Joe Hoffert

A QoS policy configuration modeling language for publish/subscribe middleware platforms

Publish/subscribe (pub/sub) middleware platforms for event-based distributed systems often provide many configurable policies that affect end-to-end quality of service (QoS). Although the flexibility and functionality of pub/sub middleware platforms has matured, configuring their QoS policies in semantically compatible ways has become more complex. This paper makes two contributions to reducing the complexity of configuring QoS policies for event-based distributed systems. First, it evaluates various approaches for managing complex QoS policy configurations in pub/sub middleware platforms. Second, it describes a domain-specific modeling language (DSML) that automates the analysis and synthesis of semantically compatible QoS policy configurations.

Adapting distributed real-time and embedded pub/sub middleware for cloud computing environments

Enterprise distributed real-time and embedded (DRE) publish/subscribe (pub/sub) systems manage resources and data that are vital to users. Cloud computing---where computing resources are provisioned elastically and leased as a service---is an increasingly popular deployment paradigm. Enterprise DRE pub/sub systems can leverage cloud computing provisioning services to execute needed functionality when on-site computing resources are not available. Although cloud computing provides flexible on-demand computing and networking resources, enterprise DRE pub/sub systems often cannot accurately characterize their behavior a priori for the variety of resource configurations cloud computing supplies (e.g., CPU and network bandwidth), which makes it hard for DRE systems to leverage conventional cloud computing platforms. This paper provides two contributions to the study of how autonomic configuration of DRE pub/sub middleware can provision and use on-demand cloud resources effectively. We first describe how supervised machine learning can configure DRE pub/sub middleware services and transport protocols autonomically to support end-to-end quality-of-service (QoS) requirements based on cloud computing resources. We then present results that empirically validate how computing and networking resources affect enterprise DRE pub/sub system QoS. These results show how supervised machine learning can configure DRE pub/sub middleware adaptively in < 10 μsec with bounded time complexity to support key QoS reliability and latency requirements.

Evaluating Transport Protocols for Real-Time Event Stream Processing Middleware and Applications

Real-time event stream processing (RT-ESP) applications must synchronize continuous data streams despite fluctuations in resource availability. Satisfying these needs of RT-ESP applications requires predictable QoS from the underlying publish/subscribe (pub/sub) middleware. If a transport protocol is not capable of meeting the QoS requirements within a dynamic environment, the middleware must be flexible enough to tune the existing transport protocol or switch to a transport protocol better suited to the changing operating conditions. Realizing such adaptive RT-ESP pub/sub middleware requires a thorough understanding of how different transport protocols behave under different operating conditions. This paper makes three contributions to work on achieving that understanding. First, we define ReLate2, which is an evaluation metric that combines packet latency and reliability to evaluate transport protocol performance. Second, we use the ReLate2 metric to quantify the performance of various transport protocols integrated with the OMGs Data Distribution Service (DDS) QoS-enabled pub/sub middleware standard using our FLEXibleMiddleware AndTransports (FLEXMAT) prototype for experiments that capture performance data. Third, we use ReLate2 to pinpoint configurations involving sending rate, network loss, and number of receivers that show the pros and cons of the protocols.

Timely Autonomic Adaptation of Publish/Subscribe Middleware in Dynamic Environments

Quality-of-service enabled publish/subscribe (pub/sub) middleware provides powerful support for scalable data dissemination. It is difficult to maintain key quality of service properties (such as reliability and latency) in dynamic environments for distributed real-time and embedded systems (such as disaster relief operations or power grids). Managing quality of service manually is often not feasible in dynamic environments due to slow response times, the complexity of managing multiple interrelated quality of service settings, and the scale of the systems being managed. For certain domains, distributed real-time and embedded systems must be able to reflect on the conditions of their environment and adapt accordingly in a bounded amount of time. This paper describes an architecture of quality of service-enabled middleware and corresponding algorithms to support specified quality of service in dynamic environments.

Adapting and evaluating distributed real-time and embedded systems in dynamic environments

Quality of Service (QoS)-enabled publish/subscribe (pub/- sub) middleware provides much needed infrastructure for data dissemination in distributed real-time and embedded (DRE) systems. It is hard, however, to quantify the performance of mechanisms that support multiple interrelated QoS concerns, e.g., reliability, latency, and jitter. Moreover, once an appropriate mechanism is selected, it is hard to maintain QoS properties as the operating environment fluctuates since the chosen mechanism might no longer provide the needed QoS. For DRE systems operating in such environments, adjustments to mechanisms supporting QoS must be both timely and resilient to unforeseen environments. This paper describes our work to (1) define composite metrics to evaluate multiple interrelated QoS concerns and (2) analyze various adjustment techniques ( i.e., policy-based approaches, machine learning techniques) used for the QoS mechanisms of a DRE system in a dynamic environment. Our results show that (1) composite metrics quantify the support that mechanisms provide for multiple QoS concerns to ease mechanism evaluation and creation of related composite metrics and (2) neural network machine learning techniques provide the constant-time complexity needed for DRE pub/- sub systems to determine adjustments and the robustness to handle unknown environments.

Integrating Machine Learning Techniques to Adapt Protocols for QoS-enabled Distributed Real-time and Embedded Publish/Subscribe Middleware

Quality-of-service (QoS)-enabled publish/subscribe (pub/sub) middleware provides the infra-structure needed to disseminate data predictably, reliably, and scalably in distributed real-time and embedded (DRE) systems. Maintaining QoS properties as the operating environ¬ment fluctuates is challenging, however, since the chosen mechanism (e.g., transport protocol or caching algorithm for data persistence) may no longer provide the needed QoS. Moreover, some adaptation approaches are tailored for particular types of operating environments, such as environments whose configuration properties (e.g., number of data receivers or data sending rate) are known prior to runtime versus unknown until runtime. For DRE pub/sub systems operating in dynamic environments, adjustments to mechanisms must be timely, accurate for known environments, and resilient to environments unknown until runtime. Several adaptation approaches, such as policy-based [1] and reinforcement learning [2] have been developed to ensure end-to-end quality-of-service (QoS) for enterprise distributed systems in dynamic operating environments. Not all approaches are applicable for DRE pub/sub systems, however, due to their stringent accuracy, timeliness, and development complexity requirements. Supervised machine learning techniques, such as artificial neural networks (ANNs) [3] and support vector machines (SVMs) [4], are promising approaches to address the accuracy, time complexity, and development complexity concerns of adaptive enterprise DRE systems. This article describes the results of research that (1) empirically evaluates supervised machine learning techniques used to adapt the transport protocols of QoS-enabled pub/sub middleware autonomically in a dynamic environment and (2) integrates multiple techniques to increase accuracy for environments known a priori and not known until runtime. Our results show that both ANNs and SVMs provide constant time complexity, low latency, and reduced de-velopment complexity. ANNs are generally more accurate in providing adaptation guidance for environments whose properties are known prior to runtime and provide sub-sec response times, whereas SVMs provide higher accuracy with sec latencies for environments whose properties are not known until runtime. Both approaches can be leveraged together with QoS-enabled pub/sub middleware to address the timeliness, accuracy, and development com-plexity needs of enterprise DRE systems executing in dynamic environments.

A taxonomy of discovery services and gap analysis for ultra-large scale systems

Timely discovery of services in ultra-large scale (ULS) systems plays a vital role in critical areas, such as national power grids, homeland security, and health care. This paper develops a taxonomy for classifying discovery services and presents an overview of existing discovery service technologies. It then classifies these discovery services using the taxonomy and performs a gap analysis for discovery services with respect to emerging ULS systems. Our results show that while discovery services are fairly mature and broadly applicable to todays systems much R&D remains to support emerging systems of ultra-large scale effectively.

Using machine learning to maintain pub/sub system QoS in dynamic environments

Quality-of-service (QoS)-enabled publish/subscribe (pub/sub) middleware provides powerful support for scalable data dissemination. It is hard, however, to maintain specified QoS properties (such as reliability and latency) in dynamic environments (such as disaster relief operations or power grids). For example, managing QoS manually is often not feasible in dynamic systems due to (1) slow human response times, (2) the complexity of managing multiple interrelated QoS settings, and (3) the scale of the systems being managed. For certain applications the systems must be able to reflect on the conditions of their environment and adapt accordingly. Machine learning techniques provide a promising adaptation approach to maintaining QoS properties of QoS-enabled pub/sub middleware in dynamic environments. These techniques include decision trees, neural networks, and linear logistic regression classifiers that can be trained on existing data to interpolate and extrapolate for new data. By training the machine learning techniques with system performance metrics in a wide variety of configurations, changes to middleware mechanisms (e.g., associations of publishers and subscribers to transport protocols) can be driven by machine learning to maintain specified QoS. This paper describes how we are applying machine learning techniques to simplify the configuration of QoS-enabled middleware and adaptive transport protocols to maintain specified QoS as systems change dynamically. The results of our work thus far show that decision trees and neural networks can effectively classify the best protocols to use. In particular, decision trees answer questions about which measurements and variables are most important when considering the reliability and latency of pub/sub systems.

Maintaining QoS for publish/subscribe middleware in dynamic environments

Emerging trends and challenges. The number and type of distributed systems that utilize publish/subscribe (pub/sub) technologies are growing due to the advantages of performance, cost, and scale compared with single computers [1, 2]. Examples of pub/sub middleware include Web Services Brokered Notification (www.oasis-open.org/committees/tc_home.php?wg_abbrev=wsn), the Java Message Service (JMS) (java.sun.com/products/jms), the CORBA Event Service (www.omg.org/technology/documents/formal/event_service.htm), and the Data Distribution Sendee (DDS) (www.omg.org/spec/DDS). These technologies support data propagation throughout a system using an anonymous subscription model that decouples event suppliers and consumers.

DQML: A Modeling Language for Configuring Distributed Publish/Subscribe Quality of Service Policies

Many publish/subscribe (pub/sub) middleware platforms provide flexibility in configuring policies that affect end-to-end quality of service (QoS). While the functionality and tunability of pub/sub middleware has increased, so has the complexity of creating semantically compatible QoS policy configurations. This paper makes two contributions to addressing these challenges. First, it describes how a domain-specific modeling language (DSML) can automate the analysis and synthesis of semantically compatible QoS policy configurations. Second, it empirically evaluates how this DSML increases productivity when generating valid QoS policy configurations. Our experimental results show a 54% reduction in development effort using DQML over manual methods.