Tradeoff Exploration between Reliability, Power Consumption, and Execution Time

Assayad, Ismail; Girault, Alain; Kalla, Hamoudi

doi:10.1007/978-3-642-24270-0_32

“…The responsiveness of embedded systems was throughly studied and evaluated by estimating the Worst-Case Execution Time (WCET) of individual tasks leading to the existence of several commercial tools and research prototypes [51]. However, the relation between the WCET analysis and power consumption is less understood, because of the challenge in assuming a direct correlation between execution bounds that involve different components such as compiler, scheduler, and hardware specifications [2,7,51]. Figure 10.…”

Section: Related Workmentioning

confidence: 99%

Don't race the memory bus: taming the GC leadfoot

Hussein

¹

,

Hosking

²

,

Payer

³

et al. 2015

Proceedings of the 2015 International Symposium on Memory Management

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Dynamic voltage and frequency scaling (DVFS) is ubiquitous on mobile devices as a mechanism for saving energy. Reducing the clock frequency of a processor allows a corresponding reduction in power consumption, as does turning off idle cores. Garbage collection is a canonical example of the sort of memory-bound workload that best responds to such scaling. Here, we explore the impact of frequency scaling for garbage collection in a real mobile device running Android's Dalvik virtual machine, which uses a concurrent collector. By controlling the frequency of the core on which the concurrent collector thread runs we can reduce power significantly. Running established multi-threaded benchmarks shows that total processor energy can be reduced up to 30 %, with end-to-end performance loss of at most 10 %.

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“…The responsiveness of embedded systems was throughly studied and evaluated by estimating the Worst-Case Execution Time (WCET) of individual tasks leading to the existence of several commercial tools and research prototypes [51]. However, the relation between the WCET analysis and power consumption is less understood, because of the challenge in assuming a direct correlation between execution bounds that involve different components such as compiler, scheduler, and hardware specifications [2,7,51]. In this paper, we demonstrate that it is necessary to define GC requirements as a function of system mechanisms such as the governor and scheduling policies.…”

Section: Related Workmentioning

confidence: 99%

Don't race the memory bus: taming the GC leadfoot

Hussein

¹

,

Hosking

²

,

Payer

³

et al. 2015

SIGPLAN Not.

2

0

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Dynamic voltage and frequency scaling (DVFS) is ubiquitous on mobile devices as a mechanism for saving energy. Reducing the clock frequency of a processor allows a corresponding reduction in power consumption, as does turning off idle cores. Garbage collection is a canonical example of the sort of memory-bound workload that best responds to such scaling. Here, we explore the impact of frequency scaling for garbage collection in a real mobile device running Android's Dalvik virtual machine, which uses a concurrent collector. By controlling the frequency of the core on which the concurrent collector thread runs we can reduce power significantly. Running established multi-threaded benchmarks shows that total processor energy can be reduced up to 30 %, with end-to-end performance loss of at most 10 %.

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“…The reliability of a task T i executed once at speed f is R i (f ) = e −λ(f )×Exe(w i ,f ) . Because the fault rate is usually very small, of the order of 10 −6 per time unit in [9,43], 10 −5 in [5], we can use the first order approximation of R i (f ) as…”

Section: Tri-criteria Problemmentioning

confidence: 99%

Scheduling for Large-Scale Systems

Vivien¹

2014

Computing Handbook, Third Edition

0

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In this chapter, scheduling is defined as the activity that consists in mapping a task graph onto a target platform. The task graph represents the application, nodes denote computational tasks, and edges model precedence constraints between tasks. For each task, an assignment (choose the processor that will execute the task) and a schedule (decide when to start the execution) are determined. The goal is to obtain an efficient execution of the application, which translates into optimizing some objective function.The traditional objective function in the scheduling literature is the minimization of the total execution time, or makespan. Section 0.2 provides a quick background on task graph scheduling, mostly to recall some definitions, notations, and well-known results.In contrast to pure makespan minimization, this chapter aims at discussing scheduling problems that arise at large scale, and involve one or several different optimization criteria, that depart from, or complement, the classical makespan objective. Section 0.3 assesses the importance of key ingredients of modern scheduling techniques: (i) multi-criteria optimization; (ii) memory management; and (iii) reliability. These concepts are illustrated in the following sections, which cover four case studies. Section 0.4 is devoted to illustrate multi-criteria optimization techniques, that mix throughput, energy and reliability. Sections 0.5 and 0.6 illustrate different memory management techniques to schedule large task graphs. Finally, Section 0.7 discusses the impact of checkpointing techniques to schedule parallel jobs at very large-scale. We conclude this chapter by stating some research directions in Section 0.8, by defining terms in Section 0.9, and by pointing to additional sources of information in Section 0.10. Background on Scheduling 0.2.1 Makespan MinimizationTraditional scheduling assumes that the target platform is a set of p identical processors, and that no communication cost is paid. In that context, a task graph is a directed acyclic vertex-weighted graph G = (V, E, w), where the set V of vertices represents the tasks, the set E of edges represents precedence constraints between tasks (e = (u, v) ∈ E if and only if u ≺ v). and the weight function w : V −→ N * gives the weight (or duration) of each task. Task weights are assumed to be positive integers. A schedule σ of a task graph is a function that assigns a start time to each task: σ : V −→ N * such that σ(u) + w(u) ≤ σ(v) whenever e = (u, v) ∈ E. In other words, a schedule preserves the dependence constraints induced by the precedence relation ≺ and embodied by the edges of the dependence graph; if u ≺ v, then the execution of u begins at time σ(u) and requires w(u) units of time, and the execution of v at time σ(v) must start after the end 1

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Tradeoff Exploration between Reliability, Power Consumption, and Execution Time

Cited by 20 publications

References 14 publications

Don't race the memory bus: taming the GC leadfoot

Don't race the memory bus: taming the GC leadfoot

Don't race the memory bus: taming the GC leadfoot

Scheduling for Large-Scale Systems

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