Efficient and robust probabilistic guarantees for real-time tasks

Abeni, Luca; Manica, Nicola; Palopoli, Luigi

doi:10.1016/j.jss.2011.12.042

Cited by 19 publications

(4 citation statements)

References 21 publications

(25 reference statements)

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“…We report the results of the comparison between the numeric solution resulting from Theorem 2 and discussed in Remark 2 (companion), the analytic approximated bound in Corollary 7 (analytic) the Cyclic Reduction algorithm [10] (CR) and the bound developed by Abeni et al [32] (gamma). We have chosen CR after a selection process in which several algorithms for the solution of general QBDP problems and implemented in the SMCSolver tool-suite [33] were tested on a set of example QBDPs derived from our application.…”

Section: A Synthetic Distributionsmentioning

confidence: 99%

An Analytical Solution for Probabilistic Guarantees of Reservation Based Soft Real-Time Systems

Palopoli

Fontanelli

Abeni

et al. 2016

IEEE Trans. Parallel Distrib. Syst.

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We show a methodology for the computation of the probability of deadline miss for a periodic real-time task scheduled by a resource reservation algorithm. We propose a modelling technique for the system that reduces the computation of such a probability to that of the steady state probability of an infinite state Discrete Time Markov Chain with a periodic structure. This structure is exploited to develop an efficient numeric solution where different accuracy/computation time trade-offs can be obtained by operating on the granularity of the model. More importantly we offer a closed form conservative bound for the probability of a deadline miss. Our experiments reveal that the bound remains reasonably close to the experimental probability in one real-time application of practical interest. When this bound is used for the optimisation of the overall Quality of Service for a set of tasks sharing the CPU, it produces a good sub-optimal solution in a small amount of time

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Section: A Synthetic Distributionsmentioning

confidence: 99%

An Analytical Solution for Probabilistic Guarantees of Reservation Based Soft Real-Time Systems

Palopoli

Fontanelli

Abeni

et al. 2016

IEEE Trans. Parallel Distrib. Syst.

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“…The analysis is significantly simpler if we adopt a resource reservation scheduler, 3 in which each task executes as if on a dedicated computer and can be analyzed independently of the other tasks (the temporal isolation property). This radical simplification makes the model easy to construct and to analyze and brought different authors to develop efficient numerical methods 20,21 or analytic bounds. 6,22,23 Importantly, in all the articles cited so far, the computation time is assumed to be independent and identically distributed (i.i.d).…”

Section: Related Workmentioning

confidence: 99%

The PROSIT tool: Toward the optimal design of probabilistic soft real‐time systems

et al. 2018

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Summary In recent years, series of important achievements have paved the way for the introduction of probabilistic analysis in the area of soft real‐time systems design. In this article, we present an extensible design tool, called PROSIT, which facilitates the access to this technology for a potentially large number of researchers and industrial practitioners. The tool enables the probabilistic analysis of the temporal performance of a real‐time task under fixed‐priority and resource reservations scheduling algorithms. For resource reservations, the tool also offers an automatic procedure for the synthesis of scheduling parameters that optimize a quality metric related to the probabilistic behavior of the tasks.

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“…There is a cost function associated with these systems, which is often related to QoS. Hence, we consider a stochastic task model based on the one presented in [16]. Then, we represent each soft-real-time task using a tuple τ i = (r i , s i , a i , d i ) where: r i is the release time of the task, s i is the service time, which follows an exponential distribution of average µ −1 (i.e., µ is the number of serviced jobs of τ i per unit time), a i is the arrival time; tasks arrive according to a renewal process with exponential distribution of average λ −1 , and d i is the absolute deadline; the relative deadline is…”

Section: B the Task Modelmentioning

confidence: 99%

Green Adaptation of Real-Time Web Services for Industrial CPS Within a Cloud Environment

Higuera-Toledano

Risco‐Martín

Arroba

et al. 2017

IEEE Trans. Ind. Inf.

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Managing energy efficiency under timing constraints is an interesting and big challenge. This work proposes an accurate power model in data centers for time-constrained servers in Cloud computing. This model, as opposed to previous approaches, does not only consider the workload assigned to the processing element, but also incorporates the need of considering the static power consumption and, even more interestingly, its dependency with temperature. The proposed model has been used in a multi-objective optimization environment in which the Dynamic Voltage and Frequency Scaling (DVFS) and workload assignment have been efficiently optimized.

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Efficient and robust probabilistic guarantees for real-time tasks

Cited by 19 publications

References 21 publications

An Analytical Solution for Probabilistic Guarantees of Reservation Based Soft Real-Time Systems

An Analytical Solution for Probabilistic Guarantees of Reservation Based Soft Real-Time Systems

The PROSIT tool: Toward the optimal design of probabilistic soft real‐time systems

Green Adaptation of Real-Time Web Services for Industrial CPS Within a Cloud Environment

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