scholarly journals Method of Resource Estimation Based on QoS in Edge Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Guangshun Li ◽  
Jianrong Song ◽  
Junhua Wu ◽  
Jiping Wang
Keyword(s):  
Markov Chain ◽  
Prediction Method ◽  
Edge Computing ◽  
Resource Estimation ◽  
Matching Method ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Delay Sensitive ◽  
Penalty Factor

With the development of Internet of Things, the number of network devices is increasing, and the cloud data center load increases; some delay-sensitive services cannot be responded to timely, which results in a decreased quality of service (QoS). In this paper, we propose a method of resource estimation based on QoS in edge computing to solve this problem. Firstly, the resources are classified and matched according to the weighted Euclidean distance similarity. The penalty factor and Grey incidence matrix are introduced to correct the similarity matching function. Then, we use regression-Markov chain prediction method to analyze the change of the load state of the candidate resources and select the suitable resource. Finally, we analyze the precision and recall of the matching method through simulation experiment, validate the effectiveness of the matching method, and prove that regression-Markov chain prediction method can improve the prediction accuracy.

scholarly journals Cooperative Edge Computing Task Offloading Strategy for Urban Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Bo Wang ◽  
Mingchu Li
Keyword(s):  
Internet Of Things ◽  
Stackelberg Game ◽  
Edge Computing ◽  
Smart Devices ◽  
Smart Device ◽  
Cloud Data Center ◽  
Two Stage ◽  
Cloud Data ◽  
Internet Of Things Technology ◽  
Task Offloading

With the continuous progress of edge computing technology and the development of the Internet of Things technology, scenarios such as smart transportation, smart home, and smart medical care enable people to enjoy the smart era’s convenience. Simultaneously, with the addition of many smart devices, a large number of tasks are submitted to the edge server, making the edge server unable to meet the needs of completing tasks submitted by the smart device. Besides, if the task is submitted to the remote cloud data center, it increases the user’s additional delay and cost. Therefore, it is necessary to improve the task offloading strategy and resource allocation scheme to solve these problems. This paper first proposes a new task offloading mechanism and then proposes a two-stage Stackelberg game model to solve each participant’s interaction problem in the task offloading mechanism and ensure the maximization of their respective interests. Finally, a theoretical analysis proves the equilibrium of the two-stage Stackelberg game. Experiments are used to prove the effectiveness of the proposed mechanism. Comparative experimental results show that the proposed model can achieve better results regarding delay and energy consumption.

Energy and quality of service-aware virtual machine consolidation in a cloud data center

2020 ◽  
Vol 76 (11) ◽  
pp. 9095-9126 ◽  
Author(s):  
Anurina Tarafdar ◽  
Mukta Debnath ◽  
Sunirmal Khatua ◽  
Rajib K. Das
Keyword(s):  
Quality Of Service ◽  
Virtual Machine ◽  
Data Center ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Virtual Machine Consolidation

scholarly journals Micro Operator Design Pattern in 5G SDN/NFV Network

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Chia-Wei Tseng ◽  
Yu-Kai Huang ◽  
Fan-Hsun Tseng ◽  
Yao-Tsung Yang ◽  
Chien-Chang Liu ◽  
...  
Keyword(s):  
Data Center ◽  
Design Pattern ◽  
Transmission Rate ◽  
Development Trend ◽  
Edge Computing ◽  
Network Services ◽  
Network Function Virtualization ◽  
Cloud Data Center ◽  
Internet Service ◽  
Cloud Data

The trend of 5G mobile networks is increasing with the number of users and the transmission rate. Many operators are turning to small cell and indoor coverage of telecom network service. With the emerging Software Defined Networking and Network Function Virtualization technologies, Internet Service Provider is able to deploy their networks more flexibly and dynamically. In addition to the change of the wireless mobile network deployment model, it also drives the development trend of the Micro Operator (μO). Telecom operators can provide regional network services through public buildings, shopping malls, or industrial sites. In addition, localized network services are provided and bandwidth consumption is reduced. The distributed architecture ofμO tackles computing requirements for applications, data, and services from cloud data center to edge network devices or to the micro data center ofμO. The service model ofμO is capable of reducing network latency in response to the low-latency applications for future 5G edge computing environment. This paper addresses the design pattern of 5G micro operator and proposes a Decision Tree Based Flow Redirection (DTBFR) mechanism to redirect the traffic flows to neighbor service nodes. The DTBFR mechanism allows differentμOs to share network resources and speed up the development of edge computing in the future.

A Self-Adaptive Prediction Algorithm for Cloud Workloads

2015 ◽  
Vol 7 (2) ◽  
pp. 65-76 ◽  
Author(s):  
Li Mao ◽  
Deyu Qi ◽  
Weiwei Lin ◽  
Chaoyue Zhu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Dynamic Scheduling ◽  
Main Idea ◽  
Prediction Method ◽  
Prediction Algorithm ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Adaptive Prediction ◽  
Self Adaptive

It is difficult to analyze the workload in complex cloud computing environments with a single prediction algorithm as each algorithm has its own shortcomings. A self-adaptive prediction algorithm combining the advantages of linear regression (LR) and a BP neural network to predict workloads in clouds is proposed in this paper. The main idea of the self-adaptive prediction algorithm is to choose the better prediction method of the future workload. Some experiments of prediction algorithms are conducted with workloads on the public cloud servers. The experimental results show that the proposed algorithm has a relatively high accuracy on the workload predictions compared with the BP neural network and LR. Furthermore, in order to use the proposed algorithm in a cloud data center, a dynamic scheduling architecture of cloud resources is designed to improve resource utilization and reduce energy consumption.

Preference Based Resource Allocation In Cloud Data Center

2018 ◽  
Vol 6 (2) ◽  
pp. 287-292
Author(s):  
M.R. Dave ◽  
◽  
H.B. Patel ◽  
B. Shrimali ◽  
◽  
...  
Keyword(s):  
Resource Allocation ◽  
Data Center ◽  
Cloud Data Center ◽  
Cloud Data

scholarly journals Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2628
Author(s):  
Mengxing Huang ◽  
Qianhao Zhai ◽  
Yinjie Chen ◽  
Siling Feng ◽  
Feng Shu
Keyword(s):  
Optimization Algorithm ◽  
Edge Computing ◽  
Solution Set ◽  
Whale Optimization Algorithm ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Multi Objective ◽  
Whale Optimization ◽  
Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

Sign in / Sign up

Export Citation Format

Share Document