Revisiting GPC and AND Connector in Real-Time Calculus

Tang, Yue; Guan, Nan; Liu, Weichen; Phan, Linh Thi Xuan; Yi, Wang

doi:10.1109/rtss.2017.00031

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…With some minor additions, it can predict if an optional feature of the NC analysis might be beneficial at a certain point. Examples are the alternative output bound formulation of [42] and flow prolongation [43]. Exhaustive flow prolongation is only feasible on single tandems, yet, learning from those can be sufficient as we show in our paper.…”

Section: Discussionmentioning

confidence: 85%

DeepTMA: Predicting Effective Contention Models for Network Calculus using Graph Neural Networks

Geyer

Bondorf

2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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Network calculus computes end-to-end delay bounds for individual data flows in networks of aggregate schedulers. It searches for the best model bounding resource contention between these flows at each scheduler. Analyzing networks, this leads to complex dependency structures and finding the tightest delay bounds becomes a resource intensive task. The exhaustive search for the best combination of contention models is known as Tandem Matching Analysis (TMA). The challenge TMA overcomes is that a contention model in one location of the network can have huge impact on one in another location. These locations can, however, be many analysis steps apart from each other. TMA can derive delay bounds with high degree of tightness but needs several hours of computations to do so. We avoid the effort of exhaustive search altogether by predicting the best contention models for each location in the network. For effective predictions, our main contribution in this paper is a novel framework combining graph-based deep learning and Network Calculus (NC) models. The framework learns from NC, predicts best NC models and feeds them back to NC. Deriving a first heuristic from this framework, called DeepTMA, we achieve provably valid bounds that are very competitive with TMA. We observe a maximum relative error below 6 %, while execution times remain nearly constant and outperform TMA in moderately sized networks by several orders of magnitude.

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Section: Discussionmentioning

confidence: 85%

DeepTMA: Predicting Effective Contention Models for Network Calculus using Graph Neural Networks

Geyer

Bondorf

2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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“…6(a) shows the results with s from 2 to 6. Under this parameter setting, the percentage of task sets where the new proposed method outperforms the original calculation grows with increasing s, while it does not perform better than the method in existing work [8].…”

Section: Methodsmentioning

confidence: 94%

“…We implement our new theoretical results in RTC Toolbox [11] and conduct experiments to evaluate their performance. The new proposed method (denoted by new) is compared with the original GPC (denoted by org), and the existing work in [8](denoted by ext). Task sets of two different parameter settings are generated, under which both single GPC and GPC network are considered .…”

Section: Methodsmentioning

confidence: 99%

“…This work was further generalized in [10], which eliminates the dependency of Finitary RTC on the semantics of GPC and applied Finitary RTC to the level of RTC-operators. For precision, [8] proposed to reduce the unused resource from total resource before calculating the output arrival curves, which improves the precision of output arrival curves.…”

Section: Introductionmentioning

confidence: 99%

“…First, we revise the proof of output curves in GPC and complement the missing deduction steps, which further clarifies the correctness of the original results. Second, we propose a new method for calculating output arrival curves, which generates more precise results based on the connection between output arrival curves and the number of accumulated events, rather than focusing on the interaction of between arrival and service curves as in [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving the Analysis of GPC in Real-Time Calculus

Tang

Jiang

Guan

2019

Dependable Software Engineering. Theories, Tools, and Applications

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Real-Time Calculus (RTC) is a framework for modeling and performance analysis of real-time networked systems. In RTC, workload and resources are modeled as arrival and service curves, and processing semantics are modeled by abstract components. Greedy Processing Component (GPC) is one of the fundamental abstract components in RTC, which processes incoming events in a greedy fashion as long as there are available resources. The relations between inputs and outputs of GPC have been established, which are consistent with its behaviors. In this paper, we first revise the original proof of calculating output curves in GPC, and then propose a new method to obtain tighter output arrival curves. Experiment results show that the precision of output arrival curves can be improved by our method compared with the original calculation and existing work.

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Towards Customizable CPS: Composability, Efficiency and Predictability

2017

Formal Methods and Software Engineering

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Revisiting GPC and AND Connector in Real-Time Calculus

Cited by 6 publications

References 19 publications

DeepTMA: Predicting Effective Contention Models for Network Calculus using Graph Neural Networks

DeepTMA: Predicting Effective Contention Models for Network Calculus using Graph Neural Networks

Improving the Analysis of GPC in Real-Time Calculus

Towards Customizable CPS: Composability, Efficiency and Predictability

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