Dynamic Power Management Research Articles

Optimization of Energy-Efficient Algorithms for Real-Time Data Administering in Wireless Sensor Networks for Precision Agriculture

Precision agriculture relies heavily on wireless sensor networks (WSNs) to monitor environmental conditions and manage resources efficiently. However, traditional WSN algorithms face significant challenges in energy management, resulting in frequent battery replacements, high maintenance costs, and reduced network reliability. Additionally, issues with high data latency and short network lifetimes hinder real-time decision-making and continuous monitoring. This study aims to address these challenges by developing energy-efficient algorithms that enhance the performance and longevity of WSNs in precision agriculture. The objectives include creating adaptive sampling, dynamic power management, and sleep scheduling algorithms to optimize energy consumption; improving data transmission efficiency and reducing latency through advanced data aggregation techniques; and extending network lifetime with balanced energy load distribution and efficient power usage strategies. The study introduces innovative algorithms that significantly reduce energy consumption and extend network lifetime, contributing to the field with adaptive and dynamic methodologies. Extensive simulations demonstrate that the proposed algorithms achieve a 30% reduction in energy consumption, a 20% decrease in data latency, and a 40% increase in network lifetime compared to traditional methods. Additionally, the packet delivery ratio improved by 10%, and computational overhead was reduced by 30%, highlighting the efficiency and reliability of the proposed solutions. These results are consistent with recent advancements in WSN optimization techniques, such as those utilizing cognitive radio and deep learning, validating the potential of the proposed algorithms for real-world application.

An advanced LVRT controlled DSCC-STATCOM for reactive power compensation

<span lang="EN-US">This article focuses on the control of STATCOM for the improvement of reactive power compensation during grid faults and unbalanced load. The problem encountered with convectional voltage source converter based STATCOM for reactive power management and capacitor voltage balancing during grid faults are analyzed and its operational limitations are studied. To remove the above problems, an adaptive low voltage ride through control technique based modular double star cascaded converter (DSCC) STATCOM is proposed. In this dynamic control, the positive reactive currents and negative reactive currents are separately controlled for reactive power management and a zero (Vo) sequence voltage is inserted for the management of active power. This advanced control method effectively balances the submodule capacitor voltage within its prescribed limits by managing the equal distribution of active power between converter legs and provides dynamic reactive power management during grid faults. The effectiveness of the DSCC-STATCOM, with proposed control method is verified using MATLAB/Simulink software-based simulations under single line to ground fault and unbalanced load.</span>

PoMic: Dynamic Power Management of VM-Microservices in Overcommitted Cloud

PoMic: Dynamic Power Management of VM-Microservices in Overcommitted Cloud

Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for燞omogeneous MPSoCs

Increasing the life span and efficiency of Multiprocessor System on Chip (MPSoC) by reducing power and energy utilization has become a critical chip design challenge for multiprocessor systems. With the advancement of technology, the performance management of central processing unit (CPU) is changing. Power densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip size. When energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor (CMOS) circuits and reduces the speed by 10%–15% because excessive on-chip temperature shortens the chip’s life cycle. In this paper, we address the scheduling & energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling (EA-EDF) based technique for multiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor system-on-chip while lowering energy and power consumption. The selection of core and migration of tasks prevents the system from reaching its maximum energy utilization while effectively using the dynamic power management (DPM) policy. Increase in the execution of tasks the temperature and utilization factor on-chip increases that dissipate more power. The proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple CPUs. The performance of the EA-EDF algorithm was evaluated by an extensive set of experiments, where excellent results were reported when compared to other current techniques, the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%–4.7% on a utilization of 6%, 36% & 46% at 520 & 624 MHz operating frequency when particularly in comparison to other energy-aware methods for MPSoCs. Tasks are running and accurately scheduled to make an energy-efficient processor by controlling and managing the thermal effects on-chip and optimizing the energy consumption of MPSoCs.

An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip (MPSoC) has become an integral chip design issue for multiprocessor systems. The performance measurement of computational systems is changing with the advancement in technology. Due to shrinking and smaller chip size power densities on-chip are increasing rapidly that increasing chip temperature in multi-core embedded technologies. The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor (CMOS) circuits because high on-chip temperature adversely affects the life span of the chip. In this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first (EA-EDF) scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip (SOC). Dynamic power management (DPM) enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task migration. Task migration avoids peak temperature values in the multi-core system. High utilization factor ( on central processing unit (CPU) core consumes more energy and increases the temperature on-chip. Our technique switches the core by migrating such task to a core that has less temperature and is in a low power state. The proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature core. The effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and works. The simulation results show the improvement in performance by optimizing 4.8% on 9%, 16%, 23% and 25% at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can schedule more tasks to make an energy-efficient processor by controlling and managing the energy consumption of MPSoC.

Enhancement of system performance using PeSche scheduling algorithm on multiprocessors

The scheduling techniques have been investigated by the job execution process in a system to maximize multiprocessor utilization. Dynamic Power Management (DPM) and Dynamic Voltage and Frequency Scaling (DVFS) represent two general strategies for lowering energy use. Performance enhanced Scheduling (PeSche) is a proposed scheduling algorithm designed for an optimal solution. CodeBlocks were utilized to run extensive simulations. In terms of computing performance (average waiting time and average turnaround time), the PeSche scheduling algorithm outperformed recently reported scheduling algorithms such as SJF, RR, FCFS, Priority, and SJF-LJF. The PeSche scheduling algorithm yielded better results by assigning priority in terms of energy-time ratio, programming running time, total energy, and total time than existing algorithms. In comparison to Minimum Energy Schedule (MES) and Slack Utilization for Reduced Energy (SURE), PeSche consumed less energy.

Analysis of Power Management for Hard Real-Time Embedded System between Adaptive Power Management Method and Idle Energy Reduction Method

Static and dynamic power management have become crucial in real-world applications of embedded systems development. While many technologies and algorithms can reduce power consumption efficiently, like dynamic voltage and frequency scaling, varying voltage and frequency make overall power consumption less efficient. The comprehensive embedded system spends time and energy on the transition between states, voltage, and frequency. To better understand how these problems can be addressed, this paper will explore two methods, the adaptive power management method and the idle state energy reduction method, to mitigate issues on the algorithm side. Furthermore, this paper is intended to give researchers and real-time embedded systems developers an overview of power management methods and to help them better develop the embedded systems.

Power control management system model using wireless sensor network

Several researchers would study the development of controlling and centralized management approaches in response to improvements in wireless communication, microsystems, microcontrollers, deeply integrated computers, deeply integrated devices, and the expanding demand for more productive controlled electric equipment. The objective is to develop a computerized device that employs Wireless Sensor Networks (WSN) and Dynamic Power Management (DPM) to supervise and troubleshoot the electrical system. The Intelligent Sensor Modules (ISM) and isolated data automation systems, that are in charge of Signal Detection, Computing, and Transmission, have been deployed on two network architectures (SDCT). The data collection devices must be accepted by the Remote Servers (RS) design, and the monitoring inverter serves as the wired connection's gateway. A DPM approach is used by the sensor networks to extend the WSN's lifespan. By simply extending the sleep duration, which could be modified according to the application operating on the sensor network, the life of the node can be easily increased.

Cloud Data Centre Energy Utilization Estimation

Due to the growth of the internet and internet-based software applications, cloud data center demand has increased. Cloud data centers have thousands of servers that are 24×7 working for users; it is the strong witness of enormous energy consumption for the operation of the cloud data center. However, server utilization is not remaining the same all the time, so, from an economic feasibility point of view, energy management is an essential activity for cloud resource management. Some well-known energy management techniques for cloud data centers generally used are dynamic voltage and frequency scaling (DVFS), dynamic power management (DPM), and task scheduling-based techniques. The present work is based on an analytical approach to integrating resource provisioning with sophisticated task scheduling; the authors estimate energy utilization by cloud data centers using iDR cloud simulator. The work is intended to optimize power consumption in the cloud data center.

Energy aware fixed priority scheduling in mixed-criticality systems

Most of studies about energy management for MC systems are based on dynamic priority scheme. The disadvantages of dynamic priority scheme are high system overhead and poor predictability. Unlike previous studies, we focus on the problem of scheduling mixed-criticality (MC) periodic tasks with minimizing energy consumption in MC systems based on fixed priority scheme. Firstly, we explain a criticality rate monotonic scheduling (CRMS) and propose the sufficient schedulability condition of CRMS. Secondly, we compute the energy minimization uniform scaled speed and present an optimal static solution algorithm based on CRMS. The extra workload of the high criticality level (HI) task executes with the maximum processor speed in the high criticality mode (HI-mode). But this algorithm does not exploit the slack time generated from the HI task in the low criticality mode (LO-mode). For energy efficiency, we propose a dynamic fixed priority energy minimization algorithm which exploits the slack time generated from the HI task in LO-mode to save energy. In addition, it combines a dynamic voltage and frequency scaling technique and a dynamic power management technique to reduce energy consumption. Finally, the experiments are applied to evaluate the performance of the proposed algorithm and the experimental results show that the proposed algorithm can save up 23.89% energy compared with other existing algorithms.

TherMa-MiCs: Thermal-Aware Scheduling for Fault-Tolerant Mixed-Criticality Systems

Multicore platforms are becoming the dominant trend in designing Mixed-Criticality Systems (MCSs), which integrate applications of different levels of criticality into the same platform. A well-known MCS is the dual-criticality system that is composed of low-criticality and high-criticality tasks. The availability of multiple cores on a single chip provides opportunities to employ fault-tolerant techniques, such as N-Modular Redundancy (NMR), to ensure the reliability of MCSs. However, applying fault-tolerant techniques will increase the power consumption on the chip, and thereby on-chip temperatures might increase beyond safe limits. To prevent thermal emergencies, urgent countermeasures, like Dynamic Voltage and Frequency Scaling (DVFS) or Dynamic Power Management (DPM) will be triggered to cool down the chip. Such countermeasures, however, might not only lead to suspending low-criticality tasks, but also it might lead to violating timing constraints of high-criticality tasks. In order to prevent such severe scenarios, it is indispensable to consider a temperature constraint within the scheduling process of fault-tolerant MCSs. Therefore, this paper presents, for the first time, a thermal-aware scheduling scheme for fault-tolerant MCSs, named TherMa-MiCs. In particular, TherMa-MiCs, satisfies the temperature constraint jointly with the timing constraints of the high-criticality tasks, while attempting to maximize the QoS of low-criticality tasks under the predefined constraints. At the same time, a reliability target is satisfied by employing the well-known N-Modular Redundancy (NMR) fault-tolerant technique. Experimental results show that our proposed scheme meets the temperature and timing constraints, while at the same time, improving the QoS of low-criticality tasks, with an average of 44%.

Distributed Dynamic Event-Triggered Power Management Strategy for Global Economic Operation in High-Power Hybrid AC/DC Microgrids

With the increasing integration of distributed generators (DGs), renewable energy sources (RESs), and energy storage systems (ESSs) in the hybrid AC/DC microgrid (HMG), the paralleled bidirectional interlinking converters (BILCs) play a significant role in the economic power interaction (EPI) between AC and DC subgrids. This paper proposes a distributed dynamic event-triggered control (ETC) strategy for the HMG to realize the global economic operation. In each subgrid, economic power sharing (EPS) among DGs can be achieved by economic droop controllers with no global information. The proposed distributed dynamic ETC includes a continuous-time part and a discrete-time part, where the former part realizes the EPI between two subgrids, while the latter part ensures the proportional power sharing between BILCs. Thus, a particular over-stressed BILC can be avoided, and system reliability and scalability are significantly improved. Additionally, only data from neighboring BILCs at the event-triggered times are involved, which greatly minimizes the communication burden and costamong BILCs. Furthermore, the asymptotic stability of the closed-loop system dynamics and the communication time delay stability are proven. Finally, the hardware-in-loop experimental results are conducted to demonstrate the effectiveness of the proposed control strategy.

Dynamic power management based on model predictive control for hybrid-energy-storage-based grid-connected microgrids

In this study, an efficient and reliable dynamic power management system (PMS) is proposed for microgrids (μGs) based on hybrid energy storage systems. Owing tothe differences in the response times of the different components (i.e., the battery, supercapacitor, and fuel cell) of the μG, efficiently allocating the power between the different devices is a challenging task for system designers. To address this issue and optimize the operation of the μG, a dynamic PMS is necessary. The contribution of this study is the application of model predictive control (MPC) for operating and controlling such μGs. The MPC method is used to manage power electronic devices such as DC-DC converters (for the battery, fuel cell, and supercapacitor) and grid-connected inverters. After the proposed PMS is successfully implemented in simulations, itsreal-time performance is validatedunder different operating scenarios that could occur in actual situations. The proposed PMS is found to be effective in controlling the control and operation of the μG.It can regulate the DC bus voltage during transient power imbalances, control the fuel cell current slope as per the requirement, and smoothen the operation between isolated and grid-connected modes.

Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM

With the computational systems of even embedded devices becoming ever more powerful, there is a need for more effective and pro-active methods of dynamic power management. The work presented in this paper demonstrates the effectiveness of a reinforcement-learning based dynamic power manager placed in a software framework. This combination of Q-learning for determining policy and the software abstractions provide many of the benefits of co-design, namely, good performance, responsiveness and application guidance, with the flexibility of easily changing policies or platforms. The Q-learning based Quality of Service Manager (2QoSM) is implemented on an autonomous robot built on a complex, powerful embedded single-board computer (SBC) and a high-resolution path-planning algorithm. We find that the 2QoSM reduces power consumption up to 42% compared to the Linux on-demand governor and 10.2% over a state-of-the-art situation aware governor. Moreover, the performance as measured by path error is improved by up to 6.1%, all while saving power.

Dynamic Network State Learning Model for Mobility Based WMSN Routing Protocol

The rising demand of wireless multimedia sensor networks (WMSNs) has motivated academia-industries to develop energy efficient, Quality of Service (QoS) and delay sensitive communication systems to meet major real-world demands like multimedia broadcast, security and surveillance systems, intelligent transport system, etc. Typically, energy efficiency, QoS and delay sensitive transmission are the inevitable requirements of WMSNs. Majority of the existing approaches either use physical layer or system level schemes that individually can’t assure optimal transmission decision to meet the demand. The cumulative efficiency of physical layer power control, adaptive modulation and coding and system level dynamic power management (DPM) are found significant to achieve these demands. With this motivation, in this paper a unified model is derived using enhanced reinforcement learning and stochastic optimization method. Exploiting physical as well as system level network state information, our proposed dynamic network state learning model (NSLM) applies stochastic optimization to learn network state-activity that derives an optimal DPM policy and PHY switching scheduling. NSLM applies known as well as unknown network state variables to derive transmission and PHY switching policy, where it considers DPM as constrained Markov decision process (MDP) problem. Here,the use of Hidden Markov Model and Lagrangian relaxation has made NSLM convergence swift that assures delay-sensitive, QoS enriched, and bandwidth and energy efficient transmission for WMSN under uncertain network conditions. Our proposed NSLM DPM model has outperformed traditional Q-Learning based DPM in terms of buffer cost, holding cost, overflow, energy consumption and bandwidth utilization.

Run-Time Hierarchical Management of Mapping, Per-Cluster DVFS and Per-Core DPM for Energy Optimization

Heterogeneous cluster-based multi/many-core systems (e.g., ARM big.LITTLE, supporting dynamic voltage and frequency scaling (DVFS) at cluster level and dynamic power management (DPM) at core level) have attracted much attention to optimize energy on modern embedded systems. For concurrently executing applications on such a platform, this paper aims to study how to appropriately apply the three system configurations (mapping, DVFS, and DPM) to reduce both dynamic and static energy. To this end, this paper first formulates the dependence of the three system configurations on heterogeneous cluster-based systems as a 0–1 integrated linear programming (ILP) model, taking into account run-time configuration overheads (e.g., costs of DPM mode switching and task migration). Then, with the 0–1 ILP model, different run-time strategies (e.g., considering the three configurations in fully separate, partially separate, and holistic manners) are compared based on a hierarchical management structure and design-time prepared data. Experimental case studies offer insights into the effectiveness of different management strategies on different platform sizes (e.g., #cluster × #core, 2 × 4, 2 × 8, 4 × 4, 4 × 8), in terms of application migration, energy efficiency, resource efficiency, and complexity.

Energy-saving service management technology of internet of things using edge computing and deep learning

The purpose is to solve the problems of high transmission rate and low delay in the deployment of mobile edge computing network, ensure the security and effectiveness of the Internet of things (IoT), and save resources. Dynamic power management is adopted to control the working state transition of Edge Data Center (EDC) servers. A load prediction model based on long-short term memory (LSTM) is creatively proposed. The innovation of the model is to shut down the server in idle state or low utilization in EDC, consider user mobility and EDC location information, learn the global optimal dynamic timeout threshold strategy and N-policy through trial and error reinforcement learning method, reasonably control the working state switching of the server, and realize load prediction and analysis. The results show that the performance of AdaGrad optimization solver is the best when the feature dimension is 3, the number of LSTM network layers is 6, the time series length is 30–45, the batch size is 128, the training time is 788 s, the number of units is 250, and the number of times is 350. Compared with the traditional methods, the proposed load prediction model and power management mechanism improve the prediction accuracy by 4.21%. Compared with autoregressive integrated moving average (ARIMA) load prediction, the dynamic power management method of LSTM load prediction can reduce energy consumption by 12.5% and realize the balance between EDC system performance and energy consumption. The system can effectively meet the requirements of multi-access edge computing (MEC) for low delay, high bandwidth and high reliability, reduce unnecessary energy consumption and waste, and reduce the cost of MEC service providers in actual operation. This exploration has important reference value for promoting the energy-saving development of Internet-related industries.

Survey on energy efficient scheduling techniques on cloud computing

With ever-growing technical advances, performance of complex scientific and engineering applications has arrived at petaflops and exaflops range. However, massive power drawn from the large scale computing infrastructure has caused commensurate rise in electricity consumption, escalating data center ownership costs besides leaving carbon footprints. Judicious scheduling of complex applications with an objective to reduce overall makespan and reduced energy consumption has become one of the biggest confront in the realm of computing architectures. This paper presents a survey on energy efficient scheduling algorithms based on dynamic voltage and frequency scaling (DVFS) and dynamic power management (DPM) techniques. The parameters considered are mainly the makespan, processor energy (dynamic and static) consumption, and network energy (communication) consumption, wherever appropriate during task scheduling.

Energy Efficient Framework for a AIoT Cardiac Arrhythmia Detection System Wearable during Sport

The growing market of wearables is expanding into different areas of application such as devices designed to improve and monitor sport activities. This in turn is pushing research on low-cost, very low-power wearable systems with increased analysis capabilities. This paper proposes integrated energy-aware techniques and a convolutional neural network (CNN) for a cardiac arrhythmia detection system that can be worn during sport training sessions. The dynamic power management strategy (DPMS) is programmed into an ultra-low-power microcontroller, and in combination with a photovoltaic (PV) energy harvesting (EH) circuit, achieves a battery-life extension towards a self-powered operation. The CNN-based analysis filters, scales the image, and using a bicubic technique, interpolates the measurements to subsequently classify the electrocardiogram (ECG) signal into normal and abnormal patterns. Experimental results show that the EH-DPMS achieves an extension in the battery charge for a total of 14.34% more energy available, which represents 12 consecutive workouts of 45 min without the need to manually recharge it. Furthermore, an arrhythmia detection precision of 98.6% is achieved among the experimental sessions using 55,222 images for training the system with the MIT-BIH, QT, and long-term ST databases, and 1320 implemented on a wearable system. Therefore, the proposed wearable system can be used to monitor an athlete’s condition, reducing the risk of abnormal heart conditions during sports activities.

Dynamic Modeling, Stability Analysis, and Power Management of Shipboard DC Hybrid Power Systems

This article proposes a framework for the stability analysis and control testing of marine hybrid power systems with dc power architecture. The dynamics of such active systems are increasingly influenced by interactive modes, such as the highly dynamic loads and varying load sharing scenarios, electromechanical modes, and integration of energy storage systems (ESS). Hence, a dynamic model of the entire system is developed, including the power electronics and ESS, electromechanical systems, different controllers, low level and high level, and propulsion loads. The proposed analytical model is used to establish not only the small-signal stability analysis but also time-domain simulations. Then, a set of dynamic analyses and tests has been performed to identify the stability challenges that a vessel may be exposed to during a real operation. The suggestions for improving the system performance are given as the control modification at different levels. To emulate the real operation, a ship operational profile is used for the tests. Finally, the proposed dynamic model is verified with the experimental results conducted in a full-scale hybrid power systems laboratory. The results show that the system dynamics can be affected significantly by the interaction of the high- and low-level controls of the converters, which is usually neglected in conventional models.