A Hybrid Service Selection and Composition Model for Cloud-Edge Computing in the Internet of Things

Abstract

Cloud-edge computing is a hybrid model of computing where resources and services provided via the Internet of Things (IoT) between large-scale and long-term data informs of the cloud layer and smallscale and short-term data as edge layer. The main challenge of the cloud service providers is to select the optimal candidate services that are doing the same work but offer different Quality of Service (QoS) values in IoT applications. Service composition in cloud-edge computing is an NP-hard problem; therefore, many meta-heuristic methods introduced to solve this issue. Also, the correctness of meta-heuristic and machine learning algorithms for evaluating service composition problem should be proven using formal methods to guarantee functional and non-functional specifications. In this paper, a hybrid Artificial Neural Networkbased Particle Swarm Optimization (ANN-PSO) Algorithm presented to enhance the QoS factors in cloudedge computing. To illustrate the correctness and improve the reachability rate of candidate composited services and QoS factors for the proposed hybrid algorithm, we present a formal verification method based on a labeled transition system to check some critical Linear Temporal Logics (LTL) formulas. The experimental results illustrated the high performance of the proposed model in terms of minimum verification time, memory consumption, and guaranteeing critical specifications rules as the Linear Temporal Logic (LTL) formulas. Also, we observed that the proposed model has optimal response time, availability, and price with maximum fitness function value than other service composition algorithms.INDEX TERMS Cloud-edge computing, Internet of Things, service composition, formal verification, quality of service, artificial neural network, and particle swarm optimization.

A fog‐based fault‐tolerant and QoE‐aware service composition in smart cities

“…Besides the aforementioned approaches focusing on relevant smart city problems, a number of works addressed the service composition problem for IoT /Web services, 5‐11 and CMfg 15‐21 . In this regard, a couple of works provided a systematic literature review of composition techniques in IoT and CMfg and their open challenges 12‐14,22 .…”

“…In this regard, a couple of works provided a systematic literature review of composition techniques in IoT and CMfg and their open challenges 12‐14,22 . These techniques can be categorized based on whether they focus on service composition architecture 6,9,10 or optimal/near optimal solving of service composition problem 5,8,11,15‐21 …”

“…Although a number of existing works have already focused on service composition for web services and IoT, 5‐14 and cloud manufacturing (CMfg), 15‐22 the literature lacks adequate specialized solutions addressing the composition of urban services in smart cities. In this context, the response time to a service composition request is of great importance for citizens.…”

Congestion‐aware adaptive decentralised computation offloading and caching for multi‐access edge computing networks

“…An active queue management‐based green cloud model introduced to minimise the latency and energy wastage [42]. In [43], a hybrid artificial neural network‐based particle swarm optimisation algorithm investigated to improve user quality of experience in cloud‐edge computing platform.…”

Load balancing edge server placement method with QoS requirements in wireless metropolitan area networks

“…First, the population density of urban areas in WMANs is relatively high, and the computing performance of edge servers can be used more effectively. Second, because of the large scale of WMANs, MEC service providers can exploit economies of scale to improve the acceptability of MEC services to the public [17, 18].…”