Cost-Efficient and Quality of Experience-aware Player Request Scheduling and Rendering Server Allocation for Edge Computing Assisted Multiplayer Cloud Gaming

2021 ◽  
pp. 1-1
Author(s):  
Yongqiang Gao ◽  
Chaoyu Zhang ◽  
Zhulong Xie ◽  
Zhengwei Qi ◽  
Jiantao Zhou
Keyword(s):  
Quality Of Experience ◽  
Edge Computing ◽  
Server Allocation ◽  
Cost Efficient

Mobile Edge Computing

2019 ◽  
Vol 10 (3) ◽  
pp. 23-46
Author(s):  
Michael P. J. Mahenge ◽  
Chunlin Li ◽  
Camilius A. Sanga
Keyword(s):  
Mobile Network ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Efficient Design ◽  
Potential Gain ◽  
Backhaul Link ◽  
Task Processing ◽  
Cost Efficient ◽  
The Cost

The overwhelming growth of resource-intensive and latency-sensitive applications trigger challenges in legacy systems of mobile cloud computing (MCC) architecture. Such challenges include congestion in the backhaul link, high latency, inefficient bandwidth usage, insufficient performance, and quality of service (QoS) metrics. The objective of this study was to find out the cost-efficient design that maximizes resource utilization at the edge of the mobile network which in return minimizes the task processing costs. Thus, this study proposes a cooperative mobile edge computing (coopMEC) to address the aforementioned challenges in MCC architecture. Also, in the proposed approach, resource-intensive jobs can be unloaded from users' equipment to MEC layer which is potential for enhancing performance in resource-constrained mobile devices. The simulation results demonstrate the potential gain from the proposed approach in terms of reducing response delay and resource consumption. This, in turn, improves performance, QoS, and guarantees cost-effectiveness in meeting users' demands.

scholarly journals In-Network Caching and Edge Computing-Based Live Broadcasting Optimization for Football Competitions

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Zhigang Li
Keyword(s):  
Video Compression ◽  
Video Transmission ◽  
Quality Of Experience ◽  
Direct Relationship ◽  
Edge Computing ◽  
Convolution Kernel ◽  
Scheduling Problem ◽  
Optimization Framework ◽  
The World

The business of football competitions is called the number one sport in the world, thanks to more than one billion people’s attention. With the development of big convergence media, the live broadcasting of football competitions gradually becomes industrialization and commercialization, which has a direct relationship with economic growth. For the live broadcasting of football competitions, the users focus more on quality of experience, i.e., definition and instantaneity. In terms of such two metrics, the current live broadcasting schemes are difficult to cover them well. Therefore, this paper exploits the emerging in-network caching and edge computing technologies to optimize the live broadcasting of football competitions, shorten for IELB. At first, the live broadcasting optimization framework based on in-network caching and edge computing is presented. Then, the auction-based method is used to address the task scheduling problem in the edge computing. In addition, a video compression algorithm based on adaptive convolution kernel is introduced to accelerate the video transmission and guarantee users to obtain the contents of football competitions as quickly as possible. The proposed IELB has been verified based on the collected real football competitions dataset by evaluating response time, and the experimental results demonstrate that IELB is feasible and efficient.

Enhancing quality of experience in mobile edge computing using deep learning based data offloading and cyberattack detection technique

2021 ◽  
Author(s):  
Anwer Mustafa Hilal ◽  
Manal Abdullah Alohali ◽  
Fahd N. Al-Wesabi ◽  
Nadhem Nemri ◽  
Hasan J. Alyamani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Quality Of Experience ◽  
Edge Computing ◽  
Detection Technique ◽  
Mobile Edge Computing ◽  
Data Offloading

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

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.

scholarly journals Quality-of-Experience-Aware Incentive Mechanism for Workers in Mobile Device Cloud

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sajeeb Saha ◽  
Md. Ahsan Habib ◽  
Tamal Adhikary ◽  
Md. Abdur Razzaque ◽  
Md. Mustafizur Rahman ◽  
...  
Keyword(s):  
Mobile Device ◽  
Quality Of Experience ◽  
Incentive Mechanism ◽  
Mobile Device Cloud
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