Data-intensive Service Provision Research Articles

Data-Intensive Service Provision Based on Particle Swarm Optimization

Data-Intensive Service Provision Based on Particle Swarm Optimization

Recent research advances in cloud computing and big data

Recent research advances in cloud computing and big data

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

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.

Facilitating an ant colony algorithm for multi-objective data-intensive service provision

The explosion of enormous sources of digital data has led to greater dependence on data-intensive services. Applications based on data-intensive services have become one of the most challenging applications in cloud computing. The service provision, and in particular service composition, will face new challenges as the services and data grow. In this paper, we will evaluate an ant colony system to resolve the multi-objective data-intensive service composition problem. The algorithm for a multi-objective context will get a set of Pareto-optimal solutions considering two objectives at the same time: the total cost and the total execution time of a composite service.