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.

scholarly journals MultiCuckoo: Multi-Cloud Service Composition Using a Cuckoo-Inspired Algorithm for the Internet of Things Applications

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 56737-56749 ◽  
Author(s):  
Heba Kurdi ◽  
Fadwa Ezzat ◽  
Lina Altoaimy ◽  
Syed Hassan Ahmed ◽  
Kamal Youcef-Toumi
Keyword(s):  
Internet Of Things ◽  
Service Composition ◽  
Cloud Service ◽  
The Internet ◽  
Cloud Service Composition ◽  
The Internet Of Things ◽  
Multi Cloud

scholarly journals An Efficient Multi-Cloud Service Composition Using A Distributed Multiagent-based, Memory-driven Approach

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Phillip Kendrick ◽  
Thar Baker ◽  
Zakaria Maamar ◽  
Abir Hussain ◽  
Rajkumar Buyya ◽  
...  
Keyword(s):  
Service Composition ◽  
Cloud Service ◽  
Cloud Service Composition ◽  
Multi Cloud

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.

A hybrid formal verification approach for QoS-aware multi-cloud service composition

2019 ◽  
Vol 23 (4) ◽  
pp. 2453-2470 ◽  
Author(s):  
Alireza Souri ◽  
Amir Masoud Rahmani ◽  
Nima Jafari Navimipour ◽  
Reza Rezaei
Keyword(s):  
Formal Verification ◽  
Service Composition ◽  
Cloud Service ◽  
Cloud Service Composition ◽  
Multi Cloud

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%.

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