Energy efficient backup overloading schemes for fault tolerant scheduling of real-time tasks

Bansal, Sandeep; Bansal, Rakesh Kumar; Arora, Kiran

doi:10.1016/j.sysarc.2020.101901

Cited by 11 publications

(2 citation statements)

References 38 publications

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“…The study in [68] has proposed two energy-aware fault-tolerance scheduling algorithms based on a primarybackup approach called ''Fault-tolerant Energy Efficient task scheduling with Delayed and Overloaded backups (FEED-O)'', and ''FEEDO with Dynamic-deferring (FEED-OD)''. Primary tasks will be executed on a DVS-enabled processor, while the backup copies are scheduled on the auxiliary processor with DPM for reducing energy consumption.…”

Section: A Standby-sparing (Ss) and Primary/backup (P/b)mentioning

confidence: 99%

A Survey of Fault-Tolerance Techniques for Embedded Systems From the Perspective of Power, Energy, and Thermal Issues

et al. 2022

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The relentless technology scaling has provided a significant increase in processor performance, but on the other hand, it has led to adverse impacts on system reliability. In particular, technology scaling increases the processor susceptibility to radiation-induced transient faults. Moreover, technology scaling with the discontinuation of Dennard scaling increases the power densities, thereby temperatures, on the chip. High temperature, in turn, accelerates transistor aging mechanisms, which may ultimately lead to permanent faults on the chip. To assure a reliable system operation, despite these potential reliability concerns, fault-tolerance techniques have emerged. Specifically, fault-tolerance techniques employ some kind of redundancies to satisfy specific reliability requirements. However, the integration of fault-tolerance techniques into real-time embedded systems complicates preserving timing constraints. As a remedy, many task mapping/scheduling policies have been proposed to consider the integration of fault-tolerance techniques and enforce both timing and reliability guarantees for real-time embedded systems. More advanced techniques aim additionally at minimizing power and energy while at the same time satisfying timing and reliability constraints. Recently, some scheduling techniques have started to tackle a new challenge, which is the temperature increase induced by employing fault-tolerance techniques. These emerging techniques aim at satisfying temperature constraints besides timing and reliability constraints. This paper provides an in-depth survey of the emerging research efforts that exploit fault-tolerance techniques while considering timing, power/energy, and temperature from the real-time embedded systems' design perspective. In particular, the task mapping/scheduling policies for fault-tolerance real-time embedded systems are reviewed and classified according to their considered goals and constraints. Moreover, the employed fault-tolerance techniques, application models, and hardware models are considered as additional dimensions of the presented classification. Lastly, this survey gives deep insights into the main achievements and shortcomings of the existing approaches and highlights the most promising ones.INDEX TERMS Fault-tolerance, embedded systems, real-time computing, scheduling, power/energy minimization, thermal-aware design.

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Section: A Standby-sparing (Ss) and Primary/backup (P/b)mentioning

confidence: 99%

A Survey of Fault-Tolerance Techniques for Embedded Systems From the Perspective of Power, Energy, and Thermal Issues

et al. 2022

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“…In [16] also the fault-tolerance concept implements in big data. Based on aforementioned work, it is clear that fault tolerance is not important in RTS only, but in every domain [9][10][11][12][13][14][15][16][17].…”

Section: Background and Preliminariesmentioning

confidence: 99%

Fault Tolerance in the Joint EDF-RMS Algorithm: A Comparative Simulation Study

Sharma¹,

Nitin²,

Dahiya³

2022

Computers, Materials &Amp; Continua

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Failure is a systemic error that affects overall system performance and may eventually crash across the entire configuration. In Real-Time Systems (RTS), deadline is the key to successful completion of the program. If tasks effectively meet the deadline, it means the system is working in pristine order. However, missing the deadline means a systemic fault due to which the system can crash (hard RTS) or degrade inclusive performance (soft RTS). To fine-tune the RTS, tolerance is the critical issue and must be handled with extreme care. This article explains the context of fault tolerance with improvised Joint EDF-RMS algorithm in RTS. The backup method has been derived to prevent the system from being recursively migrating the same task. If any task migrates three times, this migrated task will get shifted to the backup queue. This backup queue assigns the task to a backup processor and is destined for final execution. For performance evaluation purposes, a relative graph between fault and failure rates, failure and total processor utilization along with other averages have been evaluated. Furthermore, these archived results are compared with fault-tolerant Earliest Deadline First (EDF) and Rate Monotonic Scheduling (RMS) algorithms independently in relatively similar conditions. These comparisons show better performance against overloading conditions.

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TEFRED: A Temperature and Energy Cognizant Fault-Tolerant Real-Time Scheduler Based on Deadline Partitioning for Heterogeneous Platforms

Sharma

Das

Moulik

2022

Lecture Notes in Computer Science

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Energy efficient backup overloading schemes for fault tolerant scheduling of real-time tasks

Cited by 11 publications

References 38 publications

A Survey of Fault-Tolerance Techniques for Embedded Systems From the Perspective of Power, Energy, and Thermal Issues

A Survey of Fault-Tolerance Techniques for Embedded Systems From the Perspective of Power, Energy, and Thermal Issues

Fault Tolerance in the Joint EDF-RMS Algorithm: A Comparative Simulation Study

TEFRED: A Temperature and Energy Cognizant Fault-Tolerant Real-Time Scheduler Based on Deadline Partitioning for Heterogeneous Platforms

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