Modeling and Simulation of QoS-aware Web Service Selection for Provider Profit Maximization

Abstract

Web Services (WSs) are a new breed of web application that is gaining momentum. One of the research challenges it brings along is the establishment of an interoperable semantic framework suitable to represent not only the functional, but also the non-functional features of WSs, i.e. their Quality-of-Service (QoS), which in most cases are not included in the WS description. Nevertheless, integrating QoS features in WS profiles is to the advantage of both users and providers, as it supports QoS-aware WS selection and composition addressing the user's QoS requirements, while enabling WS providers to increase their profits. The purpose of this paper is twofold. On the one hand, it aims to present a QoS ontology framework adequate for WS provision. On the other hand, it studies the problem of providers that receive concurrent requests of numerous customers for WSs demonstrating different bandwidth and price properties. It introduces the “Selective Multiple Choice Knapsack Problem” that aims to identify the services that should be delivered to the customers in order to maximize the provider's profit, subject to maximum bandwidth constraints on the server side. Thus, the QoS-awareness in this version of this problem is limited to the consideration of two QoS parameters: service bandwidth and cost. The problem is solved by a proposed algorithm that has been empirically evaluated via numerous experiments.

Geographic Location-Based Network-Aware QoS Prediction for Service Composition

“…In recent approaches, the service execution contexts, including bandwidth [15], policy context [16], device context [13], etc, are also considered in the QoS models for the composition problems. In cloud computing environments, point-to-point network delay [17], including network latency between adjacent services [4] is also considered in service compositions.…”

A dynamically self-configurable service process engine

“…In recent years, QoS support for Web services has become a widely investigated area and has shown to be an effective mechanism in Web services' discovery particularly in discriminating between services that share similar functionalities. This information can be obtained from QoS description of Web Service [13,25,31], estimated …”

Optimizing execution path of scientific workflow by gradual removal of QoS constraint violations in Reverse Order

“…In Algorithm 1, the WHILE loop (step 13) can terminate when the violation space is reduced to be less than zero. Since the global minimum QoS value is guaranteed to be less than the user-expressed constraint value (steps 8-12), the violation space will be less than zero after finite execution steps (steps [13][14][15][16][17][18][19][20][21][22][23][24][25]. Let us consider the worst case, assuming that K is the maximum candidate service instance number among all included abstract services in a scientific workflow plan, the maximum scanning number in WHILE loop that can make violation space to be less than zero is K .…”

“…However, this method is only useful in a limited business field. Some work is also proposed to maximize provider's profits or user's profits [13,15], while our work emphasizes on an optimized execution path for making sure of meeting all end-to-end QoS constraints by gradually removing QoS violations in Reverse Order instead of directly judging constraint conditions to detect multiple execution paths. Many other QoS-based service selection methods are considered in limited resource environments such as gird communities [31,32], but they only emphasize the utility of using grid resources while ignoring the overall QoS constraints.…”