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

Hoffert, Joe; Schmidt, Douglas C.; Gokhale, Aniruddha

doi:10.1007/978-3-642-05148-7_45

Cited by 8 publications

(16 citation statements)

References 17 publications

(16 reference statements)

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“…Our prior work [1] showed how to quantitatively evaluate reliability and average latency via the ReLate2 metric, which multiplies the average latency by the percent packet loss as follows:…”

Section: Composite Metrics For Scaal Applicationsmentioning

confidence: 99%

“…We present experimental results using the ReLate2Jit metric. Our experimental environment is similar to the one used for our ReLate2 results [1], except that we use the OpenSplice DDS rather than OpenDDS. We send data from a publisher to subscribers while varying the sending rate, the percent loss in the network, and the number of subscribers.…”

Section: Composite Metrics For Scaal Applicationsmentioning

confidence: 99%

“…We build on previous composite metrics of reliability and average latency [1] to include the concerns of jitter, average network bandwidth usage, and network burstiness, i.e., standard deviation of network bandwidth usage. Moreover, we analyze the composability of various QoS concerns and analyze techniques to manage adaptations for DRE systems in dynamic environments.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Hoffert

Schmidt

Gokhale

2010

Proceedings of the First International Workshop on Data Dissemination for Large Scale Complex Critical Infrastructures

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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.

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Section: Composite Metrics For Scaal Applicationsmentioning

confidence: 99%

Section: Composite Metrics For Scaal Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Hoffert

Schmidt

Gokhale

2010

Proceedings of the First International Workshop on Data Dissemination for Large Scale Complex Critical Infrastructures

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“…Table 3 outlines the data that is being collected to classify and evaluate middleware performance. The ReLate2 value [7] is a metric that evaluates both reliability determined by the number of packets received by an application and packet latency. ReLate2 is calculated by multiplying the average latency by the percent packet loss.…”

Section: Evaluation Setupmentioning

confidence: 99%

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

Hoffert

Mack

Schmidt

2009

Proceedings of the 8th International Workshop on Adaptive and Reflective MIddleware

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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.

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“…Our ADAMANT middleware uses the Adaptive Network Transports (ANT) [10] to select the transport protocol(s) that best address multiple QoS concerns for given computing resources. ANT provides infrastructure for composing and configuring transport protocols using the scalable reliable multicast-based Ricochet transport protocol [3].…”

Section: Introductionmentioning

confidence: 99%

Adapting Distributed Real-Time and Embedded Pub/Sub Middleware for Cloud Computing Environments

2010

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Abstract. 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.

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

Cited by 8 publications

References 17 publications

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

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

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

Adapting Distributed Real-Time and Embedded Pub/Sub Middleware for Cloud Computing Environments

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