Percent Energy Savings Research Articles

Energy-Efficient Framework for Time and Wavelength Division Multiplexed Passive Optical Networks

For the first time, to the best of our knowledge, an energy-efficient framework that optimizes the number of active wavelengths and uses sleep/doze mode to improve the energy savings of a delay-constrained time and wavelength division multiplexed passive optical network is presented in this work. In the proposed framework, the optical network units (ONUs) operate under the maximum polling cycle time that satisfies a given delay constraint to achieve maximum possible energy savings at the ONUs. When the average bandwidth requested by an ONU exceeds the maximumallowable bandwidth, a new wavelength is introduced to the network to satisfy delay constraints. Conversely, when the bandwidth requested by an ONU is small, idle wavelengths are switched off to increase energy savings at the optical line terminal (OLT). The proposed framework is analyzed using online and offline just-in-time dynamic wavelength and bandwidth allocation (DWBA) algorithms. The performance of both algorithms, under the proposed framework, is evaluated in terms of average delay, percentage of energy savings at the ONU, and percentage of energy savings at the OLT. Our analytical and simulation results indicate that under the proposed framework, the average delay of the network remains below a specified maximum allowable delay, in both algorithms. The online DWBA algorithm, however, results in improved energy savings, both at the OLT and at the ONUs, compared to the offline DWBA algorithm.

Power-Aware Computing in Wearable Sensor Networks: An Optimal Feature Selection

Wearable sensory devices are becoming the enabling technology for many applications in healthcare and well-being, where computational elements are tightly coupled with the human body to monitor specific events about their subjects. Classification algorithms are the most commonly used machine learning modules that detect events of interest in these systems. The use of accurate and resource-efficient classification algorithms is of key importance because wearable nodes operate on limited resources on one hand and intend to recognize critical events (e.g., falls) on the other hand. These algorithms are used to map statistical features extracted from physiological signals onto different states such as health status of a patient or type of activity performed by a subject. Conventionally selected features may lead to rapid battery depletion, mainly due to the absence of computing complexity criterion while selecting prominent features. In this paper, we introduce the notion of power-aware feature selection, which aims at minimizing energy consumption of the data processing for classification applications such as action recognition. Our approach takes into consideration the energy cost of individual features that are calculated in real-time. A graph model is introduced to represent correlation and computing complexity of the features. The problem is formulated using integer programming and a greedy approximation is presented to select the features in a power-efficient manner. Experimental results on thirty channels of activity data collected from real subjects demonstrate that our approach can significantly reduce energy consumption of the computing module, resulting in more than $30$ percent energy savings while achieving $96.7$ percent classification accuracy.

GreenBSN: Enabling Energy-Proportional Cellular Base Station Networks

Base station (BS) networks in 3G cellular infrastructure do not consume energy in proportion to their carried traffic load. Our measurements show that the 3G traffic exhibits high fluctuations both in time and over space, thus incurring energy waste. In this paper, we propose Green base station networks (GreenBSN) to approximate network-wide energy proportionality using non-load-adaptive BSes. The instrument is a traffic-driven approach. By leveraging the inherent temporal-spatial traffic dynamics and node deployment heterogeneity, we power off under-utilized BSes under light traffic. Our evaluation on four regional 3G networks shows that GreenBSN yields up to 53 percent energy savings in dense large cities and 23 percent in sparsely deployed regions.

Rule-based approaches for energy savings in an ambient intelligence environment

This paper presents a novel real-world application for energy savings in a Smart Building environment. The proposed system unifies heterogeneous wireless sensor networks under a Semantic Web Service middleware. Two complementary and mutually exclusive rule-based approaches for enforcing energy-saving policies are proposed: a reactive agent based on production rules and a deliberative agent based on defeasible logic. The system was deployed at a Greek University, showing promising experimental results (at least 4% daily savings). Although the percentage of energy savings may seem low, the greatest merit of the method is ensuring that no energy is wasted by constantly enforcing the policies.

Using Multilevel Phase Change Memory to Build Data Storage: A Time-Aware System Design Perspective

This paper advocates a time-aware design methodology for using multilevel per cell (MLC) phase-change memory (PCM) in data storage systems such as solid-state disk and disk cache. It is well known that phase-change material resistance drift gradually reduces memory device noise margin and degrades the raw storage reliability. Intuitively, due to the time-dependent nature of resistance drift, if we can dynamically adjust storage system operations adaptive to the time and, hence, memory cell resistance drift, we may improve various PCM-based data storage system performance metrics. Under such an intuitive time-aware system design concept, we propose three specific design techniques, including time-aware variable-strength error correction code (ECC) decoding, time-aware partial rewrite, and time-aware read-&-refresh. Since PCM-based data storage systems have to use powerful ECC whose decoding can be energy-hungry, the first technique aims to minimize the ECC decoding energy consumption. The second technique improves the data retention limit when using partial rewrite in MLC PCM, and the third technique can further improve the efficiency of time-aware variable-strength ECC decoding. Using hypothetical 2-bit/cell PCM with device parameters from recent device research as a test vehicle, we carry out mathematical analysis and trace-based simulations, which show that these techniques can improve the data retention limit by few orders of magnitude, and enable up to 97 and 79 percent energy savings for PCM-based solid-state disk and PCM-based disk cache.

Communication Energy Modeling and Optimization through Joint Packet Size Analysis of BSN and WiFi Networks

In this paper, we present an optimal packet size solution that optimizes the communication energy consumption in the heterogeneous wireless networks. More specifically, we consider a heterogeneous network system composed of a body sensor network (BSN) and a WiFi network. Then, based on the analysis of data communication in the BSN and WiFi (BSN-WiFi) network, we formulate a communication energy consumption optimization model with the constraints of throughput and time delay. Mathematically, we convert this model into a geometric programming problem, which is then numerically solved. The optimal solution can be applied in both BSN and WiFi network to dynamically select packet payload sizes according to real-time packet delivery ratios (PDRs). Since PDRs are time-varying, we tabulate a packet payload size lookup table for online packet size selection using PDRs as indices. Finally, we collect PDRs from a deployed BSN-WiFi network and evaluate the energy optimization model. The performance evaluation results show that, in comparison with fixed packet size solutions, our optimal solutions achieve up to 70 percent energy savings in a BSN(TDMA)-WiFi network and 68 percent in a BSN(CSMA)-WiFi network.

GreenDCN: A General Framework for Achieving Energy Efficiency in Data Center Networks

The popularization of cloud computing has raised concerns over the energy consumption that takes place in data centers. In addition to the energy consumed by servers, the energy consumed by large numbers of network devices emerges as a significant problem. Existing work on energy-efficient data center networking primarily focuses on traffic engineering, which is usually adapted from traditional networks. We propose a new framework to embrace the new opportunities brought by combining some special features of data centers with traffic engineering. Based on this framework, we characterize the problem of achieving energy efficiency with a time-aware model, and we prove its NP-hardness with a solution that has two steps. First, we solve the problem of assigning virtual machines (VM) to servers to reduce the amount of traffic and to generate favorable conditions for traffic engineering. The solution reached for this problem is based on three essential principles that we propose. Second, we reduce the number of active switches and balance traffic flows, depending on the relation between power consumption and routing, to achieve energy conservation. Experimental results confirm that, by using this framework, we can achieve up to 50 percent energy savings. We also provide a comprehensive discussion on the scalability and practicability of the framework.

Sleep/doze controlled dynamic bandwidth allocation algorithms for energy-efficient passive optical networks

In this work, we present a comparative study of two just-in-time (JIT) dynamic bandwidth allocation algorithms (DBAs), designed to improve the energy-efficiency of the 10 Gbps Ethernet passive optical networks (10G-EPONs). The algorithms, termed just-in-time with varying polling cycle times (JIT) and just-in-time with fixed polling cycle times (J-FIT), are designed to achieve energy-savings when the idle time of an optical network unit (ONU) is less than the sleep-to-active transition time. This is made possible by a vertical-cavity surface-emitting laser (VCSEL) ONU that can transit into sleep or doze modes during its idle time. We evaluate the performance of the algorithms in terms of polling cycle time, power consumption, percentage of energy-savings, and average delay. The energy-efficiency of a VCSEL ONU that can transition into sleep or doze mode is compared to an always-ON distributed feedback (DFB) laser ONU. Simulation results indicate that both JIT and J-FIT DBA algorithms result in improved energy-efficiency whilst J-FIT performs better in terms of energy-savings at low network loads. The J-FIT DBA however, results in increased average delay in comparison to the JIT DBA. Nonetheless, this increase in average delay is within the acceptable range to support the quality of service (QoS) requirements of the next-generation access networks.

Utilization-Based Resource Partitioning for Power-Performance Efficiency in SMT Processors

Simultaneous multithreading (SMT) increases processor throughput by allowing parallel execution of several threads. However, fully sharing processor resources may cause resource monopolization by a single thread or other misallocations, resulting in overall performance degradation. Static resource partitioning techniques have been suggested, but are not as effective as dynamic ones since program behavior does change over the course of its execution. In this paper, we propose an Adaptive Resource Partitioning Algorithm (ARPA) that dynamically assigns resources to threads according to changes in thread behavior. ARPA analyzes the resource usage efficiency of each thread in a given time period and assigns more resources to threads which can use them more efficiently. Its purpose is to improve the efficiency of resource utilization, thereby improving overall instruction throughput. Our simulation results on a set of 42 multiprogramming workloads show that ARPA outperforms the traditional fetch policy ICOUNT by 55.8 percent with regard to overall instruction throughput and achieves a 33.8 percent improvement over Static Partitioning. It also outperforms the current best dynamic resource allocation technique, Hill-climbing, by 5.7 percent. Considering fairness accorded to each thread, ARPA attains 43.6, 18.5, and 9.2 percent improvements over ICOUNT, Static Partitioning, and Hill-climbing, respectively, using a common fairness metric. We also explore the energy efficiency of dynamically controlling the number of powered-on reorder buffer entries for ARPA. Compared with ARPA, our energy-aware resource partitioning algorithm achieves 10.6 percent energy savings, while the performance loss is negligible.

Wireless Lighting Control: A Life Cycle Cost Evaluation of Multiple Lighting Control Strategies

This study evaluated the energy impacts and cost differences of multiple lighting control strategies for a controls-only retrofit of a theoretical 1970's or 1980's office building, analyzed in Boston and Los Angeles. The minimum control requirements of ASHRAE 90.1–2007 established an energy baseline, and six control systems were assessed. Life-cycle costs were evaluated using equipment and installation cost estimates, provided by an independent contractor, and commissioning cost estimates, provided by an independent commissioning agent.Results suggest that advanced systems can achieve nearly 50 percent energy savings compared to code-compliant controls, and that the approach to space-planning can significantly influence the lighting energy density. The cost analysis shows that wireless lighting controls required lower investment to achieve a high level of functionality for retrofit applications. The cost of commissioning was found to generally be offset by the utility rebates available in both locations. This all combines to illustrate that advanced networked lighting control systems can lead to a lower lifetime cost of ownership compared to more traditional systems.

An Adaptive Interleaving Technique for Memory Performance-per-Watt Management

With the increased complexity of platforms coupled with data centers' servers sprawl, power consumption is reaching unsustainable limits. Researchers have addressed data centers' performance-per-watt management at different hierarchies going from server clusters to servers to individual components within the server platform. This paper addresses performance-per-watt maximization of memory subsystems in a data center. Traditional memory power management techniques rely on profiling the utilization of memory modules and transitioning them to some low-power mode when they are sufficiently idle. However, fully interleaved memory presents an interesting research challenge because data striping across memory modules reduces the idleness of individual modules to warrant transitions to low-power states. In this paper, we present a novel technique for performance-per-watt maximization of interleaved memory by dynamically reconfiguring (expanding or contracting) the degree of interleaving to adapt to incoming workload. The reconfigured memory hosts the application's working set on a smaller set of modules in a manner that exploits the platform's memory hierarchy architecture. This creates the opportunity for the remaining memory modules to transition to low-power states and remain in those states for as long as the performance remains within given acceptable thresholds. The memory power expenditure is minimized subject to application memory requirements and end-to-end memory access delay constraints. This is formulated as a performance-per-watt maximization problem and solved using an analytical memory power and performance model. Our technique has been validated on a real server using SPECjbb benchmark and on a trace-driven memory simulator using SPECjbb and gcc memory traces. On the server, our techniques are shown to give about 48.8 percent (26.7 kJ) energy savings compared to traditional techniques measured at 4.5 percent. The maximum improvement in performance-per-watt was measured at 88.48 percent. The simulator showed 89.7 percent improvement in performance-per-watt compared to the best performing traditional technique.

Gradient Boundary Detection for Time Series Snapshot Construction in Sensor Networks

In many applications of sensor networks, the sink needs to keep track of the history of sensed data of a monitored region for scientific analysis or supporting historical queries. We call these historical data a time series of value distributions or snapshots. Obviously, to build the time series snapshots by requiring all of the sensors to transmit their data to the sink periodically is not energy efficient. In this paper, we introduce the idea of gradient boundary and propose the gradient boundary detection (GBD) algorithm to construct these time series snapshots of a monitored region. In GBD, a monitored region is partitioned into a set of subregions and all sensed data in one subregion are within a predefined value range, namely, the gradient interval. Sensors located on the boundaries of the subregions are required to transmit the data to the sink and, then, the sink recovers all subregions to construct snapshots of the monitored area. In this process, only the boundary sensors transmit their data and, therefore, energy consumption is greatly reduced. The simulation results show that GBD is able to build snapshots with a comparable accuracy and has up to 40 percent energy savings compared with the existing approaches for large gradient intervals.

ZettaRAM: A Power-Scalable DRAM Alternative through Charge-Voltage Decoupling

ZettaRAMtrade is a nascent memory technology with roots in molecular electronics. It uses a conventional DRAM architecture except that the conventional capacitor is replaced with a new molecular capacitor. The molecular capacitor has a discrete threshold voltage, above which all molecules are charged and below which all molecules are discharged. Thus, while voltage still controls charging/discharging, the fixed charge deposited on the molecular capacitor is voltage-independent. Charge-voltage decoupling makes it possible to lower voltage from one memory generation to the next while still maintaining the minimum critical charge for reliable operation, whereas DRAM voltage scaling is constrained by charge. Voltage can be scaled inexpensively and reliably by engineering new, more favorable molecules. We analyze how three key molecule parameters influence voltage and then evaluate 23 molecules in the literature. Matching DRAM density and speed, the best molecule yields 61 percent energy savings. While the fixed charge is voltage-independent, speed is voltage-dependent. Thus, voltage is padded for competitive latency. We propose dynamically modulating the padding based on criticality of memory requests, further extending ZettaRAM's energy advantage with negligible system slowdown. Architectural management extends the best molecule's energy savings to 77 percent and extracts energy savings from six otherwise uncompetitive molecules

High-performance, power-aware distributed computing for scientific applications

The PowerPack framework enables distributed systems to profile, analyze, and conserve energy in scientific applications using dynamic voltage scaling. For one common benchmark, the framework achieves more than 30 percent energy savings with minimal performance impact.

A Holistic Approach to Designing Energy-Efficient Cluster Interconnects

Designing energy-efficient clusters has recently become an important concern to make these systems economically attractive for many applications. Since the cluster interconnect is a major part of the system, the focus of this paper is to characterize and optimize the energy consumption in the entire interconnect. Using a cycle-accurate simulator of an InfiniBand Architecture (IBA) compliant interconnect fabric and actual designs of its components, we investigate the energy behavior on regular and irregular interconnects. The energy profile of the three major components (switches, network interface cards (NICs), and links) reveals that the links and switch buffers consume the major portion of the power budget. Hence, we focus on energy optimization of these two components. To minimize power in the links, first we investigate the dynamic voltage scaling (DVS) algorithm and then propose a novel dynamic link shutdown (DLS) technique. The DLS technique makes use of an appropriate adaptive routing algorithm to shut down the links intelligently. We also present an optimized buffer design for reducing leakage energy in 70nm technology. Our analysis on different networks reveals that, while DVS is an effective energy conservation technique, it incurs significant performance penalty at low to medium workload. Moreover, energy saving with DVS reduces as the buffer leakage current becomes significant with 70nm design. On the other hand, the proposed DLS technique can provide optimized performance-energy behavior (up to 40 percent energy savings with less than 5 percent performance degradation in the best case) for the cluster interconnects.

Optimizing array-intensive applications for on-chip multiprocessors

With energy consumption becoming one of the first-class optimization parameters in computer system design, compilation techniques that consider performance and energy simultaneously are expected to play a central role. In particular, compiling a given application code under performance and energy constraints is becoming an important problem. In this paper, we focus on an on-chip multiprocessor architecture and present a set of code optimization strategies. We first evaluate an adaptive loop parallelization strategy (i.e., a strategy that allows each loop nest to execute using a different number of processors if doing so is beneficial) and measure the potential energy savings when unused processors during execution of a nested loop are shut down (i.e., placed into a power-down or sleep state). Our results show that shutting down unused processors can lead to as much as 67 percent energy savings at the expense of up to 17 percent performance loss in a set of array-intensive applications. To eliminate this performance penalty, we also discuss and evaluate a processor preactivation strategy based on compile-time analysis of nested loops. Based on our experiments, we conclude that an adaptive loop parallelization strategy combined with idle processor shut down and preactivation can be very effective in reducing energy consumption without increasing execution time. We then generalize our strategy and present an application parallelization strategy based on integer linear programming (ILP). Given an array-intensive application, our optimization strategy determines the number of processors to be used in executing each loop nest based on the objective function and additional compilation constraints provided by the user/programmer. Our initial experience with this constraint-based optimization strategy shows that it is very successful in optimizing array-intensive applications on on-chip multiprocessors under multiple energy and performance constraints.

Optimal Insulation of Structures with Varying Thermal Conductivity

The optimal distribution of a limited amount of insulation material on a composite structure is obtained. Three cases for the variation of the composite wall thermal conductivity were considered. These are 1) power, 2) linear, and 3) exponential types of variation of wall thermal conductivity with an in-plane direction along the surface of the wall. The surfaces of the wall are assumed to either have constant temperatures or be exposed to ambient at given temperatures. The variation of the optimum insulation thickness is shown to be relatively smooth and easily applicable for all three profiles of thermal conductivity. For a certain optimum length of composite structure, optimal distribution of the insulation material provides maximum percent energy savings. Optimal distribution of the insulation material may require partially covering the surface.

Fifty percent energy savings with automatic lighting controls

An electronically ballasted lighting control system was demonstrated at an office building in the San Francisco Bay Area. The system was used to demonstrate the energy savings and demand-reduction capabilities of a lighting control system designed to exploit all major control strategies, including scheduling, daylighting, and lumen maintenance. After 9 months of operation, lighting energy savings of approximately 50% relative to previous usage have been consistently achieved. A novel, two-part control photocell that permits successful implementation of daylighting and lumen maintenance strategies was demonstrated. >

Enhanced Control of Induction Heating Processes of Welded Tubes

This paper presents research results on improved low-cost automatic control of induction heating processes applied to seam-annealing and to preheatin for insulation of welded tubes. It has been done by introducing additional temperature loop at local control of medium -frequency generators and coordination by tracking a family of overal static characteristics, which define sets of local set-point commands. Local three-term controls are designet using the idea of extended frequency characteristics, while overal control is following the idea of direct coordination by penalty function tracking of commands. All models have been derivet and identified in plant environment, and family of overal characteristics also discretized into look-up tables. Benefits are estimated up to five percent energy savings and higher quality processing, while retaining simple control scheme.

Economic analysis of energy management for cooling systems in Kuwait

Analysis of the economics of energy management measures for A/C systems in Kuwait shows that, as the daily utilization increases, larger additional investment may be made and the system cost would still break even. It is shown that, for the same percentage of energy savings, larger additional investment can be made in air-cooled than water-cooled systems.