scholarly journals Popularity-Guided Cost Optimization for Live Streaming in Mobile Edge Computing

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Tao He ◽  
Kunxin Zhu ◽  
Zhipeng Chen ◽  
Ruomei Wang ◽  
Fan Zhou
Keyword(s):  
Cost Optimization ◽  
Edge Computing ◽  
Live Streaming ◽  
Mobile Edge Computing ◽  
Efficient Delivery ◽  
Streaming Service ◽  
Cloud Servers ◽  
The Cost ◽  
Capacity Cost ◽  
Overlay Model

Live streaming service usually delivers the content in mobile edge computing (MEC) to reduce the network latency and save the backhaul capacity. Considering the limited resources, it is necessary that MEC servers collaborate with each other and form an overlay to realize more efficient delivery. The critical challenge is how to optimize the topology among the servers and allocate the link capacity so that the cost will be lower with delay constraints. Previous approaches rarely consider server collaborations for live streaming service, and the scheduling delay is usually ignored in MEC, leading to suboptimal performances. In this paper, we propose a popularity-guided overlay model which takes the scheduling delay into consideration and utilizes MEC collaboration to achieve efficient live streaming service. The links and servers are shared among all channel streams and each stream is pushed from cloud servers to MEC servers via the trees. Considering the optimization problem is NP-hard, we propose an effective optimization framework called cost optimization for live streaming (COLS) to predict the channel popularity by a LSTM model with multiscale input data. Finally, we compute topology graph by greedy scheme and allocate the capacity with convex programming. Experimental results show that the proposed approach achieves higher prediction accuracy, reducing the capacity cost by more than 40% with an acceptable delay compared with state-of-the-art schemes.

scholarly journals Cost-Effective Resource Sharing in an Internet of Vehicles-Employed Mobile Edge Computing Environment

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 594 ◽  
Author(s):  
Tri Nguyen ◽  
Tien-Dung Nguyen ◽  
Van Nguyen ◽  
Xuan-Qui Pham ◽  
Eui-Nam Huh
Keyword(s):  
Video Streaming ◽  
Resource Sharing ◽  
Vehicular Network ◽  
Edge Computing ◽  
Mixed Integer ◽  
Mobile Users ◽  
Vehicle Speed ◽  
Mobile Edge Computing ◽  
Internet Of Vehicles ◽  
The Cost

By bringing the computation and storage resources close proximity to the mobile network edge, mobile edge computing (MEC) is a key enabling technology for satisfying the Internet of Vehicles (IoV) infotainment applications’ requirements, e.g., video streaming service (VSA). However, the explosive growth of mobile video traffic brings challenges for video streaming providers (VSPs). One known issue is that a huge traffic burden on the vehicular network leads to increasing VSP costs for providing VSA to mobile users (i.e., autonomous vehicles). To address this issue, an efficient resource sharing scheme between underutilized vehicular resources is a promising solution to reduce the cost of serving VSA in the vehicular network. Therefore, we propose a new VSA model based on the lower cost of obtaining data from vehicles and then minimize the VSP’s cost. By using existing data resources from nearby vehicles, our proposal can reduce the cost of providing video service to mobile users. Specifically, we formulate our problem as mixed integer nonlinear programming (MINP) in order to calculate the total payment of the VSP. In addition, we introduce an incentive mechanism to encourage users to rent its resources. Our solution represents a strategy to optimize the VSP serving cost under the quality of service (QoS) requirements. Simulation results demonstrate that our proposed mechanism is possible to achieve up to 21% and 11% cost-savings in terms of the request arrival rate and vehicle speed, in comparison with other existing schemes, respectively.

scholarly journals Optimal Offloading Decision Strategies and Their Influence Analysis of Mobile Edge Computing

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3231 ◽  
Author(s):  
Jiuyun Xu ◽  
Zhuangyuan Hao ◽  
Xiaoting Sun
Keyword(s):  
Energy Consumption ◽  
Time Delay ◽  
Branch And Bound ◽  
Edge Computing ◽  
Optimal Decision ◽  
Mobile Edge Computing ◽  
Wireless Devices ◽  
Influence Analysis ◽  
Cloud Servers ◽  
And Storage

Mobile edge computing (MEC) has become more popular both in academia and industry. Currently, with the help of edge servers and cloud servers, it is one of the substantial technologies to overcome the latency between cloud server and wireless device, computation capability and storage shortage of wireless devices. In mobile edge computing, wireless devices take responsibility with input data. At the same time, edge servers and cloud servers take charge of computation and storage. However, until now, how to balance the power consumption of edge devices and time delay has not been well addressed in mobile edge computing. In this paper, we focus on strategies of the task offloading decision and the influence analysis of offloading decisions on different environments. Firstly, we propose a system model considering both energy consumption and time delay and formulate it into an optimization problem. Then, we employ two algorithms—Enumerating and Branch-and-Bound—to get the optimal or near-optimal decision for minimizing the system cost including the time delay and energy consumption. Furthermore, we compare the performance between two algorithms and draw the conclusion that the comprehensive performance of Branch-and-Bound algorithm is better than that of the other. Finally, we analyse the influence factors of optimal offloading decisions and the minimum cost in detail by changing key parameters.

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 Deep Reinforcement Learning-Empowered Resource Allocation for Mobile Edge Computing in Cellular V2X Networks

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 372
Author(s):  
Dongji Li ◽  
Shaoyi Xu ◽  
Pengyu Li
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Vehicular Networks ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Dynamic Environment ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Computing Paradigm ◽  
Cloud Servers

With the rapid development of vehicular networks, vehicle-to-everything (V2X) communications have huge number of tasks to be calculated, which brings challenges to the scarce network resources. Cloud servers can alleviate the terrible situation regarding the lack of computing abilities of vehicular user equipment (VUE), but the limited resources, the dynamic environment of vehicles, and the long distances between the cloud servers and VUE induce some potential issues, such as extra communication delay and energy consumption. Fortunately, mobile edge computing (MEC), a promising computing paradigm, can ameliorate the above problems by enhancing the computing abilities of VUE through allocating the computational resources to VUE. In this paper, we propose a joint optimization algorithm based on a deep reinforcement learning algorithm named the double deep Q network (double DQN) to minimize the cost constituted of energy consumption, the latency of computation, and communication with the proper policy. The proposed algorithm is more suitable for dynamic scenarios and requires low-latency vehicular scenarios in the real world. Compared with other reinforcement learning algorithms, the algorithm we proposed algorithm improve the performance in terms of convergence, defined cost, and speed by around 30%, 15%, and 17%.

scholarly journals An Algorithm to Minimize Energy Consumption and Elapsed Time for IoT Workloads in a Hybrid Architecture

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2914
Author(s):  
Julio C. S. dos Anjos ◽  
João L. G. Gross ◽  
Kassiano J. Matteussi ◽  
Gabriel V. González ◽  
Valderi R. Q. Leithardt ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Cost Model ◽  
Edge Computing ◽  
Task Completion ◽  
Hybrid Architecture ◽  
Mobile Edge Computing ◽  
Simulated Environment ◽  
Task Processing ◽  
Iot Devices ◽  
The Cost

Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to cloud computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in mobile edge computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among cloud or mobile edge computing (MEC) server or local IoT devices to achieve better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%.

scholarly journals A Multi-Criteria Multi-Cloud Service Composition in Mobile Edge Computing

2020 ◽  
Vol 12 (18) ◽  
pp. 7661
Author(s):  
Beibei Pang ◽  
Fei Hao ◽  
Doo-Soon Park ◽  
Carmen De Maio
Keyword(s):  
Energy Consumption ◽  
Service Composition ◽  
Cloud Service ◽  
Edge Computing ◽  
Formal Concept ◽  
Mobile Edge Computing ◽  
Multiple Clouds ◽  
Cloud Service Composition ◽  
Cloud Servers ◽  
Multi Cloud

The development of mobile edge computing (MEC) is accelerating the popularity of 5G applications. In the 5G era, aiming to reduce energy consumption and latency, most applications or services are conducted on both edge cloud servers and cloud servers. However, the existing multi-cloud composition recommendation approaches are studied in the context of resources provided by a single cloud or multiple clouds. Hence, these approaches cannot cope with services requested by the composition of multiple clouds and edge clouds jointly in MEC. To this end, this paper firstly expands the structure of the multi-cloud service system and further constructs a multi-cloud multi-edge cloud (MCMEC) environment. Technically, we model this problem with formal concept analysis (FCA) by building the service–provider lattice and provider–cloud lattice, and select the candidate cloud composition that satisfies the user’s requirements. In order to obtain an optimized cloud combination that can efficiently reduce the energy consumption, money cost, and network latency, the skyline query mechanism is utilized for extracting the optimized cloud composition. We evaluate our approach by comparing the proposed algorithm to the random-based service composition approach. A case study is also conducted for demonstrating the effectiveness and superiority of our proposed approach.

TRAFFIC OFFLOADING METHOD FOR V2X/5G NETWORKS BASED ON THE EDGE COMPUTING SYSTEM

2020 ◽  
Author(s):  
А.Г. ВЛАДЫКО ◽  
А.С. МУТХАННА ◽  
А.Е. КУЧЕРЯВЫЙ
Keyword(s):  
Data Transfer ◽  
Computing System ◽  
Edge Computing ◽  
System Structure ◽  
Mobile Edge Computing ◽  
Efficient Operation ◽  
Traffic Offloading ◽  
5G Network ◽  
Cloud Servers ◽  
System Power

Разработана структура системы V2X(Vehicle-to-Everything), позволяющей на базе сети 5G организовать взаимодействие между объектами дорожной инфраструктуры, беспилотными и высокоавтоматизированными транспортными средствами, пешеходами и другими сетями. Для производительной работы в условиях плотного трафика транспортных средств в системе V2X использованы технология мобильных граничных вычислений MEC (Mobile Edge Computing) и алгоритм выгрузки трафика, позволяющий управлять нагрузкой данных на граничные и облачные серверы. Результаты моделирования для различных сценариев показали, что предложенные структура и алгоритм выгрузки могут значительно снизить задержки в передаче данных и энергопотребление системы. The V2X (Vehicle-to-Everything) system structure has been developed, which allows, based on 5G network, to organize interaction between road infrastructure objects, unmanned and highly automated vehicles, pedestrians, and other networks. For efficient operation in conditions of dense vehicle traffic, the V2X system uses the mobile edge computing technology and a traffic offloading algorithm, which allows managing the data load on the edge cloud servers. Simulation results for various scenarios showed that the proposed structure and offloading algorithm can significantly reduce data transfer delays and system power consumption.

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