Improving dynamic voltage scaling algorithms with PACE

By Jacob R. Lorch, Alan Jay Smith. Measurement and Modeling of Computer Systems, 2001

439 citations recorded by the source corpus.

Subfields: Hardware and Architecture, Information Systems

View Paper: https://www.semanticscholar.org/paper/5a9c74d872efbadf4fae3390d604b6437e7f7ac8

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Citations in the corpus

Managing server energy and operational costs in hosting centers (2005, neutral): “[17], [19],[25], [28], [37]).”
Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer (2015, methodology): “Then the CCI can be modeled as a random variable denoted as X and L X gives the number of CPU cycles for computing L-bit input data [16].”
Energy-optimal mobile application execution: Taming resource-poor mobile devices with cloud clones (2012, background): “As shown in [9], [15], [16], the number of CPU cycles per bit can be modeled by a Gamma distribution.”
Power-rate-distortion analysis for wireless video communication under energy constraints (2005, background): “scaling (DVS) , has been recently developed [18], [20].”
Dynamic voltage scaling on a low-power microprocessor (2001, background): “To increase the scope for improvements at the OS level, several researchers have proposed to use soft deadlines [5, 14].”
Dynamic and aggressive scheduling techniques for power-aware real-time systems (2001, background): “Lorch and Smith addressed the variable voltage schedu ling of tasks withsoftdeadlines in [14].”
Energy-efficient soft real-time CPU scheduling for mobile multimedia systems (2003, methodology): “We once considered an alternative approach, often used in simulations, that approximates a calculated speed with two bounding supported speeds [14, 17, 23].”
Improving smartphone user experience by balancing performance and energy with probabilistic QoS guarantee (2016, background): “Over the past decade, a large body of research has been published on optimizing energy efficiency [10,13,16,18,23, 24, 38, 43, 44, 47].”
Energy Harvesting for Structural Health Monitoring Sensor Networks (2008, background): “Stochastic DVS estimates a program’s execution time using stochastic techniques [81, 82].”
Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services (2008, methodology): “Dynamic Voltage/Frequency Scaling (DVFS) has been developed as a standard technique to achieve power efficiency of processors [19, 13, 17].”
A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems (2010, background): “In contrast to intertask, intratask approaches leverage fine grained information about the structure of programs and adjust the processor frequency and voltage within the tasks [35] [36].”
Optimization of Radio and Computational Resources for Energy Efficiency in Latency-Constrained Application Offloading (2014, methodology): “Following [6] and [21], [13] models the number of cycles Nc required to complete the execution of an application with a probability p close to 1, as the product between the number of input bits and a factor that depends on the probability p and the computation complexity of the algorithm.”
Power-aware scheduling for periodic real-time tasks (2004, background): “In the intratask DVS research, Lorch and Smith addressed the scheduling of aperiodic tasks with soft deadlines in [18].”
Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel (2013, methodology): “For a given input data size, L, it can be derived from [2], [26] as W = LX, (6)”