Power Management Policies Research Articles

ControlPULP: A RISC-V On-Chip Parallel Power Controller for Many-Core HPC Processors with FPGA-Based Hardware-In-The-Loop Power and Thermal Emulation

High-performance computing (HPC) processors are nowadays integrated cyber-physical systems demanding complex and high-bandwidth closed-loop power and thermal control strategies. To efficiently satisfy real-time multi-input multi-output (MIMO) optimal power requirements, high-end processors integrate an on-die power controller system (PCS). While traditional PCSs are based on a simple microcontroller (MCU)-class core, more scalable and flexible PCS architectures are required to support advanced MIMO control algorithms for managing the ever-increasing number of cores, power states, and process, voltage, and temperature variability. This paper presents ControlPULP, an open-source, HW/SW RISC-V parallel PCS platform consisting of a single-core MCU with fast interrupt handling coupled with a scalable multi-core programmable cluster accelerator and a specialized DMA engine for the parallel acceleration of real-time power management policies. ControlPULP relies on FreeRTOS to schedule a reactive power control firmware (PCF) application layer. We demonstrate ControlPULP in a power management use-case targeting a next-generation 72-core HPC processor. We first show that the multi-core cluster accelerates the PCF, achieving 4.9x speedup compared to single-core execution, enabling more advanced power management algorithms within the control hyper-period at a shallow area overhead, about 0.1% the area of a modern HPC CPU die. We then assess the PCS and PCF by designing an FPGA-based, closed-loop emulation framework that leverages the heterogeneous SoCs paradigm, achieving DVFS tracking with a mean deviation within 3% the plant’s thermal design power (TDP) against a software-equivalent model-in-the-loop approach. Finally, we show that the proposed PCF compares favorably with an industry-grade control algorithm under computational-intensive workloads.

Artificial Intelligence Driven Power Optimization in IOT-Enabled Wireless Sensor Networks

The widespread use of Wireless Sensor Networks (WSN) in Internet of Things (IoT) causes energy efficiency issues. This paper proposes an AI-based solution to this problem. The propose an AI-Driven Power Optimization framework for IoT-enabled WSN using Deep Q-Network (DQN) and Dynamic Voltage and Frequency Scaling (DVFS). These techniques can adapt to changing network conditions and reduce power consumption when used together. Sensor nodes provide environmental parameters, battery status, and network behavior data to the AI-driven framework DQN is implemented after data preprocessing to learn and make power management decisions using reinforcement learning. Neural network-driven agent operates in a state and action space. It optimizes energy use with rewards. Real-time hardware power adjustment is done using DVFS. DVFS precise control and AI-driven decision-making create a comprehensive power optimization strategy. AI adapts to new challenges and optimizes network lifespan by improving its power management policies. Experimental implementations of the proposed framework show significant energy savings, network lifespan extension, and QoS improvements. AI-Driven Power Optimization in IoT-enabled WSN is proven effective and flexible. This study shows the potential of AI, specifically DQN and DVFS, in IoT-enabled WSN. This AI-Driven Power Optimization framework addresses energy efficiency and improves IoT sensor networks. AI improves power optimization in IoT-enabled WSN making IoT deployments more sustainable and resilient.

An Adaptive Wireless Power Sharing Control for Multiterminal HVDC

Power sharing among multiterminal high voltage direct current terminals (MT-HVDC) is mainly developed based on a priority or sequential manners, which uses to prevent the problem of overloading due to a predefined controller coefficient. Furthermore, fixed power sharing control also suffers from an inability to identify power availability at a rectification station. There is a need for a controller that ensures an efficient power sharing among the MT-HVDC terminals, prevents the possibility of overloading, and utilizes the available power sharing. A new adaptive wireless control for active power sharing among multiterminal (MT-HVDC) systems, including power availability and power management policy, is proposed in this paper. The proposed control strategy solves these issues and, this proposed controller strategy is a generic method that can be applied for unlimited number of converter stations. The rational of this proposed controller is to increase the system reliability by avoiding the necessity of fast communication links. The test system in this paper consists of four converter stations based on three phase-two AC voltage levels. The proposed control strategy for a multiterminal HVDC system is conducted in the power systems computer aided design/electromagnetic transient design and control (PSCAD/EMTDC) simulation environment. The simulation results significantly show the flexibility and usefulness of the proposed power sharing control provided by the new adaptive wireless method.

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.

Q-learning based control for energy management of series-parallel hybrid vehicles with balanced fuel consumption and battery life

The present study investigates an energy management strategy based on reinforcement learning for series-parallel hybrid vehicles. Hybrid electric vehicles allow using more advanced power management policies because of their complexity of power management. Towards this feature, a Q-Learning algorithm is proposed to design an energy management strategy. Compared to previous studies, an online reward function is defined to optimize fuel consumption and battery life cycle. Moreover, in the provided method, prior knowledge of the cycle and exact modeling of the vehicle are not required. The introduced strategy is simulated for four driving cycles in MATLAB software linked with ADVISOR. The simulation results show that in the HWFET cycle, the fuel consumption decreases by 1.25 %, and battery life increases by 65% compared to the rule-based method implemented in ADVISOR. Also, the results for the other driving cycles confirm the self-improvement property. In addition, it has been depicted that in the case of change in the driving cycle, the method performance has been maintained and gained better performance than the rule-based controller.

Reinforcement Learning-Based Power Management Policy for Mobile Device Systems

This paper presents a power management policy that utilizes reinforcement learning to increase the power efficiency of mobile device systems based on a multiprocessor system-on-a-chip (MPSoC). The proposed policy predicts a system’s characteristics and learns power management controls to adapt to the variations in the system. We consider the behavioral characteristics of systems that run on mobile devices under diverse scenarios. Therefore, the policy can flexibly manage the system power regardless of the application scenario and achieve lower energy consumption without compromising the user satisfaction. The average energy per unit quality of service (QoS) of the proposed policy is lower than that of the previous six dynamic voltage/frequency scaling governors by 31.66%. Furthermore, we reduce the runtime overhead by implementing the proposed policy as hardware. We implemented the policy on the field programmable gate array (FPGA) and construct a communication interface between the central processing units (CPUs) and the hardware of the proposed policy. Decision-making by the hardware-implemented policy is 3.92 times faster than by the software-implemented policy.

Modelling user satisfaction for power-usage optimisation of computer fleets

Power consumption costs of computer fleets can be one of the main operational costs of medium or large-sized office sites. In order to optimise the power consumption of the fleet, a set of optimal power management policies must be generated and enforced.Generating these policies is an optimisation problem of finding the power off timeout value for each computer that maximises energy savings while guaranteeing user satisfaction. To solve this problem, understanding the computer utilisation patterns of the users and defining a metric of user satisfaction is fundamental.This paper presents a method to analyse user activity and inactivity, extract models from previously recorded utilisation logs and use them to manage a whole computer fleet. A tool that implements this method is also introduced. This tool generates power management policies from utilisation logs. It analyses the effects of variations in fleet characteristics on policies by means of discrete event simulation. It also seeks to understand the behavioural patterns of users over weekly periods. Finally, it generates utilisation logs from high level descriptions of fleets. This tool offers a simulation to study diverse fleet configurations and generation of synthetic fleets.

Predictive management approach for the coordination of wind and water-based power supplies

This paper addresses the coordinated operation between wind farms and reservoir hydropower plants in networks with weak transmission capacity. The joint operation of the energy sources is captured by a Virtual Power Plant (VPP) representation. With the aim of maximising renewable energy penetration while ensuring a suitable VPP integration, a supervisory controller for the energy dispatch layer is proposed. To deal with the variable nature of both resources, the controller has been designed into a model predictive control framework including mixed-integer quadratic programming (given the hybrid considered model) for solving the related optimisation problem, and complementary techniques as constraints softening and time-varying weighting. The results indicate that the predictive control approach achieves a better Independent System Operator (ISO) reference tracking that, together with reservoir management, are the most important factors to ensure a suitable VPP incorporation to the main grid. As a case study, a sub-network of the Argentinian power system is tackled. The controller performance is quantitatively and qualitatively assessed under uncertain conditions and compared with other approaches over a nine-year period. By means of the proposed power management policy, the ISO’s effort to balance the sub-network is reduced 56% with regard to other approaches without any water spillage.

A Security Monitoring Framework for Mobile Devices

Quite often, organizations are confronted with the burden of managing mobile device assets, requiring control over installed applications, security, usage profiles or customization options. From this perspective, the emergence of the Bring Your Own Device (BYOD) trend has aggravated the situation, making it difficult to achieve an adequate balance between corporate regulations, freedom of usage and device heterogeneity. Moreover, device and information protection on mobile ecosystems are quite different from securing other device assets such as laptops or desktops, due to their specific characteristics and limitations—quite often, the resource overhead associated with specific security mechanisms is more important for mobile devices than conventional computing platforms, as the former frequently have comparatively less computing capabilities and more strict power management policies. This paper presents an intrusion and anomaly detection framework specifically designed for managed mobile device ecosystems, that is able to integrate into mobile device and management frameworks for complementing conventional intrusion detection systems. In addition to presenting the reference architecture for the proposed framework, several implementation aspects are also analyzed, based on the lessons learned from developing a proof-of-concept prototype that was used for validation purposes.

Idleness-Aware Dynamic Power Mode Selection on the i.MX 7ULP IoT Edge Processor

Power management is a crucial concern in micro-controller platforms for the Internet of Things (IoT) edge. Many applications present a variable and difficult to predict workload profile, usually driven by external inputs. The dynamic tuning of power consumption to the application requirements is indeed a viable approach to save energy. In this paper, we propose the implementation of a power management strategy for a novel low-cost low-power heterogeneous dual-core SoC for IoT edge fabricated in 28 nm FD-SOI technology. Ss with more complex power management policies implemented on high-end application processors, we propose a power management strategy where the power mode is dynamically selected to ensure user-specified target idleness. We demonstrate that the dynamic power mode selection introduced by our power manager allows achieving more than 43% power consumption reduction with respect to static worst-case power mode selection, without any significant penalty in the performance of a running application.

Chip-Specific Power Delivery and Consumption Co-Management for Process-Variation-Aware Manycore Systems Using Reinforcement Learning

Energy efficiency has become a critical design metric for high-performance systems. Various power management techniques have been proposed for the processor cores such as dynamic voltage and frequency scaling (DVFS), whereas few solutions consider the power losses suffered on the power delivery system (PDS), despite the fact that they have a significant impact on the overall energy efficiency of the system. With the explosive growth of system complexity and highly dynamic workloads variations, it is also challenging to find the optimal power management policies which can effectively match the power delivery with the power consumption. In addition, process variations (PVs) add heterogeneity to systems and make traditional power management methods less effective. To tackle the above problems, we propose a reinforcement-learning-based Chip-Specific Power co-Management (CSPM) scheme for PV-aware manycore systems. Both PDS and processor cores are jointly adjusted by distributed agents with modular Q-learning to improve the overall energy efficiency of the system. System characteristics are naturally included in the learning process to obtain chip-specific policies. Experimental results show that when applied to PV-aware manycore systems with a hybrid PDS constructed by both on- and off-chip voltage regulators, the proposed method achieves a 60.1% reduction of the overall energy delay product (EDP) of the system, on average, compared to a traditional DVFS approach.

ECOGreen: Electricity Cost Optimization for Green Datacenters in Emerging Power Markets

Modern datacenters need to tackle efficiently the increasing demand for computing resources while minimizing energy usage and monetary costs. Power market operators have recently introduced emerging demand-response programs, in which electricity consumers regulate their power usage following provider requests to reduce monetary costs. Among different programs, regulation service (RS) reserves are particularly promising for datacenters due to the high credit gain possibilities and datacenters' flexibility in regulating their power consumption. Therefore, it is essential to develop bidding strategies for datacenters to participate in emerging power markets together with power management policies that are aware of power market requirements at runtime. In this paper we propose ECOGreen, a holistic strategy to jointly optimize the datacenter RS problem and virtual machine (VM) allocation that satisfies the hour-ahead power market constraints in the presence of electrical energy storage (EES) and renewable energy. We first find the best power and reserve bidding values as well as the number of active servers in a fast analytical way that works well in practice. Then, we present an online adaptive policy that modulates datacenter power consumption by controlling VMs CPU resource limits and efficiently utilizing demand-side EES and renewable power, while guaranteeing quality-of-service (QoS) constraints. Our results demonstrate that ECOGreen can provide 76 percent of the datacenter power consumption on average as reserves to the market, due to largely operating on renewable sources and EES. This translates into ECOGreen saving up to 71 percent electricity costs when compared to other state-of-the-art datacenter electricity cost minimization techniques that participate in the power market.

Multidevice Collaborative Power Management Through Decentralized Knowledge Sharing

Minimizing energy consumption while satisfying the user-specified performance requirement is a primary design objective for mobile devices. To achieve this, off-the-shelf mobile devices are usually equipped with dynamic voltage and frequency scaling (DVFS)-enabled processors to tradeoff performance for energy reduction through online adjustment of operating points. Recently, reinforcement learning algorithms have been widely studied and show great potential for runtime DVFS control because of their adaptability to the changing environment. However, the rapid evolution of hardware and the ever-growing diversity of mobile applications increase the system complexity dramatically and make it hard for the learning agent to quickly obtain an efficient power management policy. To address this challenge, we propose a decentralized collaborative Q-learning (DCQL)-based approach in this article to solve the DVFS control problem of multicore mobile processors. By exchanging learning experiences and knowledge among multiple devices, DCQL increases the convergence rate of the learning algorithm and improves the quality of the derived power management policy. On each device, the 4-phase action selection strategy is applied for efficient exploration of the action space defined by various power settings. Experimental results on realistic applications show that DCQL can achieve 3.4%–8.0% energy reduction over various existing approaches while providing an average of $3.4\times $ speedup over the state-of-the-art individual learning algorithm. We also show that the proposed approach can scale well with the number of cores through clustered DVFS control.

Network Packet Processing Mode-Aware Power Management for Data Center Servers

In data center servers, power management (PM) exploiting Dynamic Voltage and Frequency Scaling (DVFS) for processors can play a crucial role to improve energy efficiency. However, we observe that current PM policies (i.e., governors) not only considerably increase tail response time (i.e., violate a given Service Level Objective (SLO)) but also hurt energy efficiency. Tackling limitations of current PM governors, we propose NMAP, Network packet processing Mode-Aware Power management. NMAP improves energy efficiency while satisfying given SLOs, considering packet processing status on a core for PM by monitoring transitions between network packet processing modes - interrupt and polling. Tracking the transitions, NMAP detects moments that a core cannot process packets fast enough and forces the core to immediately raise the voltage and frequency (V/F) state. As a result, NMAP can provide not only low response time but also low energy consumption. Our experiment shows that NMAP improves tail response time by up to 4.1× compared with the ondemand governor, reducing energy by up to 44.6 percent compared with the performance governor.

Co-Adjusting Voltage/Frequency State and Interrupt Rate for Improving Energy-Efficiency of Latency-Critical Applications

As the power/energy consumption is one of the major contributors to the Total Cost of Ownership (TCO), improving power/energy efficiency is crucial for large-scale data centers where latency-critical applications are commonly accommodated while computing resources are usually under-utilized. For improving the power/energy efficiency of processors, most of the commercial processors support Dynamic Voltage and Frequency Scaling (DVFS) technology that enables to adjust Voltage and Frequency state (V/F state) of the processor dynamically. In particular, for the latency-critical applications, many prior studies propose power management policies using the DVFS for the latency-critical applications, which minimizes the performance degradation or satisfies the Service Level Objectives (SLOs) constraints. Meanwhile, although the interrupt rate also affects the response latency and energy efficiency of latency-critical applications considerably, those prior studies just introduce policies for V/F state adjustment while not considering the interrupt rate. Therefore, in this article, we investigate the impact of adjusting the interrupt rate on the tail response latency and energy consumption. Through our experimental results, we observe that adjusting interrupt rate along with V/F state management varies the performance and energy consumption considerably, and provides an opportunity to reduce energy further by showing latency overlap between different V/F states. Based on the observation, we show the quantitative potential in improving energy efficiency of co-adjusting V/F state and interrupt rate with a simple management policy, called Co-PI. Co-PI searches the most energy-efficient combination of the V/F state and interrupt rate from the latency and energy tables that we obtain through offline profiling, and reflect the combination to the core and NIC. Co-PI reduces energy consumption by 34.1% and 25.1% compared with performance and ondemand governors while showing the almost same tail response latency with the performance governor that operates cores at the highest V/F state statically.

Power Management Policies in Distributed Generation and Potential Opportunities for Future Research

In this paper widespread prior-art search of the distributed generation and its power management policies are presented, the conclusions derived by various authors for different objective statements are explored. In Microgrids with non conventional distributed generation systems, power quality issues are increasing due to power electronic converters involved for the energy conversion. Hence, the purpose of this paper is to showcase the unmet needs of the microgrids in terms of power quality problems and succeeded by the case study on the different available compensation techniques, algorithms and devices. Finally, reflected the importunate gaps available and promising opportunities for future research.

Effective Resource Planning for Interference Aware Throughput Maximization of Wireless Body Area Networks

Cross-layer design is an in-depth analysis area in especially wireless Body Area networks and is necessary under energy and throughput limitations. To improve network performance the all the layer increases energy consumption; thus, the collaborations connecting these layers ought to be examined. This study develops an optimal interrupt aware connection planning and power management policy that assist a certain data rate on each connection in the network, including signal to interference and noise ratio(SINR) and highest transmission power controls at one another node or center.in each connection as link cost metrics Creating an interrupt-aware development problem by considering the power of each node in the routing path and the associated delay in each connection.the problem is to discover an scheduling link planning, power management and routing policy that maximizes the life span and throughput and minimizes the end-to-end delay as well as the energy link cost subject to definite controls. To provide mathematical guides to our schemes and relationships between the parameters. In our proposed work is an advance step towards our distributed joint planning, power management and routing algorithm for wireless Body Area networks utilizing cross layer design.

On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

Wireless sensor networks have recently been widely used in several applications and scenarios, especially because they have the ability and flexibility for establishing a scalable and reliable wireless network. Cooperative multi-robotic systems (CMRS) are one example of these applications where establishing a wireless network between robots is essential and paramount to their operation. Further, these robots can utilize their mobility to provide sensing functionality for areas that are not covered by the static sensor. This can be achieved by equipping the robots with specific sensors to sense the area of interest (AoI) and report the sensed data to a remote monitoring center for further processing and decision-making. However, the nodes that form the sensor network have limited energy, and, as such, efficient algorithms in clusters’ formation, packets’ routing, and energy and mobility management are paramount. In this paper, a literature survey is presented containing the most related works that have been proposed to solve these challenges utilizing fuzzy logic. Most of the literature work attempted to utilize a de-centralized approach, where certain input parameters such as the residual energy, communication link quality, network congestion status, the nodes’ distance to the sink node and its location with respect to the other nodes, and the data and their sampling rate are all used as inputs to the fuzzy logic controller. These input parameters are used to determine several performance vital factors such as the cluster formation and its cluster head, best route to the sink node, optimal power management policies in terms of sleep/awake times needed to maximize the network lifetime, nodes’ mobility management policies to maintain network connectivity, and best route in terms of packet loss and delay.

Dynamic Resource Management of Heterogeneous Mobile Platforms via Imitation Learning

The complexity of heterogeneous mobile platforms is growing at a rate faster than our ability to manage them optimally at runtime. For example, state-of-the-art systems-on-chip (SoCs) enable controlling the type (Big/Little), number, and frequency of active cores. Managing these platforms becomes challenging with the increase in the type, number, and supported frequency levels of the cores. However, existing solutions used in mobile platforms still rely on simple heuristics based on the utilization of cores. This paper presents a novel and practical imitation learning (IL) framework for dynamically controlling the type (Big/Little), number, and the frequencies of active cores in heterogeneous mobile processors. We present efficient approaches for constructing an Oracle policy to optimize different objective functions, such as energy and performance per Watt (PPW). The Oracle policies enable us to design low-overhead power management policies that achieve near-optimal performance matching the Oracle. Experiments on a commercial platform with 19 benchmarks show on an average 101% PPW improvement compared to the default interactive governor.

Adaptive battery aware power management of a computer with self power-managed components

Dynamic power management strategies are generally used to achieve efficient power consumption of battery operated computer systems. Such computer systems usually integrate a number of built-in power-management policies. These policies are generally integrated into device drivers and cannot be changed. This paper addresses the problem of adaptive dynamic power management of a battery operated computer with self-power managed components. The power management task is split into Component Power Manager (CPM) and Global Power Manager (GPM). The CPM is the local-level policy that is pre-defined and can't change. The GPM cannot overwrite the CPM policy. A Service Flow Controller (SFC) is incorporated to control the service request generation for a specific component. The GPM uses model free reinforcement learning to adequately guide SFC actions. Moreover, the GPM implements Reinforcement learning based battery power management aiming at optimizing the battery's State of Charge (SoC) and improving its lifetime. This is performed by letting the GPM adapt the system quality of services to the actual battery SoC. Experiments on measured data traces confirmed the effectiveness of the proposed approach. Up to 57.2% of maximum SoC savings are obtained while good performance levels are maintained. Compared to prior reference studies, the proposed approach is model free, event driven, adapts to non-stationary workloads, considers multiple types of user applications, models the battery nonlinear characteristics, can handle SoC degradation and performance at the same time, and is capable to achieve deep and wide SoC degradation/performance tradeoff curves.