Power Management Techniques for Data Centers: A Survey

With growing use of internet and exponential growth in amount of data to be stored and processed (known as 'big data'), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become essential. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several recent architectural techniques designed for power management of data centers. We also present a classification of these techniques based on their characteristics. This paper aims to provide insights into the techniques for improving energy efficiency of data centers and encourage the designers to invent novel solutions for managing the large power dissipation of data centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy Efficiency, Green Computing, DVFS, Server Consolidatio

Energy-aware scheduling in distributed computing systems

Distributed computing systems, such as data centers, are key for supporting modern computing demands. However, the energy consumption of data centers has become a major concern over the last decade. Worldwide energy consumption in 2012 was estimated to be around 270 TWh, and grim forecasts predict it will quadruple by 2030. Maximizing energy efficiency while also maximizing computing efficiency is a major challenge for modern data centers. This work addresses this challenge by scheduling the operation of modern data centers, considering a multi-objective approach for simultaneously optimizing both efficiency objectives. Multiple data center scenarios are studied, such as scheduling a single data center and scheduling a federation of several geographically-distributed data centers. Mathematical models are formulated for each scenario, considering the modeling of their most relevant components such as computing resources, computing workload, cooling system, networking, and green energy generators, among others. A set of accurate heuristic and metaheuristic algorithms are designed for addressing the scheduling problem. These scheduling algorithms are comprehensively studied, and compared with each other, using statistical tools to evaluate their efficacy when addressing realistic workloads and scenarios. Experimental results show the designed scheduling algorithms are able to significantly increase the energy efficiency of data centers when compared to traditional scheduling methods, while providing a diverse set of trade-off solutions regarding the computing efficiency of the data center. These results confirm the effectiveness of the proposed algorithmic approaches for data center infrastructures.Los sistemas informáticos distribuidos, como los centros de datos, son clave para satisfacer la demanda informática moderna. Sin embargo, su consumo de energético se ha convertido en una gran preocupación. Se estima que mundialmente su consumo energético rondó los 270 TWh en el año 2012, y algunos prevén que este consumo se cuadruplicará para el año 2030. Maximizar simultáneamente la eficiencia energética y computacional de los centros de datos es un desafío crítico. Esta tesis aborda dicho desafío mediante la planificación de la operativa del centro de datos considerando un enfoque multiobjetivo para optimizar simultáneamente ambos objetivos de eficiencia. En esta tesis se estudian múltiples variantes del problema, desde la planificación de un único centro de datos hasta la de una federación de múltiples centros de datos geográficmentea distribuidos. Para esto, se formulan modelos matemáticos para cada variante del problema, modelado sus componentes más relevantes, como: recursos computacionales, carga de trabajo, refrigeración, redes, energía verde, etc. Para resolver el problema de planificación planteado, se diseñan un conjunto de algoritmos heurísticos y metaheurísticos. Estos son estudiados exhaustivamente y su eficiencia es evaluada utilizando una batería de herramientas estadísticas. Los resultados experimentales muestran que los algoritmos de planificación diseñados son capaces de aumentar significativamente la eficiencia energética de un centros de datos en comparación con métodos tradicionales planificación. A su vez, los métodos propuestos proporcionan un conjunto diverso de soluciones con diferente nivel de compromiso respecto a la eficiencia computacional del centro de datos. Estos resultados confirman la eficacia del enfoque algorítmico propuesto

Power Management for Cloud-Scale Data Centers

Recent years have seen the rapid growth of large and geographically distributed data centers deployed by Internet service operators to support various services such as cloud computing. Consequently, high electricity bills, as well as negative environmental implications (e.g., CO2 emission and global warming) come along. In this thesis, we first propose a novel electricity bill capping algorithm that not only minimizes the electricity cost, but also enforces a cost budget on the monthly bill for cloud-scale data centers that impact the power markets. Our solution first explicitly models the impacts of the power demands induced by cloud-scale data centers on electricity prices and the power consumption of cooling and networking in the minimization of electricity bill. In the second step, if the electricity cost exceeds a desired monthly budget due to unexpectedly high workloads, our solution guarantees the quality of service for premium customers and trades off the request throughput of ordinary customers. We formulate electricity bill capping as two related constrained optimization problems and propose efficient algorithms based on mixed integer programming. We then propose GreenWare, a novel middleware system that conducts dynamic request dispatching to maximize the percentage of renewable energy used to power a network of distributed data centers, subject to the desired cost budget of the Internet service operator. Our solution first explicitly models the intermittent generation of renewable energy, e.g., wind power and solar power, with respect to varying weather conditions in the geographical location of each data center. We then formulate the core objective of GreenWare as a constrained I optimization problem and propose an efficient request dispatching algorithm based on linear-fractional programming (LFP)

Green metascheduler architecture to provide QoS in cloud computing

The aim of this paper is to propose and evaluate GreenMACC (Green Metascheduler Ar-\ud chitecture to Provide QoS in Cloud Computing), an extension of the MACC architecture\ud (Metascheduler Architecture to provide QoS in Cloud Computing) which uses greenIT\ud techniques to provide Quality of Service. The paper provides an evaluation of the perfor-\ud mance of the policies in the four stages of scheduling focused on energy consumption and\ud average response time. The results presented con rm the consistency of the proposal as\ud it controls energy consumption and the quality of services requested by di erent users of\ud a large-scale private cloud

A Novel Cloud-Based Temperature Monitoring Service to Datacenter Environment

As temperature-efficient of cloud datacenters becomes one of many challenges faces the engineers in today’s business environment, virtualized monitoring the resources over these datacenters is the cloud provider’s interest in a way the providers can control their temperature remotely anywhere around the world. Techniques proposed by existing works are focused on executing many cloud modules on less cloud-server which cannot be practical because massive volume of cloud modules will be scheduled by cloud tenants. This motivated us to present a novel solution and propose a real-time temperature aware for cloud network datacenters. The proposed paradigm is constructed based on defining dual thresholds per datacenter to control the level of temperature. Moreover, we have set the upper threshold to 25 degree and the lower one to 15 degree. Surpassing these values alerts network administrator through cloud-based software service to operate the cooling unit(s). The microcontrollers that have been incorporated are both Arduino and GSM Shield. The simulation results showed that our heuristic had the ability to control the temperature per datacenter approximately 63%

Power-Thermal Modeling and Control of Energy-Efficient Servers and Datacenters

Recently, the energy-efficiency constraints have become the dominant limiting factor for datacenters due to their unprecedented increase of growing size and electrical power demands. In this chapter we explain the power and thermal modeling and control solutions which can play a key role to reduce the power consumption of datacenters considering time-varying workload characteristics while maintaining the performance requirements and the maximum temperature constraints. We first explain simple-yet-accurate power and temperature models for computing servers, and then, extend the model to cover computing servers and cooling infrastructure of datacenters. Second, we present the power and thermal management solutions for servers manipulating various control knobs such as voltage and frequency of servers, workload allocation, and even cooling capability, especially, flow rate of liquid cooled servers). Finally, we present the solution to minimize the server clusters of datacenters by proposing a solution which judiciously allocates virtual machines to servers considering their correlation, and then, the joint optimization solution which enables to minimize the total energy consumption of datacenters with hybrid cooling architecture (including the computing servers and the cooling infrastructure of datacenters)

Effective and Economical Content Delivery and Storage Strategies for Cloud Systems

Cloud computing has proved to be an effective infrastructure to host various applications and provide reliable and stable services. Content delivery and storage are two main services provided by the cloud. A high-performance cloud can reduce the cost of both cloud providers and customers, while providing high application performance to cloud clients. Thus, the performance of such cloud-based services is closely related to three issues. First, when delivering contents from the cloud to users or transferring contents between cloud datacenters, it is important to reduce the payment costs and transmission time. Second, when transferring contents between cloud datacenters, it is important to reduce the payment costs to the internet service providers (ISPs). Third, when storing contents in the datacenters, it is crucial to reduce the file read latency and power consumption of the datacenters. In this dissertation, we study how to effectively deliver and store contents on the cloud, with a focus on cloud gaming and video streaming services. In particular, we aim to address three problems. i) Cost-efficient cloud computing system to support thin-client Massively Multiplayer Online Game (MMOG): how to achieve high Quality of Service (QoS) in cloud gaming and reduce the cloud bandwidth consumption; ii) Cost-efficient inter-datacenter video scheduling: how to reduce the bandwidth payment cost by fully utilizing link bandwidth when cloud providers transfer videos between datacenters; iii) Energy-efficient adaptive file replication: how to adapt to time-varying file popularities to achieve a good tradeoff between data availability and efficiency, as well as reduce the power consumption of the datacenters. In this dissertation, we propose methods to solve each of aforementioned challenges on the cloud. As a result, we build a cloud system that has a cost-efficient system to support cloud clients, an inter-datacenter video scheduling algorithm for video transmission on the cloud and an adaptive file replication algorithm for cloud storage system. As a result, the cloud system not only benefits the cloud providers in reducing the cloud cost, but also benefits the cloud customers in reducing their payment cost and improving high cloud application performance (i.e., user experience). Finally, we conducted extensive experiments on many testbeds, including PeerSim, PlanetLab, EC2 and a real-world cluster, which demonstrate the efficiency and effectiveness of our proposed methods. In our future work, we will further study how to further improve user experience in receiving contents and reduce the cost due to content transfer

Energy consumption in cloud computing environments

The conference aimed at supporting and stimulating active productive research set to strengthen the technical foundations of engineers and scientists in the continent, through developing strong technical foundations and skills, leading to new small to medium enterprises within the African sub-continent. It also seeked to encourage the emergence of functionally skilled technocrats within the continent.Datacentres are becoming indispensable infrastructure for supporting the services offered by cloud computing. Unfortunately, they consume a great deal of energy accounting for 3% of global electrical energy consumption. The effect of this is that, cloud providers experience high operating costs, which leading to increased Total Cost of Ownership (TCO) of datacentre infrastructure. Moreover, there is increased carbon dioxide emissions that affects the universe. This paper presents a survey on the various ways in which energy is consumed in datacentre infrastructure. The factors that influence energy consumption within a datacentre is presented as well.Strathmore University; Institute of Electrical and Electronics Engineers (IEEE