James R. Edmondson

Evaluating technologies for tactical information management in net-centric systems

Recent trends in distributed real-time and embedded (DRE) systems motivate the development of tactical information management capabilities that ensure the right information is delivered to the right place at the right time to satisfy quality of service (QoS) requirements in heterogeneous environments. A promising approach to building and evolving large-scale and long-lived tactical information management systems are standards-based QoS-enabled publish/subscribe (pub/sub) platforms that enable applications to communicate by publishing information they have and subscribing to information they need in a timely manner. Since there is little existing evaluation of how well these platforms meet the performance needs of tactical information management, this paper provides two contributions: (1) it describes three common architectures for the OMG Data Distribution Service (DDS), which is a QoS-enabled pub/sub platform standard, and (2) it evaluates three implementations of these architectures to investigate their design tradeoffs and to compare their performance. Our results show that DDS implementations perform well in general and are well-suited for certain classes of data-critical tactical information management systems.

Tools for Continuously Evaluating Distributed System Qualities

Developers are increasingly using service-oriented middleware to develop distributed systems. This middleware raises the abstraction level for software so that distributed-system developers can focus more on application-level concerns (for instance, business logic) rather than wrestle with infrastructure-level concerns (such as software adaptation, context-awareness, and life-cycle management). Service-oriented middleware also promotes reuse of business logic and services across heterogeneous application domains, thus facilitating the development of larger, more complex systems.

QoS enabled dissemination of managed information objects in a publish-subscribe-query information broker

Net-centric information spaces have become a necessary concept to support information exchange for tactical warfighting missions using a publish-subscribe-query paradigm. To support dynamic, mission-critical and time-critical operations, information spaces require quality of service (QoS)-enabled dissemination (QED) of information. This paper describes the results of research we are conducting to provide QED information exchange in tactical environments. We have developed a prototype QoS-enabled publish-subscribe-query information broker that provides timely delivery of information needed by tactical warfighters in mobile scenarios with time-critical emergent targets. This broker enables tailoring and prioritizing of information based on mission needs and responds rapidly to priority shifts and unfolding situations. This paper describes the QED architecture, prototype implementation, testing infrastructure, and empirical evaluations we have conducted based on our prototype.

Dynamic Policy-Driven Quality of Service in Service-Oriented Systems

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.

Design of a scalable reasoning engine for distributed, real-time and embedded systems

Effective and efficient knowledge dissemination and reasoning in distributed, real-time, and embedded (DRE) systems remains a hard problem due to the need for tight time constraints on evaluation of rules and scalability in dissemination of knowledge events. Limitations in satisfying the tight timing properties stem from the fact that most knowledge reasoning engines continue to be developed in managed languages like Java and Lisp, which incur performance overhead in their interpreters due to wasted precious clock cycles on managed features like garbage collection and indirection. Limitations in scalable dissemination stem from the presence of ontologies and blocking network communications involving connected reasoning agents. This paper addresses the existing problems with timeliness and scalability in knowledge reasoning and dissemination by presenting a C++-based knowledge reasoning solution that operates over a distributed and anonymous publish/subscribe transport mechanism provided by the OMGs Data Distribution Service (DDS). Experimental results evaluating the performance of the C++-based reasoning solution illustrate microsecond-level evaluation latencies, while the use of the DDS publish/subscribe transport illustrates significant scalability in dissemination of knowledge events while also tolerating joining and leaving of system entities.

Dynamic Policy-Driven Quality of Service in Service-Oriented Information Management Systems

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 QEDs 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 QEDs 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

Automating testing of service-oriented mobile applications with distributed knowledge and reasoning

Automated testing of distributed, service-oriented applications, particularly mobile applications, is a hard problem due to challenges testers often must deal with, such as (1) heterogeneous platforms, (2) difficulty in introducing additional resources or backups of resources that fail during testing, and (3) lack of fine-grained control over test sequencing. To address these challenges, this paper describes an approach that combines portable operating system libraries with knowledge and reasoning, which together leverage the best features of centralized and decentralized testing infrastructures to support both heterogeneous systems and distributed control by reasoning on distributed testing events.

Multi-agent distributed adaptive resource allocation (MADARA)

The component placement problem involves mapping a component to a particular location and maximising component utility in grid and cloud systems. It is also an NP hard resource allocation and deployment problem, so many common grid and cloud computing libraries, such as MPICH and Hadoop, do not address this problem, even though large performance gains can occur by optimising communications between nodes. This paper provides four contributions to research on the component placement problem for grid and cloud computing environments. First, we present the multi-agent distributed adaptive resource allocation (MADARA) toolkit, which is designed to address grid and cloud allocation and deployment needs. Second, we present a heuristic called the comparison-based iteration by degree (CID) heuristic, which we use to approximate optimal deployments in MADARA. Third, we analyse the performance of applying the CID heuristic to approximate common grid and cloud operations, such as broadcast, gather and reduce. Fourth, we evaluate the results of applying genetic programming mutation to improve our CID heuristic.

QoS-enabled distributed mutual exclusion in public clouds

Popular public cloud infrastructures tend to feature centralized, mutual exclusion models for distributed resources, such as file systems. The result of using such centralized solutions in the Google File System (GFS), for instance, reduces scalability, increases latency, creates a single point of failure, and tightly couples applications with the underlying services. In addition to these quality-of-service (QoS) and design problems, the GFS methodology does not support generic priority preference or pay-differentiated services for cloud applications, which public cloud providers may require under heavy loads. This paper presents a distributed mutual exclusion algorithm called Prioritizable Adaptive Distributed Mutual Exclusion (PADME) that we designed to meet the need for differentiated services between applications for file systems and other shared resources in a public cloud. We analyze the fault tolerance and performance of PADME and show how it helps cloud infrastructure providers expose differentiated, reliable services that scale. Results of experiments with a prototype of PADME indicate that it supports service differentiation by providing priority preference to cloud applications, while also ensuring high throughput.

Approximation Techniques for Maintaining Real-Time Deployments Informed by User-Provided Dataflows within a Cloud

Distributed applications are increasingly developed by composing many participants, such as services, components, and objects. When deploying distributed applications into a mobile ad hoc cloud, the locality of application participants that communicate with each other can affect latency, power/\-battery usage, throughput, and whether or not a cloud provider can meet service-level agreements (SLA). Optimization of important communication links within a distributed application is particularly important when dealing with mission-critical applications deployed in a distributed real-time and embedded (DRE) scenario, where violation of SLAs may result in loss of property, cyber infrastructure, or lives. To complicate the optimization process, the underlying cloud environment can change during operation and an optimal deployment of the distributed application may degrade over time due to hardware failures, overloaded hosts, and other issues that are beyond the control of distributed application developers. To optimize performance of distributed applications in dynamic environments, therefore, the deployment of participants may need adapting and revising according to the requirements of application developers and the resources available in the underlying cloud environment. This paper present two contributions to the study of dynamic optimizations of user-provided deployments within a cloud. First, we present a dataflow description language that allows developers to designate key communication paths between participants within their distributed applications. Second, we describe heuristics that use this dataflow representation to identify optimal configurations for initial deployments and/or subsequent redeployments within a cloud. An experiment is presented to validate the heuristic approaches.