Cloud Computing: Survey on Energy Efficiency <s> DVFS and Alternatives. <s> Power consumption and performance are the crucial factors that determine the reliability of a CPU. In this paper, we discuss about some techniques that can be used for Instruction Level Parallelism which enhances the performance of the CPU by reducing the CPI there by reducing power consumption. We have also discussed about the power saving scheme using proper clocking strategies. We have mainly focused on implementing the simplified RISC pipeline datapath in HDL using two different clocking schemes to reduce the power consumption. We have adopted a new method which uses dual edge triggered clock that can decrease the power consumption of a pipelined datapath considerably, without sacrificing the throughput of the CPU. Finally, we have given the experimental result for our implementation of simplified RISC datapath. <s> BIB001 </s> Cloud Computing: Survey on Energy Efficiency <s> DVFS and Alternatives. <s> This paper approaches the problem of improving the service center server CPU's energy efficiency by executing dynamic frequency scaling actions and performing tradeoffs between CPU's computational performance and its power consumption. Two different algorithms are designed and implemented: an immune inspired algorithm and a fuzzy logic based algorithm. The immune inspired algorithm uses the human antigen as a model to represent the server power / performance state. Using a set of detectors the antigens are classified as self for optimal power consumption state or non-self for non-optimal power consumption state. For the non-self antigens a biologically inspired clonal selection approach is used to determine the actions that need to be executed to bring the server's CPU in an optimal power consumption state. The fuzzy logic based algorithm adaptively changes the processor performance states to the incoming workload. The algorithm also filters workload spikes because frequent p-states transition costs can outweigh the benefit of adaptation. <s> BIB002 </s> Cloud Computing: Survey on Energy Efficiency <s> DVFS and Alternatives. <s> In this paper the problem of service center servers high energy consumption is tackled by proposing a time series based CPU dynamic frequency scaling algorithm. The algorithm senses the workload changes and adapts the CPU power states thus minimizing the CPU energy consumption. Our solution analyzes the CPU workload time series for identifying the frequent workload patterns. For each frequent pattern, the corresponding dynamic frequency scaling actions are determined and associated using information about the pattern's sub-sequences trends. A workload characterization function is defined and used to identify the pattern trends. To identify the membership of the new CPU workload observations to a frequent CPU workload pattern, a sliding window based method is used. If such a match is found, the dynamic frequency scaling actions associated to the frequent pattern are executed and the pattern occurrence probability is increased. <s> BIB003 </s> Cloud Computing: Survey on Energy Efficiency <s> DVFS and Alternatives. <s> Modern high performance computing subsystems (HPC) a#x2013; including processor, network, memory, and IO a#x2013; are provided with power management mechanisms. These include dynamic speed scaling and dynamic resource sleeping. Understanding the behavioral patterns of high performance computing systems at runtime can lead to a multitude of optimization opportunities including controlling and limiting their energy usage. In this paper, we present a general purpose methodology for optimizing energy performance of HPC systems considering processor, disk and network. We rely on the concept of execution vector along with a partial phase recognition technique for on-the-fly dynamic management without any a priori knowledge of the workload. We demonstrate the effectiveness of our management policy under two real-life workloads. Experimental results show that our management policy in comparison with baseline unmanaged execution saves up to 24% of energy with less than 4% performance overhead for our real-life workloads. <s> BIB004 </s> Cloud Computing: Survey on Energy Efficiency <s> DVFS and Alternatives. <s> Excessive energy consumption has become one of the major challenges in high performance computing. Reducing the energy consumption of frequently used high performance computing applications not only saves the energy cost but also reduces the greenhouse gas emissions. This paper focuses on developing energy efficient algorithms and software for the widely used matrix-matrix multiplication, so that it is able to consume less energy in a DVFS-enabled cluster with little sacrifice in performance. The state-of-the-art practical parallel matrix matrix multiplication algorithm in ScaLAPACK partitions matrices into small blocks and distributes matrices using a two dimensional block cyclic distribution approach. Experimental results demonstrate that our energy efficient matrix-matrix multiplication algorithm can save up to 26.35% of energy with about 1% performance penalty. And the modified PDGEMM of ScaLAPACK is able to save energy more than 20% with less than 2% of performance loss. <s> BIB005 </s> Cloud Computing: Survey on Energy Efficiency <s> DVFS and Alternatives. <s> Saving data center energy consumption is a hot issue for the environment and the economy. CPU is the biggest energy consuming component in a server, and it has various energy saving technologies, such as C-state and P-state. Advances in multi-core processors and virtualization technologies have enabled many workloads to be consolidated in a physical server, and energy efficiency can be degraded due to the contention in shared resources. We observed that energy efficiency can be improved further by adjusting CPU frequency according to the degree of contention in shared resources, and the most energy-efficient CPU frequency is quite different for each situation. In this paper, we propose an energy-centric DVFS controlling method (eDVFS), which aims to minimize total energy consumption. Our experimental results show that eDVFS method is more energy-efficient and faster than current “on-demand” CPU governor. <s> BIB006 </s> Cloud Computing: Survey on Energy Efficiency <s> DVFS and Alternatives. <s> Power capping on server nodes has become an essential feature in data centers for controlling energy costs and peak power consumption. More than half of the server nodes are virtualized in today's data centers; thus, providing a practical power capping technique for consolidated virtual environments is a significant research problem. This paper proposes a power capping technique, vCap, which makes resource allocation decisions to maximize the Quality-of-Service (QoS) while meeting the power constraints in virtualized servers that run multi-threaded applications. For a given set of applications, vCap first decides which applications to co-schedule based on application scalability and then optimizes the QoS in an application-aware manner for each VM by adaptively adjusting the CPU resources. Experiments on real-life multi-core servers show that vCap provides 12% higher energy efficiency in comparison to the state-of-the-art power capping techniques, while adhering to the power cap 92% of the time within a 2W error margin. <s> BIB007
One of the most notable techniques for reducing energy consumption in a CPU is reducing its power input due to its disproportional energy consumption; this is referred to as DVFS. A large body of research work is currently trying to utilize DVFS in order to reduce energy consumption, which includes algorithms such as those presented in BIB002 and BIB003 , or combining the DVFS with other components, such as memory and disk BIB004 . Other works that utilize DVFS include BIB005 and BIB006 , whereas  propose some improvements over DVFS itself. Going even deeper into power limitation, BIB001 propose a novel clocking scheme on a pipelined RISC CPU that is able to reduce power consumption by 50%. Unlike DVFS approaches or ones using clock gating, vCAP BIB007 ] uses co-scheduling for resource allocation in order to maximize the performance under power and performance constraints. It identifies nonscalable VMs in terms of performance and consolidates them. This is an example of how the CMS domain (i.e., VM management) in combination with the Server domain can provide more benefits for energy-saving techniques.