Energy Efficiency Of Data Centers Research Articles

Data Storage Energy Efficiency in the Zettabyte Era

Data storage has become an increasingly significant issue with the widespread availability of cheap storage technology and the development of global data centres for cloud storage. This paper reports a study of energy consumption for data storage in data centres. Unlike previous estimates, which have been extrapolations of broad energy usage within the data centre, we take a bottom-up approach. Firstly, we describe the technologies that can be used for data storage. Then we build up a model of energy consumption in data centres based on tiers of data storage. Based on published forecasts of data storage growth and assumptions about improving energy efficiency in technology and data centres, we then estimate the energy consumption for data storage in data centres in 2020. We show that energy consumption will rise substantially if a “business as usual” approach is adopted. We further show that the growth in energy consumption can be mitigated somewhat by adopting a more aggressive policy of data archiving on long-term, low-energy, “cold” storage; or more ideally purging data of little or no future value. This, however, would require a change in the popular assumption that data will be available readily online and forever.

An energy-efficient power management for heterogeneous servers in data centers

Power management for heterogeneous servers has been playing a key role in improving energy efficiency in data centers. Running latency-critical web services on such scenario is still challenging due to the overheads of task transition between such servers. In this paper, we present a runtime power management system, Montgolfier, which is built on a latency-aware feedback control mechanism. It consolidates wimpy and brawny servers into composite nodes performing latency-critical applications to improve overall energy efficiency while ensuring QoS. The key idea behind Montgolfier is to mitigate the negative effect of server switches by dynamic load prediction and to determine thin-provisioned configurations in fine-grain manner within servers for daily fluctuating loads. Our evaluation results show that Montgolfier reduces energy consumption by up to 34.9% without violating any QoS constraints.

Virtual machine optimization to achieve energy efficient optimum resource utilization in cloud data center

Objectives: Cloud offers multiple benefits through its data center-based services. The whole world uses these services that are hosted by physical machines. Millions of virtual machines get an optimized utilization of hardware to utilize these physical machines. The unbalanced distribution of virtual machines to physical machines offers an in-efficient utilization of data center hardware that will also lead to more carbon emission and harm the environment as well. Methods/material: A learning function is needed to offer energy efficiency in the cloud data center through VM optimization through its optimal allocation to physical machines. Therefore, an optimal VM placement and migration algorithm is a challenge that is addressed in this paper to reach an efficient energy optimization and resource utilization level. Finding/Novelty: The proposed algorithm is led by a learning function that takes into account the available number of physical machines, number of virtual machines, incoming requests and decides to run an optimal number of physical machines to obtain energy efficiency level for the cloud data center by migrating the virtual machines (VMs). Keywords: Datacenter; Cloud computing; Energy efficiency; VM placement; VM migration; Green Cloud

An Energy-Efficient Networking Approach in Cloud Services for IIoT Networks

We study the problem of the energy-efficient networking in cloud services with geographically distributed data centers for industrial Internet-of-Things (IIoT) networks, specially for multimedia IIoT networks. This is significantly challenged by dynamic end-to-end request demands and unbalanced link energy efficiency, unbalanced and time-varying link utilization, and bandwidth and delay constraints for service requirements. To solve these issues, we propose a multi-constraint optimization model for the energy efficiency optimization in cloud computing services where data centers are geographically distributed and are interconnected by cloud networks. Our model jointly optimizes energy efficiency in data centers and cloud networks. An intelligent heuristic algorithm is presented to solve this model for dynamic request demands between different data centers and between data centers and users. This is implemented by combining the niche genetic algorithm and the random depth-first search. Simulation results for energy-efficient networking show that better gains in network energy efficiency can be achieved by our joint optimization. Joint optimization between industrial data centers and industrial cloud networks can further improve energy savings and link utilization for time-varying requests.

Efficient VM migrations using forecasting techniques in cloud computing: a comprehensive review

High cost of data centers’ energy consumption and its environmental effects such as CO2 emissions have inspired numerous researches to provide more efficient VM management approaches. VM migration is one of the critical VM management activities whose performance has a direct effect on the energy efficiency of cloud data centers (DCs). To conduct a more effective migration process and reduce the number of VM migrations, some of the VM management frameworks apply prediction algorithms to forecast various migration and VM-related factors. This paper presents an extensive survey and taxonomy of the predictive VM migration approaches adapted for the cloud DCs. For this purpose, it first provides the key issues regarding the VM migration and then classifies them based on their applied prediction algorithm. It illustrates the main contributions of each scheme and describes how prediction methods are integrated into the VM migration process, to make it more effective. Moreover, a comparison of the predictive migration schemes is provided. Finally, the concluding remarks and future research areas are specified.

A Review on Virtual Machine Positioning and Consolidation Strategies for Energy Efficiency in Cloud Data Centers

The cloud data center consumes massively more and more energy which is considered inacceptable. Therefore further efforts are needed to improve the energy efficiency of such data centers by using Server Consolidation to minimize the number of Active Physical Machines (APMs) in a data center setting. Strategies for positioning and transformation of VM maintain their usefulness as a roadmap to maximum consolidation. The latest techniques do complex restructuring, thus optimizing VM's positioning. The paper provides a detailed state-of - the-art strategies for VM positioning and consolidation that help improve energy efficiency in cloud data centers. A comparison is provided here between the strategies that revealed the worthiness, limitations and suggestions of strengthening other methods along the way.

CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds

Cloud computing offers hardware and software resources delivered as services. It provides solutions for dynamic as well as “pay as you go” provision of resources. Energy consumption of these resources is high which leads to higher operational costs and carbon emissions in data centers. A number of research studies have been conducted on energy efficiency of data centers, but most of them concentrate on single factor energy consumption, i.e., energy consumed by CPU only, and energy consumption by Random Access Memory (RAM) is neglected. However, recently the focus has been turned towards impact of energy consumption by RAM on data centers. Studies have shown that RAM consumes about 25% of joint energy consumed by a server’s CPU and RAM. In this paper, two energy-aware virtual machine (VM) consolidation schemes are proposed that take into account a server’s capacity in terms of CPU and RAM to reduce the overall energy consumption. The proposed schemes are compared with existing schemes using CloudSim simulator. The results show that the proposed schemes reduce the energy cost with improved Service Level Agreement (SLA).

Energy-Efficient Hybrid Framework for Green Cloud Computing

The increasing growth in the demand for cloud computing services, due to the increasing digital transformation and the high elasticity of the cloud, requires more efforts to improve the electrical energy efficiency of cloud data centers. In this paper, an energy-efficient hybrid (EEH) framework for improving the efficiency of consuming electrical energy in data centers is proposed and evaluated. The proposed framework is based on both the request scheduling and servers consolidation approaches rather than depending only on one approach as in the existing related works. The EEH framework sorts the customers' requests (tasks) according to their time and power needs before performing the scheduling. It has a scheduling algorithm that considers power consumption when taking its scheduling decisions. It also has a consolidation algorithm that determines the underloaded servers to be slept or hibernated, the overloaded servers, the virtual machines to be migrated and the servers that will receive migrated virtual machines. In addition, the EEH framework includes a migration algorithm for transferring migrated virtual machines to new servers. Results of simulation experiments indicate the superiority of the EEH framework to the utilization of one approach only to reduce power consumption in terms of power usage effectiveness (PUE), data center energy productivity (DCEP), average execution time, throughput and cost saving.

Virtual Machine Consolidation with Multiple Usage Prediction for Energy-Efficient Cloud Data Centers

Virtual machine consolidation aims at reducing the number of active physical servers in a data center so as to decrease the total power consumption. In this context, most of the existing solutions rely on aggressive virtual machine migration, thus resulting in unnecessary overhead and energy wastage. Besides, virtual machine consolidation should take into account multiple resource types at the same time, since CPU is not the only critical resource in cloud data centers. In fact, also memory and network bandwidth can become a bottleneck, possibly causing violations in the service level agreement. This article presents a virtual machine consolidation algorithm with multiple usage prediction (VMCUP-M) to improve the energy efficiency of cloud data centers. In this context, multiple usage refers to both resource types and the horizon employed to predict future utilization. Our algorithm is executed during the virtual machine consolidation process to estimate the long-term utilization of multiple resource types based on the local history of the considered servers. The joint use of current and predicted resource utilization allows for a reliable characterization of overloaded and underloaded servers, thereby reducing both the load and the power consumption after consolidation. We evaluate our solution through simulations on both synthetic and real-world workloads. The obtained results show that consolidation with multiple usage prediction reduces the number of migrations and the power consumption of the servers while complying with the service level agreement.

QoS-aware flow scheduling for energy-efficient cloud data centre network

It is highly valuable to achieve energy-efficient cloud data centres, which always act as the basic infrastructures. This paper thus aims at reducing the energy consumption of network devices in cloud data centres by flexible flow scheduling. For such an aim, it is necessary to guarantee the flow-level performance, which is a critical requirement for quality of service (QoS) in production data centres. Hence, this paper takes both energy reduction and QoS into account for flow scheduling, and then proposes a three-phase framework to control the energy consumption for cloud data centre network (DCN) while guaranteeing flow-level performance. The core idea is to turn off the unnecessary switches off. From extensive simulations, it is shown that our solution could not only reduce about 20% of energy on average than the case with all switches on, but also maintain good flow-level performance, stability and fault tolerance simultaneously.

Research progress and development trend of cross-layer energy efficiency optimization in data centers

Energy consumption has become a main obstacle hindering the further development of data centers. To solve this problem, many data center energy efficiency optimization studies have emerged in academia and industry in recent years. This review introduces current global research on the data center energy saving problem from the perspective of optimizing cross-layer energy efficiency, including the refrigeration and power supply system energy efficiency optimization based on IT load dispatching as well as unified energy efficiency optimization of IT and refrigeration systems, and then projects trends in these areas.

Silicon Photonic Switch Topologies and Routing Strategies for Disaggregated Data Centers

Disaggregation enabled by silicon photonic switch fabrics is a path to low-cost and energy-efficient data centers. The routing strategy, which can be seamlessly incorporated into the switch control plane, potentially provides an additional dimension for the physical-layer performance optimization, at no extra cost. In this paper, we analyze the role of optical routing strategies for silicon photonic switch fabrics. We define and quantify the number of global switching states in various switching topologies and discuss their relationship to the number of switch permutations. We propose a topology-agnostic approach that is shown to optimize fabric-wide switch path power penalties and consequently reduce the dynamic-range requirement on receivers. Additionally, it potentially compensates for device fabrication variations by taking advantage of the redundancy in switching states over switch permutations; thus, increasing fabrication tolerance. Significant power penalty improvements are demonstrated via both our simulation and test platforms, even for moderate-scale silicon switches.

Measurement, Analysis, and Enhancement of Multipath TCP Energy Efficiency for Datacenters

Multipath TCP (MPTCP) has recently been suggested as a promising transport protocol to boost the utilization of underlaying datacenter networks, yet it also increases the host CPU power consumption. It remains unclear whether datacenters can indeed benefit from using MPTCP from the perspective of energy efficiency. Through realworld measurement of MPTCP, we show that the energy efficiency of MPTCP is largely related to the flow completion time and the existence of link-sharing subflows. In particular, we find that the link-sharing subflows in MPTCP will significantly elevate the CPUs’ power consumption on hosts. To make the matter worse, it will also reduce the transmission efficiency for both throughput-sensitive long flows and latency-sensitive short flows. To address such a problem, we present MPTCP-D, an energy-efficient enhancement of MPTCP in datacenter networks. MPTCP-D incorporates a novel congestion control algorithm that improves energy efficiency by minimizing the flow completion time. It also has a build-in subflow elimination mechanism that precludes link-sharing subflows from increasing the host CPU power consumption. We implement MPTCP-D in the Linux kernel, analyze the parameter selection in the algorithm and study its performance through packet-level simulation and on Amazon EC2. Our results show that, without degrading the performance of the long flow throughput and the short flow completion time, MPTCP-D reduces the long flow energy consumption by up to 72% compared to DCTCP for data transfers, and reduces the short flow power consumption by up to 46% compared to MPTCP with link-sharing subflows.

Real-Time Workflows Oriented Hybrid Scheduling Approach With Balancing Host Weighted Square Frequencies in Clouds

High energy consumption in cloud data centers has become one of the main obstacles to green cities, and an urgent problem to be solved. So far, a large number of scheduling algorithms have been developed to reduce energy consumption for executing workflows. However, most existing algorithms have obvious defects in energy and resource efficiency, because they schedule workflow tasks to hosts directly and ignore that the host is so powerful that a single workflow task cannot make full use of its resources. To resolve the issue, a new scheduling architecture is designed for cloud data centers. Then, two principles are derived: one suggests that hybrid scheduling tasks from different workflows is helpful for improving resource utilization; the other one deduces that balancing host weighted square frequencies can minimize total power consumption of active hosts under a given resource requirement. On the basis of the scheduling architecture and the two principles, an oNline schEduling AlgoriThm, namely NEAT, is proposed to schedule dynamic workflows with deadlines. Furthermore, three strategies for dynamically adjusting the available virtual machines (VMs) and active hosts are proposed and integrated into NEAT to improve the energy and resource efficiency for cloud data centers. Finally, the proposed NEAT is compared with three existing algorithms using real-world workflow traces to demonstrate its superior performance with respect to energy and resource efficiency while guaranteeing the timing requirements of workflows. Compared with baseline algorithms, NEAT is capable of reducing energy consumption for cloud data centers by an average of 40% in the range from 34.0% to 45.67%.

Immersion cooling as the next technology for data center cooling: A review

The utility of the world internet is on the rise as it is recorded that 46% of the world’s population have become internet users who generates data traffic of up to 8 zettabytes daily. This increase has triggered the growth of data center infrastructure as processing, storage and communication system in the digital world. The data center itself has contributed 1.5% to the total world electricity consumption and this is expected to increase with time. The proportion of energy used in the data center covers 52% for information technology (IT) equipment, 38% for cooling and 10% for supporting devices. One of the problems faced by these centers over the years is the cooling of the information technology (IT) components. This paper describes a cooling model that has the potential to improve the efficiency of energy used in data centers. Numerous studies have been carried out and one of them involves the use of immersion cooling technique as it promises improvements in the energy efficiency of data centers, using dielectric fluids that have high heat capacity. Several types of fluid used in this method are identified and discussed in this paper.

SDN Based Energy Efficient Cloud Data Center Networks

Cloud computing has led to the tremendous growth of IT organizations, which serves as the means of delivering services to large number of consumers globally, by providing anywhere, anytime easy access to resources and services. The primary concern over the increasing energy consumption by cloud data centers is mainly due to the massive emission of greenhouse gases, which contaminate the atmosphere and tend to worsen the environmental conditions. The major part of huge energy consumption comes from large servers, high speed storage devices and cooling equipment, present in cloud data centers. These serve as the basis for fulfilling the increasing need for computing resources. These in turn bestow additional cost of resources. The goal is to focus on energy savings through effective utilization of resources. This necessitates the need for developing a green-aware, energy-efficient framework for cloud data center networks. The Software Defined Networking (SDN) are chosen as they aid in studying the behaviour of networks from the overall perspective of software layer, rather than decisions from each individual device, as in case of conventional networks. The central objective of this paper is dedicated to survey on various existing SDN based energy efficient cloud data center networks.

Optimal energy-efficient policies for data centers through sensitivity-based optimization

In this paper, we propose a novel dynamic decision method by applying the sensitivity-based optimization theory to find the optimal energy-efficient policy of a data center with two groups of heterogeneous servers. Servers in Group 1 always work at high energy consumption, while servers in Group 2 may either work at high energy consumption or sleep at low energy consumption. An energy-efficient control policy determines the switch between work and sleep states of servers in Group 2 in a dynamic way. Since servers in Group 1 are always working with high priority to jobs, a transfer rule is proposed to migrate the jobs in Group 2 to idle servers in Group 1. To find the optimal energy-efficient policy, we set up a policy-based Poisson equation, and provide explicit expressions for its unique solution of performance potentials by means of the RG-factorization. Based on this, we characterize monotonicity and optimality of the long-run average profit with respect to the policies under different service prices. We prove that the bang-bang control is always optimal for this optimization problem, i.e., we should either keep all servers sleep or turn on the servers such that the number of working servers equals that of waiting jobs in Group 2. As an easy adoption of policy forms, we further study the threshold-type policy and obtain a necessary condition of the optimal threshold policy. We hope the methodology and results derived in this paper can shed light to the study of more general energy-efficient data centers.

CSL-driven and energy-efficient resource scheduling in cloud data center

High energy consumption in data center has serious impacts on environment. Energy-efficient scheduling under Customer Satisfaction Level (CSL) constraint has become a key problem of cloud computing in data centers. In this paper, a CSL-driven and energy-efficient resource scheduling (CDEERS) framework has been designed to optimize energy efficiency and CSL in cloud data centers. To achieve different goals in different CSL states, three scheduling strategies (aiming to minimize energy saving, maximize CSL and maximize CSL per energy, respectively) are auto-adaptively applied to consolidate resources according to CSL states. To identify CSL states, a metric based on Service Level Agreement (SLA) violation rate (MSVR) is designed and applied in the proposed method. Additionally, the Weighted Moving Average (WMA) model is applied to predict the future state of CSL and optimize the resource allocation. Simulation result shows that the MSVR improves energy efficiency (CSL per energy) by 51.1% and 30.4% compared with traditional metric based on workload (MW) and metric based on response time (MRT), respectively, during the whole test period. It reduces the SLA violation rate by 7.9% and the number of host overloads by 9.6%, respectively, while prediction mechanism is applied. It shows that CDEERS has better energy efficiency than these static scheduling methods with single objection in the condition of dynamic state of CSL.

Sensor Communication Model Using Cyber-Physical System Approach for Green Data Center

Energy consumption in distributed computing system gains a lot of attention recently after its processing capacity becomes significant for better business and economic operations. Comprehensive analysis of energy efficiency in high-performance data center for distributed processing requires ability to monitor a proportion of resource utilization versus energy consumption. In order to gain green data center while sustaining computational performance, a model of energy efficient cyber-physical communication is proposed. A real-time sensor communication is used to monitor heat emitted by processors and room temperature. Specifically, our cyber-physical communication model dynamically identifies processing states in data center while implying a suitable air-conditioning temperature level. The information is then used by administration to fine-tune the room temperature according to the current processing activities. Our automated triggering approach aims to improve edge computing performance with cost-effective energy consumption. Simulation experiments show that our cyber-physical communication achieves better energy consumption and resource utilization compared with other cooling model.

The Numerical Simulation of the Airflow Distribution and Energy Efficiency in Data Centers with Three Types of Aisle Layout

The energy consumption of fast-growing data centers is drawing attentions from not only energy organizations and institutions all over the world, but also charity groups, such as Greenpeace, and research shows that the power consumption of air conditioning makes up a large proportion of the electricity cost in data centers. Therefore, more detailed investigations of air conditioning power consumption are warranted. Three types of airflow distributions with different aisle layouts (the open aisle, the closed cold aisle, and the closed hot aisle) were investigated with Computational Fluid Dynamics (CFD) methods in a typical data center of four rows of racks in this study. To evaluate the results of thermal and bypass phenomenon, the temperature increase index (β) and the energy utilization index (ηr) were used. The simulations show that there is a better trend of the β index and ηr index both closed cold aisle and closed hot aisle compared with free open aisle. Especially with high air flow rate, the β index decreases and the ηr index increases considerably. Moreover, the results prove the closed aisles (both closed cold aisle and closed hot aisle) can not only significantly improve the airflow distribution, but also reduce the mixture of cold and heat flow, and therefore improve energy efficiency. In addition, it proves the design of the closed aisles can meet the increasing density of installations and our simulation method could evaluate the cooling capacity easily.