A Simple Model for Estimating Power Consumption of a Multicore Server System

Kim, Min-Joong; Ju, Yoondeok; Chae, Jinseok; Park, Moonju

doi:10.14257/ijmue.2014.9.2.15

Cited by 15 publications

(7 citation statements)

References 13 publications

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“…We obtain several lessons from developing the precise power consumption model. We believe that modeling power consumptions is as an important research topic in networking communities as in hardware/system communities wherein power consumption models of memory devices [10] and server systems [21] are developed.…”

Section: Resultsmentioning

confidence: 99%

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Empirically modeling how a multicore software ICN router and an ICN network consume power

Hasegawa

Nakai

Ohsugi

et al. 2014

Proceedings of the 1st ACM Conference on Information-Centric Networking

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ICN (Information Centric Networking) has received much attention due to its built-in functionalities such as caching and mobility-support. One of the important research challenges is to reduce the power consumed by ICN networks because ICN's packet forwarding and packet-level caching are power-hungry. As the first step to achieve power-efficient ICN networks, this paper develops a power consumption model of a multicore software ICN router while taking into account the power consumed by power-hungry computation. This paper makes the following three contributions: First, the model is one of the first realistic models which consider ICN packet forwarding and packet-level caching. Second, the model is represented as a concise set of equations with just a few parameters. Third, we apply the model to estimate power consumed by simple networks.

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Section: Resultsmentioning

confidence: 99%

“…The objectives of this subsection are to validate that the power consumed by accessing the DDR3 device is proportional to the rate of accessing it [21] and to decide the constants and in equation (3).…”

Section: Power Consumed By Memory Devicementioning

confidence: 99%

Empirically modeling how a multicore software ICN router and an ICN network consume power

Hasegawa

Nakai

Ohsugi

et al. 2014

Proceedings of the 1st ACM Conference on Information-Centric Networking

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“…() A large amount of work regards modeling power for various types of computing units, starting from load, frequency, and other hardware counters. For instance, single and dual core CPU power is modeled in the work of Dargie by considering the relation between the probability distribution functions of load and power, while servers with up to 8 cores are studied in the works of Takouna et al and Kim et al GPU power is estimated from load measures in the work of Ma et al These methods do not allow for advance prediction in real life scenarios, since load and hardware counters cannot be known in advance, unless they can be predicted through other methods. Our method is significantly different in that we model total system power starting exclusively from workload measures, without the need to monitor the individual components, enabling advance prediction of power .…”

Section: Related Workmentioning

confidence: 99%

A data‐driven approach to modeling power consumption for a hybrid supercomputer

Sîrbu

Babaoğlu

2018

Concurrency and Computation

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Summary Power consumption of current High Performance Computing systems has to be reduced by at least one order of magnitude before they can be scaled up towards ExaFLOP performance. While we can expect novel hardware technologies and architectures to contribute towards this goal, significant advances have to come also from software technologies such as proactive and power‐aware scheduling, resource allocation, and fault‐tolerant computing. Development of these software technologies in turn relies heavily on our ability to model and accurately predict power consumption in large computing systems. In this paper, we present a data‐driven model of power consumption for a hybrid supercomputer (which held the top spot in the Green500 ranking in June 2013) that combines CPU, GPU, and MIC technologies to achieve high levels of energy efficiency. Our model takes as input workload characteristics—the number and location of resources that are used by each job at a certain time—and calculates a predicted power consumption at the system level. The model is application‐code‐agnostic and is based solely on a data‐driven predictive approach, where log data describing the past jobs in the system are employed to estimate future power consumption. For this, three different model components are developed and integrated. The first employs support vector regression to predict power usage for jobs before these are started. The second uses a simple heuristic to predict the length of jobs, again before they start. The two predictions are then combined to estimate power consumption due to the job at all computational elements in the system. The third component is a linear model that takes as input the power consumption at the computing units and predicts system‐wide power consumption. Our method achieves highly‐accurate predictions starting solely from workload information and user histories. The model can be applied to power‐aware scheduling and power capping: alternative workload dispatching configurations can be evaluated from a power perspective and more efficient ones can be selected. The methodology outlined here can be easily adapted to other HPC systems where the same types of log data are available.

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“…However, building an accurate power model to estimate the power consumption as a function of the CPU load of a PM is a difficult task. An accurate power model usually requires a large number of input data which can cause additional overhead [25]. For example, McCullough et al [30] shows that the power consumption of a PM with a single core can be modeled with 97% accuracy where the accuracy lies within 94-98% for a PM with multiple cores.…”

Section: Uncertainty In the Power Model For Pmsmentioning

confidence: 99%

Robust optimization for energy-efficient virtual machine consolidation in modern datacenters

2018

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Energy efficient virtual machine (VM) consolidation in modern data centers is typically optimized using methods such as Mixed Integer Programming, which typically require precise input to the model. Unfortunately, many parameters are uncertain or very difficult to predict precisely in the real world. As a consequence, a once calculated solution may be highly infeasible in practice. In this paper, we use methods from robust optimization theory in order to quantify the impact of uncertainty in modern data centers. We study the impact of different parameter uncertainties on the energy efficiency and overbooking ratios such as e.g. VM resource demands, migration related overhead or the power consumption model of the servers used. We also show that setting aside additional resource to cope with uncertainty of workload influences the overbooking ration of the servers and the energy consumption. We show that, by using our model, Cloud operators can calculate a more robust migration schedule leading to higher total energy consumption. A more risky operator may well choose a more opportunistic schedule leading to lower energy consumption but also higher risk of SLA violation.

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A Simple Model for Estimating Power Consumption of a Multicore Server System

Cited by 15 publications

References 13 publications

Empirically modeling how a multicore software ICN router and an ICN network consume power

Empirically modeling how a multicore software ICN router and an ICN network consume power

A data‐driven approach to modeling power consumption for a hybrid supercomputer

Robust optimization for energy-efficient virtual machine consolidation in modern datacenters

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