Dynamic Power Management Algorithm Research Articles

Expected Energy Optimization for Real-Time Multiprocessor SoCs Running Periodic Tasks with Uncertain Execution Time

Energy optimization plays an increasingly critical role in designing an embedded real-time multiprocessor System on Chip (MPSoC). Dynamic Voltage Frequency Scaling (DVFS) and Dynamic Power Management (DPM) are preferable techniques to optimize energy consumption. However, previous DVFS and DPM algorithms were mostly designed for inter-task scheduling, without sufficient exploration on intra-task scheduling for further energy reduction. This paper presents a new intra-task scheduling approach considering the probabilistic distribution of task execution time, and it optimizes the mathematical expectation of power consumption (expected power consumption) for periodic dependent tasks with uncertain execution time running on MPSoCs using DVFS and DPM. The energy-efficient scheduling problem can be formulated by means of mixed integer linear programming (MILP) with the proposed technique. Moreover, we also propose a technique to compress the exploration space by reorganizing the probabilistic profiling information of all tasks. Our experimental results on synthetic and realistic benchmarks show that the proposed approach achieves up to 30 percent energy savings compared with other existing methods.

An Online Learning Methodology for Performance Modeling of Graphics Processors

Approximately 18 percent of the 3.2 million smartphone applications rely on integrated graphics processing units (GPUs) to achieve competitive performance. Graphics performance, typically measured in frames per second, is a strong function of the GPU frequency, which in turn has a significant impact on mobile processor power consumption. Consequently, dynamic power management algorithms have to assess the performance sensitivity to the frequency accurately to choose the operating frequency of the GPU effectively. Since the impact of GPU frequency on performance varies rapidly over time, there is a need for online performance models that can adapt to varying workloads. This paper presents a light-weight adaptive runtime performance model that predicts the frame processing time of graphics workloads at runtime without apriori characterization. We employ this model to estimate the frame time sensitivity to the GPU frequency, i.e., the partial derivative of the frame time with respect to the GPU frequency. The proposed model does not rely on any parameter learned offline. Our experiments on commercial platforms with common GPU benchmarks show that the mean absolute percentage error in frame time and frame time sensitivity prediction are 4.2 and 6.7 percent, respectively.

Algorithmic Optimization of Thermal and Power Management for Heterogeneous Mobile Platforms

State-of-the-art mobile platforms are powered by heterogeneous system-on-chips that integrate multiple CPU cores, a GPU, and many specialized processors. Competitive performance on these platforms comes at the expense of increased power density due to their small form factor. Consequently, the skin temperature, which can degrade the experience, becomes a limiting factor. Since using a fan is not a viable solution for hand-held devices, there is a strong need for dynamic thermal and power management (DTPM) algorithms that can regulate temperature with minimal performance impact. This paper presents a DTPM algorithm, which uses a practical temperature prediction methodology based on system identification. The proposed algorithm dynamically computes a power budget using the predicted temperature. This budget is used to throttle the frequency and number of cores to avoid temperature violations with minimal impact on the system performance. Our experimental measurements on two different octa-core big.LITTLE processors and common Android applications demonstrate that the proposed technique predicts the temperature with less than 5% error across all benchmarks. Using this prediction, the proposed DTPM algorithm successfully regulates the maximum temperature and decreases the temperature violations by one order of magnitude while also reducing the total power consumption on average by 7% compared with the default solution.

Economic Dispatch with Demand Response in Smart Grid: Bargaining Model and Solutions

This paper proposes an economic dispatch strategy for the electricity system with one generation company, multiple utility companies and multiple consumers, which participate in demand response to keep the electricity real-time balance. In the wholesale markets, multiple utility companies will commonly select a reliable agent to negotiate with the generation company on the wholesale price. It is challengeable to find a wholesale price to run the electricity market fairly and effectively. In this study, we use the multiple utility companies’ profits to denote the utility function of the agent and formulate the interaction between the agent and the generation company as a bargaining problem, where the wholesale price was enforced in the bargaining outcome. Then, the Raiffa–Kalai–Smorodinsky bargaining solution (RBS) was utilized to achieve the fair and optimal outcome. In the retail markets, the unfavorable disturbances exist in the power management and price when the consumers participate in the demand response to keep the electricity real-time balance, which motivates us to further consider the dynamic power management algorithm with the additive disturbances, and then obtain the optimal power consumption and optimal retail price. Based on the consumers’ utility maximization, we establish a price regulation model with price feedback in the electricity retail markets, and then use the iterative algorithm to solve the optimal retail price and the consumer’s optimal power consumption. Hence, the input-to-state stability condition with additive electricity measurement disturbance and price disturbance is given. Numerical results demonstrate the effectiveness of the economic dispatch strategy.

Energy-Aware Disk Storage Management : Online Approach with Application in DBMS

Energy consumption has become a first-class optimization goal in design and implementation of data-intensive computing systems. This is particularly true in the design of database management systems (DBMS), which was found to be the major consumer of energy in the software stack of modern data centers. Among all database components, the storage system is one of the most power-hungry elements. In previous work, dynamic power management (DPM) techniques that make real-time decisions to transition the disks to low-power modes are normally used to save energy in storage systems. In this paper, we tackle the limitations of DPM proposals in previous contributions. We introduced a DPM optimization model integrated with model predictive control (MPC) strategy to minimize power consumption of the disk-based storage system while satisfying given performance requirements. It dynamically determines the state of disks and plans for inter-disk data fragment migration to achieve desirable balance between power consumption and query response time. Via analyzing our optimization model to identify structural properties of optimal solutions, we propose a fast-solution heuristic DPM algorithm that can be integrated in large-scale disk storage systems for efficient state configuration and data migration. We evaluate our proposed ideas by running simulations using extensive set of synthetic workloads based on popular TPC benchmarks. Our results show that our solution significantly outperforms the best existing algorithm in both energy savings and response time.

Enhancing DPM Techniques in Outdoor Industrial WSN Applications

The main challenge during designing a new node is reducing the power consumption as much as possible to maximize the lifetime of wireless sensor network (WSN) since most nodes are powered from a finite source of energy, in general nonrechargeable battery. In this paper we propose a solution to minimize the power consumed by the microcontroller (MCU), which is the main processing component in the node. Our energy power optimization solution is based on two techniques: firstly enhance dynamic power management (DPM) policies by reducing MCU consumption during standby mode; secondly, focus on temperature impact on low power consumption. In this context, a microcontroller dynamic power management (MDPM) algorithm is proposed to improve DPM scheme. This algorithm is deployed on a measurement circuit able to calculate the consumption during the different low power modes in real environments conditions and then selects the better one. These two techniques are combined in a novel way to provide an efficient energy solution for wireless sensor networks nodes. Our solution is validated and qualified with STM32F446RE MCU.

Constrained Energy Optimizationin Heterogeneous Platforms UsingGeneralized Scaling Models

Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. We first present models for power consumption, response time and energy consumption of heterogeneous mobile platforms. Then, we use these models to optimize the energy consumption of baseline platforms under response time and temperature constraints with and without introducing new resources. We show that the optimal design choices depend on dynamic power management algorithm, and adding new resources is more energy efficient than scaling existing resources alone.

Online learning of timeout policies for dynamic power management

Dynamic power management (DPM) refers to strategies which selectively change the operational states of a device during runtime to reduce the power consumption based on the past usage pattern, the current workload, and the given performance constraint. The power management problem becomes more challenging when the workload exhibits nonstationary behavior which may degrade the performance of any single or static DPM policy. This article presents a reinforcement learning (RL)-based DPM technique for optimal selection of timeout values in the different device states. Each timeout period determines how long the device will remain in a particular state before the transition decision is taken. The timeout selection is based on workload estimates derived from a Multilayer Artificial Neural Network (ML-ANN) and an objective function given by weighted performance and power parameters. Our DPM approach is further able to adapt the power-performance weights online to meet user-specified power and performance constraints, respectively. We have completely implemented our DPM algorithm on our embedded traffic surveillance platform and performed long-term experiments using real traffic data to demonstrate the effectiveness of the DPM. Our results show that the proposed learning algorithm not only adequately explores the power-performance trade-off with nonstationary workload but can also successfully perform online adjustment of the trade-off parameter in order to meet the user-specified constraint.

Performance Comparison of Two Different Energy Conservation Mac Protocols

In Wireless Sensor Networks (WSN), neglecting the effects of varying channel quality can lead to an unnecessary wastage of precious battery resources. This can result in the rapid depletion of sensor energy and partitioning of the network. Fairness is a critical issue when accessing a shared wireless channel and fair scheduling must be employed to provide proper flow of information in WSN. In this paper, a channel adaptive MAC protocol with a traffic-aware dynamic power management algorithm is developed for efficient packet scheduling and queuing in a sensor network, with time varying characteristics of wireless channel taken into consideration. To avoid buffer overflow and achieve fairness for the poor quality nodes, a Load prediction algorithm is designed. In addition to these, a traffic aware dynamic power management scheme is also designed to minimize energy consumption by continuously turning off the radio interface of unnecessary nodes that are not included in the routing path. By Simulation results, the proposed protocol is seen to achieve high Throughput and fairness while reducing the energy consumption when compared to a traffic adapted sleep/listening MAC protocol

An adaptive hybrid dynamic power management algorithm for mobile devices

We propose a novel power efficient adaptive hybrid dynamic power management (AH-DPM) algorithm. To adapt well to bursty request arrival patterns with self-similarity and a service provider (SP, i.e., hard disk or WLAN NIC, in this paper) with multiple inactive states, the proposed AH-DPM first derives the average idle time of the SP in the bursty (ON) period and non-bursty (OFF) period separately. Then, to achieve better power saving, we use the average idle time in the ON period to adjust the timeout value more precisely and use the average idle time in the OFF period to decide which inactive state the SP should be switched to. Experimental results based on real traces show that, for the hard disk, the average power consumption of the proposed AH-DPM is better than that of the Adaptive Timeout (ATO), Machine Learning (ML), Predictive, Static Timeout (STO), and Stochastic algorithms. In addition, the average response time of the proposed AH-DPM algorithm is still lower than that specified in a typical hard disk specification. As to the WLAN NIC, experimental results show that the average power consumption of the proposed AH-DPM is comparable to that of the Oracle (theoretically optimal), ATO, and Predictive algorithms, and is better than that of the ML, STO, and Stochastic algorithms. However, the average packet transmission delay of the proposed AH-DPM is better than that of the ATO and Predictive algorithms. Therefore, by providing a better tradeoff between average power consumption and average response time (or average packet transmission delay), the proposed AH-DPM algorithm is very feasible for extending the battery lifetime of ever increasing mobile devices that are equipped with hard disks and WLAN NICs.

Guest Editorial: Special Issue on EUC 2007

Embedded and ubiquitous computing is an exciting new paradigm that provides computing and communication services all the time and everywhere. Its systems are now affecting every aspect of our life to the point that they are hidden inside various appliances. This emergence is a natural outcome of research and technological advances in embedded systems, pervasive computing and communications, wireless networks, mobile computing, distributed computing and agent technologies, etc. This special issue has collected best papers presented in the 2007 IFIP International Conference on Embedded and Ubiquitous Computing (EUC 2007), held in Taipei, Taiwan, ROC. These papers present state of the art in the areas of real-time and embedded software, power-aware hardware management, network security, embedded compiler techniques, mobility and security in advanced wireless networks, and data crawling. Best papers in the EUC 2007 conference were invited for submission. All the invited papers had undergone an intensive review process. The Editor-in-Chief agreed with our recommendation of the acceptance of eleven high quality papers. The first four papers focus on software techniques in embedded and ubiquitous systems. The first paper, entitled “Task Scheduling for Context Minimization in Dynamically Reconfigurable Platforms”, by Nei-Chiung Perng et al. describes a real-time approach to reducing the number of FPGA configuration contexts in dynamic reconfigurable systems. The second paper, entitled “Real-Time Embedded Software Design for Mobile and Ubiquitous Systems” by Pao-Ann Hsiung et al., describes a component-based software architecture that supports automatic synthesis and verification, aiming at high software design productivity. The third paper, entitled “SIGMA system: A Multi-OS Environment for Embedded Systems”, by Wataru Kanda et al., introduces a virtualization technique to support multiple operating systems for multi-processor embedded architectures. The fourth paper, entitled “Compiling for Reduced Bit-Width Queue Processors” by Arquimedes Canedo et al., presents a compiler technique for generating compact codes for embedded processors. The next three papers are on hardware management for embedded and ubiquitous computing. The fifth paper in this special issue, entitled “Energy-Efficient Considerations on a Variable-Bitrate PCI-Express Device”, by Jian-Jia Chen et al., presents a dynamic power management (DPM) algorithm for variable-bitrate PCI devices for power saving. The sixth paper, entitled “Design and Synthesis of An Multiprocessor System-on-Chip Architecture for Real-Time Biomedical Signal Processing in Gamma Cameras”, by Kai Sun et al., discusses communication synthesis and areacost reduction for an MPSOC Gamma Camera. The seventh paper, entitled “Variable Length Pattern Matching for Hardware Network Intrusion Detection System”, by Chun Xue et al., presents a high-performance hardware-based network intrusion detection system. The last four papers are related to wireless-network applications. The eighth paper in this special issue, entitled “A Lightweight Authentication Protocol for Low-Cost RFID” by Hung-Yu Chien et al., proposes a lightweight L.-P. Chang (*) Department of Computer Science, National Chiao-Tung University, Hsin-Chu, Taiwan, Republic of China e-mail: lpchang@cs.nctu.edu.tw

DYNAMIC POWER MANAGEMENT IN AN EMBEDDED SYSTEM FOR MULTIPLE SERVICE REQUESTS

Power is increasingly becoming a design constraint for embedded systems. Dynamic Power Management algorithms enable optimal utilization of hardware at runtime. The present work attempts to arrive at an optimal policy to reduce the energy consumption at system level, by selectively placing components into low power states. A new, simple algorithm for power management systems involving multiple requests and services, proposed here, has been obtained from stochastic queuing models. The proposed policy is event driven and based on a Deterministic Markov Nonstationary Policy model (DMNSP). The proposed policy has been tested using a Java-based event driven simulator. The test results show that there is about 23% minimum power saving over the existing schemes with less impact on performance or reliability.

Online strategies for dynamic power management in systems with multiple power-saving states

Online dynamic power management (DPM) strategies refer to strategies that attempt to make power-mode-related decisions based on information available at runtime. In making such decisions, these strategies do not depend upon information of future behavior of the system, or any a priori knowledge of the input characteristics. In this paper, we present online strategies, and evaluate them based on a measure called the competitive ratio that enables a quantitative analysis of the performance of online strategies. All earlier approaches (online or predictive) have been limited to systems with two power-saving states (e.g., idle and shutdown). The only earlier approaches that handled multiple power-saving states were based on stochastic optimization. This paper provides a theoretical basis for the analysis of DPM strategies for systems with multiple power-down states, without resorting to such complex approaches. We show how a relatively simple "online learning" scheme can be used to improve the competitive ratio over deterministic strategies using the notion of "probability-based" online DPM strategies. Experimental results show that the algorithm presented here attains the best competitive ratio in comparison with other known predictive DPM algorithms. The other algorithms that come close to matching its performance in power suffer at least an additional 40% wake-up latency on average. Meanwhile, the algorithms that have comparable latency to our methods use at least 25% more power on average.

Mobile power management for wireless communication networks

For fixed quality-of-service constraints and varying channel interference, how should a mobile node in a wireless network adjust its transmitter power so that energy consumption is minimized? Several transmission schemes are considered, and optimal solutions are obtained for channels with stationary, extraneous interference. A simple dynamic power management algorithm based on these solutions is developed. The algorithm is tested by a series of simulations, including the extraneous-interference case and the more general case where multiple, mutually interfering transmitters operate in a therefore highly responsive interference environment. Power management is compared with conventional power control for models based on FDMA/TDMA and CDMA cellular networks. Results show improved network capacity and stability in addition to substantially improved battery life at the mobile terminals.