Cloud Computing: Survey on Energy Efficiency <s> INTERACTIONS BETWEEN CLOUD COMPUTING INFRASTRUCTURE DOMAINS <s> In this paper a circuit of setup is proposed to measure instant current values of CPU, it may improve the accuracy during estimating power consumption of CPU in instructions process running. Plug-in of the CPU during the measurements corresponds to the regular circuit. Gained results can be used to develop the appropriate mathematical model of CPU's power consumption and optimize its software in future. <s> BIB001 </s> Cloud Computing: Survey on Energy Efficiency <s> INTERACTIONS BETWEEN CLOUD COMPUTING INFRASTRUCTURE DOMAINS <s> The use of High Performance Computing (HPC) in commercial and consumer IT applications is becoming popular. They need the ability to gain rapid and scalable access to high-end computing capabilities. Cloud computing promises to deliver such a computing infrastructure using data centers so that HPC users can access applications and data from a Cloud anywhere in the world on demand and pay based on what they use. However, the growing demand drastically increases the energy consumption of data centers, which has become a critical issue. High energy consumption not only translates to high energy cost, which will reduce the profit margin of Cloud providers, but also high carbon emissions which is not environmentally sustainable. Hence, energy-efficient solutions are required that can address the high increase in the energy consumption from the perspective of not only Cloud provider but also from the environment. To address this issue we propose near-optimal scheduling policies that exploits heterogeneity across multiple data centers for a Cloud provider. We consider a number of energy efficiency factors such as energy cost, carbon emission rate, workload, and CPU power efficiency which changes across different data center depending on their location, architectural design, and management system. Our carbon/energy based scheduling policies are able to achieve on average up to 30% of energy savings in comparison to profit based scheduling policies leading to higher profit and less carbon emissions. <s> BIB002 </s> Cloud Computing: Survey on Energy Efficiency <s> INTERACTIONS BETWEEN CLOUD COMPUTING INFRASTRUCTURE DOMAINS <s> Thermal management is critical for clusters because of the increasing power consumption of modern processors, compact server architectures and growing server density in data centers. Thermal balancing mitigates hot spots in a cluster through dynamic load distribution among servers. This paper presents two Control-theoretical Thermal Balancing (CTB) algorithms that dynamically balance the temperatures of different servers based on online measurements. CTB features controllers rigorously designed based on optimal control theory and a difference equation model that approximates the thermal dynamics of clusters. Control analysis and simulation results demonstrate that CTB achieves robust thermal balancing under a wide range of uncertainties: (1) when different tasks incur different power consumptions on the CPUs, (2) when servers experience different ambient temperatures, and (3) when servers experience thermal faults. <s> BIB003 </s> Cloud Computing: Survey on Energy Efficiency <s> INTERACTIONS BETWEEN CLOUD COMPUTING INFRASTRUCTURE DOMAINS <s> We have developed PowerMon and PowerMon2, low-cost power monitoring devices that operate inside commodity computer systems, to analyze performance and power consumption tradeoffs in computer applications. Inserted between a system's power supply and motherboard, PowerMon monitors voltage and current on six DC rails and reports measurements at a rate of up to fifty samples per second through a USB interface, allowing monitoring by the target or a separate host. PowerMon2 has a smaller form factor that fits in a standard 3.5" hard drive bay, allowing it to be used in a 1U server chassis. It features a faster measurement rate of up to 1024 Hz on a single channel or 3072 Hz divided among multiple channels. PowerMon2 also adds two measurement channels for additional peripherals, such as disks and graphical processing units. The PowerMon devices have been used to resolve and highlight variations in power consumption and energy efficiency at the subsystem level during separate operating phases of scientific performance benchmarks, such as NAS BT and SP. The device parts cost is low enough to provision an entire cluster for power monitoring and adaptation. Complete schematics, board layouts, and source code for the PowerMon devices are available under a BSD-style open source license at ilab.renci.org/powermon. <s> BIB004 </s> Cloud Computing: Survey on Energy Efficiency <s> INTERACTIONS BETWEEN CLOUD COMPUTING INFRASTRUCTURE DOMAINS <s> Live migration of virtual machine (VM) provides a significant benefit for virtual server mobility without disrupting service. It is widely used for system management in virtualized data centers. However, migration costs may vary significantly for different workloads due to the variety of VM configurations and workload characteristics. To take into account the migration overhead in migration decision-making, we investigate design methodologies to quantitatively predict the migration performance and energy cost. We thoroughly analyze the key parameters that affect the migration cost from theory to practice. We construct two application-oblivious models for the cost prediction by using learned knowledge about the workloads at the hypervisor (also called VMM) level. This should be the first kind of work to estimate VM live migration cost in terms of both performance and energy in a quantitative approach. We evaluate the models using five representative workloads on a Xen virtualized environment. Experimental results show that the refined model yields higher than 90% prediction accuracy in comparison with measured cost. Model-guided decisions can significantly reduce the migration cost by more than 72.9% at an energy saving of 73.6%. <s> BIB005 </s> Cloud Computing: Survey on Energy Efficiency <s> INTERACTIONS BETWEEN CLOUD COMPUTING INFRASTRUCTURE DOMAINS <s> Although cloud computing has rapidly emerged as a widely accepted computing paradigm, the research on cloud computing is still at an early stage. Cloud computing suffers from different challenging issues related to security, software frameworks, quality of service, standardization, and power consumption. Efficient energy management is one of the most challenging research issues. The core services in cloud computing system are the SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service). In this paper, we study state-of-the-art techniques and research related to power saving in the IaaS of a cloud computing system, which consumes a huge part of total energy in a cloud computing system. At the end, some feasible solutions for building green cloud computing are proposed. Our aim is to provide a better understanding of the design challenges of energy management in the IaaS of a cloud computing system. <s> BIB006 </s> Cloud Computing: Survey on Energy Efficiency <s> INTERACTIONS BETWEEN CLOUD COMPUTING INFRASTRUCTURE DOMAINS <s> Energy consumption of the Information and Communication Technology (ICT) sector has grown exponentially in recent years. A major component of the today’s ICT is constituted by the data centers which have experienced an unprecedented growth in their size and population, recently. The Internet giants like Google, IBM and Microsoft house large data centers for cloud computing and application hosting. Many studies, on energy consumption of data centers, point out to the need to evolve strategies for energy efficiency. Due to large-scale carbon dioxide (\(\mathrm{CO}_2\)) emissions, in the process of electricity production, the ICT facilities are indirectly responsible for considerable amounts of green house gas emissions. Heat generated by these densely populated data centers needs large cooling units to keep temperatures within the operational range. These cooling units, obviously, escalate the total energy consumption and have their own carbon footprint. In this survey, we discuss various aspects of the energy efficiency in data centers with the added emphasis on its motivation for data centers. In addition, we discuss various research ideas, industry adopted techniques and the issues that need our immediate attention in the context of energy efficiency in data centers. <s> BIB007 </s> Cloud Computing: Survey on Energy Efficiency <s> INTERACTIONS BETWEEN CLOUD COMPUTING INFRASTRUCTURE DOMAINS <s> The energy consumption issue in distributed computing systems raises various monetary, environmental and system performance concerns. Electricity consumption in the US doubled from 2000 to 2005. From a financial and environmental standpoint, reducing the consumption of electricity is important, yet these reforms must not lead to performance degradation of the computing systems. These contradicting constraints create a suite of complex problems that need to be resolved in order to lead to 'greener' distributed computing systems. This book brings together a group of outstanding researchers that investigate the different facets of green and energy efficient distributed computing. Key features: One of the first books of its kind Features latest research findings on emerging topics by well-known scientists Valuable research for grad students, postdocs, and researchers Research will greatly feed into other technologies and application domains <s> BIB008 </s> Cloud Computing: Survey on Energy Efficiency <s> INTERACTIONS BETWEEN CLOUD COMPUTING INFRASTRUCTURE DOMAINS <s> Introduction to Data Center Energy Flow and Thermal Management.- Fundamentals of Data Center Air Flow Management.- Peeling the Power Onion of Data Centers.- Understanding and Managing IT Power Consumption:A Measurement-based Approach.- Monitoring and Control of Data Centers.- Energy Efficiency Metrics.- Data Center Metrology and Measurement-Based Modeling Methods.- Numerical modeling of Data Center Clusters.- Exergy Analysis of Data Center Thermal Management Systems.- Reduced Order Modeling-based Energy Efficient and Adaptable Design.- Statistical Design and Analysis Methods for Data Center Thermal Management.- Two-Phase On-Chip Cooling Systems for Green Data Centers.- Emerging Trends in Data Center Cooling Technologies. <s> BIB009 </s> Cloud Computing: Survey on Energy Efficiency <s> INTERACTIONS BETWEEN CLOUD COMPUTING INFRASTRUCTURE DOMAINS <s> The growing economical and environmental cost of data centers due to the high energy consumption is becoming a major issue. “Green Data Centers” refers to, energy aware, energy efficient and CO2 emission minimizing designs, protocols, devices, infrastructures and algorithms for data centers. Today's data centers are provisioned for peak load. However, it is shown that servers are idle most of the time. Idle servers and the connected network elements are consuming considerable amount of energy. In this survey, we identify the key enablers of green data center research. Firstly we overview the green metrics that are applicable to data centers. Then we describe the most recent stage of research and give a taxonomy of the related work. We focus on computing and networking proposals for green data centers, even though we briefly describe some other green research related to data centers such as cloud computing, cooling. <s> BIB010 </s> Cloud Computing: Survey on Energy Efficiency <s> INTERACTIONS BETWEEN CLOUD COMPUTING INFRASTRUCTURE DOMAINS <s> The fast growing number of data centers in cloud raises high energy consumption problem. Thus there is a need for “Greening” in data center. ”Greening” means applying energy-saving techniques and designs in data center for power saving. Although cloud data centers nowadays are over-provisioned for high load, however, it is shown that the average data center load is only 30% [1]. There is lack of resource allocation mechanisms in data centers that caused the above problem. In this article, we review the “green” strategies, which are applied in data center to reduce energy consumption. First, we give the overview of the green metrics for data centers. Then, we present the most recent research in this area and give taxonomy of the related work. <s> BIB011 </s> Cloud Computing: Survey on Energy Efficiency <s> INTERACTIONS BETWEEN CLOUD COMPUTING INFRASTRUCTURE DOMAINS <s> The power consumption of presently available Internet servers and data centers is not proportional to the work they accomplish. The scientific community is attempting to address this problem in a number of ways, for example, by employing dynamic voltage and frequency scaling, selectively switching off idle or underutilized servers, and employing energy-aware task scheduling. Central to these approaches is the accurate estimation of the power consumption of the various subsystems of a server, particularly, the processor. We distinguish between power consumption measurement techniques and power consumption estimation models. The techniques refer to the art of instrumenting a system to measure its actual power consumption whereas the estimation models deal with indirect evidences (such as information pertaining to CPU utilization or events captured by hardware performance counters) to reason about the power consumption of a system under consideration. The paper provides a comprehensive survey of existing or proposed approaches to estimate the power consumption of single-core as well as multicore processors, virtual machines, and an entire server. <s> BIB012
Cloud computing infrastructure represents a tightly coupled system composed of domains described in previous sections. Although, each domain can be analyzed separately, each domain influences another. Therefore, the entire cloud computing infrastructure, from the data center building to the smallest component, such as a CPU, has to be analyzed as a whole as well. In this section, we provide an overview of interactions between different infrastructure domains. Appliance. The appliance is the smallest unit of manageable elements in cloud computing and represents the software that a user ultimately interacts with. For this reason, the greatest energy savings require studies of relations between appliances and all other domains, in particular the CMS and servers. Optimization of appliances to specific types of hardware may bring significant energy savings. For example, GeneralPurpose GPUs (GPGPU) are very energy-efficient provided that the application (or its parts) is implemented to make the most of GPGPU advantages. Similarly, some appliances can be run on microservers equipped with processors, such as ARMs, but not all can be easily ported without significant performance penalties. Even for a given hardware type, its power state may affect specific appliances in different ways. For example, depending on appliance characteristics, changes of CPU frequency and voltage will cause different performance and power usage values for CPU-bound and data-intensive applications. CMS. The CMS, being at the center of application placement and scheduling management, must take these facts into consideration since its influence on other domains can be large. For instance, the local temperature and thus the behavior of fans and cooling infrastructure can be managed in thermal-aware solutions BIB003  . However, most CMSs do not encompass this aspect in their solution, missing an important point. Finally, the way a system is implemented (e.g., scalability, components), how it interacts with underlying layers (e.g., hardware components, communication libraries, etc.), and how it is designed (e.g., architecture, supporting modules) affects the overall energy efficiency of the infrastructure. Losses and wastes are caused by both inefficiency of underlying layers and by their interactions with certain systems. Servers. To support smart scheduling, hardware matching, and optimal decision making, the CMS needs detailed information about appliances and underlying hardware. This information includes progress, performance, state, and data size, as well as hardware metrics. In the context of energy efficiency, the most notable metric is the power consumption of a server. It is usually acquired with a power metering device, such as PowerMon BIB004 , or those integrated in the Power Distribution Unit (PDU) or UPS unit. More detailed measurements can be performed for each component of a server, such as measuring instant current values of CPU power consumption with a circuit, as proposed in BIB001 . Modeling VM power consumption is a next step in obtaining more detailed monitoring data BIB012 . Furthermore, power consumption reductions can also be studied at a global scale via resource allocation.  propose cost reduction in a geographically distributed system. Their objective is to handle efficiently the variability between the energy costs of data centers and their architectural differences. They also use the time zone where these are located, as well as their proximity to green power sources. A similar approach is followed by BIB002 . Network. Compared to power consumption for computing and cooling in a data center, the power consumption for network transport is still relatively small. As a result, this enables the location of computation at data centers where, for example, energy from renewable resources is available or where less energy for cooling is required due to a cool climate. This flexibility of computational placement enabled by efficient high-bandwidth network connections can result in a significant reduction of energy consumption for computation. However, when placing computation far from the end user, this also results in an increased latency limited by the speed of light in the optical fiber. Additionally, when using migration, the impact on the network cannot be completely ignored. Indeed, even if several researchers suggest that the impact of traffic can be ignored in terms of power consumption (i.e., the switches and routers consume roughly the same amount of energy no matter the bytes transferred  ]), it cannot be so when considering that network components can be switched off or bandwidth adapted as in Adaptive Link Rate (ALR) for saving energy when not being used. Using models for power consumption during migrations BIB005 ] can add to overall power consumption awareness when using such optimizations. Cooling and power supply. The domains of cooling, power supply, and data center building are only briefly covered in this article, but surveys by BIB007 , , and BIB006 cover these from the energy efficiency perspective.  and BIB008 cover data center building, cooling, and power supply as related to energy efficiency, as well as cost. A comprehensive description of energy-efficient thermal management methods for data centers can be also found in BIB009 . Metrics. Finally, the overall energy efficiency of a data center can be measured using the Power Usage Effectiveness (PUE), which represents the ratio between total energy consumption of the facility and the ICT equipment. Details of PUE levels and measurement specification were defined in  . However, in this article, we focus on ICT equipment optimization, and PUE is not sufficient to represent such a level of detail. A metric such as Data Center infrastructure Effectiveness (DCiE)  shows the inverse of PUE and thus inherits the same shortages. For example, PUE Scalability measures power proportionality-how the used power scales with load  . Additionally, metrics focused on IT energy efficiency have been proposed. Examples of such metrics include TUE and ITUE, introduced in  , which express total energy delivered into a data center divided by energy consumed by computational components and total energy delivered into ICT equipment divided by energy consumed by computational components, respectively. Other metrics include Carbon Usage Effectiveness (CUE), Water Usage Effectiveness (WUE), and Energy Reuse Effectiveness (ERE) and are covered in surveys BIB011 BIB010 .