Real-time dynamic voltage scaling for low-power embedded operating systems

By Padmanabhan Pillai, Kang G. Shin. Symposium on Operating Systems Principles, 2001

1,377 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4ed50ef3ebdc06a2a4480f7a49a5403515862ea6

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Citations in the corpus

Power-aware scheduling for periodic real-time tasks (2004, methodology): “Shin et al. [26] proposed an intratask DVS framework for aperiodic real-time tasks with (possibly) precedence constraints .”
ECOSystem: managing energy as a first class operating system resource (2002, background): “The term "throttling" (which we have used in a very general sense) is most often associated with the growing literature on voltage/clock scheduling [24, 25, 13, 33, 12, 34, 16, 9, 27, 26] for processors that support dynamic voltage scaling.”
JouleSort: a balanced energy-efficiency benchmark (2007, background): “These costs vary depending upon the installation, but they are growing rapidly and have the potential eventually to outstrip the cost of hardware [2].”
A framework of energy efficient mobile sensing for automatic user state recognition (2009, background): “The problem of energy management on mobile devices has been well-explored in the literature such as [28, 29, 30, 31, 32].”
The design, implementation, and evaluation of a compiler algorithm for CPU energy reduction (2003, neutral): “[37] P. Pillai and K. Shin.”
Power Aware Scheduling of Bag-of-Tasks Applications with Deadline Constraints on DVS-enabled Clusters (2007, background): “Many studies have been done on DVS real-time scheduling on single processor systems [19, 20, 23].”
Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer (2015, background): “Another energy-efficient approach is to control the CPU-cycle frequencies under the deadline constraint by exploiting the fact that lowering the frequencies reduces the energy consumption [15]–[17].”
Scheduling with dynamic voltage/speed adjustment using slack reclamation in multi-processor real-time systems (2001, background): “4 ms to change volt age and 40 s to change frequency [22].”
Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers (2011, background): “…are generally composed of CMOS circuits is given by: P = αV 2 f + I leak V + P short , where P is the power dissipation, V is the supply voltage, f is the clock frequency, I leak is the leakage current, and P short is the short circuit power dissipated during the voltage switching process [8] [48].”
Cyber–Physical Systems: A Perspective at the Centennial (2012, background): “resource reservation for real-time applications, in [160]– [162] on a control theoretical approach to performance and throughput management of computing systems, in [163] on real-time scheduling algorithms for power management in Kim and Kumar: Cyber–Physical Systems: A Perspective at the Centennial”
Improving smartphone user experience by balancing performance and energy with probabilistic QoS guarantee (2016, background): “From this perspective, it is imperative to ensure user satisfaction even at the cost of additional power; however, due to ﬁnite energy capacity, it is not typically optimal for mobile platforms to sustain high performance states, and as this paper will demonstrate, providing guarantees on QoS…”
Managing performance vs. accuracy trade-offs with loop perforation (2011, background): “Moreover, for real-time computations, loop perforation may free up the time required to enable additional synergistic energy-saving optimizations such as voltage scaling [28].”
DORA: Optimizing Smartphone Energy Efficiency and Web Browser Performance under Interference (2018, background): “predict and operate processors at an optimal energy-efficient setting [21]–[31] but also allows designs to control the degree of memory interference experienced in a multiprogrammed setting [32]–[35].”