scholarly journals Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Guang-Shun Li ◽  
Ying Zhang ◽  
Mao-Li Wang ◽  
Jun-Hua Wu ◽  
Qing-Yan Lin ◽  
...  
Keyword(s):  
Resource Management ◽  
Response Time ◽  
Stackelberg Game ◽  
Edge Computing ◽  
Iteration Algorithm ◽  
Data Resource ◽  
Management Framework ◽  
Computing Services ◽  
Cloud Computing Services ◽  
Promising Solution

With the emergence and development of the Internet of Vehicles (IoV), quick response time and ultralow delay are required. Cloud computing services are unfavorable for reducing delay and response time. Mobile edge computing (MEC) is a promising solution to address this problem. In this paper, we combined MEC and IoV to propose a specific vehicle edge resource management framework, which consists of fog nodes (FNs), data service agents (DSAs), and cars. A dynamic service area partitioning algorithm is designed to balance the load of DSA and improve the quality of service. A resource allocation framework based on the Stackelberg game model is proposed to analyze the pricing problem of FNs and the data resource strategy of DSA with a distributed iteration algorithm. The simulation results show that the proposed framework can ensure the allocation efficiency of FN resources among the cars. The framework achieves the optimal strategy of the participants and subgame perfect Nash equilibrium.

scholarly journals A Thread-Oriented Memory Resource Management Framework for Mobile Edge Computing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 45881-45890 ◽  
Author(s):  
Zongwei Zhu ◽  
Fan Wu ◽  
Jing Cao ◽  
Xi Li ◽  
Gangyong Jia
Keyword(s):  
Resource Management ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Management Framework ◽  
Memory Resource

scholarly journals Markov Approximation for Task Offloading and Computation Scaling in Mobile Edge Computing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Wenchen Zhou ◽  
Weiwei Fang ◽  
Yangyang Li ◽  
Bo Yuan ◽  
Yiming Li ◽  
...  
Keyword(s):  
Optimal Solution ◽  
Task Assignment ◽  
Edge Computing ◽  
Computation Offloading ◽  
Approximation Technique ◽  
Markov Approximation ◽  
Mobile Edge Computing ◽  
Computing Services ◽  
Cloud Computing Services ◽  
Task Offloading

Mobile edge computing (MEC) provides cloud-computing services for mobile devices to offload intensive computation tasks to the physically proximal MEC servers. In this paper, we consider a multiserver system where a single mobile device asks for computation offloading to multiple nearby servers. We formulate this offloading problem as the joint optimization of computation task assignment and CPU frequency scaling, in order to minimize a tradeoff between task execution time and mobile energy consumption. The resulting optimization problem is combinatorial in essence, and the optimal solution generally can only be obtained by exhaustive search with extremely high complexity. Leveraging the Markov approximation technique, we propose a light-weight algorithm that can provably converge to a bounded near-optimal solution. The simulation results show that the proposed algorithm is able to generate near-optimal solutions and outperform other benchmark algorithms.

Challenges in the Cloud Computing Model of Resource Management Issues Such as Service Level Agreements and Services Models

2021 ◽  
Vol 9 (3) ◽  
pp. 42-51
Author(s):  
Mohammed Tuays Almuqati
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Service Level ◽  
Service Level Agreements ◽  
It Industry ◽  
Computing Model ◽  
Computing Services ◽  
Cloud Computing Services ◽  
Management Issues ◽  
New Infrastructure

Cloud computing has recently emerged as a new model for hosting and delivering services over the internet. Cloud computing has many advantages, such as the ability to increase capacity or add capabilities without the need to invest in new infrastructure. It can also fulfil technological requirements in a fast and automated manner. In recent years, cloud computing has changed the IT industry; in fact, it is one of the industry's fastest growing phenomena. However, as more information about people and businesses becomes available in the cloud, concerns about the safety of this environment will increase. In addition, some challenges to the use of this service exist. This paper presents the results of a survey about cloud computing and outlines the main concepts of the technology along with examples of appropriate usage. It also discusses resource management issues such as service level agreements and highlights the challenges faced by users when choosing cloud computing services.

scholarly journals A Survey on Secure Deployment of Mobile Services in Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Mengmeng Cui ◽  
Yiming Fei ◽  
Yin Liu
Keyword(s):  
Autonomous Vehicles ◽  
Mobile Network ◽  
Edge Computing ◽  
Internet Of Vehicles ◽  
It Service ◽  
Computing Services ◽  
Service Deployment ◽  
Cloud Computing Services ◽  
Complete Survey ◽  
Research Studies

Mobile edge computing (MEC) is an emerging technology that is recognized as a key to 5G networks. Because MEC provides an IT service environment and cloud-computing services at the edge of the mobile network, researchers hope to use MEC for secure service deployment, such as Internet of vehicles, Internet of Things (IoT), and autonomous vehicles. Because of the characteristics of MEC which do not have terminal servers, it tends to be deployed on the edge of networks. However, there are few related works that systematically introduce the deployment of MEC. Also, secure service deployment frameworks with MEC are even rare. For this reason, we have conducted a comprehensive and concrete survey of recent research studies on secure deployment. Although numerous research studies and experiments about MEC service deployment have been conducted, there are few systematic summaries that conclude basic concepts and development strategies about secure service deployment of commercial MEC. To make up for the gap, a detailed and complete survey about relative achievements is presented.

scholarly journals Availability Modeling of Computing Systems with Virtual Architectures

2021 ◽  
Author(s):  
Ricardo Paharsingh
Keyword(s):  
Response Time ◽  
Virtual Machines ◽  
High Availability ◽  
Software Systems ◽  
Cloud Provider ◽  
Computing Systems ◽  
Computing Services ◽  
Processing Power ◽  
Cloud Computing Services ◽  
Availability Modeling

Cloud computing services are built on the premise of high availability. These services are sold to customers who are expecting a reduced cost particularly in the area of failures and maintenance. At the Infrastructure as a Service (IaaS) layer resources is sold to customers as virtual machines (VMs) with CPU and memory specifications. Both these resources are not necessarily guaranteed. This is because virtual machines can share the same hardware resources. If resources aren't allocated properly, one virtual machine for example, may use up too much CPU power reducing the processing power available to other virtual machines. This can result in response time failures. In this research a framework is developed that integrates hardware, software and response time failures. Response time failures occur when a request is made to a server and does not complete on time. The framework allows the cloud purchaser to test the system under stressed conditions, allocating more or less virtual machines to determine the availability of the system. The framework also allows the cloud provider to separately evaluate the availability of the hardware and other software systems.

scholarly journals Analisis Perbandingan Performa Virtualisasi Server Sebagai Basis Layanan Infrastructure As A Service Pada Jaringan Cloud

2019 ◽  
Vol 19 (1) ◽  
pp. 1-8
Author(s):  
Deni Marta ◽  
M. Angga Eka Putra ◽  
Guntoro Barovih
Keyword(s):  
Cloud Computing ◽  
Response Time ◽  
Resource Utilization ◽  
Service Providers ◽  
Performance Measurements ◽  
Infrastructure As A Service ◽  
Server Virtualization ◽  
Computing Services ◽  
Cloud Computing Service ◽  
Cloud Computing Services

Cloud Computing provides convenience and comfort to every service. Infrastructure as a Service is one of the cloud computing services that is a choice of several users, it is very important to know the performance of each existing platform in order to get the maximum result according to our needs. In this study, testing 3 platforms of cloud computing service providers are VMWare ESXi, XenServer, and Proxmox, using action research methods. From the results of performance measurements, then analyzed and compared with the minimum and maximum limits. The tested indicators are response time, throughput, and resource-utilization as a comparison of server virtualization performance implementations. In the resource utilization testing when the condition of installing an operating system, CPU usage on the Proxmox platform shows the lowest usage of 10.72%, and the lowest RAM usage of 53.32% also on the Proxmox platform. In the resource test utilization when idle state shows the lowest usage of 5.78% on the Proxmox platform, while the lowest RAM usage is 57.25% on the VMWare ESXi platform. The mean resource utilization tests indicate that the Proxmox platform is better. At the throughput test when the upload measurement of the XenServer platform is better 1.37 MB/s, while the throughput test when the download of the VMWare ESXi platform is better than 1.39 MB/s. On response time testing shows the platform VMWare ESXi as the fastest is 0.180 sec.

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