Optimal Power-Down Strategies

Augustine, John; Irani, Sandy; Swamy, Chaitanya

doi:10.1137/05063787x

Cited by 82 publications

(91 citation statements)

References 11 publications

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“…In reality, switching on a machine from a sleep state requires some energy and it may save energy to leave a machine to idle if jobs will be scheduled on it again soon [1], [6]. To take this advantage, different optimization criteria have to be considered.…”

Section: Summary Extensions and Future Workmentioning

confidence: 99%

“…In this definition a job [s J , c J ] is considered as not being processed at time c J . For instance, a machine processing jobs [1,2], [2,3], [1,3] is considered to be processing 2 jobs at time 2. Note that this is consistent with the definition of the term overlapping, and equivalent to saying that the intervals do not contain their completion time, i.e.…”

Section: Notations and Preliminariesmentioning

confidence: 99%

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Optimizing busy time on parallel machines

Mertzios

Shalom

Voloshin

et al. 2015

Theoretical Computer Science

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Abstract-We consider the following fundamental scheduling problem in which the input consists of n jobs to be scheduled on a set of identical machines of bounded capacity g (which is the maximal number of jobs that can be processed simultaneously by a single machine). Each job is associated with a start time and a completion time; it is supposed to be processed from the start time to the completion time (and in one of our extensions it has to be scheduled also in a continuous number of days; this corresponds to a two-dimensional version of the problem). We consider two versions of the problem. In the scheduling minimization version the goal is to minimize the total busy time of machines used to schedule all jobs. In the resource allocation maximization version the goal is to maximize the number of jobs that are scheduled for processing under a budget constraint given in terms of busy time. This is the first study of the maximization version of the problem. The minimization problem is known to be NP-Hard, thus the maximization problem is also NP-Hard. We consider various special cases, identify cases where an optimal solution can be computed in polynomial time, and mainly provide constant factor approximation algorithms for both minimization and maximization problems. Some of our results improve upon the best known results for this job scheduling problem. Our study has applications in power consumption, cloud computing and optimizing switching cost of optical networks.

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Section: Summary Extensions and Future Workmentioning

confidence: 99%

Section: Notations and Preliminariesmentioning

confidence: 99%

Optimizing busy time on parallel machines

Mertzios

Shalom

Voloshin

et al. 2015

Theoretical Computer Science

View full text Add to dashboard Cite

show abstract

“…If a machine is busy over several contiguous time interval, then we can replace it with several machines that satisfy the assumption, changing neither the feasibility nor the measure of the schedule. For example, if a machine is busy over [1,2] and [3,4], we can replace this machine with two machines, one busy over [1,2], the other over [3,4] and this does not change the total busy time.…”

Section: Notations and Preliminariesmentioning

confidence: 99%

“…Two intervals are said to be overlapping if their intersection contains more than one point. For example, the two intervals [1,2] and [2,3] are considered to be non-overlapping. For a set I of intervals we define SPAN(I) A (partial) schedule is a (partial) function from the set of jobs J to the set of machines M. A schedule is said to be valid if every machine processes at most g jobs at any given time.…”

Section: Notations and Preliminariesmentioning

confidence: 99%

“…In this definition a job [r J , c J ] is considered as not being processed at time c J . For instance, a machine processing jobs [1,2], [2,3], [1,3] is considered to be processing two jobs at time 2. Note that this is consistent with the definition of the notion overlapping intervals, and equivalent to saying that the intervals do not contain their completion time, i.e.…”

Section: Notations and Preliminariesmentioning

confidence: 99%

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Online Optimization of Busy Time on Parallel Machines

Shalom

Voloshin

Wong

et al. 2012

Lecture Notes in Computer Science

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Abstract. We consider the following online scheduling problem in which the input consists of n jobs to be scheduled on identical machines of bounded capacity g (the maximum number of jobs that can be processed simultaneously on a single machine). Each job is associated with a release time and a completion time between which it is supposed to be processed. When a job is released, the online algorithm has to make decision without changing it afterwards. We consider two versions of the problem. In the minimization version, the goal is to minimize the total busy time of machines used to schedule all jobs. In the resource allocation maximization version, the goal is to maximize the number of jobs that are scheduled under a budget constraint given in terms of busy time. This is the first study on online algorithms for these problems. We show a rather large lower bound on the competitive ratio for general instances. This motivates us to consider special families of input instances for which we show constant competitive algorithms. Our study has applications in power aware scheduling, cloud computing and optimizing switching cost of optical networks.

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Modeling and simulation of global and sleep states in ACPI‐compliant energy‐efficient cloud environments

Xavier

Rossi

Rose

et al. 2016

Concurrency and Computation

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SUMMARYThe more large-scale data centers infrastructure costs increase, the more simulation-based evaluations are needed to understand better the trade-off between energy and performance and support the development of new energy-aware resource allocation policies. Specifically, in the cloud computing field, various simulators are able to predict and measure the behavior of applications on different architectures using different resource allocation policies. Yet, only a few of them have the ability to simulate energy-saving strategies, and none of them support the complete advanced configuration and power interface (ACPI) specification. ACPI defines a terminology for all possible power states of a machine and their associated power rate. The hardware industry has relied on ACPI to provide up-to-date standard interfaces for hardware discovery, configuration, power management, and monitoring, enabling a better understanding of the energy consumption level of different hardware states, referred to as ACPI G-states, S-states, and P-states. In this paper, we improve the modeling and simulation of the ACPI G/S-states and show not only that these states offer different energy-saving levels but also that state transitions consume energy. In addition, we model the latency to transit between two states and the effects on the turnaround time when the transitions are not performed conservatively. Furthermore, the equations provide essential information to quantify the trade-off between energy consumption and performance and assist in the analysis/decision on which strategy fits better in the environment and how it could be refined. Our expanded energy model was implemented in CloudSim and validated with simulation-based experiments with a very high level of accuracy, with a standard deviation of at most 6%.

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Optimal Power-Down Strategies

Cited by 82 publications

References 11 publications

Optimizing busy time on parallel machines

Optimizing busy time on parallel machines

Online Optimization of Busy Time on Parallel Machines

Modeling and simulation of global and sleep states in ACPI‐compliant energy‐efficient cloud environments

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