Data Center Energy Consumption Modeling: A Survey


Information centers are vital, energy-hungry infrastructures that run giant-scale.Net-primarily based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in information centers. In this paper, we tend to survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their elements. We conduct an in-depth study of the present literature on information center power modeling, covering a lot of than two hundred models. We tend to organize these models in an exceedingly hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Beneath hardware-centric approaches we begin from the digital circuit level and move on to describe higher-level energy consumption models at the hardware element level, server level, data center level, and at last systems of systems level. Beneath the software-centric approaches we have a tendency to investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to spot multiple problems prevalent in power modeling of different levels of knowledge center systems, as well as: i) few modeling efforts targeted at power consumption of the entire information center ii) many state-of-the-art power models are primarily based on some CPU or server metrics, and iii) the effectiveness and accuracy of these power models stay open questions. Based mostly on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Systematic Analysis of Fine-Grained Mobility Prediction with On-Device Contextual Data ABSTRACT: The concept of predicting the mobility of users is widely discussed within the research community. Numerous studies
PROJECT TITLE : Objective-Variable Tour Planning for Mobile Data Collection in Partitioned Sensor Networks ABSTRACT: Wireless sensor networks can achieve greater energy efficiency and more even load distribution through the collection
PROJECT TITLE : Location-Flexible Mobile Data Service in Overseas Market ABSTRACT: Mobile network operators, also known as MNOs, are the companies that are responsible for providing wireless data services. These services are based
PROJECT TITLE : Parallel Fractional Hot-Deck Imputation and Variance Estimation for Big Incomplete Data Curing ABSTRACT: The fractional hot-deck imputation, also known as FHDI, is a method for handling multivariate missing data
PROJECT TITLE : Representation Learning from Limited Educational Data with Crowdsourced Labels ABSTRACT: It has been demonstrated that representation learning plays a significant part in the unprecedented success of machine learning

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry