Research on Edge Service Composition Method Based on BAS Algorithm

Edge services are transferred data processing, application running, and implementation of some functional services from cloud central server to network edge server to provide services. Combined edge service can effectively reduce task computation in the cloud, shorten transmission distance of processing data, quickly decompose task of service request, and select the optimal edge service combination to provide service for users. BAS is an efficient intelligent optimization algorithm, which can achieve efficient optimization and neither need to know the specific form of function nor need gradient information. This paper designs an edge service composition model based on edge computing and proposes a method about edge service composition by BAS optimization algorithm. Our proposed method has obvious advantages in service composition efficiency compared with service composition method based on PSO or WPA heuristic algorithm. Compared with cloud service composition method, our proposed method has advantages of shorter service response time, low cost, and high quality of user experience.

Trust Enforced Cloud Service Composition Based on Teaching–Learning-Based Optimization – STORM

In the arena of Cloud Computing, the emergence of social networks and IoT increased the number of available services on the cloud platform, making service composition and optimal selection (SCOS) in Cloud Manufacturing (CMfg), NP-hard. The existing approaches for addressing SCOS often fail to offer assistance with maximized trust and satisfied QoS preferences. Hence, this research paper presents a novel Teach Inglea Rning-based Optimization a Lgorithm (TIROL) for achieving the optimal solution for truST enforced clOud seRvice coMposition (STORM) to assist CMfg for improving the trust value with satisfied QoS preference(s). The performance of the proposed framework has been validated using the synthetic dataset generated from different test-cases. Experimental results show that the proposed framework is reliable and outperforms the SOTA approaches in terms of trust value maximization.

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

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.