Energy Consumption Of Subsystems Research Articles

AN EXPERIMENT BASED EXAMINATION OF ENERGY OVERHEAD THROUGH VM MIGRATION

Abstract: The use of cloud computing has increased in recent days with tremendous benefits to users and also provoked to rise in total ownership cost due to swift increase in energy consumption of data centers. Out of various approaches, best of virtual machine placement policies can be exploited to reduce the energy consumption during migration in data centers. The virtual machine live migration during placement has enough potential to reduce energy consumption with certain level of utilization. One of the mechanisms to achieve energy efficiency is to manage the migration time parameter of live migration with appropriate network bandwidth in communication aware connected data centers. In spite of this, virtual machine size and network bandwidth have a great impact on energy consumption of sub systems during virtual machine live migration. To follow this study, in this paper, analysis has been made to see the impact of migration time on energy consumption of sub systems during guest virtual machine live migration. For experimentation, Kernel-based Virtual Machine (KVM) hypervisor and Virt Manager was used to perform live migration on Ubuntu 14.04 Linux machines in various conditions. Afterwards, the noted observations are validated with Pearson’s Correlation Coefficient statistical approach to study the strength of relationship of defined parameters.

Energy efficiency assessment of grinding strategy

Purpose – This paper aims to set the framework for measuring the energy performance of a manufacturing process. The availability and affordability of energy is becoming a critical parameter nowadays, affecting the whole lifecycle of the product, and hence the production phase as well. The energy efficiency of the grinding process, as a widely used manufacturing process in the industry, is assessed with regard to the selected process strategies. Design/methodology/approach – To assess the grinding machine tool energy performance, a measuring framework is designed, implemented and validated. The process strategy effect on the energy consumption is experimentally assessed through energy audits of the grinding machine tool. Such energy audits provide better insights into the way subsystems composing a machine tool affect the energy consumption. Findings – It is revealed that the proper selection of process strategy can significantly reduce the energy consumption. The amount of energy consumed for the actual process is less than the energy required for maintaining the processing environment (e.g. for the coolant pump delivering coolant fluid in the processing area). The key finding is that the measuring framework can be used for the understanding and analysis of the energy consumption of the various machine tool components. Additionally, for the grinding process itself, the energy audits indicate that reducing the processing duration can significantly reduce the overall energy. Originality/value – The main novel contribution of the present paper is the development of a measurement framework for assessing the energy consumption of subsystems running simultaneously when processing a workpiece. Grinding process energy demand is analysed in detail, allowing for the first time to consider energy consumption as a manufacturing decision criterion.

Runtime energy consumption estimation for server workloads based on chaotic time-series approximation

This article proposes a runtime model that relates server energy consumption to its overall thermal envelope, using hardware performance counters and experimental measurements. While previous studies have attempted system-wide modeling of server power consumption through subsystem models, our approach is different in that it links system energy input to subsystem energy consumption based on a small set of tightly correlated parameters. The proposed model takes into account processor power, bus activities, and system ambient temperature for real-time prediction on the power consumption of long running jobs. Using the HyperTransport and QuickPath Link structures as case studies and through electrical measurements on example server subsystems, we develop a chaotic time-series approximation for runtime power consumption, arriving at the Chaotic Attractor Predictor (CAP). With polynomial time complexity, CAP exhibits high prediction accuracy, having the prediction errors within 1.6% (or 3.3%) for servers based on the HyperTransport bus (or the QuickPath Links), as verified by a set of common processor benchmarks. Our CAP is a superior predictive mechanism over existing linear auto-regressive methods, which require expensive and complex corrective steps to address the nonlinear and chaotic aspects of the underlying physical system.

System-Wide Leakage-Aware Energy Minimization Using Dynamic Voltage Scaling and Cache Reconfiguration in Multitasking Systems

System optimization techniques are widely used to improve energy efficiency as well as overall performance. Dynamic voltage scaling (DVS) is well studied and known to be successful in reducing processor energy consumption. Due to the increasing significance of the memory subsystem's energy consumption, dynamic cache reconfiguration (DCR) techniques are recently proposed at the aim of improving cache subsystem's energy efficiency. As the manufacturing technology scales into the order of nanometers, leakage current, which leads to static power consumption, becomes a significant contributor in the overall power dissipation. In this paper, we consider various system components and study their impact on system-wide energy consumption under different processor voltage levels as well as cache configurations. Based on the observation, we efficiently integrate DVS and DCR techniques together to make decisions judiciously so that the total energy consumption is minimized. Our studies show that considering only DVS or DCR and ignoring the impact from other system components may lead to incorrect conclusions in overall energy savings. Experimental results demonstrate that our approach outperforms existing leakage-aware DVS techniques by 47.6% and leakage-oblivious DVS + DCR technique by up to 23.5%.