Dynamic priority-adjustment for real-time flows in software-defined networks

An, Namwon; Ha, Taejin; Park, Kyung‐Joon; Lim, Hyuk

doi:10.1109/netwks.2016.7751167

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Li et al [38] introduce a middleware solution that assigns priority levels to network flows based on three different classes of data: expedited forwarding, assured forwarding and best effort. Similarly, An et al [6] assign priorities based on the type of data traffic (i.e., delay-sensitive or delay-tolerant traffic), and Shi et al [54] assign priorities based on the semantics of data. Finally, Bröring et al [17] allocate bandwidth to SDN network flows based on video-specific application-level QoS constraints (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…Given the heterogeneity of this information and the limited networking resources for delivering notifications, we believe event prioritization is necessary in such mission-critical settings. Existing data exchange systems [6,38,50,54,58,65] provide mechanisms for event prioritization either by manually assigning priorities to specific data flows or by dynamically assigning them based on the application-level data flows/types (e.g., video data) or even QoS-specific requirements (e.g., delay-sensitive apps). However, such systems cannot be customized to support mission-critical applications with dynamic changes of the app requirements, interested data recipients, data flows/types and the networking conditions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

PrioDeX

Bouloukakis

Benson

Scalzotto³

et al. 2021

ACM Trans. Internet Things

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Real-time event detection and targeted decision making for emerging mission-critical applications require systems that extract and process relevant data from IoT sources in smart spaces. Oftentimes, this data is heterogeneous in size, relevance, and urgency, which creates a challenge when considering that different groups of stakeholders (e.g., first responders, medical staff, government officials, etc.) require such data to be delivered in a reliable and timely manner. Furthermore, in mission-critical settings, networks can become constrained due to lossy channels and failed components, which ultimately add to the complexity of the problem. In this article, we propose PrioDeX, a cross-layer middleware system that enables timely and reliable delivery of mission-critical data from IoT sources to relevant consumers through the prioritization of messages. It integrates parameters at the application, network, and middleware layers into a data exchange service that accurately estimates end-to-end performance metrics through a queueing analytical model. PrioDeX proposes novel algorithms that utilize the results of this analysis to tune data exchange configurations (event priorities and dropping policies), which is necessary for satisfying situational awareness requirements and resource constraints. PrioDeX leverages Software-Defined Networking (SDN) methodologies to enforce these configurations in the IoT network infrastructure. We evaluate our approach using both simulated and prototype-based experiments in a smart building fire response scenario. Our application-aware prioritization algorithm improves the value of exchanged information by 36% when compared with no prioritization; the addition of our network-aware drop rate policies improves this performance by 42% over priorities only and by 94% over no prioritization.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PrioDeX

Bouloukakis

Benson

Scalzotto³

et al. 2021

ACM Trans. Internet Things

View full text Add to dashboard Cite

show abstract

“…These approaches provide the scalability and QoS level needed for wide-area networks but are not sufficient for industrial scenarios, which require strict buffer management as provided by our proposed models. 5) Measurement-based Frameworks: A wide range of proposals build the network state by retrieving it from the data plane [9]- [15]. This step adds a non-negligible delay to the flow request processing.…”

Section: B Sdn-based Qos Networking Frameworkmentioning

confidence: 99%

DetServ: Network Models for Real-Time QoS Provisioning in SDN-Based Industrial Environments

Guck

Bemten

Kellerer

2017

IEEE Trans. Netw. Serv. Manage.

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Industrial networks require real-time guarantees for the flows they carry. That is, flows have hard end-to-end delay requirements that have to be deterministically guaranteed. While proprietary extensions of Ethernet have provided solutions, these often require expensive forwarding devices. The rise of Software-Defined Networking (SDN) opens the door to the design of centralized traffic engineering frameworks for providing such real-time guarantees. As part of such a framework, a network model is needed for the computation of worst-case delays and for access control. In this article, we propose two network models based on network calculus theory for providing deterministic services (DetServ). While our first model, the multi-hop model (MHM), assigns a rate and a buffer budget to each queue in the network, our second model, the threshold-based model (TBM), simply fixes a maximum delay for each queue. Via a packet-level simulation, we confirm that the delay bounds guaranteed by both models are never exceeded and that no packet loss occurs. We further show that the TBM provides more flexibility with respect to the characteristics of the flows to be embedded and that it has the potential of accepting more flows in a given network. Finally, we show that the runtime cost for this increase in flexibility stays reasonable for online request processing in industrial scenarios.

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“…SDN-based approaches have been used in general IoT networks to: configure QoS-enabled routing [36,45]; differentiate pub/sub subscriptions at the network level and prioritize them separately to provide bounded queueing delays [57]; manage wireless IoT sensor networks [21]; for meet real-time data flow processing delays through prioritization [3]. Recent research [9,11,53] used SDN to enable high performance pub/sub through network-level multicast.…”

Section: Background On Iot Data Exchangementioning

confidence: 99%

FireDeX

Benson

Bouloukakis

Grant

et al. 2018

Proceedings of the 19th International Middleware Conference

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Real-time event detection and targeted decision making for emerging mission-critical applications, e.g. smart fire fighting, requires systems that extract and process relevant data from connected IoT devices in the environment. In this paper, we propose FireDeX, a cross-layer middleware that facilitates timely and effective exchange of data for coordinating emergency response activities. FireDeX adopts a publish-subscribe data exchange paradigm with brokers at the network edge to manage prioritized delivery of mission-critical data from IoT sources to relevant subscribers. It incorporates parameters at the application, network, and middleware layers into a data exchange service that accurately estimates end-to-end performance metrics (e.g. delays, success rates). We design an extensible queueing theoretic model that abstracts these cross-layer interactions as a network of queues, thereby making it amenable for rapid analysis. We propose novel algorithms that utilize results of this analysis to tune data exchange configurations (event priorities and dropping policies) while meeting situational awareness requirements and resource constraints. FireDeX leverages Software-Defined Networking (SDN) methodologies to enforce these configurations in the IoT network infrastructure. We evaluate its performance through simulated experiments in a smart building fire response scenario. Our results demonstrate significant improvement to mission-critical data delivery under a variety of conditions. Our application-aware prioritization algorithm improves the value of exchanged information by 36% when compared with no prioritization; the addition of our network-aware drop rate policies improves this performance by 42% over priorities only and by 94% over no prioritization.

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Dynamic priority-adjustment for real-time flows in software-defined networks

Cited by 6 publications

References 10 publications

PrioDeX

PrioDeX

DetServ: Network Models for Real-Time QoS Provisioning in SDN-Based Industrial Environments

FireDeX

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