Flexible provisioning of Web service workflows

Web services promise to revolutionise the way computational resources and business processes are offered and invoked in open, distributed systems, such as the Internet. These services are described using machine-readable meta-data, which enables consumer applications to automatically discover and provision suitable services for their workflows at run-time. However, current approaches have typically assumed service descriptions are accurate and deterministic, and so have neglected to account for the fact that services in these open systems are inherently unreliable and uncertain. Specifically, network failures, software bugs and competition for services may regularly lead to execution delays or even service failures. To address this problem, the process of provisioning services needs to be performed in a more flexible manner than has so far been considered, in order to proactively deal with failures and to recover workflows that have partially failed. To this end, we devise and present a heuristic strategy that varies the provisioning of services according to their predicted performance. Using simulation, we then benchmark our algorithm and show that it leads to a 700% improvement in average utility, while successfully completing up to eight times as many workflows as approaches that do not consider service failures

Flexible Provisioning of Service Workflows

Service-oriented computing is a promising paradigm for highly distributed and complex computer systems. In such systems, services are offered by provider agents over a computer network and automatically discovered and provisioned by consumer agents that need particular resources or behaviours for their workflows. However, in open systems where there are significant degrees of uncertainty and dynamism and where the agents are self-interested, the provisioning of these services needs to be performed in a more flexible way than has hitherto been considered. To this end, we devise a number of heuristics that vary provisioning according to the predicted performance of provider agents. We then empirically benchmark our algorithms and show that they lead to a 350% improvement in average utility, while successfully completing 5-6 times as many workflows as current approaches

An Effective Strategy for the Flexible Provisioning of Service Workflows

Recent advances in service-oriented frameworks and semantic Web technologies have enabled software agents to discover and invoke resources over large distributed systems, in order to meet their high-level objectives. However, most work has failed to acknowledge that such systems are complex and dynamic multi-agent systems, where service providers act autonomously and follow their own decision-making procedures. Hence, the behaviour of these providers is inherently uncertain - services may fail or take uncertain amounts of time to complete. In this work, we address this uncertainty and take an agent-oriented approach to the problem of provisioning service providers for the constituent tasks of abstract workflows. Specifically, we describe an algorithm that uses redundancy to deal with unreliable providers, and we demonstrate that it achieves an 8-14% improvement in average utility over previous work, while performing up to 6 times as well as approaches that do not consider service uncertainty. We also show that our algorithm performs well in the presence of inaccurate service performance information

Service-oriented computing : agents, semantics, and engineering : AAMAS 2007 International Workshop, SOCASE 2007, Honolulu, HI, USA, May 14, 2007 : proceedings

Executing Semantic Web Services with a Context-Aware Service Execution Agent.- An Effective Strategy for the Flexible Provisioning of Service Workflows.- Using Goals for Flexible Service Orchestration.- An Agent-Based Approach to User-Initiated Semantic Service Interconnection.- A Lightweight Agent Fabric for Service Autonomy.- Semantic Service Composition in Service-Oriented Multiagent Systems: A Filtering Approach.- Towards a Mapping from BPMN to Agents.- Associated Topic Extraction for Consumer Generated Media Analysis.- An MAS Infrastructure for Implementing SWSA Based Semantic Services.- A Role-Based Support Mechanism for Service Description and Discovery.- WS2JADE: Integrating Web Service with Jade Agents.- Z-Based Agents for Service Oriented Computing

Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high-performance applications, such as local clusters, high-performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications

HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation

Historically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing nterest among the cloud providers to demonstrate the capability to perform large-scale scientific computing. In this paper, we discuss results from the CMS experiment using the Fermilab HEPCloud facility, which utilized both local Fermilab resources and virtual machines in the Amazon Web Services Elastic Compute Cloud. We discuss the planning, technical challenges, and lessons learned involved in performing physics workflows on a large-scale set of virtualized resources. In addition, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources.Comment: 15 pages, 9 figure