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As real-time systems are safety critical, guaranteeing a high reliability threshold is as important as meeting all deadlines. Periodic tasks are replicated to mitigate the negative impact of transient faults, which leads to redundancy and high energy consumption. On the other hand, energy saving is widely identified as increasingly relevant issues in real-time systems. In this paper, we formalize this challenging tri-criteria optimization problem, i.e., minimizing the expected energy consumption while enforcing the reliability threshold and meeting all task deadlines, and propose several mapping and scheduling heuristics to solve it. Specifically, a novel approach is designed to (i) map an arbitrary number of replicas onto processors, (ii) schedule each replica of each task instance on its assigned processor with less temporal overlap. The platform is composed of processing units with different characteristics, including speed profile, energy cost and fault rate. The heterogeneity of the computing platform makes the problem more complicated, because different mappings achieve different levels of reliability and consume different amounts of energy. Moreover, scheduling plays an important role in energy saving, as the expected energy consumption is the average over all failure scenarios. Once a task replica is successful, the other replicas of that task instance can be canceled, which calls for minimizing the overlap between any replica pair. Finally, to quantitatively analyze our methods, we derive a theoretical lower-bound for the expected energy consumption. Comprehensive experiments are conducted on a large set of execution scenarios and parameters. The comparison results reveal that our strategies perform better than the random baseline under almost all settings, with an average gain in energy consumption of more than 40%, and our best heuristic achieves an excellent performance: its energy saving is only 2% less than the lower-bound on average. [ABSTRACT FROM AUTHOR]

Getting cities and communities smarter means to use Information and Communication Technology (ICT) to transform their "metabolism" in order to offer sustainable services for people. This mission is a strategic but also very complex process that involves worldwide stakeholders (i.e., governments, entities, businesses and citizens), areas and infrastructures (e.g., buildings, homes, offices, urban parks, industrial plants). As part of this process, balancing sustainability and comfort objectives with energy efficiency and cost-saving requirements is a challenge. The Internet of Things (IoT) paradigm can help this process from the collection to the management of large amount of data. Generally, the conventional notion of sustainability is related to three main aspects, i.e., environmental, economic and social. If protection and resource conservation are typically environmental objectives, at the same time prosperity and continuity in urban contexts are two economical goals. Social well-being and equity complete the characterization of sustainability. Following this approach, in this paper we present and discuss the #SmartMe Energy system, implemented at the University of Messina as part of the #SmartMe crowdfunding project. It allows employees to optimize the use of all the electrical equipments. The result is the reduction of energy consumptions and costs also improving sustainability and comfort inside offices, thus becoming "smart offices". [ABSTRACT FROM AUTHOR]

Cloud data centers have become overwhelmed with data-intensive applications due to the limited computational capabilities of mobile terminals. Mobile edge computing is emerging as a potential paradigm to host application execution at the edge of networks to reduce transmission delays. Compute nodes are usually distributed in edge environments, enabling crucially efficient task scheduling among those nodes to achieve reduced processing time. Moreover, it is imperative to conserve edge server energy, enhancing their lifetimes. To this end, this paper proposes a novel task scheduling algorithm named Energy-aware Double-fitness Particle Swarm Optimization (EA-DFPSO) that is based on an improved particle swarm optimization algorithm for achieving energy efficiency in an edge computing environment along with minimal task execution time. The proposed EA-DFPSO algorithm applies a dual fitness function to search for an optimal tasks-scheduling scheme for saving edge server energy while maintaining service quality for tasks. Extensive experimentation demonstrates that our proposed EA-DFPSO algorithm outperforms the existing traditional scheduling algorithms to achieve reduced task completion time and conserve energy in an edge computing environment.

The most common task of GPUs is to render images in real time. When rendering a 3D scene, a key step is to determine which parts of every object are visible in the final image. There are different approaches to solve the visibility problem, the Z-Test being the most common. A main factor that significantly penalizes the energy efficiency of a GPU, especially in the mobile arena, is the so-called overdraw, which happens when a portion of an object is shaded and rendered but finally occluded by another object. This useless work results in a waste of energy; however, a conventional Z-Test only avoids a fraction of it. In this article we present a novel microarchitectural technique, the Omega-Test, to drastically reduce the overdraw on a Tile-Based Rendering (TBR) architecture. Graphics applications have a great degree of inter-frame coherence, which makes the output of a frame very similar to the previous one. The proposed approach leverages the frame-to-frame coherence by using the resulting information of the Z-Test for a tile (a buffer containing all the calculated pixel depths for a tile), which is discarded by nowadays GPUs, to predict the visibility of the same tile in the next frame. As a result, the Omega-Test early identifies occluded parts of the scene and avoids the rendering of non-visible surfaces eliminating costly computations and off-chip memory accesses. Our experimental evaluation shows average EDP savings in the overall GPU/Memory system of 26.4 percent and an average speedup of 16.3 percent for the evaluated benchmarks. [ABSTRACT FROM AUTHOR]

In this article, we present an Online Learning Artificial Neural Network (ANN) model that is able to predict the performance of tasks in lower frequency levels and safely optimize real-time embedded systems’ power saving operations. The proposed ANN model is supported by feature selection, which provides the most relevant variables to describe shared resource contention in the selected multicore architecture. The variables are used at runtime to produce a performance trace that encompasses sufficient information for the ANN model to predict the impact of a frequency change on the performance of tasks. A migration heuristic encompassing a weighted activity vector is combined with the ANN model to dynamically adjust frequencies and also to trigger task migrations among cores, enabling further optimization by solving resource contentions and balancing the load among cores. The proposed solution achieved energy-savings of 24.97 percent on average when compared to the run-to-end approach, and it did it without compromising the criticality of any single task. The overhead incurred in terms of execution time was 0.1791 percent on average. Each prediction added 15.3585 $\mu s$ μ s on average and each retraining cycle triggered at frequency adjustments was never larger than 100 $\mu s$ μ s . [ABSTRACT FROM AUTHOR]

This article proposes an analytical method for determining the traffic characteristics of multi-service switching networks in elastic optical networks. The method takes into consideration the limitations of W-S-W (wavelength-space-wavelength) switching networks in the execution of multi-slot connections in line with the concept of elastic optical networks. The results obtained on the basis of the developed analytical model are compared with the results obtained from the simulation experiments for a selected structure of a switching network. The study confirms the high accuracy of the proposed model. The model can be used to optimize and evaluate the effectiveness of blocking optical switching networks.

Single-issue processor cores are very energy efficient but suffer from the von Neumann bottleneck, in that they must explicitly fetch and issue the loads/storse necessary to feed their ALU/FPU. Each instruction spent on moving data is a cycle not spent on computation, limiting ALU/FPU utilization to 33 percent on reductions. We propose “Stream Semantic Registers” to boost utilization and increase energy efficiency. SSR is a lightweight, non-invasive RISC-V ISA extension which implicitly encodes memory accesses as register reads/writes, eliminating a large number of loads/stores. We implement the proposed extension in the RTL of an existing multi-core cluster and synthesize the design for a modern 22 nm technology. Our extension provides a significant, 2x to 5x, architectural speedup across different kernels at a small 11 percent increase in core area. Sequential code runs 3x faster on a single core, and 3x fewer cores are needed in a cluster to achieve the same performance. The utilization increase to almost 100 percent in leads to a 2x energy efficiency improvement in a multi-core cluster. The extension reduces instruction fetches by up to 3.5x and instruction cache power consumption by up to 5.6x. Compilers can automatically map loop nests to SSRs, making the changes transparent to the programmer. [ABSTRACT FROM AUTHOR]

This article describes the development of a software architectural framework for implementing compute-aware control systems, where the term “compute-aware” describes controllers that can modify existing low-level computing platform power managers in response to the needs of the physical system controller. This level of interaction means that high-level decisions can be made as to when to operate the computing platform in a power-savings mode or a high-performance mode in response to situation awareness of the physical system. The framework is demonstrated experimentally on a mobile robot platform. In this example, a situation-aware governor is developed that adjusts the speed of the processor based on the physical performance of the robot as it traverses a path through obstacles. The results show that the situation-aware governor results in overall power savings of up to 38.9 percent with 1.3 percent degradation in performance compared to the static high-power strategy. [ABSTRACT FROM AUTHOR]

We analyze time and energy performance of distributed computations in heterogeneous systems with hierarchical memory. Different levels of memory hierarchy have different time and energy efficiency. Core memory may be too small to hold whole load to be processed, while computations using external storage are expensive in time and energy. In order to avoid the costs of processing the load in the external memory, it is allowed that the load is distributed to the worker processors in multiple installments. A minimum energy solution is found by use of mixed integer linear programming under a limit on schedule length. Two types of fast heuristics with several variants are also examined. The trade-off between the criteria of processing time and energy is studied. Key features of optimum solutions are analyzed. It is shown that holding machines in a diverse set of energy modes and limited use of the out-of-core memory can be beneficial for the time and energy performance. The proposed scheduling algorithms are evaluated in the terms of solution quality and runtimes. • Processing big volumes of data on heterogeneous systems with memory hierarchy considered. • Heuristics and MIP-based scheduling algorithms proposed. • Time–energy performance trade-off has local optima caused by idle machine power usage. • Holding machines in a diverse set of energy modes is advantageous. • Heuristic solutions approx. 3 times worse in quality, but in 4–5 orders of magnitude shorter time. [ABSTRACT FROM AUTHOR]

Smartphones and a growing number of wearable devices are equipped with powerful sensors. This has led to increased interest in developing applications using sensor feeds from such devices to offer services across a wide variety of domains including healthcare, entertainment, environmental monitoring and transportation. However, most of these applications require continuous sensing, which places a heavy demand on the typically limited battery power of devices. This paper presents ShareSens, our approach to opportunistically merge sensing requirements of independent applications. We achieve this using sensing schedulers for sensors, which determine the lowest sensing rate which would satisfy all requests, and then use custom filters to send out only the required data to each application. Applications can request fixed sampling rates or ranges of rates, creating the opportunity for sending them higher rates than minimally required—“for free.” Sensing requests made through our ShareSens API are forwarded to the relevant schedulers, which determine the optimum sensing rates to satisfy all requests, and set up filters to deliver required feeds to the applications. The design and implementation of ShareSens is presented, along with results from our experimental work on the power savings that can be achieved by using it. [ABSTRACT FROM AUTHOR]

This paper presents an application-transparent execution context for mobile operating systems that reflects the criticality of current execution on the user interactivity. We track Android / Linux system-level events including semantic syscalls, UI actions, and standard inter-process and intra-process communication interfaces that signal the initiation and propagation of interactivity-related executions. This interactive context enables new optimizations in CPU scheduling and power state management. We recognize that online resource scheduling is susceptible to inaccurate contexts—e.g., misidentifying an interactive execution as background leads to possible priority inversion by schedulers. In particular, low-level OS events such as kernel block / wake-up carry ambiguous semantics that could induce false context propagations. We selectively expose such uncertainties to the schedulers so that executions with uncertain contexts are prioritized behind interactive executions but otherwise run as fast as possible to minimize the priority inversion. We further recognize the importance of prompt propagation of the interactivity context at the earliest possible moment to facilitate immediate prioritization. We utilize the interactivity context to enable differential per-core frequency control and background workload consolidation. Experiments on a quad-core smartphone with a dozen mobile workload scenarios show that we can, on average, save 13 percent energy with only 1 percent degradation on the interactive performance. [ABSTRACT FROM AUTHOR]

Cloud datacentres are acknowledged as being massive energy consumers, which may have significant environment impacts. Service providers have an ethical responsibility to reduce the environmental impact of server resources and a simultaneous and complementary commercial desire to reduce energy costs. Zombie servers in the datacenters are one of the primary sources of undesirable energy expenditures by incurring idle resources during task execution. This paper investigates the cause, impact and energy-related implications of zombie servers. Important outcomes of this paper are the characterization of the diversity among the workload behaviors in resource consumption and the quantification of the presence of idle CPU and memory resources during task execution causing server zombieness. The undesirable power consumption of zombie servers is determined based on the profiles of currently available servers and their corresponding environmental implications are illustrated in this paper. Empirical analysis shows that cloud workloads are highly heterogeneous in resource consumption pattern and CPU resources may display 75.6% of idleness relative to their allocated level, while memory is 25.5% idle. The report concludes that significant reductions in power consumption and CO2 emission can be achieved by provisioning a realistic level of resources to servers, which are scaled to suit the anticipated workloads.

Managing the power consumption of display-related components in mobile devices is difficult because of performance degradation. Therefore, eliminating hidden workloads, such as redundant frames, is preferable, as it directly reduces power without affecting the user experience. Our preliminary study shows that the default launcher of the Android Open Source Project (AOSP) and popular applications, such as Instagram and Pinterest, generate redundant frames. In this paper, we propose a scheme to optimize the power consumption of the smartphone's display-related components by preventing redundant frames generation. By analyzing the frame-generation process, we observe that redundant frame generation is possible in the current Android framework. We then propose a scheme that recognizes and prevents redundant frame generation before actual frame generation (i.e., frame rendering in the GPU). The proposed scheme utilizes a display list, which was introduced in recent Android smartphones for efficient frame generation. We implemented the proposed scheme on Nexus smartphones. On the Nexus 5, the proposed solution reduced the energy of the AOSP default launcher, Instagram, and Pinterest by 40, 35.4, and 39.6 percent, respectively. Furthermore, the experimental results with a general usage scenario showed that our scheme prevented about 35 percent of redundant frame generation with a false-positive rate of 1.8 percent. [ABSTRACT FROM AUTHOR]

The use of wireless sensor networks (WSNs) has increased rapidly over the last years. Due to their low resources, sensors come along with new issues regarding network security and energy consumption. Focusing on the network availability, previous studies proposed to protect clustered network against denial of service attacks with the use of traffic monitoring agents on some nodes. Those control nodes have to analyze the traffic inside a cluster and to send warnings to the cluster head whenever an abnormal behavior (e.g., high packets throughput or non-retransmission of packets) is detected. But if the control nodes ( cNodes) die out of exhaustion, they leave the network unprotected. To better fight against attacks, we try to enhance this solution by renewing periodically the election process. Furthermore, we propose three energy-aware and secure methods to designate the cNodes in a hierarchically clustered WSN. The first one is a simple self-election process where nodes randomly designate themselves. It leads to a better load balancing than a static method (i.e., with no renewal), but we argue that we can obtain better results by considering the remaining energy of the nodes at cNodes selection time. Hence, the second algorithm is purely based on the residual energy of the sensors. We discuss limitations of this deterministic process concerning security and cluster coverage and suggest workarounds. These improvements lead us to the third mechanism. It is based on residual energy too, but it includes a democratic election process in which nodes in the cluster vote to optimize the cNode role attribution. Results obtained from simulation experiments with the ns-2 tool are provided to analyze the energy repartition in the network and to compare the three selection algorithms. All experimental outcomes show improvements of the load balancing in the network, while maintaining good detection coverage, in regard to static selection. Furthermore, the analysis of the respective performances of the three mechanisms is used as a basis to establish recommendations regarding the use cases of those methods. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The power modeling of mobile application processors (APs) is a challenging task due to their complexity. The existing power models and their associated devices have mostly been made obsolete by recent hardware developments. In this paper, we propose an enhanced power model used in modern mobile devices. The model accurately estimates the power consumption of AP component and utilizes the runtime usage information of each hardware component. We evaluated the model accuracy using various benchmarks, as well as popular smartphone applications with multiple devices that employ different APs. The evaluation shows that our model achieves the mean absolute percentage error (MAPE) of 5.1%. [ABSTRACT FROM AUTHOR]

This paper considers secure energy-efficient routing in the presence of multiple passive eavesdroppers. Previous work in this area has considered secure routing assuming probabilistic or exact knowledge of the location and channel-state-information (CSI) of each eavesdropper. In wireless networks, however, the locations and CSIs of passive eavesdroppers are not known, making it challenging to guarantee secrecy for any routing algorithm. We develop an efficient (in terms of energy consumption and computational complexity) routing algorithm that does not rely on any information about the locations and CSIs of the eavesdroppers. Our algorithm guarantees secrecy even in disadvantaged wireless environments, where multiple eavesdroppers try to eavesdrop each message, are equipped with directional antennas, or can get arbitrarily close to the transmitter. The key is to employ additive random jamming to exploit inherent non-idealities of the eavesdropper’s receiver, which makes the eavesdroppers incapable of recording the messages. We have simulated our proposed algorithm and compared it with the existing secrecy routing algorithms in both single-hop and multi-hop networks. Our results indicate that when the uncertainty in the locations of eavesdroppers is high and/or in disadvantaged wireless environments, our algorithm outperforms existing algorithms in terms of energy consumption and secrecy. [ABSTRACT FROM AUTHOR]

On emerging multicore systems, task replication is a powerful way to achieve high reliability targets. In this paper, we consider the problem of achieving a given reliability target for a set of periodic real-time tasks running on a multicore system with minimum energy consumption. Our framework explicitly takes into account the coverage factor of the fault detection techniques and the negative impact of Dynamic Voltage Scaling (DVS) on the rate of transient faults leading to soft errors. We characterize the subtle interplay between the processing frequency, replication level, reliability, fault coverage, and energy consumption on DVS-enabled multicore systems. We first develop static solutions and then propose dynamic adaptation schemes in order to reduce the concurrent execution of the replicas of a given task and to take advantage of early completions. Our simulation results indicate that through our algorithms, a very broad spectrum of reliability targets can be achieved with minimum energy consumption thanks to the judicious task replication and frequency assignment. [ABSTRACT FROM PUBLISHER]

Nowadays, a significant part of all network accesses comes from embedded and battery-powered devices, which must be energy efficient. This paper demonstrates that a hardware (HW) implementation of network security algorithms can significantly reduce their energy consumption compared to an equivalent software (SW) version. The paper has four main contributions: (i) a new feature extraction algorithm, with low processing demands and suitable for hardware implementation; (ii) a feature selection method with two objectives—accuracy and energy consumption; (iii) detailed energy measurements of the feature extraction engine and three machine learning (ML) classifiers implemented in SW and HW—Decision Tree (DT), Naive-Bayes (NB), and k-Nearest Neighbors (kNN); and (iv) a detailed analysis of the tradeoffs in implementing the feature extractor and ML classifiers in SW and HW. The new feature extractor demands significantly less computational power, memory, and energy. Its SW implementation consumes only 22 percent of the energy used by a commercial product and its HW implementation only 12 percent. The dual-objective feature selection enabled an energy saving of up to 93 percent. Comparing the most energy-efficient SW implementation (new extractor and DT classifier) with an equivalent HW implementation, the HW version consumes only 5.7 percent of the energy used by the SW version. [ABSTRACT FROM PUBLISHER]

In a design based on the Charm++ parallel programming framework, an adaptive runtime system dynamically interacts with a datacenter's resource manager to control power by intelligently scheduling jobs, reallocating resources, and reconfiguring hardware. It simultaneously manages reliability by cooling the system to the running application's optimal level and maintains performance through load balancing. [ABSTRACT FROM PUBLISHER]

The effectiveness and the simple implementation of physical layer jammers make them an essential threat for wireless networks. In a multihop wireless network, where jammers can interfere with the transmission of user messages at intermediate nodes along the path, one can employ jamming oblivious routing and then employ physical-layer techniques (e.g., spread spectrum) to suppress jamming. However, whereas these approaches can provide significant gains, the residual jamming can still severely limit system performance. This motivates the consideration of routing approaches that account for the differences in the jamming environment between different paths. First, we take a straightforward approach where an equal outage probability is allocated to each link along a path and develop a minimum energy routing solution. Next, we demonstrate the shortcomings of this approach and then consider the joint problem of outage allocation and routing by employing an approximation to the link outage probability. This yields an efficient and effective routing algorithm that only requires knowledge of the measured jamming at each node. Numerical results demonstrate that the amount of energy saved by the proposed methods with respect to a standard minimum energy routing algorithm, especially for parameters appropriate for terrestrial wireless networks, is substantial. [ABSTRACT FROM PUBLISHER]

Power budgeting is an essential aspect of networks-on-chip (NoC) to meet the power constraint for on-chip communications while assuring the best possible overall system performance. For simplicity and ease of implementation, existing NoC power budgeting schemes treat all the individual routers uniformly when allocating power to them. However, such homogeneous power budgeting schemes ignore the fact that the workloads of different NoC routers may vary significantly, and thus may provide excess power to routers with low workloads, whereas insufficient power to those with high workloads. In this paper, we formulate the NoC power budgeting problem in order to optimize the network performance over a power budget through per-router frequency scaling. We take into account of heterogeneous workloads across different routers as imposed by variations in traffic. Correspondingly, we propose a fine-grained solution using an agile algorithm with low time complexity. Frequency of each router is set individually according to its contribution to the average network latency while meeting the power budget. Experimental results have confirmed that with fairly low runtime and hardware overhead, the proposed scheme can help save up to 50 percent application execution time when compared with the latest proposed methods. [ABSTRACT FROM PUBLISHER]

Despite numerous studies to reduce the power consumption of the display-related components of mobile devices, previous works have led to a deterioration in user experience due to compromised graphic quality. In this paper, we propose an effective scheme to reduce the energy consumption of the display subsystems of mobile devices without compromising user experience. In preliminary experiments, we noticed that mobile devices typically perform redundant display updates even if the display content does not change. Based on this observation, we first propose a metric called the content rate, which is defined as the number of meaningful frame changes in a second. Our scheme then estimates an optimal refresh rate based on the content rate in order to eliminate redundant display updates. Also proposed is the flicker compensation technique, which prevents the flickering problem caused by the reduced refresh rate. Extensive experiments conducted on the latest smartphones demonstrated that our system effectively reduces the overall power consumption of mobile devices by 35 percent while simultaneously maintaining satisfactory display quality. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel censoring policy for energy-efficient transmissions in energy-harvesting sensors. The problem is formulated as an infinite-horizon Markov Decision Process (MDP). The objective to be optimized is the expected sum of the importance (utility) of all transmitted messages. Assuming that such importance can be evaluated at the transmitting node, we show that, under certain conditions on the battery model, the optimal censoring policy is a threshold function on the importance value. Specifically, messages are transmitted only if their importance is above a threshold whose value depends on the battery level. Exploiting this property, we propose a model-based stochastic scheme that approximates the optimal solution, with less computational complexity and faster convergence speed than a conventional Q-learning algorithm. Numerical experiments in single-hop and multi-hop networks confirm the analytical advantages of the proposed scheme. [ABSTRACT FROM AUTHOR]

It is well known that the cost of executing the sequential portion of a program will limit and sometimes even eclipse the gains brought by processing in parallel the rest of the program. This means that serious consideration should be brought to bear on accelerating the execution of this unavoidable sequential part. Such acceleration can be done by boosting the operating frequency in a symmetric multicore processor. In this paper, we derive a performance and power model to describe the implications of this approach. From our model, we show that the ratio of performance over energy during the sequential part improves with an increase in the number of cores. In addition, we demonstrate how to determine with the proposed model the optimal frequency boosting ratio which maximizes energy efficiency. [ABSTRACT FROM PUBLISHER]

The problem of cooperative data censoring in battery-powered multihop sensor networks is analyzed in this paper. We are interested in scenarios where nodes generate messages (which are related to the sensor measurements) that can be graded with some importance value. Less important messages can be censored in order to save energy for later communications. The problem is modeled using a joint Markov Decision Process of the whole network dynamics, and a theoretically optimal censoring policy, which maximizes a long-term reward, is found. Though the optimal censoring rules are computationally prohibitive, our analysis suggests that, under some conditions, they can be approximated by a finite collection of constant-threshold rules. A centralized algorithm for the computation of these thresholds is proposed. The experimental simulations show that cooperative censoring policies are energy-efficient, and outperform other non-cooperative schemes. [ABSTRACT FROM AUTHOR]

Emerging applications of wireless sensor networks mandate extensive in-network information processing and communication while requiring energy efficiency. Dense deployments of wireless nodes and shared wireless channel pose severe interference constraints. Several scheduling schemes in literature propose interference-aware message scheduling with the objective of energy minimization, but the problem of joint scheduling of tasks and messages for energy minimization in interference-aware manner has not been studied. We formulate a Mixed Integer Linear Program (MILP) for the joint scheduling of computation tasks and communication messages in data collection tree based networks. We propose a three phase heuristic which first performs joint scheduling of tasks and messages and then reduces the energy consumption of the network by using the energy saving techniques like Dynamic Voltage Scaling (DVS) for tasks and Dynamic Modulation Scaling (DMS) for messages. These techniques tradeoff energy with latency. However, in dense deployments of WSN with small transmitter receiver distances, DMS does not monotonically reduce the energy consumption. We use this knowledge to efficiently perform slack allocation. We present a Mixed Integer Linear Programming (MILP) formulation to obtain the optimal solution. We evaluate the performance of the proposed algorithm for a variety of scenarios and our results show that the energy savings obtained by the proposed algorithm competes closely with that of the MILP solution. [ABSTRACT FROM AUTHOR]

Power mismatching between supply and demand has emerged as a top issue in modern datacenters that are under-provisioned or powered by intermittent power supplies. Recent proposals are primarily limited to leveraging uninterruptible power supplies (UPS) to handle power mismatching, and therefore lack the capability of efficiently handling the irregular peak power mismatches. In this paper we propose hPower, the first heterogeneous energy buffering strategy that incorporates supercapacitors into existing datacenters to handle power mismatch. Our technique exploits power supply diversity and smart load assignment to provide efficiency-aware and emergency-aware power mismatch management. We show that hPower could improve energy efficiency by 30 percent, extend UPS lifetime by 4.3×, and reduce system downtime by 36 percent. It allows datacenters to adapt themselves to various power supply anomalies, thereby improving operational efficiency and resiliency. [ABSTRACT FROM PUBLISHER]

Interactive data visualization tools for residential energy data are instrumental indicators for analyzing end user behavior. These visualizations can be used as continuous home feedback systems and can be accessed from mobile devices using touch-based applications. Visualizations have to be carefully selected in order for them to partake in the behavioral transformation that end users are encouraged to adopt. In this paper, six energy data visualizations are evaluated in a randomized controlled trial fashion to determine the optimal data visualization tool. Conventional visualizations, namely bar, line, and stacked area, are compared against enhanced charts, namely spiral, heatmap, and stacked bar, in terms of effectiveness, aesthetic, understandability, and three analysis questions. The study is conducted through a questionnaire in a mobile application. The application, created through React Native, is circulated to participants in multiple countries, collecting 133 responses. From the received responses, conventional plots scored higher understandability (by 22.74%), effectiveness (by 13.44%), and aesthetic (by 10.54%) when compared with the enhanced visualizations. On the flipside, enhanced plots generated higher correct analysis questions' responses by 8% compared to the conventional counterparts. From the 133 collected responses, and after applying the unpaired t-test, conventional energy data visualization plots are considered superior in terms of understandability, effectiveness, and aesthetic. • Distributed a questionnaire about energy data visualization built in a mobile app. • Enhanced charts in the energy field have been created. • Comparison between enhanced and conventional energy visualization charts is applied. • Quantitative and qualitative questions asked in the questionnaire. • Received 133 participants' responses from multiple countries. [ABSTRACT FROM AUTHOR]

• A novel system aiming at gas demand response from domestic gas boilers is presented. • A novel methodology to calculate the gas demand baseline is provided. • Neural networks are used to forecast the indoor conditions and the boiler modulation. • Optimization of consumer's welfare from gas supply and market participation. • The presented gas DR system handles efficiently the so-called "rebound effect" This paper presents a novel, fully automated system for the implementation of explicit gas Demand Response in the Gas Balancing Market, with real-time control instructions provided to domestic gas boilers. The Demand Response is applied in both the upward and downward direction, enabling the respective gas consumer to provide both upward and downward Balancing Services to the Gas Transmission System Operator. The system targets at the welfare maximization of domestic gas consumers, i.e., the revenues attained by providing Balancing Services to the Gas Transmission System Operator minus the cost of gas supply, while maintaining the house's indoor temperature within the residents' comfort limits. Real-time data are derived from interconnected commercial thermostats and used by two Recurrent Neural Networks for each house, in order to attain forecasts for the indoor temperature change and the expected boiler's modulation levels for the next future time intervals. Such forecasted condition changes within each house are then considered in an optimization model that results in the optimal instruction signal that must be provided to the boiler. The signal concerns the boiler operation level for the following 5 min duration. A Genetic Algorithm is employed for the optimization problem solution. The whole system is deployed using a containerized software architecture to ensure scalability and full-service availability. The implemented real-world tests exhibit that domestic consumers can increase their profits from both gas consumption minimization and from the provision of gas DR services in real-time. [ABSTRACT FROM AUTHOR]

In the era of platforms hosting multiple applications with variable reliability needs, worst-case platform-wide fault-tolerance decisions are neither optimal nor desirable. As a solution to this problem, designs commonly employ adaptive fault-tolerance strategies that provide each application with the reliability level actually needed. However, in the CGRA domain, the existing schemes either only allow to shift between different levels of modular redundancy (duplication, triplication, etc.) or protect only a particular region of a device (e.g. configuration memory, computation, or data memory). To complement these strategies, we propose private fault-tolerance environments which, in addition to modular redundancy, also provide low cost sub-modular (e.g. residue mod 3) redundancy capable of handling both permanent and temporary faults in configuration memory, computation, communication, and data memory. In addition, we also present adaptive configuration scrubbing techniques which prevent fault accumulation in the configuration memory. Simulation results using a few selected algorithms (FFT, matrix multiplication, and FIR filter) show that the approach proposed is capable of providing flexible protection with energy overhead ranging from 3.125 % to 107 % for different reliability levels. Synthesis results have confirmed that the proposed architecture reduces the area overhead for self-checking (58 %) and fault-tolerant (7.1 %) versions, compared to the state of the art adaptive reliability techniques. [ABSTRACT FROM AUTHOR]

Power dissipation trends are leading high-performance processors to a regime in which all chip elements cannot be operated simultaneously at maximum frequency. Consequently, energy-efficiency will increase even more in importance, and performance must be achieved within strict power budgets. Current designs employ techniques such as dynamic voltage and frequency scaling (DVFS) to provide power-performance tradeoffs for both single and multi-threaded workloads. In power-dominated regimes, processors will be run at or near the minimum voltage. Frequency can be reduced to save power, but there is no scaling strategy for increasing performance with high energy-efficiency if the processor is operating at its maximum frequency (and minimum voltage). In this paper, we evaluate the energy-efficiency of processor composability—dynamically aggregating small energy-efficient physical cores into larger logical processors—as a method of scaling single-threaded performance up and down, comparing composability to the energy-efficiency of voltage and frequency scaling. We measure the power breakdowns of the baseline composable microarchitecture (the TFlex microarchitecture, based on an EDGE ISA) and compare the energy-efficiency and performance to one processor designed for power-efficiency (XScale) and another designed for high-performance (a variant of the Power-4) using normalized power models for as fair a comparison as possible. The study shows that composing multiple dual-issue cores (up to eight) provides performance scaling that is as energy-efficient as frequency scaling in a balanced microarchitecture, and is considerably more efficient than scaling the voltage to achieve additional performance once the maximum frequency at the minimum voltage is attained. [ABSTRACT FROM AUTHOR]

This paper presents a novel framework that includes an inhomogeneous (time-variant) Hidden Markov Model (HMM) and learning from data concepts. The framework either recognizes or estimates user contextual inferences called ‘user states’ within the concept of Human Activity Recognition (HAR) for future context-aware applications. Context-aware applications require continuous data acquisition and interpretation from one or more sensor reading(s). Therefore, device battery lifetimes need to be extended due to the fact that constantly running built-in sensors deplete device batteries rapidly. In this sense, a framework is constructed to fulfill requirements needed by applications and to prolong device battery lifetimes. The ultimate goal of this paper is to present an accurate user state representation model, and to maximize power efficiency while the model operates. Most importantly, this research intends to create and clarify a generic framework to guide the development of future context-aware applications. Moreover, topics such as user profile adaptability and variant sensory sampling operations are examined. The proposed framework is validated by simulations and implemented in a HAR-based application by the smartphone accelerometer. According to the results, the proposed framework shows an increase in power efficiency of 60% for an accuracy range from 75% up to 96%, depending on user profiles. [ABSTRACT FROM AUTHOR]

Increased power dissipation in computing devices has led to a sharp rise in thermal hotspots, creating thermal runaway. To reduce the additional power requirement caused by increased temperature, current approaches apply cooling mechanisms to remove heat or apply management techniques to avoid thermal emergencies by slowing down heat generation. This paper proposes to tackle the heat management problem of computing platforms with a fundamentally new approach — instead of heat removal using cooling mechanisms and heat avoidance using dynamic thermal/power management techniques, this work investigates the mechanisms to recover wasted heat into reusable energy for sustainable computing. [ABSTRACT FROM PUBLISHER]