimal allocation of resources in MEC and cloud computing. However, this function considers many factors, which will seriously affect the efficiency of allocation in real applications.…”

Section: Related Workmentioning

confidence: 99%

Task Offloading and Resource Allocation Strategy Based on Deep Learning for Mobile Edge Computing

Yu¹,

Xu²,

Zhou³

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

For the problems of unreasonable computation offloading and uneven resource allocation in Mobile Edge Computing (MEC), this paper proposes a task offloading and resource allocation strategy based on deep learning for MEC. Firstly, in the multiuser multiserver MEC environment, a new objective function is designed by combining calculation model and communication model in the system, which can shorten the completion time of all computing tasks and minimize the energy consumption of all terminal devices under delay constraints. Then, based on the multiagent reinforcement learning system, system benefits and resource consumption are designed as rewards and losses in deep reinforcement learning. Dueling-DQN algorithm is used to solve the system problem model for obtaining resource allocation method with the highest reward. Finally, the experimental results show that when the learning rate is 0.001 and discount factor is 0.90, the performance of proposed strategy is the best. Furthermore, the proportions of reducing energy consumption and shortening completion time are 52.18% and 34.72%, respectively, which are better than other comparison strategies in terms of calculation amount and energy saving.

show abstract

Section: Related Workmentioning

confidence: 99%

Task Offloading and Resource Allocation Strategy Based on Deep Learning for Mobile Edge Computing

Yu¹,

Xu²,

Zhou³

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…In view of this, this paper focuses on the joint optimization of route planning and resource allocation to improve the quality of automatic driving of CVs in transportation system. Our work is different from previous related work in two aspects: (1) We use the collaborative route planning between CVs to actively balance the resource load of the edge cloud and achieve cross-domain load balancing between the traffic flow and the edge cloud resource domains to improve resource utilization. (2) Due to the mutual interference of CVs competing for the same edge cloud resource, we consider dynamically adjusting the edge cloud resource allocation strategy to adapt to the change of service demand of collaborative route planning of CVs, balancing the service delay of MEC and the travel time of CVs.…”

Section: Introductionmentioning

confidence: 99%

“…processing some data-intensive tasks locally with their limited perceptual range and computational power, making it difficult to optimize traffic safety and efficiency. To address the above challenges, multi-access edge computing (MEC) has gained more attention for researchers [1][2][3]. MEC enables CVs to process some intensive and delay-critical computation tasks for emerging vehicular applications through offloading tasks to the edge servers (ESs) rather than executing them locally, the system performance will be enormously improved.…”

mentioning

confidence: 99%

Cross-domain coordination of resource allocation and route planning for the edge computing-enabled multi-connected vehicles

Xue

Guo²,

Li³

et al. 2023

J Cloud Comp

View full text Add to dashboard Cite

Multi-access edge computing (MEC) has unique interests in processing intensive and delay-critical computation tasks for vehicular application through computation offloading. However, due to the spatial inhomogeneity and dynamic mobility of connected vehicles (CVs), the edge servers (ESs) must dynamically adjust their resource allocation schemes to effectively provide computation offloading services for CVs. In this case, we propose a MEC framework supporting the collaboration of CVs, and incorporate digital twins (DTs) into wireless network to mitigate the unreliable long-distance communication between CVs and ESs. To solve the contradiction between the task change requirements of CVs and ES resources, we proactively balance the computation resources load of ESs by appropriately cooperative route planning of CVs, and achieve cross-domain load balancing between traffic flow and edge cloud resources domains. Furthermore, we jointly formulate route planning and resource allocation to balance the travel and service time delay by considering the mobility of CVs, distributed resources of ESs and the deadline sensitive vehicular tasks comprehensively. Besides, considering the coupled relationship between route planning and resource allocation, an alternating optimization algorithm is proposed to solve the formulated problem. we decompose it into two sub-problems. Firstly, a reinforcement learning method is used to optimize the route planning of CVs with fixed resource allocation. Then, an online learning and iterative algorithm is used to optimize the resource allocation strategy of edge cloud with fixed route selection. In order to demonstrate that our suggested scheme is more effective than other comparison schemes, a comprehensive series of experiments are conducted.

show abstract

“…Advantageously, MEC enables the processing of data closer to their origin [4], avoiding the bottleneck that is encountered in cloud computing. This decentralization yields many advantages, such as reduction of network latency, increase of the security of services, and energy saving [5]. MEC works well for IoT devices with limited levels of mobility.…”

Section: Introductionmentioning

confidence: 99%

Efficient Authentication Scheme for UAV-Assisted Mobile Edge Computing

Alhassan¹,

Khan²

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Preserving privacy is imperative in the new unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture to ensure that sensitive information is protected and kept secure throughout the communication. Simultaneously, efficiency must be considered while developing such a privacy-preserving scheme because the devices involved in these architectures are resource constrained. This study proposes a lightweight and efficient authentication scheme for the UAV-assisted MEC environment. The proposed scheme is a hardware-based password-less authentication mechanism that is based on the fact that temporal and memory-related efficiency can be significantly improved while maintaining the data security by adopting a hardwarebased solution with a simple implementation. The proposed scheme works in four stages: system initialization, EU registration, EU authentication, and session establishment. It is implemented as a single hardware chip comprising registers and XOR gates, and it can run the entire process in one clock cycle. Consequently, the proposed scheme has significantly higher efficiency in terms of runtime and memory consumption compared to other prevalent methods in the area. Simulations are conducted to evaluate the proposed authentication algorithm. The results show that the scheme has an average execution time of 0.986 ms and consumes average memory of 34 KB. The hardware execution time is approximately 0.39 ns, which is a significantly less than the prevalent schemes, whose execution times range in milliseconds. Furthermore, the security of the proposed scheme is examined, and it is resistant to brute-force attacks. Around 1.158 × 10 77 trials are required to overcome the system's security, which is not feasible using fastest available processors.

show abstract

A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing

Cited by 8 publications

References 23 publications

Task Offloading and Resource Allocation Strategy Based on Deep Learning for Mobile Edge Computing

Task Offloading and Resource Allocation Strategy Based on Deep Learning for Mobile Edge Computing

Cross-domain coordination of resource allocation and route planning for the edge computing-enabled multi-connected vehicles

Efficient Authentication Scheme for UAV-Assisted Mobile Edge Computing

Contact Info

Product

Resources

About