A high-level energy consumption model for heterogeneous data centers

Zhang, Xiao; Lu, Jianjun; Qin, Xiao; Zhao, Xiaonan

doi:10.1016/j.simpat.2013.05.006

Cited by 46 publications

(19 citation statements)

References 8 publications

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“…In the verification stage, each feature vector in test set is inputted into the trained model, and thus, the power consumption of this vector is predicted. To evaluate the efficiency of FSDL model, we compare our approach with other five methods including power regression model [26], cubic model [27], PMC model [28], AEC model [30], CMP model [31]. Fig.…”

Section: Analysis and Comparison Of Experimental Resultsmentioning

confidence: 99%

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Power consumption model based on feature selection and deep learning in cloud computing scenarios

Yang

2020

IET Communications

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Section: Analysis and Comparison Of Experimental Resultsmentioning

confidence: 99%

“…Zhang et al [27] find that the cubic polynomial model could obtain better results whether EC swing in a small range or a wide range, exceeding the linear model that is only suitable for a small range.…”

Section: Related Workmentioning

confidence: 99%

Power consumption model based on feature selection and deep learning in cloud computing scenarios

Yang

2020

IET Communications

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“…Some energy consumption models monitor several performance counters related to the CPU in order to estimate power consumption (18). Zhang et al validate that energy usage of an application is directly related to the amount of CPU time it uses (19). Our paper focuses on critical power and regards running time as the main indicator of energy consumption of the system.…”

Section: Assessment For Energymentioning

confidence: 99%

“…In formula (19) the smallest p is the order of the model; p can be determined by the AIC criterion method.…”

Section: Prediction Of the Next Targeted Clustermentioning

confidence: 99%

Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model

Han

Zhou

Jia

et al. 2019

Information Sciences

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A Wireless Sensor Network (WSN) security strategy is imperative to counteract security threats, and energy consumption directly affects the network life-time of a wireless sensor; thus an attempt to exploit a low-consumption Intrusion Detection System (IDS) to detect malicious attacks makes a lot of sense. Current Intrusion Detection Systems can detect just some specific attacks and the network life time is reduced due to their high energy consumption. For the purpose of energy limitation and high efficiency, this paper proposes an intrusion detection model based on game theory and an autoregressive model. The paper not only improves the autoregressive theory model to a non-cooperative complete information static game model, but makes a prediction about attack pattern based on the reliable model. The proposed approach improves on previous approaches in two main ways: it takes energy consumption of the intrusion detection process into account, and it obtains the optimal defense strategy, balancing detection efficiency and the system's energy consumption by analyzing the model's mixed Nash equilibrium solution. In the simulation experiment, the running time of the process is regarded as the main indicator of energy consumption of the system. The simulation results show that the IDS proposed not only effectively predicts the attack time and the next targeted cluster based on the game theory, but also reduces energy consumption.

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“…An idle server represents 60-70% of the power consumed when it is fully utilized [12,43]. The power consumption in a computer node is mainly dictated by the CPU resource [3,4].…”

Section: Power-efficiency Logicmentioning

confidence: 99%

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Sampaio

Barbosa

Prodan

2015

Simulation Modelling Practice and Theory

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a b s t r a c tCloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU-and network-intensive applications. To address these challenges, we propose an interference-and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.

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A high-level energy consumption model for heterogeneous data centers

Cited by 46 publications

References 8 publications

Power consumption model based on feature selection and deep learning in cloud computing scenarios

Power consumption model based on feature selection and deep learning in cloud computing scenarios

Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

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