SUMMARY SOA middleware has emerged as a powerful and popular distributed computing paradigm because of its high‐level abstractions for composing systems and encapsulating platform‐level details and complexities. Control of some details encapsulated by SOA middleware is necessary, however, to provide managed QoS for SOA systems that require predictable performance and behavior. This paper presents a policy‐driven approach for managing QoS in SOA systems called QoS enabled dissemination (QED). QED includes services for: (1) specifying and enforcing the QoS preferences of individual clients; (2) mediating and aggregating QoS management on behalf of competing users; and (3) shaping information exchange to improve real‐time performance. We describe QED's QoS services and mechanisms in the context of managing QoS for a set of Publish‐Subscribe‐Query information management services. These services provide a representative case study in which CPU and network bottlenecks can occur, client QoS preferences can conflict, and system‐level QoS requirements are based on higher level, aggregate end‐to‐end goals. We also discuss the design of several key QoS services and describe how QED's policy‐driven approach bridges users to the underlying middleware and enables QoS control based on rich and meaningful context descriptions, including users, data types, client preferences, and information characteristics. In addition, we present experimental results that quantify the improved control, differentiation, and client‐level QoS enabled by QED. Copyright © 2011 John Wiley & Sons, Ltd.

Service-oriented architecture (SOA) middleware has emerged as a powerful and popular distributed computing paradigm due to its high-level abstractions for composing systems and hiding platform-level details. Control of some details hidden by SOA middleware is necessary, however, to provide managed quality of service (QoS) for SOA systems that need predictable performance and behavior. This paper presents a policy-driven approach for managing QoS in SOA systems. We discuss the design of several key QoS services and empirically evaluate their ability to provide QoS under CPU overload and bandwidth-constrained situations.

Several key aspects of coordination such as teamwork, roles, and communication, are enabled and driven by, and even largely defined by, various systems of regulation. One key feature of all these elements in human coordination is their dynamic nature. We have developed a framework to provide a dynamic regulatory system for supporting coordination in human-robot teamwork. This framework supports the definition and functions of roles within teams, as well as the creation of subteams and the roles within them. It also serves to regulate communications in support of coordination. We have demonstrated our system with a team of two humans and five robots performing advanced coordination while trying to apprehend an intruder hiding on a cluttered pier. This work lays the foundation for human-robot coordination based on dynamic regulation.

Complex services in service-oriented architectures such as the grid typically require to be configured in multiple ways that cannot be anticipated by service designers; we illustrate this requirement by studying the myGrid registry, a grid registry capable of supporting annotations of service descriptions by third-party users. Instead, services have to be conceived so that they can be configured at deployment and run time. We argue that KAoS is a powerful and flexible language that can help define such configurations. Using our registry case study, we examine the requirements that the definition of such complex configurations brings on policy languages and explain how they can be satisfied. Specifically, we use role-value maps to express constraints between property values; we introduce a notion of PolicySet with associated parameters that support constraints within a well defined scope; finally, we define a notion of context that allows us to refer to property values that were extant in past execution environments. Essentially, these concepts allow us to add constraints to values in policy definitions, to organise policies in coherent and structure blocks, and to refer to the execution history. The paper discusses these concepts and how they are implemented in a binding of the KAoS policy language to the myGrid registry.