scholarly journals Blockchain-Enabled Task Offloading and Resource Allocation in Fog Computing Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiaoge Huang ◽  
Xuesong Deng ◽  
Chengchao Liang ◽  
Weiwei Fan
Keyword(s):  
Resource Allocation ◽  
Stackelberg Game ◽  
Fog Computing ◽  
User Privacy ◽  
Total Energy Consumption ◽  
Total Delay ◽  
Resource Allocation Algorithm ◽  
Network Utility ◽  
Optimal Resource ◽  
Task Offloading

To address the data security and user privacy issues in the task offloading process and resource allocation of the fog computing network, a blockchain-enabled fog computing network task offloading model is proposed in this paper. Furthermore, to reduce the network utility which is defined as the total energy consumption of the fog computing network and the total delay of the blockchain network, a blockchain-enabled fog computing network task offloading and resource allocation algorithm (TR-BFCN) is proposed to jointly optimize the task offloading decision and resource allocation. Finally, the original nonconvex optimization problem is converted into two suboptimization problems, namely, task offloading decisions and computational resource allocations. Moreover, a two-stage Stackelberg game model is designed to obtain the optimal amount of purchased resource and the optimal resource pricing. Simulation results show that the proposed TR-BFCN algorithm can effectively reduce the network utility compared with other algorithms.

scholarly journals ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 660
Author(s):  
Marios Avgeris ◽  
Dimitrios Spatharakis ◽  
Dimitrios Dechouniotis ◽  
Aris Leivadeas ◽  
Vasileios Karyotis ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Mobile Applications ◽  
Cloud Infrastructure ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Optimal Resource ◽  
Prediction Mechanism ◽  
The Cost ◽  
Different Characteristics ◽  
Task Offloading

Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one.

An Optimal Resource Allocation Algorithm in Cloud Computing Environment

2015 ◽  
Vol 733 ◽  
pp. 779-783 ◽  
Author(s):  
Lu Dai ◽  
Jian Hua Li
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Optimization ◽  
Scientific Basis ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Optimal Resource ◽  
And Performance

Resource allocation is a key technology of cloud computing. At present, the most of studies on resource allocation mainly focus on improving the overall performance by balancing the load of data center. This paper will design the experimental platform of resource allocation algorithm, energy optimization and performance analysis, obtain original achievements in scientific research ,for the resource allocation method based on immune algorithm and energy optimization in cloud computing to provide innovative ideas and scientific basis. This research has important significance for further study on resource allocation and energy optimization in cloud computing environment.

scholarly journals Resource Allocation for Edge Computing without Using Cloud Center in Smart Home Environment: A Pricing Approach

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6545
Author(s):  
Huan Liu ◽  
Shiyong Li ◽  
Wei Sun
Keyword(s):  
Resource Allocation ◽  
Home Environment ◽  
Smart Home ◽  
Smart Homes ◽  
Research Area ◽  
Edge Computing ◽  
Allocation Model ◽  
Resource Allocation Algorithm ◽  
Optimal Resource ◽  
Smart Home Environment

Recently, more and more smart homes have become one of important parts of home infrastructure. However, most of the smart home applications are not interconnected and remain isolated. They use the cloud center as the control platform, which increases the risk of link congestion and data security. Thus, in the future, smart homes based on edge computing without using cloud center become an important research area. In this paper, we assume that all applications in a smart home environment are composed of edge nodes and users. In order to maximize the utility of users, we assume that all users and edge nodes are placed in a market and formulate a pricing resource allocation model with utility maximization. We apply the Lagrangian method to analyze the model, so an edge node (provider in the market) allocates its resources to a user (customer in the market) based on the prices of resources and the utility related to the preference of users. To obtain the optimal resource allocation, we propose a pricing-based resource allocation algorithm by using low-pass filtering scheme and conform that the proposed algorithm can achieve an optimum within reasonable convergence times through some numerical examples.

scholarly journals Multi-task offloading scheme for UAV-Enabled Fog Computing networks

2020 ◽  
Author(s):  
Xujie LI ◽  
Lingjie Zhou ◽  
Ying Sun
Keyword(s):  
Optimization Problem ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
System Model ◽  
Total Delay ◽  
Single Node ◽  
Fireworks Algorithm ◽  
Multiple Tasks ◽  
Random Algorithm ◽  
Task Offloading

Abstract In UAV-enabled fog Computing networks, how to efficiently offload multiple tasks to the computing nodes is a challenge combinatorial optimization problem. In this paper, in order to optimize the total delay for the UVA-Enabled Fog Computing networks, a simple scheduling algorithm and a multi-task offloading scheme based on fireworks algorithm (FWA) are proposed. First, the system model of multiple tasks offloading in UVA-Enabled fog computing networks is described in detail. Then, a simple scheduling algorithm is proposed to optimize the delay of the tasks allocated to a single node. Based on the scheduling algorithm, a multi-task offloading scheme for all tasks and all computing nodes is presented. Finally, simulation results show that the performance of proposed scheduling algorithm and offloading strategy outperform than that of genetic algorithm and random algorithm. This result can provide an effective optimization for multi-task offloading in UVA-Enabled Fog Computing networks.

scholarly journals Resource Allocation Algorithms for Indoor D2D Communication

2020 ◽  
Vol 9 (5) ◽  
pp. 1292-1296
Keyword(s):  
Resource Allocation ◽  
Base Station ◽  
D2d Communication ◽  
Throughput Maximization ◽  
Resource Allocation Algorithm ◽  
Main Challenge ◽  
Device To Device ◽  
Efficient Resource ◽  
Optimal Resource ◽  
Allocation Algorithms

Device to Device (D2D) communication in cellular networks is defined as direct communication between two mobile users without traversing the data through the base station (BS). Indoor D2D communication refers to transmission between two users within a building or in a closed space. Resource allocation is a plan for using available resources efficiently and the resources are allocated for optimal functioning of the D2D network. The algorithms for optimizing D2D network is characterized by the parameters like matching network, noise, throughput maximization and few more. In this work, our aim is to develop resource allocation algorithms for indoor D2D communication. An efficient resource allocation algorithm for device to device communication and a suitable frequency allocation technique in order to avoid call blockage should be designed. The main challenge in this work is to allocate resources to D2D users without affecting cellular users efficiency. These optimal resource allocation works efficiently and also adapt to time and location variation. The process involved in each algorithm is elaborated.

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