With the proliferation of Web services as a business solution to enterprise application integration, ranking and selecting the best Web services among the providers become an important factor in the success of the business solution. Quality of Service (QoS) determines the quality and usability of a service including its price, performance, reliability, integrity, accessibility, availability, interoperability, and security. Given a set of QoS attributes from a variety of sources, it is a challenge to sort through all of them and be able to get the best services that meet QoS requirement. In this paper, we describe a novel method by which Web services can be ranked and selected automatically based on a number of observed QoS parameters and feedback responses learned from prior knowledge. This new approach treats the observed Web services QoS attributes and target Web services relationship, represented by a matrix, as a statistical problem. Using Singular Value Decomposition (SVD) technique, and an user assisted weighting system, implicit higher order correlations among Web services and their associated QoS attributes are extracted and used to estimate the selection of recommended Web services.

Monitoring resource utilization is an essential task in utility computing (UC). Typically, a UC manager with adaptors is used to orchestrate and collect the monitoring results. However, large IT systems, such as those in data centers are required to monitor dynamic resources running in distributed platforms, thus demanding a complex UC manager structure. This paper introduces a novel approach that utilizes a platform specific aspect-oriented programming (AOP) tool to dynamically weave a monitoring Web service into a running resource to enable communication with a UC manager exposed with standard monitoring Web service interfaces. This AO-Web service approach provides a simple yet powerful and effective means for the dynamic monitoring of distributed resources running in heterogeneous platforms.

Electrical power management in large-scale IT systems such as commercial data- centers is an application area of rapidly growing interest from both an economic and ecological perspective, with billions of dollars and millions of metric tons of CO2 emissions at stake annually. Businesses want to save power without sac- rificing performance. This paper presents a reinforcement learning approach to simultaneous online management of both performance and power consumption. We apply RL in a realistic laboratory testbed using a Blade cluster and dynam- ically varying HTTP workload running on a commercial web applications mid- dleware platform. We embed a CPU frequency controller in the Blade servers’ firmware, and we train policies for this controller using a multi-criteria reward signal depending on both application performance and CPU power consumption. Our testbed scenario posed a number of challenges to successful use of RL, in- cluding multiple disparate reward functions, limited decision sampling rates, and pathologies arising when using multiple sensor readings as state variables. We describe innovative practical solutions to these challenges, and demonstrate clear performance improvements over both hand-designed policies as well as obvious “cookbook” RL implementations.

In order to make software components more flexible and reusable, it is desirable to provide business users with facilities to assemble and control them without their needing programming knowledge. This paper describes a fully functional prototype middleware system where variability is externalized so that core applications need not be altered for anticipated changes. In this system, application behavior modification is fast and easy, making this middleware suitable for frequently changing programs.

We address why, and especially how, to represent business rules in e-commerce contracts.By contracts, we mean descriptions of goods and services offered or sought, including ancillary agreements detailing terms of a deal.We observe that rules are useful in contracts to represent conditional relationships, e.g., in terms & conditions, service provisions, and surrounding business processes, and we illustrate this point with several examples.We analyze requirements (desiderata) for representing such rules in contracts.The requirements include: declarative semantics so as to enable shared understanding and interoperability; prioritized conflict handling so as to enable modular updating/revision; ease of parsing; integration into WWW-world software engineering; direct executability; and computational tractability.We give a representational approach that consists of two novel aspects.First, we give a new fundamental knowledge representation formalism: a generalized version of Courteous Lugic Programs (CLP), which expressively extends declarative ordinary logic programs (OLP) to include prioritized conflict handling, thus enabling modularity in specifying and revising rule-sets.Our approach to im-