Dynamic Energy Consumption Research Articles

Retractable membrane ceilings for indoor thermal environment of residential buildings

Indoor thermal environment of residential buildings has attracted considerable attention due to increasing requirements of building performance and dramatical reduction in non-renewable energy. Utilizing new methods to enhance building performance is necessary to achieve low-energy consumption or sustainable buildings. In this study, a retractable membrane ceiling used as part of the second floor in a typical residential building is proposed to address this research topic.The effects of retractable membrane ceiling on indoor thermal environment are composed of temperature distribution, thermal comfort and energy consumption. It is found that temperature distribution and characteristics are spatial and temporal dependent and that performance enhancement with membrane ceiling unfolding is validated compared with membrane ceiling folding, i.e. 3.7 °C and 0.9 °C. To assess thermal comfort, a revised PMV–PPD method is employed to calculate predicted mean vote and predicted percentage of dissatisfied values. The specific values are within comfort ranges during 11:00–18:00 for both folding and unfolding cases. Moreover, thermal comfort curve with respect to membrane unfolding is smooth and wide, which means that the membrane ceiling can ensure smooth indoor environment. For energy consumption, dynamic energy consumption with membrane unfolding is lower than that with membrane folding. The relationships between outdoor air temperature and energy consumption are established on the basis of experimental results. The comparisons between correlation equations again demonstrate that using retractable membrane ceiling is a direct and efficient way to improve thermal performance and control indoor thermal environment.

A Code Generator for Energy-Efficient Wavefront Parallelization of Uniform Dependence Computations

Energy is now critical in all aspects of computing. We address a class of programs that includes so-called “stencil computations.” We address energy optimization of such programs. Since optimizing for speed alone already minimizes energy for most components, we seek to further improve the energy consumption by reducing the total number of off-chip memory accesses without sacrificing execution time . Our strategy uses two-level tiling: we first partition the iteration space into “passes,” each of which is tiled and parallelized. Here, the schedules that map the original program to the multi-pass, parametrically tiled code are specified by polynomials. They are more general than affine multidimensional schedules used by state of the art polyhedral compilers, so generating such codes automatically is an important open problem, and goes beyond the motivation of energy efficiency. We develop a parametric tiled code generator supporting our energy-efficient parallelization strategy. We give a simple linear regression model for energy as a function of performance counters. Our experimental validation on three platforms shows a reduction by about 74 percent (resp. 75 and 67 percent) of the dynamic memory energy consumption on an 8-core Xeon E5-2650 v2 (resp. 6-core Xeon E5-2620 v2 and 6-core Xeon E5-2602 v3). This leads to a reduction in the total energy of the program by 2 to 14 percent.

Counter Advance for Reliable Encryption in Phase Change Memory

The use of hardware encryption and new memory technologies such as phase change memory (PCM) are gaining popularity in a variety of server applications such as cloud systems. While PCM provides energy and density advantages over conventional DRAM memory, it faces endurance challenges. Such challenges are exacerbated when employing memory encryption as the stored data is essentially randomized, losing data locality and reducing or eliminating the effectiveness of energy and endurance aware encoding techniques. This results in increasing dynamic energy consumption and accelerated wear out. In this paper we propose counter advance , a technique to leverage the process of encryption to improve reliability and lifetime while maintaining low-energy and low-latency operation. Counter advance is compatible with standard error-correction codes (ECC) and error correction pointers (ECP), the standard for mitigating endurance faults in PCM. Counter advance achieves the same fault tolerance using three ECP pointers for a $10^{-4}$ cell failure rate compared to the leading approach to consider energy savings and reliability for encrypted PCM (SECRET) using five ECP pointers. At a failure rate of $10^{-2}$ , counter advance can achieve an uncorrectable bit error rate (UBER) of 10 $^{-10}$ , compared to ${ for SECRET, using six ECP pointers. This leads to a lifetime improvement of 3.8× while maintaining comparable energy consumption and access latency.

Performance and Energy-Efficient Design of STT-RAM Last-Level Cache

Recent research has proposed having a die-stacked last-level cache (LLC) to overcome the memory wall. Lately, spin-transfer-torque random access memory (STT-RAM) caches have received attention, since they provide improved energy efficiency compared with DRAM caches. However, recently proposed STT-RAM cache architectures unnecessarily dissipate energy by fetching unneeded cache lines (CLs) into the row buffer (RB). In this paper, we propose a selective read policy for the STT-RAM which fetches those CLs into the RB that are likely to be reused. In addition, we propose a tags-update policy that reduces the number of STT-RAM writebacks. This reduces the number of reads/writes and thereby decreases the energy consumption. To reduce the latency penalty of our selective read policy, we propose the following performance optimizations: 1) an RB tags-bypass policy that reduces STT-RAM access latency; 2) an LLC data cache that stores the CLs that are likely to be used in the near future; 3) an address organization scheme that simultaneously reduces LLC access latency and miss rate; and 4) a tags-to-column mapping policy that improves access parallelism. For evaluation, we implement our proposed architecture in the Zesto simulator and run different combinations of SPEC2006 benchmarks on an eight-core system. We compare our approach with a recently proposed STT-RAM LLC with subarray parallelism support and show that our synergistic policies reduce the average LLC dynamic energy consumption by 75% and improve the system performance by 6.5%. Compared with the state-of-the-art DRAM LLC with subarray parallelism, our architecture reduces the LLC dynamic energy consumption by 82% and improves system performance by 6.8%.

Adaptive online mobile charging for node failure avoidance in wireless rechargeable sensor networks

Recent breakthrough progress of wireless energy transfer technology and rechargeable lithium battery technology emerge the Wireless Rechargeable Sensor Networks (WRSNs). To avoid sensor node failure, how to schedule the Mobile Charger (MC) to recharge sensor nodes in WRSNs is very challenging. Previous works that predetermining the charging path of MC cannot adapt to the diversity and dynamic energy consumption of sensors in actual environment and may lead to problematic schedules. Many online charging schemes are proposed to overcome the challenges, but they still get performance limitation due to leaving out of consideration about the energy depletion issue resulted from not timely and/or unfair charging response. Particularly, the node failure problem will be worse when there are a large number of charging requirements in the network. In this paper, we address the node failure avoidance mobile charing for WRSNs, which aims to minimize the number of invalid nodes due to sensor node energy depletion in the charging process. We first consider the dynamic energy consumption rate of the node based on both its history statistics and real time energy consumption. Then we propose two efficient online charging algorithms named PA and INMA, respectively. PA selects the next charging node according to the charging probability of the requesting nodes, whereas INMA always chooses the nodes which make the least number of other requesting nodes suffer from energy depletion as the charging candidates. Furthermore, to achieve high charging efficiency, the node with the shortest time to finish the charging will be selected as the next charging node if there are multiple nodes in the candidate set. Simulation results demonstrate that the proposed algorithms can effectively solve the node energy depletion problem with lower charging latency and charging cost in comparison with other current existing online charging schemes.

RCSS: A Real-Time On-Demand Charging Scheduling Scheme for Wireless Rechargeable Sensor Networks.

With the emergence of edge computing, a large number of devices such as sensor nodes have been deployed in the edge network to sense and process data. However, how to provide real-time on-demand energy for these edge devices is a new challenge issue of edge networks. In real-world applications of edge computing, sensor nodes usually have different task burdens due to the environmental impact, which results in a dynamic change of the energy consumption rate at different nodes. Therefore, the traditional periodical charging mode cannot meet the nodes charging demand that have dynamic energy consumption. In this paper, we propose a real-time on-demand charging scheduling scheme (RCSS) under the condition of limited mobile charger capacity. In the process of building the charging path, RCSS adequately considers the dynamic energy consumption of different node, and puts forward the next node selection algorithm. At the same time, a method to determine the feasibility of charging circuit is also proposed to ensure the charging efficiency. During the charging process, RCSS is based on adaptive charging threshold to reduce node mortality. Compared with existing approaches, the proposed RCSS achieves better performance in the number of survival nodes, the average service time and charging efficiency.

A modified energy-saving skyhook for active suspension based on a hybrid electromagnetic actuator

This study proposes a modified energy-saving skyhook consisting of active control, energy regeneration, and switch. The modified skyhook coordinates the contradiction between dynamic performance and energy consumption of electromagnetic active suspension. The control principle is analyzed, the switch condition between active control and energy recovery is provided, and the switch control system is designed for simulation. Results demonstrate that the presented strategy can coordinate the dynamic performance and energy consumption effectively. The realization structure, namely, a hybrid electromagnetic actuator, is then designed to satisfy the control requirements. It integrates a linear motor and a hydraulic damper. The linear motor is used for active control or energy regeneration, while the hydraulic damper is used to guarantee basic dynamic performance. The structural dimension of hybrid electromagnetic actuator is optimized to increase air gap flux density with the volume and weight limitation. A prototype is fabricated, and a bench test is conducted. Results show that the structure can satisfy the control requirements. Some errors within a reasonable range are also observed between the test and the simulation because the simulation model is prepared under ideal conditions.

Analyze the energy consumption characteristics and affecting factors of Taiwan's convenience stores-using the big data mining approach

This study applies big data mining, machine learning analysis technique and uses the Waikato Environment for Knowledge Analysis (WEKA) as a tool to discuss the convenience stores energy consumption performance in Taiwan which consists of (a). Influential factors of architectural space environment and geographical conditions; (b). Influential factors of management type; (c). Influential factors of business equipment; (d). Influential factors of local climatic conditions; (e). Influential factors of service area socioeconomic conditions. The survey data of 1,052 chain convenience stores belong to 7-Eleven, Family Mart and Hi-Life groups by Taiwan Architecture and Building Center (TABC) in 2014. The implicit knowledge will be explored in order to improve the traditional analysis technique which is unlikely to build a model for complex, inexact and uncertain dynamic energy consumption system for convenience stores. The analysis process comprises of (a). Problem definition and objective setting; (b). Data source selection; (c). Data collection; (d). Data preprocessing/preparation; (e). Data attributes selection; (f). Data mining and model construction; (g). Results analysis and evaluation; (h). Knowledge discovery and dissemination. The key factors influencing the convenience stores energy consumption and the influence intensity order can be explored by data attributes selection. The numerical prediction model for energy consumption is built by applying regression analysis and classification techniques. The optimization thresholds of various influential factors are obtained. The different cluster data are compared by using clustering analysis to verify the correlation between the factors influencing the convenience stores energy consumption characteristic. The implicit knowledge of energy consumption characteristic obtained by the aforesaid analysis can be used to (a). Provide the owners with accurate predicted energy consumption performance to optimize architectural space, business equipment and operations management mode; (b). The design planners can obtain the optimum design proposal of Cost Performance Ratio (C/P) by planning the thresholds of various key factors and the validation of prediction model; (c). Provide decision support for government energy and environment departments, to make energy saving and carbon emission reduction policies, in order to estimate and set the energy consumption scenarios of convenience store industry.

On the Limitations of Analyzing Worst-Case Dynamic Energy of Processing

This article examines dynamic energy consumption caused by data during software execution on deeply embedded microprocessors, which can be significant on some devices. In worst-case energy consumption analysis, energy models are used to find the most costly execution path. Taking each instruction’s worst-case energy produces a safe but overly pessimistic upper bound. Algorithms for safe and tight bounds would be desirable. We show that finding exact worst-case energy is NP-hard, and that tight bounds cannot be approximated with guaranteed safety. We conclude that any energy model targeting tightness must either sacrifice safety or accept overapproximation proportional to data-dependent energy.

Exploring potential pathways towards fossil energy-related GHG emission peak prior to 2030 for China: An integrated input-output simulation model

This study develops a dynamic integrated input-output simulation model to explore potential pathways towards GHG emission peak prior to 2030 for China. Dynamic energy consumption intensities and GHG emission intensities (GHGEIs) of sectors (household), as well as various levels of economic growth are set in 4 scenarios (each containing 4 sub-scenarios). The impacts of changes in the added value (reflected as industrial restructuring) and changes in GHGEIs (reflected as technological advancement and intensified policies) of 10 target sectors including both promoted and constrained ones on the peak are elaborated. In the Business-as-Usual scenario, no emission peak could appear before 2040 along the historical trends without taking further intensified emission reduction policies. In Scenario 1 and 2, when economic growth is maintained at higher levels, sole dependence on changes in either added value or GHGEIs of sectors could curb GHG emissions, however without contributing to a peak timing before 2030. The peak timing could be advanced to 2026 (10.85 × 109 t CO2-e), 2025 (10.77 × 109 t CO2-e), 2024 (10.69 × 109 t CO2-e) and 2023 (10.65 × 109 t CO2-e) corresponding to different levels of economic growth in Scenario 3, where industrial restructuring and intensified energy and GHG emission reduction policies are involved. The results are expected to provide references to future planning of energy utilization and GHG emission reduction from the perspective of both the country and sectors.

Bi-Objective Optimization of Data-Parallel Applications on Homogeneous Multicore Clusters for Performance and Energy

Performance and energy are now the most dominant objectives for optimization on modern parallel platforms composed of multicore CPU nodes. The existing intra-node and inter-node optimization methods employ a large set of decision variables but do not consider problem size as a decision variable and assume a linear relationship between performance and problem size and between energy consumption and problem size. We demonstrate using experiments of real-life data-parallel applications on modern multicore CPUs that these relationships have complex (non-linear and even non-convex) properties and, therefore, that the problem size has become an important decision variable that can no longer be ignored. This key finding motivates our work in this paper. In this paper, we first formulate the bi-objective optimization problem for performance and energy (BOPPE) for data-parallel applications on homogeneous clusters of modern multicore CPUs. It contains only one but heretofore unconsidered decision variable, the problem size. We then present an efficient and exact global optimization algorithm called <i>ALEPH</i> that solves the <i>BOPPE</i> . It takes as inputs, discrete functions of performance and dynamic energy consumption against problem size and outputs the globally Pareto-optimal set of solutions. The solutions are the workload distributions, which achieve inter-node optimization of data-parallel applications for performance and energy. While existing solvers for <i>BOPPE</i> give only one solution when the problem size and number of processors are fixed, our algorithm gives a diverse set of globally Pareto-optimal solutions. The algorithm has time complexity of <inline-formula><tex-math notation="LaTeX">$O(m^2 \times p^2)$</tex-math></inline-formula> where <inline-formula> <tex-math notation="LaTeX">$m$</tex-math></inline-formula> is the number of points in the discrete speed/energy function and <inline-formula> <tex-math notation="LaTeX">$p$</tex-math></inline-formula> is the number of available processors. We experimentally study the efficiency and scalability of our algorithm for two data parallel applications, matrix multiplication and fast Fourier transform, on a modern multicore CPU and homogeneous clusters of such CPUs. Based on our experiments, we show that the average and maximum sizes of the globally Pareto-optimal sets determined by our algorithm are 15 and 34 and 7 and 20 for the two applications respectively. Comparing with load-balanced workload distribution solution, the average and maximum percentage improvements in performance and energy respectively demonstrated for the first application are (13%,97%) and (18%,71%). For the second application, these improvements are (40%,95%) and (22%,127%). Assuming 5 percent performance degradation from the optimal is acceptable, the average and maximum improvements in energy consumption demonstrated for the two applications respectively are 9 and 44 and 8 and 20 percent. Using the algorithm and its building blocks, we also present a study of interplay between performance and energy. We demonstrate how <i>ALEPH</i> can be combined with <i> DVFS</i> -based Multi-Objective Optimization (MOP) methods to give a better set of (globally Pareto-optimal) solutions.

Read-Tuned STT-RAM and eDRAM Cache Hierarchies for Throughput and Energy Optimization

As capacity and complexity of on-chip cache memory hierarchy increases, the service cost to the critical loads from last level cache (LLC), which are frequently repeated, has become a major concern. The processor may stall for a considerable interval while waiting to access the data stored in the cache blocks in LLC, if there are no independent instructions to execute. To provide accelerated service to the critical loads requests from LLC, this paper concentrates on leveraging the additional capacity offered by replacing SRAM-based L2 with spin-transfer torque random access memory (STT-MRAM) to accommodate frequently accessed cache blocks in exclusive read mode in favor of reducing the overall read service time. Our proposed technique improves the temporal locality while preventing cache thrashing via sufficient accommodation of the frequently read reused fraction of working set that may exhibit distant re-reference interval in L2. Our experimental results show that the proposed technique can reduce the L2 read miss ratio by 51.7% on average compared to conventional STT-MRAM L2 design across PARSEC and SPEC2006 workloads while significantly decreasing the L2 dynamic energy consumption.

Simulation of Dynamic Energy Consumption in Modular Construction Manufacturing Processes

Simulation of Dynamic Energy Consumption in Modular Construction Manufacturing Processes

Energy-Efficient Scheduling Algorithms for Real-Time Parallel Applications on Heterogeneous Distributed Embedded Systems

Energy consumption minimization is one of the primary design requirements for heterogeneous distributed systems. State-of-the-art algorithms are used to study the problem of minimizing the energy consumption of a real-time parallel application with precedence constrained tasks on a heterogeneous distributed system by introducing the concept of latest finish time (LFT) to reclaim the slack time based on the dynamic voltage and frequency scaling (DVFS) energy-efficient design optimization technique. However, the use of DVFS technique alone is insufficient, and the energy consumption reduction is limited because scaling down the frequency is restricted in practice. Furthermore, these studies merely minimize energy consumption through a local energy-efficient scheduling algorithm, such as reducing the energy consumption for each task on the fixed processor, rather than a global energy-efficient scheduling algorithm, such as reducing the energy consumption for each task on different processors. This study solves the problem of minimizing the energy consumption of a real-time parallel application on heterogeneous distributed systems by using the combined non-DVFS and global DVFS-enabled energy-efficient scheduling algorithms. The non-DVFS energy-efficient scheduling (NDES) algorithm is solved by introducing the concept of deadline slacks to reduce the energy consumption while satisfying the deadline constraint. The global DVFS-enabled energy-efficient scheduling (GDES) algorithm is presented by moving the tasks to the processor slacks that generate minimum dynamic energy consumptions. Results of the experiments show that the combined NDES&GDES algorithm can save up to 36.25-55.65 percent of energy compared with state-of-the-art counterparts under different scales, parallelism, and heterogeneity degrees of parallel applications.

Energy-proportional profiling and accounting in heterogeneous virtualized environments

The costs of current data centers are mostly driven by their energy consumption (specifically by the air conditioning, computing and networking infrastructure). Yet, current pricing models are usually static and rarely consider the facilities’ energy consumption per user. The challenge is to provide a fair and predictable model to attribute the overall energy costs per virtual machine (VM) in heterogeneous environments. In this paper we introduce EPAVE, a model for Energy-Proportional Accounting in VM-based Environments. EPAVE allows transparent, reproducible and predictive cost calculation for users and for Cloud providers. It provides a full-cost model that does not account only for the dynamic energy consumption of a given VM, but also includes the proportional static cost of using a Cloud infrastructure. It comes with PowerIndex, a profiling and estimation model, which is able to profile the energy cost of a VM on a given server architecture and can then estimate its energy cost on a different one. We provide performance results of PowerIndex on real hardware, and we discuss the use cases and applicability of EPAVE.

NVM Way Allocation Scheme to Reduce NVM Writes for Hybrid Cache Architecture in Chip-Multiprocessors

Hybrid cache architectures (HCAs) containing both SRAM and non-volatile memory (NVM) have been proposed to overcome the disadvantages of NVM-based cache architecture. Most previous works have concentrated on managing write-intensive blocks by storing these blocks to SRAM to reduce the number of the write operations to NVM. However, they have not focused on reducing linefill operations which also occupy a large portion of overall NVM write counts in chip-multiprocessor (CMP) environments. This paper proposes an NVM way allocation scheme, taking into account the NVM linefill counts as well as cache miss rate and the NVM write hit counts during victim selection. Three metrics are introduced to estimate the effectiveness of NVM way allocation: Miss counts change (ΔM), write counts change (ΔW), and NVM write counts change (ΔNVMW). An algorithm to minimize the write counts of NVM based on these metrics is proposed as well. Our experimental results show that dynamic energy consumption is reduced by 37.5 percent on average.

Sampling for building energy consumption with fuzzy theory

The foundation of energy saving is knowing the real status of building energy consumption. For various kinds and a great number of building energy consumption data, the fuzzy theory is applied for sampling. It would make data representational. Firstly, a fuzzy clustering method is used to classify the data set and then the samples are extracted from the subclass. A modified clustering algorithm based on entropy weight method is proposed. It can determine the number of the classification of data set. The simulation results indicate that the new method can directly determine the optimal sample size. This method is suitably applied for dynamic energy consumption data and is more accurate compared with the statistical method.

An Optimization Scheme of Enhanced Adaptive Dynamic Energy Consumption Based on Joint Network-Channel Coding in WSNs

Energy efficiency in wireless sensor networks is a crucial factor that affects the performance of the networks, as long-time and unceasing energy supply is impossible. Recent studies demonstrated that the previously proposed scheme of joint adaptive network-channel coding (JANCC) and adaptive dynamic energy consumption (ADEC) obtained an excellent performance in terms of energy efficiency. Based on the joint network-channel coding, we propose an enhanced ADEC optimization scheme, which integrates the essence of both JANCC and ADEC, and overcomes the shortcomings of them. In the scheme, an identifier vector is inserted into the feedback information. The relay node is able to acquire the specific signs of the channel codes received in error based on the identifier vector and transmit the network coded packets only related to those channel codes received in error. Through this method, it can achieve adjustments for the current round of transmission. Performance analyses and simulation results show that the proposed optimization scheme is more energy efficient than any other schemes of interest under the condition of meeting the requirement of transmission reliability.

OPTIMAS: Overwrite Purging Through In-Execution Memory Address Snooping to Improve Lifetime of NVM-Based Scratchpad Memories

SRAM-based scratchpad memories (SPMs) used in embedded systems impose high leakage power. Designing SPMs based on non-volatile memories (NVMs) were proposed as NVMs have negligible leakage power. The main problem of utilizing NVMs across the SPM is their limited number of write cycles (endurance). This problem threatens the reliability of NVM-based SPMs. To alleviate the problem of limited endurance in NVM-based SPMs, this paper proposes a method, called overwrite purging through in-execution memory address snooping (OPTIMAS). The main idea behind the proposed method is to control the lifetime of NVM-based SPMs, directly by a hardware unit, outside of the SPM mapping algorithm. This idea enables the possibility of using traditional SRAM-based mapping algorithms in NVM-based SPMs. OPTIMAS controls the wear-out of NVM-based SPMs based on the recent write behavior of each block during the runtime of the programs. OPTIMAS is evaluated from the lifetime, energy consumption, and performance point of views. The simulation results show up to two orders of magnitude improvement in lifetime, an average of 50% reduction in dynamic energy consumption, and negligible performance overhead (less than 1%). Furthermore, it is shown that while OPTIMAS is orthogonal to all other approaches, it benefits from significant lifetime improvement in comparison with the state-of-the-art approaches.

Energy-efficient mapping of large-scale workflows under deadline constraints in big data computing systems

Large-scale workflows for big data analytics have become a main consumer of energy in data centers where moldable parallel computing models such as MapReduce are widely applied to meet high computational demands with time-varying computing resources. The granularity of task partitioning in each moldable job of such big data workflows has a significant impact on energy efficiency, which remains largely unexplored. In this paper, we analyze the properties of moldable jobs and formulate a workflow mapping problem to minimize the dynamic energy consumption of a given workflow request under a deadline constraint in big data systems. Since this problem is strongly NP-hard, we design a fully polynomial-time approximation scheme (FPTAS) for a special case with a pipeline-structured workflow on a homogeneous cluster and a heuristic for the generalized problem with an arbitrary workflow on a heterogeneous cluster. The performance superiority of the proposed solution in terms of dynamic energy saving and deadline missing rate is illustrated by extensive simulation results in comparison with existing algorithms, and further validated by real-life workflow implementation and experimental results in Hadoop/YARN systems.