Quanwang Wu

Transactional and QoS-aware dynamic service composition based on ant colony optimization

Service composition facilitates seamless and flexible integration of applications from different providers. With the growing number of services that offer the same functionality but differ in non-functional properties published online, an efficient approach for dynamic service selection and composition is required. Traditionally, the problem is mostly addressed either from the quality of service (QoS) aspect or from the transaction aspect. In this paper, we first investigate the transactional properties of services and focus on how to compose individual services in a transactional manner, and then formulate the problem of transactional and QoS-aware dynamic service composition. By modeling the problem as a constrained directed acyclic graph, the ant colony optimization algorithm is utilized to seek a near-to-optimal solution efficiently. At last empirical studies are conducted and the experiments show that the proposed approach can approximate the optimal solution well while staying efficient.

A correlation-driven optimal service selection approach for virtual enterprise establishment

A virtual enterprise is an emerging business cooperation model which allows rapid response to the unpredictable market behavior and opportunity. For service oriented enterprises, where computing resources are encapsulated as services and published online, establishing a virtual enterprise can be regarded as a process of service composition. As there are increasing numbers of available services providing similar functionalities but with different quality values, and with potential business correlations among them, it is not trivial to orchestrate a composite service with optimal overall quality of service (QoS). In this paper, we formally propose a business correlation model including both quality correlations and selection correlations, and then present an efficient approach for correlation-driven QoS-aware optimal service selection based on a genetic algorithm. The genetic algorithm is tailored with niching technology, a repair operator and a penalty mechanism. The effectiveness and efficiency of the approach are demonstrated via empirical studies at last.

Broker-based SLA-aware composite service provisioning

Abstract QoS-aware service composition aims to satisfy users’ quality of services (QoS) needs during service composition. Traditional methods simply attempt to maximize user satisfaction by provisioning the composite service instance with the best QoS. These “best-effort” methods fail to take into account that there also exist other consumers competing for the service resources and their decisions of service selection/composition can impact on QoS. Since users QoS needs can be met once the demanded level is reached, in this paper, we propose an “on-demand” strategy for QoS-aware service composition to replace the traditional “best-effort” strategy. The service broker is introduced to facilitate implementation of this strategy: it first purchases a number of service instances for each component from providers and then provisions the composite services with different QoS classes to consumers. This paper focuses on how the broker follows the service level agreement (SLA) to provision composite services in the “on-demand” manner. This problem is formally expressed as the minimization of the QoS distance function between SLA and QoS of composite service instances, under a series of constraints. Heuristic approaches are proposed for the problem and experiments are conducted at last to verify their effectiveness and efficiency.

Natural neighbor-based clustering algorithm with local representatives

Clustering by identifying cluster centers is important for detecting patterns in a data set. However, many center-based clustering algorithms cannot process data sets containing non-spherical clusters. In this paper, we propose a novel clustering algorithm called NaNLORE based on natural neighbor and local representatives. Natural neighbor is a new neighbor concept and introduced to compute local density and find local representatives which are points with local maximum density. We first find local representatives and then select cluster centers from the local representatives. The density-adaptive distance is introduced to measure the distance between local representatives, which helps to solve the problem of clustering data sets with complex manifold structure. Cluster centers are characterized by higher density than their neighbors and a relatively large density-adaptive distance from any local representatives with higher density. In experiments, we compare the proposed algorithm NaNLORE with existing algorithms on synthetic and real data sets. Results show that NaNLORE performs better than existing algorithm, especially on clustering non-spherical data and manifold data.

QoS-Aware Multigranularity Service Composition: Modeling and Optimization

Quality of service (QoS)-aware optimal service composition aims to maximize the overall QoS value of the resulting composite service instance while meeting user-specified global QoS constraints. Traditional methods only consider as candidates service instances that implement one abstract service in the composite service and neglect those that could perform multiple abstract services. To overcome this shortcoming, this paper proposes the concept of generalized component services (GCSs), which is defined in a semantic manner, to expand the selection scope so as to achieve a better solution. A QoS-aware multigranularity service composition model is formulated and how to identify all the GCSs for a composite service is elaborated. A backtracking-based algorithm and an extended genetic algorithm are proposed to optimize the resulting composite service instance. Lastly, evaluation results of these algorithms are described.

Heterogeneous Virtual Machine Consolidation Using an Improved Grouping Genetic Algorithm

Virtual machine (VM) consolidation is a promising approach for improving energy efficiency of the datacenter by increasing the resource utilization of physical machines. However, the live migration technology that VM consolidation relies on is costly in itself, and this migration cost is usually heterogeneous as well as the datacenter. This paper focuses on how to pay limited migration costs to save as much energy as possible via VM consolidation in a heterogeneous cloud environment. That is, how to minimize the energy consumption while keeping most of the VMs in the datacenter unmoved. To capture these two conflicting objectives, a migration cost estimation method is first proposed and then a consolidation score function is defined for overall evaluation. To maximize the consolidation score, an improved grouping genetic algorithm (IGGA) based on a greedy heuristic and a swap operation is proposed for VM consolidation. Experiments show that IGGA performs better than existing consolidation methods.

QoS-Aware Multi-granularity Service Composition Based on Generalized Component Services

QoS-aware service composition aims to maximize overall QoS values of the resulting composite service. Traditional methods only consider service instances that implement one abstract service in the composite service as candidates, and neglect those that fulfill multiple abstract services. To overcome this shortcoming, we present the concept of generalized component services to expand the selection scope to achieve a better solution. The problem of QoS-aware multi-granularity service composition is then formulated and how to discover candidates for each generalized component service is elaborated. A genetic algorithm based approach is proposed to optimize the resulting composite service instance. Empirical studies are performed at last.

QCC: a novel clustering algorithm based on Quasi-Cluster Centers

Cluster analysis aims at classifying objects into categories on the basis of their similarity and has been widely used in many areas such as pattern recognition and image processing. In this paper, we propose a novel clustering algorithm called QCC mainly based on the following ideas: the density of a cluster center is the highest in its K nearest neighborhood or reverse K nearest neighborhood, and clusters are divided by sparse regions. Besides, we define a novel concept of similarity between clusters to solve the complex-manifold problem. In experiments, we compare the proposed algorithm QCC with DBSCAN, DP and DAAP algorithms on synthetic and real-world datasets. Results show that QCC performs the best, and its superiority on clustering non-spherical data and complex-manifold data is especially large.

Deadline-Constrained Cost Optimization Approaches for Workflow Scheduling in Clouds

Nowadays it is becoming more and more attractive to execute workflow applications in the cloud because it enables workflow applications to use computing resources on demand. Meanwhile, it also challenges traditional workflow scheduling algorithms that only concentrate on optimizing the execution time. This paper investigates how to minimize execution cost of a workflow in clouds under a deadline constraint and proposes a metaheuristic algorithm L-ACO as well as a simple heuristic ProLiS. ProLiS distributes the deadline to each task, proportionally to a novel definition of probabilistic upward rank, and follows a two-step list scheduling methodology: rank tasks and sequentially allocates each task a service which meets the sub-deadline and minimizes the cost. L-ACO employs ant colony optimization to carry out deadline-constrained cost optimization: the ant constructs an ordered task list according to the pheromone trail and probabilistic upward rank, and uses the same deadline distribution and service selection methods as ProLiS to build solutions. Moreover, the deadline is relaxed to guide the search of L-ACO towards constrained optimization. Experimental results show that compared with traditional algorithms, the performance of ProLiS is very competitive and L-ACO performs the best in terms of execution costs and success ratios of meeting deadlines.

Energy and migration cost-aware dynamic virtual machine consolidation in heterogeneous cloud datacenters

Energy efficiency has become one of the major concerns for todays cloud datacenters. Dynamic virtual machine (VM) consolidation is a promising approach for improving the resource utilization and energy efficiency of datacenters. However, the live migration technology that VM consolidation relies on is costly in itself, and this migration cost is usually heterogeneous as well as the datacenter. This paper investigates the following bi-objective optimization problem: how to pay limited migration costs to save as much energy as possible via dynamic VM consolidation in a heterogeneous cloud datacenter. To capture these two conflicting objectives, a consolidation score function is designed for an overall evaluation on the basis of a migration cost estimation method and an upper bound estimation method for maximal saved power. To optimize the consolidation score, a greedy heuristic and a swap operation are introduced, and an improved grouping genetic algorithm (IGGA) based on them is proposed. Lastly, empirical studies are performed, and the evaluation results show that IGGA outperforms existing VM consolidation methods.