Multi-Phase Ant Colony System for Multi-Party Data-Intensive Service Provision

Abstract

The rapid proliferation of enormous sources of digital data has led to greater dependence on data-intensive services. Each service may actually request or create a large amount of data sets. To compose these services will be more challenging. Issues such as autonomy, scalability, adaptability, and robustness, become difficult to resolve. In order to automate the process of reaching an agreement among service composers, service providers, and data providers, an ant-inspired negotiation mechanism is considered in this paper. We exploit a group of agents automatically negotiating to establish agreeable service contracts. Twostage negotiation procedures are used in our data-intensive service provision model, which will provide effective and efficient service selection for service composers. We also present a multi-phase, multi-party negotiation protocol, where the ant colony system is applied to select services with the best or near-optimal utility outputs. In order to adapt the ant colony system to handle the dynamic scenarios during negotiations, we also discuss several strategies for modifying the pheromone information in the first place. The experimental results show that our negotiation-based approach can facilitate the data-intensive service provision with better outcome. University of Wollongong, NSW, Australia E-mail: lw840@uowmail.edu.au, jshen@uow.edu.au ! Abstract-The rapid proliferation of enormous sources of digital data has led to greater dependence on data-intensive services. Each service may actually request or create a large amount of data sets. To compose these services will be more challenging. Issues such as autonomy, scalability, adaptability, and robustness, become difficult to resolve. In order to automate the process of reaching an agreement among service composers, service providers, and data providers, an ant-inspired negotiation mechanism is considered in this paper. We exploit a group of agents automatically negotiating to establish agreeable service contracts. Two-stage negotiation procedures are used in our data-intensive service provision model, which will provide effective and efficient service selection for service composers. We also present a multi-phase, multi-party negotiation protocol, where the ant colony system is applied to select services with the best or near-optimal utility outputs. In order to adapt the ant colony system to handle the dynamic scenarios during negotiations, we also discuss several strategies for modifying the pheromone information in the first place. The experimental results show that our negotiation-based approach can facilitate the data-intensive service provision with better outcome.

Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments

“…Furthermore, an ant‐inspired negotiation method for the data‐intensive service provision has been proposed by Wang and Shen . This method is used to automate the procedure of reaching an agreement among service composers and service providers.…”

A Systematic Review of Bio-Inspired Service Concretization

“…A graph for the data-intensive service concretization problem was given in [64]. The lifetime of the problem framework was described in [68]. In the lifetime, the first step is that the service composer tries to select a set of service candidates while the data provider provides data sets, and the second step is that if a feasible solution which satisfies the service composer's local and global QoS constraints does not exist, negotiations are performed in order to determine new quality values for each involved service.…”

“…Meanwhile, data-intensive services are used in a dynamic and changing environment, and different providers typically have conflicting objectives [72], [73]. In order to automate the process of reaching an agreement in the data-intensive service concretization problem, we proposed an ant-inspired negotiation approach [68], [74]. In addition, we proposed a multi-objective ant colony system for data-intensive service concretization [75], where we focused on the scalability and adaptability of service concretization, and particular attention is paid to multiobjective optimization related to cost and QoS attributes.…”

Multi-objective Ant Colony System for Data-Intensive Service Provision

“…The authors of [6] already proved that it was useful for service management and discovery to add biological mechanisms to services. One of our earlier studies has presented a hierarchical taxonomy of Web service composition approaches [7]. By analyzing each type of approaches with respect to their optimality, their computational efficiency, and their dynamic complexity, we observed that bio-inspired algorithms, belonging to the sub-optimal methods, could overcome the new challenging requirements of the data-intensive service provision problem.…”

“…By analyzing each type of approaches with respect to their optimality, their computational efficiency, and their dynamic complexity, we observed that bio-inspired algorithms, belonging to the sub-optimal methods, could overcome the new challenging requirements of the data-intensive service provision problem. Then we conducted a systematic review of Web service composition and selection based on three bioinspired algorithms, namely, the ant colony optimization algorithms, the genetic algorithms, and the particle swarm optimization algorithms [7].…”