Evolutionary Multi-Objective Optimization for Web Service Location Allocation Problem

Abstract

With the ever increasing number of functionally similar web services being available on the Internet, the market competition is becoming intense. Web service providers (WSPs) realize that good Quality of Service (QoS) is a key of business success and low network latency is a critical measurement of good QoS. Because network latency is related to location, a straightforward way to reduce network latency is to allocate services to proper locations. However, Web Service Location Allocation Problem (WSLAP) is a challenging task since there are multiple objectives potentially conflicting with each other and the solution search space has a combinatorial nature. In this paper, we consider minimizing the network latency and total cost simultaneously and model the WSLAP as a multi-objective optimization problem. We develop a new PSO-based algorithm to provide a set of trade-off solutions. The results show that the new algorithm can provide a more diverse range of solutions than the compared three well known multi-objective optimization algorithms. Moreover, the new algorithm performs better especially on large problems.

How to Evaluate Solutions in Pareto-Based Search-Based Software Engineering: A Critical Review and Methodological Guidance

“…Another example is the setting of the critical parameter reference point in the HV indicator, which has experienced various versions. For example, some studies set it to the worst value obtained for each objective during all runs [34], [76], [93], [105], [125], [126], [131]; some did it to precisely the boundaries of the optimization problem in SBSE [30], [54], [144]; some did it to the nadir point of the Pareto front [103]. The first two settings may overemphasize the boundary solutions (as the reference point may be far away from the set to be evaluated), while the last one may lead to the boundary solutions to contribute nothing to the HV value.…”

“…Unfortunately, as what has been revealed in Table 8, such misuse of indicators is not uncommon in the SBSE community. For example, preferring knee points yet using IGD in [37], [89]; preferring knee points yet using GD and CI in [35], [114]; and preferring extreme solutions yet using HV and IGD in [131], [134]. HV can be somehow in favor of extreme solutions if the reference point is set far away from the considered set, but IGD certainly does not prefer extreme solutions.…”

“…IGD [26] is a well-known indicator in the field (e.g., in [5], [13], [32], [37], [39], [43], [52], [80], [89], [91], [126], [131]). As the name suggests, IGD, an inversion of GD, is to measure how close the Pareto front is to the obtained solution set.…”

CREW: Cost and Reliability aware Eagle‐Whale optimiser for service placement in Fog

“…The suitability of Eagle strategy based multiobjective whale algorithm in the CREW approach is analysed by comparing with the Nondominated Sorting based Genetic Algorithm(NSGA‐II) and the MultiObjective Whale Optimisation Algorithm (MOWOA). The HyperVolume (HV) metric is a performance measure that reflects the area of region in the search space dominated by the obtained set of solutions 35 . It is highly desirable to have larger values for the hypervolume indicator.…”

Optimization of Microservice Composition Based on Artificial Immune Algorithm Considering Fuzziness and User Preference

“…Zhang et al [12] proposed a multi-time sequential QoS prediction method, called MulA-LMRBF, to facilitate users in selecting web services. Tan et al [13] established a multi-objective model that minimised network delays and total cost and improved a particle swarm optimization.…”