Cristina Bianca Pop

Optimizing the Semantic Web Service Composition Process Using Cuckoo Search

The behavior of biological individuals which efficiently deal with complex life problems represents an inspiration source in the design of meta-heuristics for solving optimization problems. The Cuckoo Search is such a meta-heuristic inspired by the behavior of cuckoos in search for the appropriate nest where to lay eggs. This paper investigates how the Cuckoo Search meta-heuristic can be adapted and enhanced to solve the problem of selecting the optimal solution in semantic Web service composition. To improve the performance of the cuckoo-inspired algorithm we define a 1-OPT heuristic which expands the search space in a controlled way so as to avoid the stagnation on local optimal solutions. The search space is modeled as an Enhanced Planning Graph, dynamically built for each user request. To identify the optimal solution encoded in the graph we define a fitness function which uses the QoS attributes and the semantic quality as selection criteria. The cuckoo-inspired method has been evaluated on a set of scenarios from the trip planning domain.

Immune-inspired method for selecting the optimal solution in web service composition

This paper presents an immune-inspired algorithm applied in the context of Web service composition to select the optimal composition solution. Our approach models Web service composition as a multi-layered process which creates a planning-graph structure along with a matrix of semantic links. We have enhanced the classical planning graph with the new concepts of service cluster and semantic similarity link. The semantic similarity links are defined between services on different graph layers and are stored in a matrix of semantic links. To calculate the degree of the semantic match between services, we have adapted the information retrieval measures of recall, precision and F_Measure. The immune-inspired algorithm uses the enhanced planning graph and the matrix of semantic links to select the optimal composition solution employing the QoS attributes and the semantic quality as the selection criteria.

Ant-Inspired Technique for Automatic Web Service Composition and Selection

This paper presents a technique for semantic Web service composition inspired by the behavior of ants. The proposed technique combines a service composition graph model with the ant colony optimization met heuristic to select the optimal composition solution. In our approach, we have considered as selection criteria the QoS attributes of the services and the semantic quality of the connections between the services involved in a composition solution.

Semantic Web Service Clustering for Efficient Discovery Using an Ant-Based Method

This paper presents an ant-inspired method for clustering semantic Web services. The method considers the degree of semantic similarity between services as the main clustering criterion. To measure the semantic similarity between two services we propose a matching method and a set of metrics. The proposed metrics evaluate the degree of match between the ontology concepts describing two services. We have tested the ant-inspired clustering method on the SAWSDL-TC benchmark and we have evaluated its performance using the Dunn Index, the Intra-Cluster Variance metric and an original metric we introduce in this paper.

A swarm-inspired data center consolidation methodology

This paper proposes a swarm-inspired data center consolidation methodology which aims at reducing the power consumption in data centers while ensuring the workload execution within the pre-established performance parameters. Each data center server is managed by an intelligent agent that deals with its power efficiency by implementing a birds migration-inspired behavior to decide on the appropriate server consolidation actions. The selected actions are executed to achieve an optimal utilization of server computing resources thus lowering power consumption. The data center servers self-organize in logical clusters according to the birds V-formation self-organizing migration model. The results are promising showing that the swarm-inspired data center consolidation methodology optimizes the utilization ratio of the data center computing resources and achieves estimated power savings of about 16%.

Cuckoo-inspired hybrid algorithm for selecting the optimal Web service composition

This paper presents a bio-inspired hybrid algorithm that selects the optimal solution in semanticWeb service composition. The proposed algorithm combines the cuckoo search metaheuristic with evolutionary computing, reinforcement learning and tabu search to optimize the selection process in terms of execution time and explored search space. We model the search space as an Enhanced Planning Graph structure which encodes all the possible composition solutions for a given user request. To establish whether a solution is optimal, the QoS attributes of the services involved in the composition as well as the semantic similarity between them are considered as evaluation criteria. The hybrid selection algorithm has been evaluated on a set of scenarios from the trip planning domain and comparatively analyzed with a tabu search-based selection algorithm.

Selecting the optimal web service composition based on a multi-criteria bee-inspired method

In this paper we present a bee-inspired method for selecting the optimal composition solution. The proposed method uses a composition graph model and a matrix of semantic links to search for the optimal composition solution. For improving the performance of the traditional bee colony optimization algorithm a 1-OPT heuristic is defined. This makes the composition solutions more diverse so as to avoid the stagnation on local optimal solutions. The optimal composition solution is identified by using a multi-criteria fitness function. The fitness function evaluates a composition solution according to QoS attributes and the semantic quality between the services involved in a composition solution.

Dynamic frequency scaling algorithms for improving the CPU's energy efficiency

This paper approaches the problem of improving the service center server CPUs energy efficiency by executing dynamic frequency scaling actions and performing tradeoffs between CPUs computational performance and its power consumption. Two different algorithms are designed and implemented: an immune inspired algorithm and a fuzzy logic based algorithm. The immune inspired algorithm uses the human antigen as a model to represent the server power / performance state. Using a set of detectors the antigens are classified as self for optimal power consumption state or non-self for non-optimal power consumption state. For the non-self antigens a biologically inspired clonal selection approach is used to determine the actions that need to be executed to bring the servers CPU in an optimal power consumption state. The fuzzy logic based algorithm adaptively changes the processor performance states to the incoming workload. The algorithm also filters workload spikes because frequent p-states transition costs can outweigh the benefit of adaptation.

Bio-inspired methods for selecting the optimal web service composition: Bees or cuckoos intelligence?

This paper analyses the impact of biological intelligence on the problem of selecting the optimal solution in Web service composition. Thus, we propose two selection methods, one inspired by the behaviour of bees searching for food and another one inspired by the behaviour of cuckoos searching for the nests where to lay eggs. The methods use a composition graph to search for the optimal solution. The quality of a composition is evaluated based on QoS and semantic quality. To comparatively analyse the proposed methods we implemented an experimental prototype and performed tests on a set of scenarios from trip planning.

Immune-inspired Web Service Composition Framework

This paper presents a new Web service composition method which combines the AI planning graph technique with an immune-inspired algorithm to find the optimal composition solution. Simultaneously with the planning graph onstruction, a matrix of semantic links is built to storethe semantic links established between the services on different layers of the graph. The planning graph and the matrix of semantic links represent the main building blocks of our immune-inspired technique for finding the optimal composition solution. We use a multi-criteria function which evaluates the composition solution in terms of its QoS attributes and the quality of the semantic match between the services involved in the solution. In order to validate our approach to automatically compose Web services, we have developed an experimental framework that integrates the planning graph composition approach and the immune-inspired selection technique. We have performed our experiments on a set of Web services from two domains.