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

Hoffert, Joe; Mack, Daniel; Schmidt, Douglas C.

doi:10.1145/1658185.1658189

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…Hoffert et al. [34] used machine learning methods for QoS parameter prediction. For example, neural networks and decision trees are good at protocol classification.…”

Section: Related Workmentioning

confidence: 99%

Using Machine Learning to Provide Reliable Differentiated Services for IoT in SDN-Like Publish/Subscribe Middleware

Shi

Zhang

Jacobsen

et al. 2019

Sensors

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At present, most publish/subscribe middlewares suppose that there are equal Quality of Service (QoS) requirements for all users. However, in many real-world Internet of Things (IoT) service scenarios, different users may have different delay requirements. How to provide reliable differentiated services has become an urgent problem. The rise of Software-Defined Networking (SDN) provides endless possibilities to improve the QoS of publish/subscribe middlewares due to its greater programmability. We can encode event topics and priorities into flow entries of SDN switches directly to meet customized requirements. In this paper, we first propose an SDN-like publish/subscribe middleware architecture and describe how to use this architecture and priority queues supported by OpenFlow switches to realize differentiated services. Then we present a machine learning method using the eXtreme Gradient Boosting (XGBoost) model to solve the difficult issue of getting the queuing delay of switches accurately. Finally, we propose a reliable differentiated services guarantee mechanism according to the queuing delay and the programmability of SDN to improve QoS, namely, a two-layer queue management mechanism. Experimental evaluations show that the delay predicted by the XGBoost method is closer to the real value; our mechanism can save end-to-end delay, reduce packet loss rate, and allocate bandwidth more reasonably.

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“…Hoffert et al. [34] used machine learning methods for QoS parameter prediction. For example, neural networks and decision trees are good at protocol classification.…”

Section: Related Workmentioning

confidence: 99%

Using Machine Learning to Provide Reliable Differentiated Services for IoT in SDN-Like Publish/Subscribe Middleware

Shi

Zhang

Jacobsen

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Another proactive approach is presented by Hoffert et al [58] who point out the difficulty in maintaining the Quality of Service (QoS) properties (such as reliability and latency) in dynamic environments such as disaster relief operations or power grids. They state that the challenge arises from the slow human response times, and the complexity of managing multiple interrelated QoS settings.…”

Section: Strategymentioning

confidence: 99%

“…Supervised learning algorithms make predictions based on a set of examples. Classifiers, decision trees, neural networks, and regression are some examples of supervised learning that can be seen in the work of Hoffert et al [62]. Unsupervised learning occurs where labelled examples are not available.…”

Section: Factors That Influence Adaptationmentioning

confidence: 99%

“…Hoffert et al [62] support the idea of supervised machine learning in dynamic environments in maintaining of QoS. They list possible supervised machine learning techniques to include decision trees, neural networks, and linear logistic regression classifiers that can be trained on existing data to interpolate and extrapolate for new data.…”

Section: Factors That Influence Adaptationmentioning

confidence: 99%

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State of runtime adaptation in service‐oriented systems: what, where, when, how and right

Mutanu

Kotonya

2019

IET softw.

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Software as a Service reflects a "service-oriented" approach to software development that is based on the notion of composing applications by discovering and invoking network-available services to accomplish some task. However, as more business organizations adopt service-oriented solutions and the demands on them grow, the problem of ensuring that the software systems can adapt fast and effectively to changing business needs, changes in their runtime environment and failures in provided services has become an increasingly important research problem. Dynamic adaptation has been proposed as a way to address the problem. However, for adaptation to be effective several other factors need to be considered. This paper identifies the key factors that influence runtime adaptation in service-oriented systems, and examines how well they are addressed in 29 adaptation approaches intended to support service-oriented systems.

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“…For those pub‐sub systems with QoS parameters, Hoffert et al . provide machine learning techniques for configuring the QoS parameters . Behnel et al .…”

Section: Related Workmentioning

confidence: 99%

Dynamic policy‐driven quality of service in service‐oriented information management systems

Loyall¹,

Gillen²,

Paulos³

et al. 2011

Softw Pract Exp

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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.

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

Cited by 8 publications

References 9 publications

Using Machine Learning to Provide Reliable Differentiated Services for IoT in SDN-Like Publish/Subscribe Middleware

Using Machine Learning to Provide Reliable Differentiated Services for IoT in SDN-Like Publish/Subscribe Middleware

State of runtime adaptation in service‐oriented systems: what, where, when, how and right

Dynamic policy‐driven quality of service in service‐oriented information management systems

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