A Multi-level Monitoring Framework for Stream-Based Coordination Programs

Nguyen, Vu Thien Nga; Kirner, Raimund; Penczek, Frank

doi:10.1007/978-3-642-33078-0_7

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…We perform our experiments with S-Net stream programs [19] with its LPEL execution layer [40]. One convenience of using this execution model is that it provides an essential monitoring framework to obtain the required information for the stream program graph [35]. Another reason is that LPEL provides a local scheduler for each PE where the time overhead is predictable, and the waiting time is negligible when the system is fully working (i.e., the arrival rate of external input messages is high enough to keep the resources busy) [34].…”

Section: Methodsmentioning

confidence: 99%

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Throughput-Driven Partitioning of Stream Programs on Heterogeneous Distributed Systems

Nguyen

Kirner

2016

IEEE Trans. Parallel Distrib. Syst.

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Section: Methodsmentioning

confidence: 99%

“…The unit of wðs t i t j Þ is Byte/message. The values of w r ðtÞ and wðs t i t j Þ can be easily obtained using an appropriate monitoring framework, for example the one we have developed for that purpose [35].…”

Section: Distributed Platformsmentioning

confidence: 99%

Throughput-Driven Partitioning of Stream Programs on Heterogeneous Distributed Systems

Nguyen

Kirner

2016

IEEE Trans. Parallel Distrib. Syst.

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“…LPEL is also facilitated with a monitoring framework which can provide the information of internal behaviours at different levels of details ranging from scheduling activities to performance measurement and resource utilisations [26].…”

Section: Lpel-a Stream Execution Layer With Efficient Schedulingmentioning

confidence: 99%

Dynamic power management for reactive stream processing on the SCC tiled architecture

et al. 2016

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Dynamic voltage and frequency scaling (DVFS) is a means to adjust the computing capacity and power consumption of computing systems to the application demands. DVFS is generally useful to provide a compromise between computing demands and power consumption, especially in the areas of resource-constrained computing systems. Many modern processors support some form of DVFS. In this article, we focus on the development of an execution framework that provides lightweight DVFS support for reactive stream processing systems (RSPS). RSPs are a common form of embedded control systems, operating in direct response to inputs from their environment. At the execution framework, we focus on support for many-core scheduling for parallel execution of concurrent programs. We provide a DVFS strategy for RSPs that is simple and lightweight, to be used for dynamic adaptation of the power consumption at runtime. The simplicity of the DVFS strategy became possible by the sole focus on the application domain of RSPs. The presented DVFS strategy does not require specific assumptions about the message arrival rate or the underlying scheduling method. While DVFS is a very active field, in contrast to most existing research, our approach works also for platforms like many-core processors, where the power settings typically cannot be controlled individually for each computational unit. We also support dynamic scheduling with variable workload. While many research results are provided with simulators, in our approach, we present a parallel execution framework with experiments conducted on real hardware, using the single-chip cloud computer many-core processor. The results of our experimental evaluation confirm that our simple DVFS strategy provides potential for significant energy saving on RSPs.

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Demand-Based Scheduling Priorities for Performance Optimisation of Stream Programs on Parallel Platforms

Nguyen

Kirner

2013

Algorithms and Architectures for Parallel Processing

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A Multi-level Monitoring Framework for Stream-Based Coordination Programs

Cited by 5 publications

References 18 publications

Throughput-Driven Partitioning of Stream Programs on Heterogeneous Distributed Systems

Throughput-Driven Partitioning of Stream Programs on Heterogeneous Distributed Systems

Dynamic power management for reactive stream processing on the SCC tiled architecture

Demand-Based Scheduling Priorities for Performance Optimisation of Stream Programs on Parallel Platforms

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