On the Viability of Microservers for Financial Analytics

Energy consumption and total cost of ownership are daunting challenges for Datacenters, because they scale disproportionately with performance. Datacenters running financial analytics may incur extremely high operational costs in order to meet performance and latency requirements of their hosted applications. Recently, ARM-based microservers have emerged as a viable alternative to high-end servers, promising scalable performance via scale-out approaches and low energy consumption. In this paper, we investigate the viability of ARM-based microservers for option pricing, using the Monte Carlo and Binomial Tree kernels. We compare an ARM-based microserver against a state-of-the-art x86 server. We define application-related but platform-independent energy and performance metrics to compare those platforms fairly in the context of datacenters for financial analytics and give insight on the particular requirements of option pricing. Our experiments show that through scaling out energyefficient compute nodes within a 2U rack-mounted unit, an ARM-based microserver consumes as little as about 60% of the energy per option pricing compared to an x86 server, despite having significantly slower cores. We also find that the ARM microserver scales enough to meet a high fraction of market throughput demand, while consuming up to 30% less energy than an Intel server

Looking for free riding: Energy efficiency incentives and Italian homeowners

We examine the effect of energy efficiency incentives on household energy efficiency home improvements. Starting in February 2007, Italian homeowners have been able to avail themselves of tax credits on the purchase and installation costs of certain types of energy efficiency renovations. We examine two such renovations—door/window replacements and heating system replacements—using multi-year cross-section data from the Italian Consumer Expenditure Survey and focusing on a narrow period around the introduction of the tax credits. Our regressions control for dwelling and household characteristics and economy-wide factors likely to influence the replacement rates. The effects of the policy are different for the two types of renovations. With window replacements, the policy is generally associated with a 30 % or stronger increase in the renovation rates and number of renovations. In the simplest econometric models, the effect is not statistically significant, but the results get stronger when we allow for heterogeneous effects across the country. With heating system replacements, simpler models suggest that the tax credits policy had no effect whatsoever or that free riding was rampant, i.e., people are now accepting subsidies for replacements that they would have done anyway. Further examination suggests a strong degree of heterogeneity in the effects across warmer and colder parts of the country, and effects in the colder areas that are even more pronounced than those for window replacements. These results should, however, be interpreted with caution due to the low rates of renovations, which imply that the effects are estimated relatively imprecisely.

Process efficiency in polymer extrusion: Correlation between the energy demand and melt thermal stability

Thermal stability is of major importance in polymer extrusion, where product quality is dependent upon the level of melt homogeneity achieved by the extruder screw. Extrusion is an energy intensive process and optimisation of process energy usage while maintaining melt stability is necessary in order to produce good quality product at low unit cost. Optimisation of process energy usage is timely as world energy prices have increased rapidly over the last few years. In the first part of this study, a general discussion was made on the efficiency of an extruder. Then, an attempt was made to explore correlations between melt thermal stability and energy demand in polymer extrusion under different process settings and screw geometries. A commodity grade of polystyrene was extruded using a highly instrumented single screw extruder, equipped with energy consumption and melt temperature field measurement. Moreover, the melt viscosity of the experimental material was observed by using an off-line rheometer. Results showed that specific energy demand of the extruder (i.e. energy for processing of unit mass of polymer) decreased with increasing throughput whilst fluctuation in energy demand also reduced. However, the relationship between melt temperature and extruder throughput was found to be complex, with temperature varying with radial position across the melt flow. Moreover, the melt thermal stability deteriorated as throughput was increased, meaning that a greater efficiency was achieved at the detriment of melt consistency. Extruder screw design also had a significant effect on the relationship between energy consumption and melt consistency. Overall, the relationship between the process energy demand and thermal stability seemed to be negatively correlated and also it was shown to be highly complex in nature. Moreover, the level of process understanding achieved here can help to inform selection of equipment and setting of operating conditions to optimise both energy and thermal efficiencies in parallel. 

Investigation of the process energy demand in polymer extrusion: a brief review and an experimental study

Extrusion is one of the fundamental production methods in the polymer processing industry and is used in the production of a large number of commodities in a diverse industrial sector. Being an energy intensive production method, process energy efficiency is one of the major concerns and the selection of the most energy efficient processing conditions is a key to reducing operating costs. Usually, extruders consume energy through the drive motor, barrel heaters, cooling fans, cooling water pumps, gear pumps, etc. Typically the drive motor is the largest energy consuming device in an extruder while barrel/die heaters are responsible for the second largest energy demand. This study is focused on investigating the total energy demand of an extrusion plant under various processing conditions while identifying ways to optimise the energy efficiency. Initially, a review was carried out on the monitoring and modelling of the energy consumption in polymer extrusion. Also, the power factor, energy demand and losses of a typical extrusion plant were discussed in detail. The mass throughput, total energy consumption and power factor of an extruder were experimentally observed over different processing conditions and the total extruder energy demand was modelled empirically and also using a commercially available extrusion simulation software. The experimental results show that extruder energy demand is heavily coupled between the machine, material and process parameters. The total power predicted by the simulation software exhibits a lagging offset compared with the experimental measurements. Empirical models are in good agreement with the experimental measurements and hence these can be used in studying process energy behaviour in detail and to identify ways to optimise the process energy efficiency.

Building Information Modelling Energy Performance Assessment on Domestic Dwellings: A Comparative Study

Building Information Modelling (BIM) is growing in pace, not only in design and construction stages, but also in the analysis of facilities throughout their life cycle. With this continued growth and utilisation of BIM processes, comes the possibility to adopt such procedures, to accurately measure the energy efficiency of buildings, to accurately estimate their energy usage. To this end, the aim of this research is to investigate if the introduction of BIM Energy Performance Assessment in the form of software analysis, provides accurate results, when compared with actual energy consumption recorded. Through selective sampling, three domestic case studies are scrutinised, with baseline figures taken from existing energy providers, the results scrutinised and compared with calculations provided from two separate BIM energy analysis software packages. Of the numerous software packages available, criterion sampling is used to select two of the most prominent platforms available on the market today. The two packages selected for scrutiny are Integrated Environmental Solutions - Virtual Environment (IES-VE) and Green Building Studio (GBS). The results indicate that IES-VE estimated the energy use in region of ±8% in two out of three case studies while GBS estimated usage approximately ±5%. The findings indicate that the introduction of BIM energy performance assessment, using proprietary software analysis, is a viable alternative to manual calculations of building energy use, mainly due to the accuracy and speed of assessing, even the most complex models. Given the surge in accurate and detailed BIM models and the importance placed on the continued monitoring and control of buildings energy use within today’s environmentally conscious society, this provides an alternative means by which to accurately assess a buildings energy usage, in a quick and cost effective manner.

Power and Energy Implications of the Number of Threads Used on the Intel Xeon Phi

Energy consumption has become an important area of research of late. With the advent of new manycore processors, situations have arisen where not all the processors need to be active to reach an optimal relation between performance and energy usage. In this paper a study of the power and energy usage of a series of benchmarks, the PARSEC and the SPLASH- 2X Benchmark Suites, on the Intel Xeon Phi for different threads configurations, is presented. To carry out this study, a tool was designed to monitor and record the power usage in real time during execution time and afterwards to compare the r

Improving fuel economy application of the organic Rankine Cycle on a hybrid bus

Globally vehicle operators are experiencing rising fuel costs and increased
running expenses as governments around the world attempt to decrease carbon dioxide emissions and fossil fuel consumption, due to global warming and the drive to reduce dependency on fossil fuels. Recent advances in hybrid vehicle design have made great strides towards more efficient operation, with regenerative braking being widely used to capture otherwise lost energy. In this paper a hybrid series bus is developed a step further, by installing another method of energy capture on the vehicle. In this case, it is in the form of the Organic Rankine Cycle (ORC). The waste heat expelled to the exhaust and coolant streams is recovered and converted to electrical energy which is then stored in the hybrid vehicles batteries. The electrical energy can then be used for the auxiliary power circuit or to assist in vehicle propulsion, thus reducing the load on the engine, thereby improving the overall fuel economy of the vehicle and reducing carbon dioxide emissions.

Mobile Wearable Communications

This special issue provides the latest research and development on wireless mobile wearable communications. According to a report by Juniper Research, the market value of connected wearable devices is expected to reach $1.5 billion by 2014, and the shipment of wearable devices may reach 70 million by 2017. Good examples of wearable devices are the prominent Google Glass and Microsoft HoloLens. As wearable technology is rapidly penetrating our daily life, mobile wearable communication is becoming a new communication paradigm. Mobile wearable device communications create new challenges compared to ordinary sensor networks and short-range communication. In mobile wearable communications, devices communicate with each other in a peer-to-peer fashion or client-server fashion and also communicate with aggregation points (e.g., smartphones, tablets, and gateway nodes). Wearable devices are expected to integrate multiple radio technologies for various applications' needs with small power consumption and low transmission delays. These devices can hence collect, interpret, transmit, and exchange data among supporting components, other wearable devices, and the Internet. Such data are not limited to people's personal biomedical information but also include human-centric social and contextual data. The success of mobile wearable technology depends on communication and networking architectures that support efficient and secure end-to-end information flows. A key design consideration of future wearable devices is the ability to ubiquitously connect to smartphones or the Internet with very low energy consumption. Radio propagation and, accordingly, channel models are also different from those in other existing wireless technologies. A huge number of connected wearable devices require novel big data processing algorithms, efficient storage solutions, cloud-assisted infrastructures, and spectrum-efficient communications technologies.

Cross-layer Energy-Efficiency Optimization of Packet Based Wireless MIMO Communication Systems

Energy in today's short-range wireless communication is mostly spent on the analog- and digital hardware rather than on radiated power. Hence,purely information-theoretic considerations fail to achieve the lowest energy per information bit and the optimization process must carefully consider the overall transceiver. In this paper, we propose to perform cross-layer optimization, based on an energy-aware rate adaptation scheme combined with a physical layer that is able to properly adjust its processing effort to the data rate and the channel conditions to minimize the energy consumption per information bit. This energy proportional behavior is enabled by extending the classical system modes with additional configuration parameters at the various layers. Fine grained models of the power consumption of the hardware are developed to provide awareness of the physical layer capabilities to the medium access control layer. The joint application of the proposed energy-aware rate adaptation and modifications to the physical layer of an IEEE802.11n system, improves energy-efficiency (averaged over many noise and channel realizations) in all considered scenarios by up to 44%.

Energy-Efficient In-Memory Data Stores on Hybrid Memory Hierarchies

Increasingly large amounts of data are stored in main memory of data center servers. However, DRAM-based memory is an important consumer of energy and is unlikely to scale in the future. Various byte-addressable non-volatile memory (NVM) technologies promise high density and near-zero static energy, however they suffer from increased latency and increased dynamic energy consumption.

This paper proposes to leverage a hybrid memory architecture, consisting of both DRAM and NVM, by novel, application-level data management policies that decide to place data on DRAM vs. NVM. We analyze modern column-oriented and key-value data stores and demonstrate the feasibility of application-level data management. Cycle-accurate simulation confirms that our methodology reduces the energy with least performance degradation as compared to the current state-of-the-art hardware or OS approaches. Moreover, we utilize our techniques to apportion DRAM and NVM memory sizes for these workloads.

Application-Level Energy Awareness for OpenMP

Power, and consequently energy, has recently attained first-class system resource status, on par with conventional metrics such as CPU time. To reduce energy consumption, many hardware- and OS-level solutions have been investigated. However, application-level information - which can provide the system with valuable insights unattainable otherwise - was only considered in a handful of cases. We introduce OpenMPE, an extension to OpenMP designed for power management. OpenMP is the de-facto standard for programming parallel shared memory systems, but does not yet provide any support for power control. Our extension exposes (i) per-region multi-objective optimization hints and (ii) application-level adaptation parameters, in order to create energy-saving opportunities for the whole system stack. We have implemented OpenMPE support in a compiler and runtime system, and empirically evaluated its performance on two architectures, mobile and desktop. Our results demonstrate the effectiveness of OpenMPE with geometric mean energy savings across 9 use cases of 15 % while maintaining full quality of service.

ALEA: Fine-Grain Energy Profiling with Basic Block Sampling

Energy efficiency is an essential requirement for all contemporary computing systems. We thus need tools to measure the energy consumption of computing systems and to understand how workloads affect it. Significant recent research effort has targeted direct power measurements on production computing systems using on-board sensors or external instruments. These direct methods have in turn guided studies of software techniques to reduce energy consumption via workload allocation and scaling. Unfortunately, direct energy measurements are hampered by the low power sampling frequency of power sensors. The coarse granularity of power sensing limits our understanding of how power is allocated in systems and our ability to optimize energy efficiency via workload allocation.
We present ALEA, a tool to measure power and energy consumption at the granularity of basic blocks, using a probabilistic approach. ALEA provides fine-grained energy profiling via sta- tistical sampling, which overcomes the limitations of power sens- ing instruments. Compared to state-of-the-art energy measurement tools, ALEA provides finer granularity without sacrificing accuracy. ALEA achieves low overhead energy measurements with mean error rates between 1.4% and 3.5% in 14 sequential and paral- lel benchmarks tested on both Intel and ARM platforms. The sampling method caps execution time overhead at approximately 1%. ALEA is thus suitable for online energy monitoring and optimization. Finally, ALEA is a user-space tool with a portable, machine-independent sampling method. We demonstrate two use cases of ALEA, where we reduce the energy consumption of a k-means computational kernel by 37% and an ocean modelling code by 33%, compared to high-performance execution baselines, by varying the power optimization strategy between basic blocks.

HpMC: An Energy- Aware Management System for Multi-Level Memory Architectures

DRAM technology faces density and power challenges to increase capacity because of limitations of physical cell design. To overcome these limitations, system designers are exploring alternative solutions that combine DRAM and emerging NVRAM technologies. Previous work on heterogeneous memories focuses, mainly, on two system designs: PCache, a hierarchical, inclusive memory system, and HRank, a flat, non-inclusive memory system. We demonstrate that neither of these designs can universally achieve high performance and energy efficiency across a suite of HPC workloads. In this work, we investigate the impact of a number of multilevel memory designs on the performance, power, and energy consumption of applications. To achieve this goal and overcome the limited number of available tools to study heterogeneous memories, we created HMsim, an infrastructure that enables n-level, heterogeneous memory studies by leveraging existing memory simulators. We, then, propose HpMC, a new memory controller design that combines the best aspects of existing management policies to improve performance and energy. Our energy-aware memory management system dynamically switches between PCache and HRank based on the temporal locality of applications. Our results show that HpMC reduces energy consumption from 13% to 45% compared to PCache and HRank, while providing the same bandwidth and higher capacity than a conventional DRAM system.

Energy Saving - Another Perspective for Parameter Optimization of P and PI Controllers

Modern control methods like optimal control and model predictive control (MPC) provide a framework for simultaneous regulation of the tracking performance and limiting the control energy, thus have been widely deployed in industrial applications. Yet, due to its simplicity and robustness, the conventional P (Proportional) and PI (Proportional–Integral) control are still the most common methods used in many engineering systems, such as electric power systems, automotive, and Heating, Ventilation and Air Conditioning (HVAC) for buildings, where energy efficiency and energy saving are the critical issues to be addressed. Yet, little has been done so far to explore the effect of its parameter tuning on both the system performance and control energy consumption, and how these two objectives are correlated within the P and PI control framework. In this paper, the P and PI controllers are designed with a simultaneous consideration of these two aspects. Two case studies are investigated in detail, including the control of Voltage Source Converters (VSCs) for transmitting offshore wind power to onshore AC grid through High Voltage DC links, and the control of HVAC systems. Results reveal that there exists a better trade-off between the tracking performance and the control energy through a proper choice of the P and PI controller parameters.

Secure data networks for electrical distribution applications

Smart Grids are characterized by the application of information communication technology (ICT) to solve electrical energy challenges. Electric power networks span large geographical areas, thus a necessary component of many Smart Grid applications is a wide area network (WAN). For the Smart Grid to be successful, utilities must be confident that the communications infrastructure is secure. This paper describes how a WAN can be deployed using WiMAX radio technology to provide high bandwidth communications to areas not commonly served by utility communications, such as generators embedded in the distribution network. A planning exercise is described, using Northern Ireland as a case study. The suitability of the technology for real-time applications is assessed using experimentally obtained latency data.