The increasing demand of the data center's computational capacity in recent years has introduced new data center operational challenges among others to maintain the service level agreements (SLA) and quality of services (QoS), while at the same time limiting energy consumption. In this paper, a stochastic operational risk assessment approach is presented that estimates the required number of spare servers in a data center considering the risk of servers' failure in operation since servers define the computational capability of a data center. A reliability index called “risk of computational resource commitment (RCRC)” is introduced that quantifies the probability of having insufficient spare servers due to failures during the operational lead time, and the complement of the RCRC shows the ability of the resources to maintain SLA of a data center. The failure rates of the servers are obtained using a Monte Carlo Simulation with the failure data, published by Google in 2019. The analysis shows that the RCRC reduces with the increasing number of spare servers, while it also stresses the energy efficiency of the data center. The RCRC index could be used in data center operation to avoid overprovisioning of the servers and to limit the number of spare servers in the data center, while creating a suitable balance between QoS and energy consumption of the 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.

The data centers host sensitive electronic devices like servers, memory, hard disks, network devices, etc., which are supplied by the power supply units. The regulated direct current (DC) output of the power supply units fluctuates with input voltage variation since they typically contain single phase switch-mode power supplies. The voltage dips caused by faults in the internal power supply system of the data center can be large enough to violate the Information Technology Industry Council (ITIC) proposed voltage-tolerance guideline. The output of the power supplies, hence the operation of the servers will be interrupted due to such voltage dips. In this paper, the outage probability of the servers caused by the voltage dips are analyzed for different fault location in the internal supply system of a data center.

The internal power condition system (IPCS) in data centers is prone to have cable faults that cause voltage dips and swells. The voltage dips and swells impact the power supply units (PSUs) with the servers. The servers connected with the PUSs restart or turn-off when the input voltage comes out of the voltage-tolerance range. This paper analyses the impact of such voltage disturbances on server outages due to a single-phase fault in the IPCS. The voltage-tolerance range of the PSUs is considered according to the guideline of the Information Technology Industry Council (ITIC). The voltage dip propagates to the healthy load sections from the fault location, while voltage swells are also observed due to sudden load reduction. Moreover, the current limitation mode of the inverter in the uninterrupted power supply (UPS) is identified as a cause of voltage dip to almost zero experienced by the PSUs. The reliability of the data center considering the outage probability of the servers are finally quantified to show the impacts of the voltage dips and swells in the IPCS.

The energy demand of data centers is increasing globally with the increasing demand for computational resources to ensure the quality of services. It is important to quantify the required resources to comply with the computational workloads at the rack-level. In this paper, a novel reliability index called loss of workload probability is presented to quantify the rack-level computational resource adequacy. The index defines the right-sizing of the rack-level computational resources that comply with the computational workloads, and the desired reliability level of the data center investor. The outage probability of the power supply units and the workload duration curve of servers are analyzed to define the loss of workload probability. The workload duration curve of the rack, hence, the power consumption of the servers is modeled as a function of server workloads. The server workloads are taken from a publicly available data set published by Google. The power consumption models of the major components of the internal power supply system are also presented which shows the power loss of the power distribution unit is the highest compared to the other components in the internal power supply system. The proposed reliability index and the power loss analysis could be used for rack-level computational resources expansion planning and ensures energy-efficient operation of the data center.

The number of data centers and the energy demand are increasing globally with the development of information and communication technology (ICT). The data center operators are facing challenges to limit the internal power losses and the unexpected outages of the computational resources or servers. The power losses of the internal power supply system (IPSS) increase with the increasing number of servers that causes power supply capacity shortage for the devices in IPSS. The aim of this paper is to address the outage probability of the computational resources or servers due to the power supply capacity shortage of the power distribution units (PDUs) in the IPSS. The servers outage probability at rack-level defines the service availability of the data center since the servers are the main computational resource of it. The overall availability of the IPSS and the power consumption models of the IPSS devices are also presented in this paper. Quantitative studies are performed to show the impacts of the power losses on the service availability and the overall availability of the IPSS for two different IPSS architectures, which are equivalent to the Tier I and Tier IV models of the data center.

Hyper-scale data centers are used to host cloud computing interfaces to support the increasing demand for storage and computational resources. For achieving specific service level agreements (SLA), this infrastructure demands highly available cloud computing systems. It is necessary to analyze the server failure incidents to determine the way of improving the reliability of the system since the computational interruption causes financial losses for the data center owners. Regarding the reliability analysis, it is important to characterize the time to failure and time to repair of the servers. In this paper, a publicly available data set from Google cloud-cluster data center will be analyzed to find the distribution function for the time to failure and the time to repair for the servers in a cloud based data centers.

In the context of modern information technology (IT) industry, cloud computing is gaining popularity for big data handling. Therefore, IT service providers like Google, Facebook and Amazon are expanding their technical resources by building data centers to improve the data processing and data storage facilities under cloud service pattern. However, data centers consume a large amount of electrical energy. In recent years, a lot of research has been done to reduce the electrical energy consumption of data centers by high performance computing. However, very few researchers have focused on the electrical energy consumption by the electrical components inside the data center. In this paper, a component based electrical energy consumption modelling approach is presented to identify the losses of different components as well as their interactions to the total electrical energy consumption of the data center. The electrical energy consumption models of servers and other components are presented as a function of server utilization.