Dynamic Energy Consumption Research Articles

Thermal-Aware Task Scheduling for Energy Minimization in Heterogeneous Real-Time MPSoC Systems

With the continuous scaling of CMOS devices, the increase in power density and system integration level have not only resulted in huge energy consumption but also led to elevated chip temperature. Thus, energy efficient task scheduling with thermal consideration has become a pressing research issue in computing systems, especially for real-time embedded systems with limited cooling techniques. In this paper, we design a two-stage energy-efficient temperature-aware task scheduling scheme for heterogeneous real-time multiprocessor system-on-chip (MPSoC) systems. In the first stage, we analyze the energy optimality of assigning real-time tasks to multiple processors of an MPSoC system, and design a task assignment heuristic that minimizes the system dynamic energy consumption under the constraint of task deadlines. In the second stage, the optimality of minimizing the peak temperature of a processor is investigated, and a slack distribution heuristic is proposed to improve the temperature profile of each processor under the thermal constraint, thus the temperature-dependent system leakage energy consumption is reduced. Through the extensive efforts made in two stages, the system overall energy consumption is minimized. Experimental results have demonstrated the effectiveness of our scheme.

Energy-aware memory management for embedded multidimensional signal processing applications

In real-time data-intensive multimedia processing applications, data transfer and storage significantly influence, if not dominate, all the major cost parameters of the design space—namely energy consumption, performance, and chip area. This paper presents an electronic design automation (EDA) methodology for the high-level design of hierarchical memory architectures in embedded data-intensive applications, mainly in the area of multidimensional signal processing. Different from the previous works, the problems of data assignment to the memory layers, of mapping the signals into the physical memories, and of banking the on-chip memory are addressed in a consistent way, based on the same formal model. This memory management framework employs techniques specific to the integral polyhedra based dependence analysis. The main design target is the reduction of the static and dynamic energy consumption in the hierarchical memory subsystem.

GPGPU Footprint Models to Estimate per-Core Power

We explore the problem of how to easily estimate the per-core power distribution of GPGPUs from the total power of all cores. We show that the dynamic energy consumption of a core for a given kernel, represented by its work footprint , is approximately proportional to the total time taken by all work units executing on that core, and the static power, represented by its core footprint , is proportional to the time that the core has assigned work. Footprints can be easily tracked using two hardware counters per GPU core. We also show how per-core power estimates can be used to compute power-performance pareto frontiers that identify opportunities for saving power and energy in cases of non-uniform work distribution by exploiting per-core DVFS support for GPGPUs.

Improving Energy Efficiency and Lifetime of Phase Change Memory using Delta Value Indicator

Phase change memory (PCM) has been studied as an emerging memory technology for last-level cache (LLC) due to its extremely low leakage. However, it consumes high levels of energy in updating cells and its write endurance is limited. To relieve the write pressure of LLC, we propose a delta value indicator (DVI) by employing a small cache which stores the difference between the value currently stored and the value newly loaded. Since the write energy consumption of the small cache is less than the LLC, the energy consumption is reduced by access to the small cache instead of the LLC. In addition, the lifetime of the LLC is further extended because the number of write accesses to the LLC is decreased. To this end, a delta value indicator and controlling circuits are inserted into the LLC. The simulation results show a 26.8% saving of dynamic energy consumption and a 31.7% lifetime extension compared to a state-of-the-art scheme for PCM.

Integrating building performance simulation in agent-based modeling using regression surrogate models: A novel human-in-the-loop energy modeling approach

Building Performance Simulation (BPS) is an established method used in the design phase of buildings to predict energy consumption and guide design choices. Despite their advanced abilities to model complex building systems, BPS tools typically fail to account for different and changing energy use characteristics of building occupants, contributing to important prediction errors. In parallel, Agent-Based Modeling (ABM) has emerged in recent years as a technique capable of capturing occupants’ dynamic energy consumption behaviors and actions. However, ABM lacks the building simulation capabilities to account for the complexity of various building systems in energy calculations. This research proposes a new modeling framework that integrates BPS in ABM using trained regression surrogate models. The framework is unique in its ability to (1) simulate energy use attributes of building occupants and facility managers, (2) translate those attributes to robust energy consumption estimates, and (3) help quantify the impact of uncertainty in human actions on the performance of the built environment. The framework is tested and illustrated in a case study on a prototype office building. Results indicate that providing occupants with control over their building systems can mitigate the effect of uncertainty in human actions on the performance of the built environment.

An Effective Model for Demand Response Management Systems of Residential Electricity Consumers

An effective demand response (DR) model framework for DR management systems in a residential premise is proposed. The proposed model consists of dynamic price signals from load serving entity (LSE), benefit function, and loss of consumer comfort. The conventional approaches generally consider these factors as independent in their formulation. Since these factors have functional relationship, the proposed model points to a holistic approach to capture the relationship. The scheduling model can be integrated with the home energy scheduler coupled to the smart meter, which facilitates the flow of dynamic price and energy consumption information to and from the household premise. The objective is to maximize net consumer surplus, which is formulated as a function of all aforementioned factors. A method for developing a practical and dynamic consumer benefit model for matured DR programs is proposed in this paper. For the purpose of showing the effectiveness of dynamic benefit function, a set of analytically formulated benefit functions is also considered. The suitability of the dynamic benefit function, as compared with other alternatives, is illustrated with an example. This paper also reports the dynamic price design from the view point of LSE, in order to cover the risks of wholesale price volatility.

LER: Least-Error-Rate Replacement Algorithm for Emerging STT-RAM Caches

Spin-transfer-torque RAMs (STT-RAMs) are the most promising technology for replacing Static RAMs (SRAMs) in on-chip caches. One of the major problems in STT-RAMs is the high error rate due to stochastic switching in write operations. Cache replacement algorithms have a major role in the number of write operations into the caches. Due to this fact, it is necessary to redesign cache replacement algorithms to consider the new challenges of STT-RAM caches. This paper proposes a cache replacement algorithm, which is called least error rate (LER) , to reduce the error rate in L2 caches. The main idea is to place the incoming block in a line that incurs the minimum error rate in write operation. This is done by comparing the contents of the incoming block with lines in a cache set. Compared with Least Recently Used (LRU) algorithm, LER reduces the error rate by 2× with about 1.4% and 3.6% performance and dynamic energy consumption overheads, respectively. Moreover, LER imposes no area overhead to system.

The Tag Filter Architecture: An energy-efficient cache and directory design

Power consumption in current high-performance chip multiprocessors (CMPs) has become a major design concern that aggravates with the current trend of increasing the core count. A significant fraction of the total power budget is consumed by on-chip caches which are usually deployed with a high associativity degree (even L1 caches are being implemented with eight ways) to enhance the system performance. On a cache access, each way in the corresponding set is accessed in parallel, which is costly in terms of energy. On the other hand, coherence protocols also must implement efficient directory caches that scale in terms of power consumption. Most of the state-of-the-art techniques that reduce the energy consumption of directories are at the cost of performance, which may become unacceptable for high-performance CMPs.In this paper, we propose an energy-efficient architectural design that can be effectively applied to any kind of cache memory. The proposed approach, called the Tag Filter (TF) Architecture, filters the ways accessed in the target cache set, and just a few ways are searched in the tag and data arrays. This allows the approach to reduce the dynamic energy consumption of caches without hurting their access time. For this purpose, the proposed architecture holds the X least significant bits of each tag in a small auxiliary X-bit-wide array. These bits are used to filter the ways where the least significant bits of the tag do not match with the bits in the X-bit array. Experimental results show that, on average, the TF Architecture reduces the dynamic power consumption across the studied applications up to 74.9%, 85.9%, and 84.5% when applied to L1 caches, L2 caches, and directory caches, respectively.

Charging utility maximization in wireless rechargeable sensor networks

Wireless energy transfer as a promising technology provides an alternative solution to prolong the lifetime of wireless rechargeable sensor networks (WRSNs). In this paper, we study replenishing energy on sensors in a WRSN to shorten energy expiration durations of sensors, by employing a mobile wireless charger to replenish sensors dynamically. We first formulate a novel sensor recharging problem with an objective of maximizing the charging utility of sensors, subject to the total traveling distance of the mobile charger per tour and the charging time window of each to-be-charged sensor. Due to the NP-hardness of the problem, we then propose an approximation algorithm with quasi-polynomial time complexity. In spite of the guaranteed performance ratio of the approximate solution, its time complexity is prohibitively high and may not be feasible in practice. Instead, we devise a fast yet scalable heuristic for the problem in response to dynamic energy consumption of sensors in the network. Furthermore, we also consider the online version of the problem where sensor replenishment is scheduled at every fixed time interval. We finally conduct extensive experiments by simulation to evaluate the performance of the proposed algorithms. Experimental results demonstrate that the proposed algorithms are very promising.

Reliability-conscious energy management for fixed-priority real-time embedded systems with weakly hard QoS-constraint

Aggressive scaling in technology size has dramatically increased the power density and degraded the reliability of real-time embedded systems. In this paper, we study the problem of reliability-conscious energy minimization for scheduling fixed-priority real-time embedded systems with weakly hard QoS-constraint. The weakly hard QoS-constraint is modeled with (m, k)-constraint, which requires that at least m out of any k consecutive jobs of a task meet their deadlines. We first propose a technique that can balance the static and dynamic energy consumption for real-time jobs with better speed determination than the classical strategies during their feasible intervals. Then based on it, we propose an adaptive fixed-priority scheduling scheme to reduce the energy consumption for the system while preserving its reliability. Through extensive simulations, our experiment results demonstrate that the proposed techniques can significantly outperform the previous research in energy performance while satisfying the weakly hard QoS-constraint under the reliability requirement.

$E^{2}$ -MAC: Energy Efficient Medium Access for Massive M2M Communications

In this paper, we investigate energy-efficient clustering and medium access control (MAC) for cellular-based M2M networks to minimize device energy consumption and prolong network battery lifetime. First, we present an accurate energy consumption model that considers both static and dynamic energy consumptions, and utilize this model to derive the network lifetime. Second, we find the cluster size to maximize the network lifetime and develop an energy-efficient cluster-head selection scheme. Furthermore, we find feasible regions where clustering is beneficial in enhancing network lifetime. We further investigate communications protocols for both intra- and inter-cluster communications. While inter-cluster communications use conventional cellular access schemes, we develop an energy-efficient and load-adaptive multiple access scheme, called n-phase CSMA/CA, which provides a tunable tradeoff between energy efficiency, delay, and spectral efficiency of the network. The simulation results show that the proposed clustering, cluster-head selection, and communications protocol design outperform the others in energy saving and significantly prolong the lifetimes of both individual nodes and the whole M2M network.

OaaS Based on Temporal Partitioning with Minimum Energy Consumption

Cloud computing is an economical solution for industry which is highly scalable and useful of virtualized resources that can be used on demand. It will have a significant impact on companies with the introduction of orchestration platforms as a Service (OaaS) to perform the services that support a variety of business processes such as BPEL. It's becoming an adoptable technology for many of the organizations, thanks to its flexibility and because it reduces total cost of ownership. Thus, an effective OaaS must meet several requests simultaneously; ensuring scalability and optimizing the use of shared resources in order to minimize energy consumption. In this paper, we will investigate three issues i) exploiting the minimum of resources to execute a maximum number of processes, ii) Preventing possible overload to the server, and iii) minimizing dynamic energy consumption which becomes one of the main challenges for large-scale computing, such as in cloud data center. As a solution for these challenges, we propose to use Workflow partitioning technique and this based on temporal dynamic reconfiguration approach. Our work aims to reduce the dynamic energy consumption; especially in communication buffers between partitions of BPEL process during partitioning. The proposed approach is based on two main steps: 1) Estimate the energy consumption of BPEL processes 2) Temporal and dynamic partitioning of BPEL process based on reconfigurable architecture in order to minimize overall energy consumption on each BPEL process.

Dynamic Characteristics and Energy Consumptions of an Indirect District Heating System Operating in Different Control Strategies

A set of dynamic models was developed for an indirect district heating system (IDHS), including pumps and adjusting valves. Firstly, dynamic simulations were conducted to find out how the supply water temperature of the boiler, indoor air temperature and fuel consumption vary with outdoor temperature. The influence of outdoor temperature could be compensated by modulating fuel consumptions, but the influences of solar radiation and indoor heat gains could not be ignored. Then, one-week operational states were simulated with actual meteorological data exerted and validated. And then, different control strategies were implemented and the one-week operational situations were simulated, respectively. The smallest indoor temperature variations could be achieved if the fuel, the primary piping network and radiator water flow rates were jointly controlled. However, the least amount of energy was consumed if the fuel consumption was controlled in terms of boiler supply temperature jointly with primary piping network flow rate controlled in terms of indoor temperature.

Investigation of the Thermal Effect of Building Envelopes of Individual Building under Intermittent Heating

The article deals with the engineering technique for determining the heating time of building envelope constructions in the conditions of intermittent heating. The authors present the results of the calculation of the temperature field in the single-layer claydit outer wall, resulting from the exact solution of nonstandard heat conduction problem. To estimate the time of heating and thermal energy consumption for heating the multilayer envelope constructions, an approximate analytical method is developed, the error of which is determined by comparing with the exact solution for a single-layer wall. With the help of this method, it is conducted the analysis of dynamic property and energy consumption for heating of claydit external wall, insulated from the inside with the help of extruded polystyrene foam.

Dynamic Fracture Behavior of the Martensitic High Strength Steel after Spot Welding

Martensitic low alloy high strength steel grade of 22MnB5, used for automotive safety parts constructions, was subject of the experimental analyses of dynamic fracture behavior. The main purpose of the performed analyses was the qualitative and quantitative evaluation of heterogeneities due to the standard spot welding technology. The dynamic resistance of heat affected zones compared to uninfluenced martensitic steel were the subject of suggested methodology for the evaluation of dynamic strength; impact tensile tests were performed after simulation of heat affected zone. Dynamic strength, energy consumption and fracture mode were evaluated at different strain rate. This allows to sketch the influence of the strain rate on the weld joint load capacity. Structural and fractography analyses were focused on an induced degradation process in terms of phase transformation and fracture mode.

An Optimization Scheme of Adaptive Dynamic Energy Consumption Based on Joint Network-Channel Coding in Wireless Sensor Networks

Energy efficiency is one of the most important factors in wireless sensor networks (WSNs). Recent research results have demonstrated that the previously suggested scheme of the joint network-channel coding (JNCC) can greatly improve the efficiency and the communications reliability of WSNs. In this paper, the effect of the number of network coding packets on the energy consumption with the JNCC model was analyzed. An adaptive dynamic energy consumption (ADEC) optimization scheme was proposed. With this scheme, the number of the packets with network coding can be dynamically adjusted based on the feedback of the bit-error rate (BER) to minimize the total energy consumption. In this scheme, the BER threshold can be estimated. Simulation results show that the proposed ADEC optimization scheme achieves an excellent energy efficiency, especially in the low SNR region.

A fault detection method for heat loss in a tyre vulcanization workshop using a dynamic energy consumption model and predictive baselines

In a tyre vulcanization workshop (TVWS), the faults of steam traps and insulating layers usually lead to great heat loss and significantly lower energy efficiency. These faults tend to be difficult to detect in practice, and hence often got ignored. This paper presents a fault detection method for heat loss at a workshop level. A dynamic and hierarchical energy consumption model (DHECM) for a TVWS is proposed to establish the expected energy consumption of the vulcanizing process from thermodynamic theory. This model allows the separation of the heat loss and the technical energy consumption from the actual energy usage. The LMBP algorithm and energy consumption factors are adopted to estimate the baselines for the heat loss and help detect the faults. This method is validated in a large TVWS in Guangzhou China. Test results show that the heat loss of the TVWS was reasonably evaluated and it accounted for as much as 44.78% of the energy consumption under its regular operations. The heat loss estimation facilitated better detection performance, and helped identify the faults at a low level.

Compiler-Assisted Refresh Minimization for Volatile STT-RAM Cache

Spin-transfer torque RAM (STT-RAM) has been proposed to build on-chip caches because of its attractive features such as high storage density and ultra low leakage power. However, long write latency and high write energy are the two challenges for STT-RAM. Recently, researchers propose to improve the write performance of STT-RAM by relaxing its non-volatility property. To avoid data losses resulting from volatility, refresh schemes have been proposed. However, refresh operations consume additional overhead. In this paper, we propose to significantly reduce the number of refresh operations through re-arranging program data layout at compilation time. An N-refresh scheme is also proposed to further reduce the number of refreshes. Experimental results show that, on average, the proposed methods can reduce the number of refresh operations by 84.2 percent, and reduce the dynamic energy consumption by 38.0 percent for volatile STT-RAM caches while incurring only 4.1 percent performance degradation.

An overview of load management techniques in smart grid

Summary Load management (LM) is supposed to have a vital role in future energy management systems. This article presents an overview and comparison of LM techniques along with related technologies and implementation challenges in smart grid. The article also covers consumer and utility concerns in context of LM to enhance readers' intuition about the topic. Two major categories of LM techniques, incentive-based and dynamic pricing-based schemes, have been discussed and compared. Most commonly used incentive-based direct load control is elaborated in detail. Dynamic pricing-based energy consumption scheduling schemes, featuring peak load reduction and consumers' energy cost minimization at residential level, are also emphasized. Furthermore, the article includes a description of dynamic pricing-based home energy management and associated optimization techniques as well as comparison of the latest schemes. Copyright © 2015 John Wiley & Sons, Ltd.

Heterogeneous Node Deployment Model Based on Area Topology Control in WSNs

Balanced energy consumption in WSNs is the key to maximize network lifetime. After analyzing laws of energy consumption of WSNs, a heterogeneous node deployment model based on area topology control is proposed in this paper. It utilizes predetermined heterogeneous node deployment to balance area energy consumption ratio and the topology control algorithm based on area energy consumption ratio to maintain dynamic equilibrium energy consumption. Simulation results show that our model takes a superior performance on maintaining network coverage, balancing energy consumption, and maximizing network lifetime. Compared with similar WSNs deployment models, our model can be more efficient to prolong network lifetime and has advantages in reducing dependence on deployment accuracy, balancing dynamic energy consumption, and improving lifetime in poor network environment.