Kaijun Ren

Building Quick Service Query List Using WordNet and Multiple Heterogeneous Ontologies toward More Realistic Service Composition

Although semantic-based composition approaches have brought some comprehensive advantages such as higher precisions and recalls, they are far from the real practice and hard to be applied in real-world applications due to the several challenging issues such as performance issues of time-consuming ontology reasoning, exponentially expanded searching time in large service repositories, lack of available and consensus ontologies, and higher using thresholds for users who do not have much semantic knowledge. To reduce these issues, in this paper, we present an innovative composition technique by building an Extended Quick Service Query List (EQSQL) for supporting more efficient and more realistic service composition. In EQSQL, data structures are specially designed to record service information and their associated semantic concepts by in advance processing semantic-related computing during service publication period. Particularly, WordNet and semantic similarities among multiple heterogeneous ontologies are exploited in our developed algorithms for forming EQSQL. As a result, EQSQL-based planning algorithm can not only achieve a quick response for a composition request, but guarantee the semantic composition quality as well. More importantly, our approaches can be scalable to the large service repositories and also significantly alleviate users or developers from the burden of using complicated semantic service composition, thus making service composition easier and more realistic. Our final experiments further demonstrate the feasibility and the efficiency of our proposed approaches.

Exploring portfolio scheduling for long-term execution of scientific workloads in IaaS clouds

Long-term execution of scientific applications often leads to dynamic workloads and varying application requirements. When the execution uses resources provisioned from IaaS clouds, and thus consumption-related payment, efficient and online scheduling algorithms must be found. Portfolio scheduling, which selects dynamically a suitable policy from a broad portfolio, may provide a solution to this problem. However, selecting online the right policy from possibly tens of alternatives remains challenging. In this work, we introduce an abstract model to explore this selection problem. Based on the model, we present a comprehensive portfolio scheduler that includes tens of provisioning and allocation policies. We propose an algorithm that can enlarge the chance of selecting the best policy in limited time, possibly online. Through trace-based simulation, we evaluate various aspects of our portfolio scheduler, and find performance improvements from 7% to 100% in comparison with the best constituent policies and high improvement for bursty workloads.

A QSQL-based Efficient Planning Algorithm for Fully-automated Service Composition in Dynamic Service Environments

Web service composition is emerging as a promising technology for supporting large-scale, sophisticated business process integration in a variety of complex e-science or e-business domains. Particularly, semantics have been proposed as a key to automatically solving the discovery and composition problem. However, most of semantic composition approaches still remain at a stage of low efficiency because of the performance issues brought by the involved ontology reasoning and manual processing. To address this problem, in this paper, we present a QSQL-based service composition algorithm towards a fully-automated service composition. QSQL (Quick Service Query List) is an efficient service query index list which can achieve about the same semantic service discovery effects as other existing semantic composition methods, but with much less reasoning. With our proposed QSQL-based service composition algorithm, composition plans can be created to meet a users query in an automatic, efficient and semantic manner. In particular, with our algorithm, most existing composition plans in QSQL can be founded and ranked by exploiting a weighted Petri net representation; which will facilitate the execution verification. The final experiment is conducted to further demonstrate the feasibility of our proposed composition approach and its efficiency.

A Global Graph-based Approach for Transaction and QoS-aware Service Composition

In Web Service Composition (WSC) area, services selection aims at selecting an appropriate candidate from a set of functionally-equivalent services to execute the function of each task in an abstract WSC according to their different QoS values. In despite of many related works, few of previous studies consider transactional constraints in QoS-aware WSC, which guarantee reliable execution of Composite Web Service (CWS) that is composed by a number of unpredictable web services. In this paper, we propose a novel global selection-optimal approach in WSC by considering both transactional constraints and end-to-end QoS constraints. With this approach, we firstly identify building rules and the reduction method to build layer-based Directed Acyclic Graph (DAG) model which can model transactional relationships among candidate services. As such, the problem of solving global optimal QoS utility with transactional constraints in WSC can be regarded as a problem of solving single-source shortest path in DAG. After that, we present Graph-building algorithms and an optimal selection algorithm to explain the specific execution procedures. Finally, comprehensive experiments are conducted based on a real-world web service QoS dataset. The experimental results show that our approach has better performance over other competing selection approaches on success ratio and efficiency.

A Periodic Portfolio Scheduler for Scientific Computing in the Data Center

The popularity of data centers in scientific computing has led to new architectures, new workload structures, and growing customer-bases. As a consequence, the selection of efficient scheduling algorithms for the data center is an increasingly costlier and more difficult challenge. To address this challenge, and contrasting previous work on scheduling for scientific workloads, we focus in this work on portfolio scheduling—here, the dynamic selection and use of a scheduling policy, depending on the current system and workload conditions, from a portfolio of multiple policies. We design a periodic portfolio scheduler for the workload of the entire data center, and equip it with a portfolio of resource provisioning and allocation policies. Through simulation based on real and synthetic workload traces, we show evidence that portfolio scheduling can automatically select the scheduling policy to match both user and data center objectives, and that portfolio scheduling can perform well in the data center, relative to its constituent policies.

A clustering based coscheduling strategy for efficient scientific workflow execution in cloud computing

Due to its advantages of cost‐effectiveness, on‐demand provisioning and easy for sharing, cloud computing has grown in popularity with the research community for deploying scientific applications such as workflows. Although such interests continue growing and scientific workflows are widely deployed in collaborative cloud environments that consist of a number of data centers, there is an urgent need for exploiting strategies which can place application datasets across globally distributed data centers and schedule tasks according to the data layout to reduce both latency and makespan for workflow execution. In this paper, by utilizing dependencies among datasets and tasks, we propose an efficient data and task coscheduling strategy that can place input datasets in a load balance way and meanwhile, group the mostly related datasets and tasks together. Moreover, data staging is used to overlap task execution with data transmission in order to shorten the start time of tasks. We build a simulation environment on Tianhe supercomputer for evaluating the proposed strategy and run simulations by random and realistic workflows. The results demonstrate that the proposed strategy can effectively improve scheduling performance while reducing the total volume of data transfer across data centers. Concurrency and Computation: Practice and Experience, 2013.© 2013 Wiley Periodicals, Inc.

Graph-Cut Based Coscheduling Strategy Towards Efficient Execution of Scientific Workflows in Collaborative Cloud Environments

Recently, cloud computing has emerged as a promising computing infrastructure for performing scientific workflows by providing on-demand resources. Meanwhile, it is convenient for scientific collaboration since different cloud environments used by the researchers are connected through Internet. However, the significant latency arising from frequent access to large datasets and the corresponding data movements across geo-distributed data centers has been an obstacle to hinder the efficient execution of data-intensive scientific workflows. In this paper, we propose a novel graph-cut based data and task co scheduling strategy for minimizing the data transfer across geo-distributed data centers. Specifically, a dependency graph is firstly constructed from workflow provenance and cut into sub graphs according to the datasets which must appear in fixed data centers by a multiway cut algorithm. Then, the sub graphs might be recursively cut into smaller ones by a minimum cut algorithm referring to data correlation rules until all of them can well fit the capacity constraints of the data centers where the fixed location datasets reside. In this way, the datasets and tasks are distributed into target data centers while the total amount of data transfer between them is minimized. Additionally, a runtime scheduling algorithm is exploited to dynamically adjust the data placement during execution to prevent the data centers from overloading. Simulation results demonstrate that the total volume of data transfer across different data centers can be significantly reduced and the cost of performing scientific workflows on the clouds will be accordingly saved.

A Risk-Driven Selection Approach for Transactional Web Service Composition

In web service composition (WSC), quality of service (QoS)-based web services selection has been a critical research issue, and many service selection methods have been presented aiming at resolving this issue, However, most of the existing methods ignore giving a risk evaluation for critical web services in WSC where transactional requirements often require compensation cost to ensure failure atomicity . To address this issue, we present a Risk-driven selection approach by incorporating the impact of failure risk of each participating task to reduce the average losses caused by execution failures of tasks for WSC. With our method, a kind of tree called failure causing tree is built to support risk losses evaluation for each task in composition web service (CWS). Particularly, our method is more suitable to the scientific computing area where many long tasks are often involved and easily lead to a lot of losses such as computing cost, communicating cost, when some failures occur in these executing tasks. The final experiment and evaluation further demonstrate the feasibility and efficiency of our method.

Grid-Based Semantic Web Service Discovery Model with QoS Constraints

In this paper, we present a model for grid-based semantic service discovery by combining the expressive power of the present ontology language and the advantages of semantic web techniques. This model is an initial work to support dynamic workflow composition of semantically described services. Our model distinguishes other service discovery models by not only supporting semantic functional matchmaking, but also supporting flexible QoS ranking that differentiates similar services offered by different service providers in grid environments.

A Weighted K-Means Clustering Based Co-scheduling Strategy towards Efficient Execution of Scientific Workflows in Collaborative Cloud Environments

Due to the advantages of cost-effectiveness, on-demand resource provision and easy for sharing, cloud computing has grown in popularity with research community for deploying scientific applications such as workflows. When such interest continues growing and workflows are widely performed in collaborative cloud environments that consist of a number of data centers, there is an urgent need for exploiting strategies which can place the application data across globally distributed data centers and schedule tasks according to the data layout to reduce both the latency and make span for workflow execution. In this paper, by utilising dependencies among datasets and tasks, we propose an efficient data and task co scheduling strategy that can place input datasets in a load balance way and meanwhile group the mostly related datasets and tasks together. We build a simulation environment on Tianhe supercomputer to evaluate the proposed strategy and run simulations by random and realistic workflows. The results demonstrate that the proposed strategy can effectively improve workflows performance while reducing the total volume of data transfer across data centers.