Web Scale Computing: The Power of Infrastructure as a Service.- Services in the Long Tail World: Challenges and Opportunities.- Services for Science.- Managing and Internet Service Bus.- Quality-Driven Business Policy Specification and Refinement for Service-Oriented Systems.- Adaptation of Web Service Composition Based on Workflow Patterns.- Protocol-Based Web Service Composition.- Design and Implementation of a Fault Tolerant Job Flow Manager Using Job Flow Patterns and Recovery Policies.- Building Mashups for the Enterprise with SABRE.- Adaptation of Service Protocols Using Process Algebra and On-the-Fly Reduction Techniques.- Automatic Workflow Graph Refactoring and Completion.- Authorization and User Failure Resiliency for WS-BPEL Business Processes.- Reasoning on Semantically Annotated Processes.- Event-Driven Quality of Service Prediction.- Automatic Realization of SOA Deployment Patterns in Distributed Environments.- The LLAMA Middleware Support for Accountable Service-Oriented Architecture.- ubiSOAP: A Service Oriented Middleware for Seamless Networking.- Towards a Service-Oriented Approach for Managing Context in Mobile Environment.- An Autonomic Middleware Solution for Coordinating Multiple QoS Controls.- Transparent Runtime Adaptability for BPEL Processes.- Organizational Constraints to Realizing Business Value from Service Oriented Architectures: An Empirical Study of Financial Service Institutions.- E-Marketplace for Semantic Web Services.- Business Driven SOA Customization.- Sound Multi-party Business Protocols for Service Networks.- Automatic Mash Up of Composite Applications.- Non-desynchronizable Service Choreographies.- A Framework for Semantic Sensor Network Services.- Context-Driven Autonomic Adaptation of SLA.- Determining QoS of WS-BPEL Compositions.- An Initial Approach to Explaining SLA Inconsistencies.- Ontology-Based Compatibility Checking for Web Service Configuration Management.- SOAlive Service Catalog: A Simplified Approach to Describing, Discovering and Composing Situational Enterprise Services.- WorldTravel: A Testbed for Service-Oriented Applications.- TCP???Compose ??? - A TCP-Net Based Algorithm for Efficient Composition of Web Services Using Qualitative Preferences.- A Runtime Quality Architecture for Service-Oriented Systems.- QoS Policies for Business Processes in Service Oriented Architectures.- Deriving Business Service Interfaces in Windows Workflow from UMM Transactions.- From Business Process Models to Web Services Orchestration: The Case of UML 2.0 Activity Diagram to BPEL.- Batch Invocation of Web Services in BPEL Process.- Formation of Service Value Networks for Decentralized Service Provisioning.- Towards Automated WSDL-Based Testing of Web Services.- Automated Service Composition with Adaptive Planning.- A Planning-Based Approach for the Automated Configuration of the Enterprise Service Bus.- Verifying Interaction Protocol Compliance of Service Orchestrations.- Specify Once Test Everywhere: Analyzing Invariants to Augment Service Descriptions for Automated Test Generation.- A Model-Driven Approach to Dynamic and Adaptive Service Brokering Using Modes.- Integrated Security Context Management of Web Components and Services in Federated Identity Environments.- Predicting and Learning Executability of Composite Web Services.- Authorization Policy Based Business Collaboration Reliability Verification.- VGC: Generating Valid Global Communication Models of Composite Services Using Temporal Reasoning.- A Framework for Advanced Modularization and Data Flow in Workflow Systems.- Model Identification for Energy-Aware Management of Web Service Systems.- LASS - License Aware Service Selection: Methodology and Framework.- Integrated and Composable Supervision of BPEL Processes.- Optimised Semantic Reasoning for Pervasive Service Discovery.- COSMA - An Approach for Managing SLAs in Composite Services.- Resource Calculations with Constraints, and Placement of Tenants and Instances for Multi-tenant SaaS Applications.- SPIN: Service Performance Isolation Infrastructure in Multi-tenancy Environment.- Management as a Service for IT Service Management.- SMART: Application of a Method for Migration of Legacy Systems to SOA Environments.- Discovering and Deriving Service Variants from Business Process Specifications.- Market Overview of Enterprise Mashup Tools.- Siena: From PowerPoint to Web App in 5 Minutes.- Exploration of Discovered Process Views in Process Spaceship.- ROME4EU: A Web Service-Based Process-Aware System for Smart Devices.- WS-Engineer 2008.- MetaCDN: Harnessing Storage Clouds for High Performance Content Delivery.- Yowie: Information Extraction in a Service Enabled World.

Service-oriented computing - ICSOC 2008 : 6th international conference Sydney, Australia, December 1-5, 2008 proceedings

Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be ‘team science’.

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Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data

The performance of contingencies of supply chain information integration: The roles of product and market complexity

Context: In cloud computing, there is a multitude of definitions and metrics for scalability, elasticity, and efficiency. However, stakeholders have little guidance for choosing fitting definitions and metrics for these quality properties, thus leading to potential misunderstandings. For example, cloud consumers and providers cannot negotiate reliable and quantitative service level objectives directly understood by each stakeholder. Objectives: Therefore, we examine existing definitions and metrics for these quality properties from the viewpoint of cloud consumers, cloud providers, and software architects with regard to commonly used concepts. Methods: We execute a systematic literature review (SLR), reproducibly collecting common concepts in definitions and metrics for scalability, elasticity, and efficiency. As quality selection criteria, we assess whether existing literature differentiates the three properties, exemplifies metrics, and considers typical cloud characteristics and cloud roles. Results: Our SLR yields 418 initial results from which we select 20 for in-depth evaluation based on our quality selection criteria. In our evaluation, we recommend concepts, definitions, and metrics for each property. Conclusions: Software architects can use our recommendations to analyze the quality of cloud computing applications. Cloud providers and cloud consumers can specify service level objectives based on our metric suggestions.

Scalability, Elasticity, and Efficiency in Cloud Computing: a Systematic Literature Review of Definitions and Metrics

There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed ‘big data’. The structural shift of the storage mechanism from traditional data management systems to NoSQL technology is due to the intention of fulfilling big data storage requirements. However, the available big data storage technologies are inefficient to provide consistent, scalable, and available solutions for continuously growing heterogeneous data. Storage is the preliminary process of big data analytics for real-world applications such as scientific experiments, healthcare, social networks, and e-business. So far, Amazon, Google, and Apache are some of the industry standards in providing big data storage solutions, yet the literature does not report an in-depth survey of storage technologies available for big data, investigating the performance and magnitude gains of these technologies. The primary objective of this paper is to conduct a comprehensive investigation of state-of-the-art storage technologies available for big data. A well-defined taxonomy of big data storage technologies is presented to assist data analysts and researchers in understanding and selecting a storage mechanism that better fits their needs. To evaluate the performance of different storage architectures, we compare and analyze the existing approaches using Brewer’s CAP theorem. The significance and applications of storage technologies and support to other categories are discussed. Several future research challenges are highlighted with the intention to expedite the deployment of a reliable and scalable storage system.

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Big data storage technologies: a survey

We consider the online list accessing problem and present a new family of competitive-optimal deterministic list update algorithms which is the largest class of such algorithms known to-date. This family, called SORT-BY-RANK (SBR), is parametrized with a real 0 ≤ α ≤ 1, where SBR(0) is the MOVE-TO-FRONT algorithm and SBR(1) is equivalent to the TIMESTAMP algorithm. The behaviour of SBR(α) mediates between the eager strategy of MOVE-TO-FRONT and the more conservative behaviour of TIMESTAMP.

We also present a family of algorithms SORT-BY-DELAY (SBD) which is parametrized by the positive integers, where SBD(1) is MOVE-TO-FRONT and SBD(2) is equivalent to TIMESTAMP. In general, SBD(k) is k- competitive for k ≥ 2. This is the first class of algorithms that is asymptotically optimal for independent, identically distributed requests while each algorithm is constant-competitive.

Empirical studies with with both generated and real-world data are also included.

Two New Families of List Update Algorithms

Managing Consistent Interfaces for Goods Tag, Production Bill of Material Hierarchy, and Release Order Template Business Objects across Heterogeneous Systems

The amount of data in the world is expanding rapidly. Every day, huge amounts of data are created by scientific experiments, companies, and end users’ activities. These large data sets have been labeled as “Big Data”, and their storage, processing and analysis presents a plethora of new challenges to computer science researchers and IT professionals. In addition to efficient data management, additional complexity arises from dealing with semi-structured or unstructured data, and from time critical processing requirements. In order to understand these massive amounts of data, advanced visualization and data exploration techniques are required.

Towards an Integrated Platform for Big Data Analysis

In service oriented architectures (SOA), the non-functional properties of services have been recognized to be highly important in addition to the functionality of services as a means to differentiate services according to quality considerations. Service level agreements (SLAs) are formalized contracts between service providers and service consumers that are used to define quality of service (QoS) properties. The violation of an SLA by the service provider typically results in a penalty to compensate the service consumer. In order to avoid such situations, the service provider needs to recognize critical service instances and to take appropriate countermeasures before a violation happens. Therefore a measure for quantifying the danger of SLA violation is needed as part of a service level management system. This paper proposes a concept for the definition and evaluation of such a metric that takes into account the underlying structure of the SLA as well as the available options for monitoring service quality parameters. Hence it becomes possible to obtain detailed information of the status of service fulfillment at runtime and to identify critical service instances. The methodology is exemplified with the availability property.

Towards Measuring the Degree of Fulfillment of Service Level Agreements

When designing services, a service provider has to define the quality of service levels that will be offered to customers. These service level objectives (SLOs) form the basis for service level agreements (SLAs) between the provider and consumers of services. The design of SLAs is a complex task which needs to combine business considerations regarding revenues, costs and penalties, and technical considerations with respect to feasible service levels. This paper proposes an approach that simultaneously takes into account business parameters and technical constraints for supporting the creation of service level agreements. Based on a statistical model derived from empirical service quality data, the relationship between service level objectives and potential penalty payments is investigated with respect to the long-term profit of the service provider. Based on the results, a practical support for deciding SLO thresholds is provided. The methodology is demonstrated and evaluated with the availability metric.

Frank Schulz

Papers

Two New Families of List Update Algorithms

Managing Consistent Interfaces for Goods Tag, Production Bill of Material Hierarchy, and Release Order Template Business Objects across Heterogeneous Systems

Towards an Integrated Platform for Big Data Analysis

Towards Measuring the Degree of Fulfillment of Service Level Agreements

Decision support for business-related design of service level agreements