MobiQoR: Pushing the Envelope of Mobile Edge Computing Via Quality-of-Result Optimization

2017 ◽  
Author(s):  
Yongbo Li ◽  
Yurong Chen ◽  
Tian Lan ◽  
Guru Venkataramani
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing

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 Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1446 ◽  
Author(s):  
Liang Huang ◽  
Xu Feng ◽  
Luxin Zhang ◽  
Liping Qian ◽  
Yuan Wu
Keyword(s):  
Deep Learning ◽  
Low Complexity ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Wireless Devices ◽  
Cloud Server ◽  
Convergence Performance ◽  
Multi Server

This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server. Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or to be offloaded to and processed at one of the edge servers or the cloud server. In this paper, we investigate low-complexity computation offloading policies to guarantee quality of service of the MEC network and to minimize WDs’ energy consumption. Specifically, both a linear programing relaxation-based (LR-based) algorithm and a distributed deep learning-based offloading (DDLO) algorithm are independently studied for MEC networks. We further propose a heterogeneous DDLO to achieve better convergence performance than DDLO. Extensive numerical results show that the DDLO algorithms guarantee better performance than the LR-based algorithm. Furthermore, the DDLO algorithm generates an offloading decision in less than 1 millisecond, which is several orders faster than the LR-based algorithm.

scholarly journals NFVMon: Enabling Multioperator Flow Monitoring in 5G Mobile Edge Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Enrique Chirivella-Perez ◽  
Juan Gutiérrez-Aguado ◽  
Jose M. Alcaraz-Calero ◽  
Qi Wang
Keyword(s):  
Mobile Networks ◽  
Communication Systems ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
High Definition ◽  
Flow Monitoring ◽  
Fifth Generation ◽  
New Generation ◽  
Monitoring Architecture

With the advances of new-generation wireless and mobile communication systems such as the fifth-generation (5G) mobile networks and Internet of Things (IoT) networks, demanding applications such as Ultra-High-Definition video applications is becoming ever popular. These applications require real-time monitoring and processing to meet the mission-critical quality of service requirements and are expected to be supported by the emerging fog or edge computing paradigms. This paper presents NFVMon, a novel monitoring architecture to enable flow monitoring capabilities of network traffic in a 5G multioperator mobile edge computing environment. The proposed NFVMon is integrated with the management plane of the Cloud Computing. NFVMon has been prototyped and a reference implementation is presented. It provides novel capabilities to provide disaggregated metrics related to the different 5G mobile operators sharing infrastructures and also about the different 5G subscribers of each of such mobile operators. Extensive experiments for evaluating the performance of the system have been conducted on a mid-sized infrastructure testbed.

scholarly journals Research on Multinode Collaborative Computing Offloading Algorithm Based on Minimization of Energy Consumption

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Dongsheng Han ◽  
Yu Liu ◽  
Junhong Ni
Keyword(s):  
Energy Consumption ◽  
Simulation Analysis ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Collaborative Computing ◽  
Additional Energy ◽  
Important Challenge ◽  
Local Edge ◽  
Distributed Deployment

Mobile edge computing (MEC) nodes are deployed at positions close to users to address excessive latency and converging flows. Nevertheless, the distributed deployment of MEC nodes and offload of computational tasks among several nodes consume additional energy. Accordingly, how to reduce the energy consumption of edge computing networks while satisfying latency and quality of service (QoS) demands has become an important challenge that hinders the application of MEC. This paper built a local-edge-cloud edge computing network and proposes a multinode collaborative computing offloading algorithm. It can be applied to smart homes, realize the development of green channels, and support local users of Internet of Things (IoT) to decompose computational tasks and offload them to multiple MEC or cloud nodes. The simulation analysis reveals that the new local-edge-cloud edge computing offload method not only reduces network energy consumption more effectively compared with traditional computing offload methods but also ensures the implementation of more data samples.

Privacy-Aware Forecasting of Quality of Service in Mobile Edge Computing

2021 ◽  
pp. 1-1
Author(s):  
Huiying Jin ◽  
Pengcheng Zhang ◽  
Hai Dong ◽  
Yuelong Zhu ◽  
Athman Bouguettaya
Keyword(s):  
Quality Of Service ◽  
Edge Computing ◽  
Mobile Edge Computing

scholarly journals Mobility-aware personalized service recommendation in mobile edge computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Hongxia Zhang ◽  
Yanhui Dong ◽  
Yongjin Yang
Keyword(s):  
Recommendation Systems ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Service Recommendation ◽  
Trajectory Prediction ◽  
Personalized Service ◽  
Significant Challenge ◽  
Memory Network ◽  
Short Memory

AbstractWith the proliferation of smartphones and an increasing number of services provisioned by clouds, mobile edge computing (MEC) is emerging as a complementary technology of cloud computing. It could provide cloud resources and services by local mobile edge servers, which are normally nearby users. However, a significant challenge is aroused in MEC because of the mobility of users. User trajectory prediction technologies could be used to cope with this issue, which has already played important roles in service recommendation systems with MEC. Unfortunately, little attention and work have been given in service recommendation systems considering users mobility. Thus, in this paper, we propose a mobility-aware personalized service recommendation (MPSR) approach based on user trajectory and quality of service (QoS) predictions. In the proposed method, users trajectory is firstly discovered by a hybrid long-short memory network. Then, given users trajectories, service QoS is predicted, considering the similarity of different users and different edge servers. Finally, services are recommended by a center trajectory strategy through MPSR. Experimental results on a real dataset show that our proposed approach can outperform the traditional recommendation approaches in terms of accuracy in mobile edge computing.

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.

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