scholarly journals Computational Resource Allocation Strategy in a Public Blockchain Supported by Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Sujie Shao ◽  
Weichao Gong ◽  
Shaoyong Guo ◽  
Xuesong Qiu
Keyword(s):  
Resource Allocation ◽  
Resource Constraints ◽  
Edge Computing ◽  
Market Model ◽  
Distributed Data ◽  
Computational Resource ◽  
Computing Power ◽  
Network Applications ◽  
Computing Services ◽  
Computationally Intensive

Blockchain, as an emerging distributed data management technology, has attracted extensive attention in recent years. In particular, a public blockchain network can ensure data security by addressing computationally intensive cryptographic tasks. Therefore, for node devices, sufficient computing power is required. However, mobile devices with limited computing power do not meet the conditions required by public blockchain network applications (OZEX, CoininAsia, BitRewards, etc.). To copy with the mentioned problems, nodes can offload computing tasks to edge computing services with low latency. This paper mainly focuses on the trade between edge computing providers (ECP) and nodes. We build a computational resource market model based on auction. Meanwhile, we propose two strategies to deal with two methods of offloading to achieve higher system profit. We also prove that the proposed strategy has individual rationality, authenticity under resource constraints. The simulation results have significance for administrators of a public blockchain network to improve the efficiency of computing resource allocation.

scholarly journals Joint Congestion Control and Resource Allocation for Delay-Aware Tasks in Mobile Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Shichao Li ◽  
Qiuyun Wang ◽  
Yunfeng Wang ◽  
Jianli Xie ◽  
Cuiran Li ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Congestion Control ◽  
Resource Constraints ◽  
Edge Computing ◽  
Subcarrier Allocation ◽  
Mobile Edge Computing ◽  
Time Sharing ◽  
Joint Power ◽  
Primal Problem ◽  
Execution Delay

Recently, in order to extend the computation capability of smart mobile devices (SMDs) and reduce the task execution delay, mobile edge computing (MEC) has attracted considerable attention. In this paper, a stochastic optimization problem is formulated to maximize the system utility and ensure the queue stability, which subjects to the power, subcarrier, SMDs, and MEC server computation resource constraints by jointly optimizing congestion control and resource allocation. With the help of the Lyapunov optimization method, the primal problem is transformed into five subproblems including the system utility maximization subproblem, SMD congestion control subproblem, SMD computation resource allocation subproblem, joint power and subcarrier allocation subproblem, and MEC server scheduling subproblem. Since the first three subproblems are all single variable problems, the solutions can be obtained directly. The joint power and subcarrier allocation subproblem can be efficiently solved by utilizing alternating and time-sharing methods. For the MEC server scheduling subproblem, an efficient algorithm is proposed to solve it. By solving the five subproblems at each slot, we propose a delay-aware task congestion control and resource allocation (DTCCRA) algorithm to solve the primal problem. Theoretical analysis shows that the proposed DTCCRA algorithm can achieve the system utility and execution delay trade-off. Compared with the intelligent heuristic (IH) algorithm, when the control parameter V increases from 10 6 to 10 7 , the total backlogs are decreased by 5.03% and the system utility is increased by 3.9% on average for the extensive performance by using the proposed DTCCRA algorithm.

scholarly journals A resource management survey for mission-critical and time-critical applications in multiaccess edge computing

2021 ◽  
Vol 2 (2) ◽  
pp. 61-80
Author(s):  
Nina Santi ◽  
Nathalie Mitton
Keyword(s):  
Resource Allocation ◽  
Resource Management ◽  
Edge Computing ◽  
Acute Management ◽  
Military Operations ◽  
Computing Power ◽  
Management Schemes ◽  
Mission Critical ◽  
Time Critical ◽  
Resource Allocation Schemes

Multiaccess Edge Computing (MEC) brings additional computing power in proximity of mobile users, reducing latency, saving energy and alleviating the network's bandwidth. This proximity is beneficial, especially for mission-critical applications where each second matters, such as disaster management or military operations. Moreover, it enables MEC resources embedded on mobile units like drones or robots that are flexible to be deployed for mission-critical applications. However, the MEC servers are capacity-limited and thus need an acute management of their resources. The mobile resources also need a smart deployment scheme to deliver their services efficiently. In this survey, we review mission-critical applications, resource allocation and deployment of mobile resources techniques in the context of the MEC. First, we introduce the technical specifics and uses of MEC in mission-critical applications to highlight their needs and requirements. Then, we discuss the resource allocation schemes for MEC and assess their fit depending on the application needs. In the same fashion, we finally review the deployment of MEC mobile resources. We believe this work could serve as a helping hand to design efficient MEC resource management schemes that respond to challenging environments such as mission-critical applications.

scholarly journals Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 58 ◽  
Author(s):  
Xuan-Qui Pham ◽  
Tien-Dung Nguyen ◽  
VanDung Nguyen ◽  
Eui-Nam Huh
Keyword(s):  
Resource Allocation ◽  
High Reliability ◽  
Resource Limitation ◽  
Optimization Techniques ◽  
Edge Computing ◽  
Mixed Integer ◽  
Node Selection ◽  
Computing Services ◽  
Iot Devices ◽  
Multi Access

The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead.

scholarly journals Hierarchical Mobile Edge Computing Architecture Based on Context Awareness

2018 ◽  
Vol 8 (7) ◽  
pp. 1160 ◽  
Author(s):  
Juyong Lee ◽  
Jihoon Lee
Keyword(s):  
Mobile Network ◽  
Content Delivery ◽  
Edge Computing ◽  
Multimedia Streaming ◽  
Mobile Edge Computing ◽  
Computing Power ◽  
Computing Services ◽  
Recent Developments ◽  
Computing Performance ◽  
Fast Access

Due to the recent developments in mobile network technology and the supply of mobile devices, services that require high computing power and fast access speed, such as machine learning and multimedia streaming, are attracting attention. Mobile Edge Computing (MEC) has emerged. MEC allows servers to be located close to users to efficiently handle these services and provides users with ultra-low latency content delivery and powerful computing services. However, there has been a lack of research into the architecture required to efficiently use the computing power and resources of MEC. So, this paper proposes hierarchical MEC architecture in which MEC servers (MECS) are arranged in a hierarchical scheme to provide users with rapid content delivery, high computing performance, and efficient use of server resources.

scholarly journals Joint Multi-domain Resource Allocation and Trajectory Optimization in UAV-assisted M-IoT Networks

2022 ◽  
Author(s):  
Liping Qian
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Trajectory Optimization ◽  
Mobile Internet ◽  
Edge Computing ◽  
Computation Offloading ◽  
Lagrangian Multiplier ◽  
Total Energy Consumption ◽  
Computing Services ◽  
Lagrangian Multiplier Method

<div>The integration of Maritime Internet of Things (M-IoT) technology and unmanned aerial/surface vehicles (UAVs/USVs) has been emerging as a promising navigational information technique in intelligent ocean systems. With the unprecedented increase of computation-intensive yet latency sensitive marine mobile Internet services, mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) have been envisioned as promising approaches to providing with the low-latency as well as reliable computing services and ultra-dense connectivity. In this paper, we investigate the energy consumption minimization based energy-efficient MEC via cooperative NOMA for the UAV-assisted M-IoT networks. We consider that USVs offload their computation-workload to the UAV equipped with the edge-computing server subject to the UAV mobility. To improve the energy efficiency of offloading transmission and workload computation, we focus on minimizing the total energy consumption by jointly optimizing the USVs’ offloaded workload, transmit power, computation resource allocation as well as the UAV trajectory subject to the USVs’ latency requirements. Despite the nature of mixed discrete and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a two-layered algorithm for solving it efficiently. Specifically, the top-layered algorithm is proposed to solve the problem of optimizing the UAV trajectory based on the idea of Deep Reinforcement Learning (DRL), and the underlying algorithm is proposed to optimize the underlying multi-domain resource allocation problem based on the idea of the Lagrangian multiplier method. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of NOMA-enabled computation offloading in terms of overall energy consumption.</div>

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