Dynamic Power Management Research Articles

Analytical Energy Model Parametrized by Workload, Clock Frequency and Number of Active Cores for Share-Memory High-Performance Computing Applications

Energy consumption is crucial in high-performance computing (HPC), especially to enable the next exascale generation. Hence, modern systems implement various hardware and software features for power management. Nonetheless, due to numerous different implementations, we can always push the limits of software to achieve the most efficient use of our hardware. To be energy efficient, the software relies on dynamic voltage and frequency scaling (DVFS), as well as dynamic power management (DPM). Yet, none have privileged information on the hardware architecture and application behavior, which may lead to energy-inefficient software operation. This study proposes analytical modeling for architecture and application behavior that can be used to estimate energy-optimal software configurations and provide knowledgeable hints to improve DVFS and DPM techniques for single-node HPC applications. Additionally, model parameters, such as the level of parallelism and dynamic power, provide insights into how the modeled application consumes energy, which can be helpful for energy-efficient software development and operation. This novel analytical model takes the number of active cores, the operating frequencies, and the input size as inputs to provide energy consumption estimation. We present the modeling of 13 parallel applications employed to determine energy-optimal configurations for several different input sizes. The results show that up to 70% of energy could be saved in the best scenario compared to the default Linux choice and 14% on average. We also compare the proposed model with standard machine-learning modeling concerning training overhead and accuracy. The results show that our approach generates about 10 times less energy overhead for the same level of accuracy.

Power-Aware Checkpointing for Multicore Embedded Systems

Increasing the number of cores integrated on a single chip offers a great potential for the implementation of fault-tolerant techniques to achieve high reliability in real-time embedded systems. Checkpointing with rollback-recovery is a well-established technique to tolerate transient faults in multicore platforms. To consider the worst-case fault occurrence scenario, checkpointing technique requires to re-execute some parts of the tasks, and that might lead to simultaneous execution of task parts with high power consumptions, which eventually might result in a peak power increase beyond the thermal design power (TDP). Exceeding TDP can elevate on-chip temperatures beyond safe limits, and thereby triggering countermeasures that throttle down the voltage and frequency levels or power gate the cores. Such countermeasures might lead to violating task deadlines and degrading the system&#x0027;s reliability. To avoid such severe scenarios, it is inevitable to consider the impact of applying fault-tolerant techniques on the power consumption and prevent violating the power constraint of the chip, i.e., TDP. This paper presents for the first time, a peak-power-aware checkpointing (PPAC) technique that tolerates a given number of faults, <i>k</i>, while at the same time meets the power constraint in hard real-time embedded systems. To do this, our proposed technique (PPAC) adjusts the timing of the checkpoints, which have lower power consumption than the tasks to the execution time points that have power spikes beyond TDP. Moreover, PPAC exploits the available slack times on the cores to delay the execution of some tasks to avoid the remaining power spikes beyond TDP, which could not be mitigated by solely adjusting checkpoints. To evaluate our technique, we extend the state-of-the-art system-level simulator, gem5, with the state-of-the-art checkpointing module in Linux. Our experimental results show that our proposed technique is able to tolerate a given number of faults without exceeding the timing and power constraints in hard real-time embedded systems. The resulting peak power reduction achieved by our technique compared to state-of-the-art techniques is an average of 23&#x0025;. Moreover, our technique employs the Dynamic Power Management (DPM) during the slack times resulting at runtime in the case of fault-free scenarios, which provides energy savings with an average of 17.28&#x0025; and up to 61.1&#x0025;.

A Lightweight Optimal Scheduling Algorithm for Energy-Efficient and Real-Time Cloud Services

To support ever-chainging user needs such as large storage volumes, web search, and high-performance computing, numerous companies have expanded their systems to cloud computing servers. Cloud environment systems generally consume large amounts of electrical power, leading to tremendously high operational costs. In addition, they require computing infrastructures to run various real-time applications such as financial analysis, cloud gaming, and web-based real-time services. To represent performance guarantees, the negotiated agreements in real-time computing, expressed as deadline (or latency), can be specified by <i>service level agreements</i> of cloud services between users and cloud server providers. Thus, a number of research works have started focusing on reducing the energy consumption and simultaneously satisfying the temporal constraint in a cloud environment. Although we previously proposed an optimal real-time scheduling algorithm for multiprocessors, it is difficult to use it for cloud environments handling a large number of cloud services because of the high computational complexity of <inline-formula> <tex-math notation="LaTeX">$\Omega (N^{3}logN)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> is the number of tasks. Thus, we introduce a real-time task scheduling algorithm for cloud computing servers, which alleviates the computational complexity of <inline-formula> <tex-math notation="LaTeX">$O(N^{2})$ </tex-math></inline-formula> from the complexity of the previous algorithm using a novel flow network-based optimization method. To the best of our knowledge, our scheduling algorithm in a cloud environment, which ensures optimality for real-time tasks and achieves energy savings using dynamic power management simultaneously, is the first in the problem domain. We show that the proposed scheduling algorithm guarantees an optimal schedule for real-time tasks and achieves energy savings simultaneously. Our experimental results show that the proposed algorithm outperforms the latest existing algorithms in terms of both time complexity and energy efficiency.

Dynamic power management modelling of a wireless sensor node

Dynamic power management modelling of a wireless sensor node

Energy-Saving Techniques for Urban Noise WSN With Kalman-Based State Estimation and Green Facade Energy Harvester

In this article, a Kalman framework is proposed for dynamic energy-saving in wireless sensor networks used to monitor urban noise pollution. The energy-saving framework implements a dynamic power management strategy (DPMS) with the Kalman algorithm that varies the sensor node&#x2019;s sleep period according to the measured noise levels. An Internet-of-Things (IoT) edge-based self-sustaining long-range (LoRa) network is developed and used for ubiquitous monitoring and analysis of urban noise pollution. The network consists of a star topology with six LoRa battery-free wireless sensing nodes deployed in M&#x00E9;rida city downtown, each node powered by a green facade structure consisting of an array of plant-microbial fuel cells (P-MFC). The sensor node&#x2019;s prototype was implemented following Mexican regulations, transmitting the data packets with the open frequency band of 915 MHz, and monitoring the LoRa network from a web page. Experimental results prove a sustainable operation with a green facade P&#x2013;MFC array power generation of 112.1 mW with an open-circuit voltage of 2.7 V and a short-circuit current of 180 mA. The sensor node&#x2019;s average power consumption is 11.2 mW; therefore, sufficient energy is generated for continuous monitoring. The efficient Kalman DPMS is also tested with the urban noise measurement estimation and adjusting the sleep period only if the urban measurement state estimation is above the threshold normativity. The system&#x2019;s low-power consumption allows to perform 70 continuous LoRa transmissions even when the energy harvester source is absent, and the power capacity of the green facade restores a supercapacitor full charge after only 4 min of a LoRa transmission. A 23.6&#x0025; of energy was saved with Kalman DPMS in comparison with a continuous measurement system of 10-min uniform-sleep period.

Dynamic power management modelling of a wireless sensor node

Dynamic power management modelling of a wireless sensor node

Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) are prone to highly constrained resources, as a result ensuring the proper functioning of the network is a requirement. Therefore, an effective WSN management system has to be integrated for the network efficiency. Our objective is to model, design, and propose a homogeneous WSN hybrid architecture. This work features a dedicated power utilization optimization strategy specifically for WSNs application. It is entitled Hybrid Energy-Efficient Power manager Scheduling (HEEPS). The pillars of this strategy are based on the one hand on time-out Dynamic Power Management (DPM) Intertask and on the other hand on Dynamic Voltage and Frequency Scaling (DVFS). All tasks are scheduled under Global Earliest Deadline First (GEDF) with new scheduling tests to overcome the Dhall effect. To minimize the energy consumption, the HEEPS predicts, defines and models the behavior adapted to each sensor node, as well as the associated energy management mechanism. HEEPS’s performance evaluation and analysis are performed using the STORM simulator. A comparison to the results obtained with the various state of the art approaches is presented. Results show that the power manager proposed effectively schedules tasks to use dynamically the available energy estimated gain up to 50%.

Dynamic Power Management Model for a Wireless Sensor Node

Dynamic power management (DPM) is an efficient technique to design low-power and energy-efficient nodes for wireless sensor networks. This article demonstrates the stochastic behaviour of an input event arrival which is modelled with first-in first-out (FIFO) queue and a single server. An event-driven sensor node is developed based on semi-Markov model. The article investigates the factors affecting the performance of the individual sensor node with detailed analysis considering power consumption and lifetime to be the performance metrics under study. The results demonstrate the impact of the change in event arrival and the probability of change detection on the performance of the node. It is observed that (i) the number of generated events increases with the change in the average value of the distribution which affects the service time in turn resulting in a variation of the server utilization, and that (ii) the increase in the detection probability increases the power consumption decreasing the lifetime of the node.

Energy-aware fixed priority scheduling with shared resources in standby-sparing systems

The problem of joint energy and reliability management for fixed priority periodic real-time tasks set with shared resources on a standby-sparing system is studied in this paper. A novel algorithm called FPMPSA is proposed. It considers frequency transition and processor state transition overheads imposed by dynamic voltage frequency scaling (DVFS) and dynamic power management (DPM). In addition, it applies the rate monotonic with dual priority scheduling policy (RM/DPP) to ensure that shared resources can be accessed in a mutual exclusive manner. Moreover, the mixed mapping method is applied to map the main task and backup task. Furthermore, the energy efficient speed of the main task SM is determined and the backup task executes with the maximum processor speed. For energy efficiency, the algorithm called DFPMPSA which is an extension of FPMPSA is proposed. It can use the dynamic slack time to reduce energy consumption. The experimental results show that DFPMPSA algorithm consumes average 8.15% less energy than that of FPMPSA.

HEART: H ybrid Memory and E nergy- A ware R eal- T ime Scheduling for Multi-Processor Systems

Dynamic power management (DPM) reduces the power consumption of a computing system when it idles, by switching the system into a low power state for hibernation. When all processors in the system share the same component, e.g., a shared memory, powering off this component during hibernation is only possible when all processors idle at the same time. For a real-time system, the schedulability property has to be guaranteed on every processor, especially if idle intervals are considered to be actively introduced. In this work, we consider real-time systems with hybrid shared-memory architectures, which consist of shared volatile memory (VM) and non-volatile memory (NVM). Energy-efficient execution is achieved by applying DPM to turn off all memories during the hibernation mode. Towards this, we first explore the hybrid memory architectures and suggest a task model, which features configurable hibernation overheads. We propose a multi-processor procrastination algorithm (HEART), based on partitioned earliest-deadline-first (pEDF) scheduling. Our algorithm facilitates reducing the energy consumption by actively enlarging the hibernation time. It enforces all processors to idle simultaneously without violating the schedulability condition, such that the system can enter the hibernation state, where shared memories are turned off. Throughout extensive evaluation of HEART, we demonstrate (1) the increase in potential hibernation time, respectively the decrease in energy consumption, and (2) that our algorithm is not only more general but also has better performance than the state of the art with respect to energy efficiency in most cases.

An efficient dynamic power management model for a stand-alone DC Microgrid using CPIHC technique

The power generation using solar photovoltaic (PV) system in microgrid requires energy storage system due to their dilute and intermittent nature. The system requires efficient control techniques to ensure the reliable operation of the microgrid. This work presents dynamic power management using a decentralized approach. The control techniques in microgrid including droop controllers in cascade with proportional-integral (PI) controllers for voltage stability and power balance have few limitations. PI controllers alone will not ensure microgrid’s stability. Their parameters cannot be optimized for varying demand and have a slow transient response which increases the settling time. The droop controllers have lower efficiency. The load power variation and steady-state voltage error make the droop control ineffective. This paper presents a control scheme for dynamic power management by incorporating the combined PI and hysteresis controller (CPIHC) technique. The system becomes robust, performs well under varying demand conditions, and shows a faster dynamic response. The proposed DC microgrid has solar PV as an energy source, a lead-acid battery as the energy storage system, constant and dynamic loads. The simulation results show the proposed CPIHC technique efficiently manages the dynamic power, regulates DC link voltage and battery’s state of charge (SoC) compared to conventional combined PI and droop controller (CPIDC).

Counterexample Generation for Probabilistic Model Checking Micro-Scale Cyber-Physical Systems.

Micro-scale Cyber-Physical Systems (MCPSs) can be automatically and formally estimated by probabilistic model checking, on the level of system model MDPs (Markov Decision Processes) against desired requirements in PCTL (Probabilistic Computation Tree Logic). The counterexamples in probabilistic model checking are witnesses of requirements violation, which can provide the meaningful information for debugging, control, and synthesis of MCPSs. Solving the smallest counterexample for probabilistic model checking MDP has been proven to be an NPC (Non-deterministic Polynomial complete) problem. Although some heuristic methods are designed for this, it is usually difficult to fix the heuristic functions. In this paper, the Genetic algorithm optimized with heuristic, i.e., the heuristic Genetic algorithm, is firstly proposed to generate a counterexample for the probabilistic model checking MDP model of MCPSs. The diagnostic subgraph serves as a compact counterexample, and diagnostic paths of MDP constitute an AND/OR tree for constructing a diagnostic subgraph. Indirect path coding of the Genetic algorithm is used to extend the search range of the state space, and a heuristic crossover operator is used to generate more effective diagnostic paths. A prototype tool based on the probabilistic model checker PAT is developed, and some cases (dynamic power management and some communication protocols) are used to illustrate its feasibility and efficiency.

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.

Microsecond Enhanced Indirect Model Predictive Control for Dynamic Power Management in MMC Units

The multi-modular converter (MMC) technology is becoming the preferred option for the increased deployment of variable renewable energy sources (RES) into electrical power systems. MMC is known for its reliability and modularity. The fast adjustment of the MMC’s active/reactive powers, within a few milliseconds, constitutes a major research challenge. The solution to this challenge will allow accelerated integration of RES, without creating undesirable stability issues in the future power system. This paper presents a variant of model predictive control (MPC) for the grid-connected MMC. MPC is defined using a Laguerre function to reduce the computational burden. This is achieved by reducing the number of parameters of the MMC cost function. The feasibility and effectiveness of the proposed MPC is verified in the real-time digital simulations. Additionally, in this paper, a comparison between an accurate mathematical and real-time simulation (RSCAD) model of an MMC is given. The comparison is done on the level of small-signal disturbance and a Mean Absolute Error (MAE). In the MMC, active and reactive power controls, AC voltage control, output current control, and circulating current controls are implemented, both using PI and MPC controllers. The MPC’s performance is tested by the small and large disturbance in active and reactive powers, both in an offline and online simulation. In addition, a sensitivity study is performed for different variables of MPC in the offline simulation. Results obtained in the simulations show good correspondence between mathematical and real-time analytical models during the transient and steady-state conditions with low MAE. The results also indicate the superiority of the proposed MPC with the stable and fast active/reactive power support in real-time simulation.

Energy Aware Resource Utilization Technique for Workflow Scheduling in Cloud Computing Environment

Heterogeneous multicore computational environment are increasingly being used for executing scientific workload. Heterogeneous computational framework aid is reducing energy dissipation for executing real-time data intensive workload by employing Dynamic Power Management (DPM) and Dynamic Voltage and Frequency Scaling (DVFS). However, reducing energy and improving performance is becoming major constraint in modelling workload scheduling model in heterogeneous computational environment. Recently, number of multi-objective based workload scheduling aimed at minimizing power budget and meeting task deadline constraint. However, these model induce significant overhead when demand and number of processing core increases. For addressing research problem this work assume that different task will have different execution path, I/O access, memory, active processing, and cache requirement. Thus, this paper present Energy Aware Resource Utilization (EARU) model by minimizing energy dissipation and utilizing cache resource more efficiently. The EARU model achieves much lesser execution time, energy consumption, and power consumption when compared with existing multi-objective based and DVFS-based workload scheduling algorithm.

Methodology for multi-objective optimization of wind turbine/battery/electrolyzer system for decentralized clean hydrogen production using an adapted power management strategy for low wind speed conditions

In this study, the utilization of renewable energy sources (RESs) like wind energy for clean hydrogen production via water electrolysis in small-scale decentralized plant is addressed. This paper proposes an approach for multi-objective optimization of wind-hydrogen production system (WHPS) while satisfying multiple technical constraints based on a dynamic power and hydrogen management strategy (PHMS). WHPS consists of wind turbines (WTs), water electrolyzer, battery bank, power converters and hydrogen tank. Multi-objective optimization consists of minimizing the total hydrogen deficit (THD), energy dump possibility (EDP), levelized cost of hydrogen (LCOH) as well as CO2 emission avoided (CO2EA) and natural gas preserved (NGP). Battery assistance for electrolyzer is enhanced through a dynamic PHMS adapted to low wind speed conditions. PHMS based multi-objective optimization algorithm is developed to find Pareto-optimal solutions. The optimization results reveal that the adequate design of PHMS plays a crucial role in minimizing THD, LCOH and EDP, while ignoring the EDP objective may reduce potentially LCOH. Under the selected base case conditions, small-scale hydrogen production via WHPS consisting of 250 kW alkaline-electrolyzer, 857.5 kW WTs, 719 kW h battery bank and 2022 kg hydrogen tank, for supplying hydrogen demand at full reliability (THD = 0%), results in LCOH of 33.70 $/kg, CO2EA of 87.75 ton/year, NGP of 28.96 ton/year and EDP of 9.96%.

Stochastic-Reinforcement Learning Assisted Dynamic Power Management Model for Zone-Routing Protocol in Mobile Ad Hoc Networks

The Zone Routing Protocol of Mobile Ad Hoc Networks is one of the most reliable and efficient routing protocols. However, maintaining Quality of Service, energy-efficiency and optimal resource management is of utmost importance to provide timely and reliable communication services. In this paper, a highly robust and efficient reinforcement learning based Dynamic Power Management (DPM) and Switching control strategy is developed. Unlike classical DPM models, our proposed model employs both system layer information and PHY layer information to perform stochastic prediction to schedule PHY switching. Here, we have applied both known and unknown node/network parameters such as node’s holding period, Bit Error Probability to perform stochastic prediction. Our proposed model intends to maintain minimum BEP and holding period while assuring maximum resource utilization. To achieve it, the overall DPM model is formulated as Controlled Markov decision process, where employing hidden Markov model with Lagrange relaxation and cost function we achieved optimal resource allocation without compromising transmission quality, latency or computational costs. Through simulation-based evaluations, the proposed model outperforms the classical learning models by 50% reduction in PHY Transmission Action, 94% lower cost consumption, 83% decrease in buffer cost/delay and 94% reduction on packet overflow.

Dynamic power management under the RUN scheduling algorithm: a slack filling approach

Effective energy-aware scheduling is paramount for current and future multiprocessor real-time systems, being Dynamic Power Management (DPM) one of the employed techniques. In this paper we extend the Reduction to Uniprocessor (RUN) algorithm making it DPM-compliant. RUN is an optimal multiprocessor real-time scheduling for periodic implicit-deadline tasks and it is known to generate low overhead in terms of preemptions and migrations. It is based on an off-line reduction of the target multiprocessor system into one or more uniprocessor systems. On-line scheduling decisions for the latter is then efficiently translated back to the original system. The developed approach in this paper, called Dynamic Slack Filling (DSF-RUN), extends RUN in two aspects. First, the RUN reduction process is adapted to take into account slack entities, properly defined to represent processor spare capacity for DPM management. Second, on-line scheduling rules of RUN are modified so as to generate long time periods in the schedule by dynamically inserting slacks. Our approach has the same off-line complexity of the original RUN algorithm and takes either quadratic or linear on-line complexity in the number of tasks, depending on the strategy used to compute the maximum allowed idle periods. Results obtained with simulations show that our approach consumes less than 10% more energy than an idealized optimum DPM strategy.

Reliable Islanded Microgrid Operation Using Dynamic Optimal Power Management

The tradeoff between the operating cost and the reliability of microgrids imposes challenges of having cost-effective power dispatch and reliable operations. The work presented in this article optimally solves this issue in a microgrid that contains photovoltaic (PV), dispatchable generator, and storage battery. Flexible control algorithms are developed to enable high penetration of PV power in islanded microgrids such that the challenge of having a cost-effective and reliable operation can be mitigated. This is achieved by creating a power flow optimization framework that considers all variables associated with the system operation. Particle swarm optimization is utilized to effectively optimize the power flow among the integrated sources and to dynamically schedule the battery state of charge. The proposed controller of each source works to fulfill the requirements of the optimization. The energy index of reliability is employed as a direct measure to quantify the reliability level of the microgrid. The effectiveness of the proposed approach is demonstrated by simulations and validated by experimental studies.

A Dynamic Power Management and Dedicated Control Strategy of a Flexible Multi-Terminal HVDC Grids for Offshore Wind Farms

Renewable energy systems are becoming more and more popular on the energy market and wind power has particularly shown a great potential in the field. Many wind farms able to produce thousands of MW are expected to be placed offshore away from the mainland. To this end, aiming to produce a significant amount of power and achieve a high performance while being economically competitive, appropriate transmission systems ought to be designed. This paper investigated a flexible Multi-terminal Direct current connection (MTDC), based on the voltage source converter (VSC) technology, connecting one or several offshore wind farms (OWFs) to one or different AC grids. The contribution of this article lies in the fact that it succeeded in setting up a balance between the produced power by the offshore wind farms and the received power by the different AC networks of the multi-terminal VSC-HVDC system, while checking grid constraints to prevent blackout. This was achieved by developing a global a supervision system based on a dynamic power flow management and dedicated control in order to fulfil an optimisation of the modulated architecture of the studied MTDC system. The multi-terminal VSC-HVDC system was simulated using MATLAB software and the provided results show the feasibility and effectiveness of the proposed control strategy.