Leveraging e-Infrastructures for Urgent Computing

Leong, Siew Hoon; Frank, Anton; Kranzlmüller, Dieter

doi:10.1016/j.procs.2013.05.388

Cited by 16 publications

(6 citation statements)

References 7 publications

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“…The system aims to provide resources quickly and efficiently to high-priority applications that must be executed without delay. However, a more recent study [6] shows that enabling shared infrastructures for UC remains challenging. One important challenge is that no existing job scheduling policies are suitable for shared infrastructures supporting UC.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, the damage to the dedicated resources can make it impossible to perform urgent computations that can help to mitigate the damages [5]. Due to these limitations, using existing shared infrastructures becomes an invaluable approach to supporting UC [6].…”

Section: Introductionmentioning

confidence: 99%

“…Several studies [1], [6] have shown that several challenges must be addressed to enable shared infrastructures for UC. One important challenge is to provide job scheduling methods that can handle urgent jobs.…”

Section: Introductionmentioning

confidence: 99%

“…This study also discussed the importance of preemption for guaranteeing the immediate execution of urgent jobs. However, most of the previous job scheduling methods do not support preemption [6]. Preemption is particularly hard to realize in shared infrastructures because the job scheduling methods rely on preemption mechanisms supported by the resource providers.…”

Section: Introductionmentioning

confidence: 99%

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Preemptive Parallel Job Scheduling for Heterogeneous Systems Supporting Urgent Computing

et al. 2021

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Dedicated infrastructures are commonly used for urgent computations. However, using dedicated resources is not always affordable due to budget constraints. As a result, utilizing shared infrastructures becomes an alternative solution for urgent computations. Since the infrastructures are meant to serve many users, the urgent jobs may arrive when regular jobs are using the necessary resources. In such a case, it is necessary to preempt the regular jobs so that urgent jobs can be executed immediately. Most conventional methods for job scheduling have focused on reducing the response times and waiting times of all jobs. However, these methods can delay urgent jobs and hinder them from being completed within a stipulated deadline. Furthermore, in heterogeneous systems with coprocessors, preemption becomes more difficult because coprocessors rely on several system software functionalities provided by the host processor. In this paper, we propose a parallel job scheduling method to effectively use shared heterogeneous systems for urgent computations. Our method employs an in-memory process swapping mechanism to preempt jobs running on the coprocessor devices. The results of our simulations show that our method can achieve a significant reduction in the response time and slowdown of regular jobs without substantial delays of urgent jobs. INDEX TERMS job scheduling, urgent computing, heterogeneous systems, preemption, process swapping.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Preemptive Parallel Job Scheduling for Heterogeneous Systems Supporting Urgent Computing

et al. 2021

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“…Existing approaches tend to focus on optimization of individual tasks or computational steps (e.g. resource allocation [9], data stream processing [10], or provisioning of storage resources [11]) rather than on optimizing the process as a whole. In particular, the correlation with data delivery has not been fully supported in target processing subsystems.…”

Section: Related Workmentioning

confidence: 99%

Dedicated IT infrastructure for Smart Levee Monitoring and Flood Decision Support

et al. 2016

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Abstract. Smart levees are being increasingly investigated as a flood protection technology. However, in large-scale emergency situations, a flood decision support system may need to collect and process data from hundreds of kilometers of smart levees; such a scenario requires a resilient and scalable IT infrastructure, capable of providing urgent computing services in order to perform frequent data analyses required in decision making, and deliver their results in a timely fashion. We present the ISMOP IT infrastructure for smart levee monitoring, designed to support decision making in large-scale emergency situations. Most existing approaches to urgent computing services in decision support systems dealing with natural disasters focus on delivering quality of service for individual, isolated subsystems of the IT infrastructure (such as computing, storage, or data transmission). We propose a holistic approach to dynamic system management during both urgent (emergency) and normal (non-emergency) operation. In this approach, we introduce a Holistic Computing Controller which calculates and deploys a globally optimal configuration for the entire IT infrastructure, based on cost-of-operation and quality-of-service (QoS) requirements of individual IT subsystems, expressed in the form of Service Level Agreements (SLAs). Our approach leads to improved configuration settings and, consequently, better fulfilment of the system's cost and QoS requirements than would have otherwise been possible had the configuration of all subsystems been managed in isolation.

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Scientific Partnership: A Pledge For a New Level of Collaboration Between Scientists and IT Specialists

Weismüller

Frank

2019

Progress in IS

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Leveraging e-Infrastructures for Urgent Computing

Cited by 16 publications

References 7 publications

Preemptive Parallel Job Scheduling for Heterogeneous Systems Supporting Urgent Computing

Preemptive Parallel Job Scheduling for Heterogeneous Systems Supporting Urgent Computing

Dedicated IT infrastructure for Smart Levee Monitoring and Flood Decision Support

Scientific Partnership: A Pledge For a New Level of Collaboration Between Scientists and IT Specialists

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