Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.

Data Center Energy Consumption Modeling: A Survey

A review of power consumption models of servers in data centers

Enhancing the efficiency and the reliability of the data center are the technical challenges for maintaining the quality of services for the end-users in the data center operation. The energy consumption models of the data center components are pivotal for ensuring the optimal design of the internal facilities and limiting the energy consumption of the data center. The reliability modeling of the data center is also important since the end-user’s satisfaction depends on the availability of the data center services. In this review, the state-of-the-art and the research gaps of data center energy consumption and reliability modeling are identified, which could be beneficial for future research on data center design, planning, and operation. The energy consumption models of the data center components in major load sections i.e., information technology (IT), internal power conditioning system (IPCS), and cooling load section are systematically reviewed and classified, which reveals the advantages and disadvantages of the models for different applications. Based on this analysis and related findings it is concluded that the availability of the model parameters and variables are more important than the accuracy, and the energy consumption models are often necessary for data center reliability studies. Additionally, the lack of research on the IPCS consumption modeling is identified, while the IPCS power losses could cause reliability issues and should be considered with importance for designing the data center. The absence of a review on data center reliability analysis is identified that leads this paper to review the data center reliability assessment aspects, which is needed for ensuring the adaptation of new technologies and equipment in the data center. The state-of-the-art of the reliability indices, reliability models, and methodologies are systematically reviewed in this paper for the first time, where the methodologies are divided into two groups i.e., analytical and simulation-based approaches. There is a lack of research on the data center cooling section reliability analysis and the data center components’ failure data, which are identified as research gaps. In addition, the dependency of different load sections for reliability analysis of the data center is also included that shows the service reliability of the data center is impacted by the IPCS and the cooling section.

A Review of Data Centers Energy Consumption and Reliability Modeling

Renewable energy is becoming an important power source for data centers, especially with the zero-carbon waste pledges made by big cloud providers. However, one of the main challenges of renewable energy sources is the high variability of power produced. Traditional approaches such as batteries or transmitting to the grid fall short on scale, overhead, or "green-ness". We propose Virtual Battery: instead of adapting the availability of power to match the computation demand we shift computational demand to meet the availability of power. Virtual batteries shift demand by requiring applications to either be flexible and delay-tolerant or proactively migrating to where power is (going to be) available. We show that using multiple virtual battery sites in combination can meet the needs of modern applications. Moreover, we show how an intelligent network and power aware co-scheduler can not only provide availability despite variability but also help mitigate migration related network overhead by over 30% in total and 4.2x at peak.

https://dl.acm.org/doi/pdf/10.1145/3484266.3487394

Redesigning Data Centers for Renewable Energy

Energy efficiency has turned into an undeniably essential worry in data centers as a result of issues related to energy consumption, including capital costs, working costs, and natural effect. While energy wastage, because of imperfect utilization of facilities and IT hardware, has to a great extent been decreased using best-practice advances, tending to energy loss in IT equipment, still requires the plan and usage of energy-aware resource management frameworks. Various methods aimed to enhance the energy efficiency in data centers have been developed in the field related to the development of resource allocation. In fact, researchers and scientists utilize numerous ways to improve effectiveness for optimized power and performance: scaling virtual machine and server preparing abilities to minimize energy consumption, enhancing resource use through exploiting its heterogeneity and consolidating the workload. To accomplish these objectives, there is multiple proposes models, calculations, algorithms, techniques, and procedures that diminish energy use using virtual machine scaling, virtual machine sizing for CPU and memory, CPU frequency adjustment, and also equipment power control for resource allocation in the server-level. In this context, we present different formulas for calculating energy consumption in cloud data centers and we propose a simplified and completed deduced formula for energy consumption estimation.

How energy consumption in the cloud data center is calculated

In this paper we challenge the common evaluation practices used in Virtual Machine (VM) consolidation, such as simulation and small testbeds, which fail to capture the fundamental trade-off between energy consumption and performance. We identify a number of over-simplifying assumptions which are typically made about the energy consumption and performance characteristics of modern networked systems. In response, we describe how more accurate models for data-center systems can be designed and used in order to create an evaluation framework that allows the exploration of the energy-performance trade-off in VM consolidation strategies with enhanced fidelity.

/pdf/challenge-resolving-data-center-power-bill-disputes-the-28pfh4g29l.pdf

Vincenzo Mancuso

Papers

Challenge: Resolving Data Center Power Bill Disputes: The Energy-Performance Trade-Offs of Consolidation