Dynamic Voltage Scaling Technique Research Articles

Energy-efficient scheduling for parallel applications with reliability and time constraints on heterogeneous distributed systems

Reliability is a crucial index of the system, and many safety-critical applications have reliability requirements and deadline constraints. In addition, in order to protect the environment and reduce system operating costs, it is necessary to minimize energy consumption as much as possible. This paper considers parallel applications on heterogeneous distributed systems and proposes two algorithms to minimize energy consumption for meeting the deadline and satisfying the reliability requirement of the applications. The first algorithm is called minimizing scheduling length while satisfying the reliability requirement (MSLSRR). It first transforms the reliability requirement of the application into the reliability requirement of the task and then assigns the task to the processor with the earliest finish time. Since the reliability generated by MSLSRR is often higher than the reliability requirement of the application, and the scheduling length is also less than the deadline, an algorithm called improving energy efficiency (IEE) is designed, which redefined the minimum reliability requirement for the task and applied dynamic voltage and frequency scaling (DVFS) technique for energy conservation. The proposed algorithms are compared with existing algorithms by using real parallel applications. Experimental results demonstrate that the proposed algorithms consume the least energy.

Online Energy-Aware Scheduling for Deadline-Constrained Applications in Distributed Heterogeneous Systems

In the current computing environment, the significance of distributed heterogeneous systems has gained prominence. The research on scheduling problems in distributed systems that consider energy consumption has garnered substantial attention due to its potential to enhance system stability, achieve energy savings, and contribute to environmental preservation. However, efficient scheduling in such systems necessitates not only the consideration of energy consumption but also the ability to adapt to the dynamic nature of the system. To tackle these challenges, we propose an online energy-aware scheduling algorithm for deadline-constrained applications in distributed heterogeneous systems, leveraging dynamic voltage and frequency scaling (DVFS) techniques. First, the algorithm models the continuously arriving applications and heterogeneous processors and proposes a novel task-sorting method to prioritize tasks, ensuring that more applications are completed within their respective deadlines. Second, the algorithm controls the selection range of processors based on the task’s subdeadline and assigns the task to the processor with the minimum energy consumption. Through experiments conducted with randomly generated applications, our approach consistently exhibits superior performance when compared to similar scheduling algorithms.

LEC-MiCs: Low-Energy Checkpointing in Mixed-Criticality Multi-Core Systems

With the advent of multicore platforms in designing Mixed-Criticality Systems (MCSs), simultaneous management of reliability and energy while guaranteeing an acceptable service level for low-criticality tasks is a crucial challenge. To ensure the reliability of the MCSs against transient faults, fault-tolerant techniques are employed which will increase energy consumption. To mitigate the energy overhead, the Dynamic Voltage and Frequency Scaling (DVFS) technique will be exploited. However, this technique might lead to violating the timing constraints of high-criticality tasks. Therefore, this paper presents, for the first time, the low-energy checkpointing technique to guarantee the reliability of multiple preemptive periodic mixed-criticality tasks in a multicore platform. In contrast to the previous works in checkpointing technique which consider a specific number of faults that all the tasks in the system should tolerate, in this paper, the number of tolerable faults for each execution section of a task, and in each voltage and frequency level is determined through proposed formulas to meet the reliability target based on safety standards. Then, our proposed method determines the number of checkpoints and their non-uniform intervals for the normal and overrun sections of each task to reduce energy consumption, respectively. Moreover, the unified demand bound function (DBF) analysis is proposed for analyzing the schedulability of the task set, where each high-criticality task meets its timing and reliability constraints, and low-criticality tasks execute based on their derived guaranteed periods in each operational mode of the system. Experimental results show that our proposed scheme meets the timing and reliability constraints while at the same time, improving the QoS of low-criticality tasks, and managing energy consumption with an average of 29.49%, and 32.78%, respectively.

Architectural and Technological Approaches for Efficient Energy Management in Multicore Processors

Benchmarks play an essential role in the performance evaluation of novel research concepts. Their effectiveness diminishes if they fail to exploit the available hardware of the evaluated microprocessor or, more broadly, if they are not consistent in comparing various systems. An empirical analysis of the consecrated Splash-2 benchmarks suite vs. the latest version Splash-4 was performed. It was shown that on a 64-core configuration, half of the simulated benchmarks reach temperatures well beyond the critical threshold of 105 °C, emphasizing the necessity of a multi-objective evaluation from at least the following perspectives: energy consumption, performance, chip temperature, and integration area. During the analysis, it was observed that the cores spend a large amount of time in the idle state, around 45% on average in some configurations. This can be exploited by implementing a predictive dynamic voltage and frequency scaling (DVFS) technique called the Simple Core State Predictor (SCSP) to enhance the Intel Nehalem architecture and to simulate it using Sniper. The aim was to decrease the overall energy consumption by reducing power consumption at core level while maintaining the same performance. More than that, the SCSP technique, which operates with core-level abstract information, was applied in parallel with a Value Predictor (VP) or a Dynamic Instruction Reuse (DIR) technique, which rely on instruction-level information. Using the SCSP alone, a 9.95% reduction in power consumption and an energy reduction of 10.54% were achieved, maintaining the performance. By combining the SCSP with the VP technique, a performance increase of 8.87% was obtained while reducing power and energy consumption by 3.13% and 8.48%, respectively.

An Energy-Efficient Wireless Power Receiver With One-Step Adiabatic-Bipolar-Supply Generating for Implantable Electrical Stimulation Applications.

This paper presents an energy-efficient wireless power receiver for implantable electrical stimulation applications, which can achieve one-step adiabatic bipolar-supply that is generated by a hybrid single-stage dual-output regulating (SSDOR) rectifiers. The structure using only four switches overcomes the disadvantages that the two output voltage values in the traditional dual-output rectifiers are close to each other. A constant-current (CC) controlled adiabatic dynamic voltage scaling (DVS) technique is proposed to minimize the voltage headroom of the stimulating drivers and improve the stimulation efficiency significantly. In addition, the receiver adopts only one general constant on-time (COT) low-frequency control to adjust the stimulation current, reducing both the power consumption and the complexity of the control circuits. The proposed receiver has been fabricated in a 0.18 μm BCD process with ±6 V voltage compliance and 2.5 mA maximum stimulating current. With a current range from ±1.5 mA to ±2.5 mA, the measured maximum average headroom voltage is only 80 mV and the peak total efficiency of the receiver is 85.6%. The functionalities of the proposed receiver have been successfully verified through in vitro experiments.

Time–Energy Correlation for Multithreaded Matrix Factorizations

The relationship between time and energy is an important aspect related to energy savings in modern multicore architectures. In this paper, we investigated and analyzed the correlation between time and energy. We compared the execution time and energy consumption of the LU factorization algorithms (versions with and without pivoting) and Cholesky with the Math Kernel Library (MKL) on a multicore machine. To reduce the energy of these multithreaded factorizations, the Dynamic Voltage and Frequency Scaling (DVFS) technique was used. This technique allows the clock frequency to be scaled without changing the implementation. In particular, we studied the correlations between time and energy using two metrics: Energy Delay Product (EDP) and Greenup, Powerup, and Speedup (GPS-UP). An experimental evaluation was performed on an Intel Xeon Gold multicore machine as a function of the number of threads and the clock speed. Our test results showed that scalability in terms of execution time, expressed by the Speedup metric, had values close to a linear function as the number of threads increased. In contrast, the scalability in terms of energy consumption, expressed by the Greenup metric, had values close to a logarithmic function as the number of threads increased. The use of the EDP and GPS-UP metrics allowed us to evaluate the impact of the optimized code (DVFS and increase in the number of threads) on the time and energy consumption and to determine a better green category representing energy savings without losing performance.

Learning to Schedule Multi-Server Jobs With Fluctuated Processing Speeds

Multi-server jobs are imperative in modern cloud computing systems. A noteworthy feature of multi-server jobs is that, they usually request multiple computing devices simultaneously for their execution. How to schedule multi-server jobs online with a high system efficiency is a topic of great concern. First, the scheduling decisions have to satisfy the service locality constraints. Second, the scheduling decisions needs to be made online without the knowledge of future job arrivals. Third, and most importantly, the actual service rate experienced by a job is usually in fluctuation because of the dynamic voltage and frequency scaling (DVFS) and power oversubscription techniques when multiple types of jobs co-locate. A majority of online algorithms with theoretical performance guarantees are proposed. However, most of them require the processing speeds to be knowable, thereby the job completion times can be exactly calculated. To present a theoretically guaranteed online scheduling algorithm for multi-server jobs without knowing actual processing speeds apriori, in this article, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Esdp</small> (Efficient Sampling-based Dynamic Programming), which learns the distribution of the fluctuated processing speeds over time and simultaneously seeks to maximize the cumulative overall utility. The cumulative overall utility is formulated as the sum of the utilities of successfully serving each multi-server job minus the penalty on the operating, maintaining, and energy cost. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Esdp</small> is proved to have a polynomial complexity and a logarithmic regret, which is a State-of-the-Art result. We also validate it with extensive simulations and the results show that the proposed algorithm outperforms several benchmark policies with improvements by up to 73%, 36%, and 28%, respectively.

Mobility-Aware and Energy-Efficient Task Offloading Strategy for Mobile Edge Workflows

With the rapid growth of the Industrial Internet of Things (IIoT), the Mobile Edge Computing (MEC) has coming widely used in many emerging scenarios. In MEC, each workflow task can be executed locally or offloaded to edge to help improve Quality of Service (QoS) and reduce energy consumption. However, most of the existing offloading strategies focus on independent applications, which cannot be applied efficiently to workflow applications with a series of dependent tasks. To address the issue, this paper proposes an energy-efficient task offloading strategy for large-scale workflow applications in MEC. First, we formulate the task offloading problem into an optimization problem with the goal of minimizing the utility cost, which is the trade-off between energy consumption and the total execution time. Then, a novel heuristic algorithm named Green DVFS-GA is proposed, which includes a task offloading step based on the genetic algorithm and a further step to reduce the energy consumption using Dynamic Voltage and Frequency Scaling (DVFS) technique. Experimental results show that our proposed strategy can significantly reduce the energy consumption and achieve the best trade-off compared with other strategies.

An Energy-Efficient Method for Recurrent Neural Network Inference in Edge Cloud Computing

Recurrent neural networks (RNNs) are widely used to process sequence-related tasks such as natural language processing. Edge cloud computing systems are in an asymmetric structure, where task managers allocate tasks to the asymmetric edge and cloud computing systems based on computation requirements. In such a computing system, cloud servers have no energy limitations, since they have unlimited energy resources. Edge computing systems, however, are resource-constrained, and the energy consumption is thus expensive, which requires an energy-efficient method for RNN job processing. In this paper, we propose a low-overhead, energy-aware runtime manager to process tasks in edge cloud computing. The RNN task latency is defined as the quality of service (QoS) requirement. Based on the QoS requirements, the runtime manager dynamically assigns RNN inference tasks to edge and cloud computing systems and performs energy optimization on edge systems using dynamic voltage and frequency scaling (DVFS) techniques. Experimental results on a real edge cloud system indicate that in edge systems, our method can reduce the energy up to 45% compared with the state-of-the-art approach.

Cloud Servers: Resource Optimization Using Different Energy Saving Techniques.

Currently, researchers are working to contribute to the emerging fields of cloud computing, edge computing, and distributed systems. The major area of interest is to examine and understand their performance. The major globally leading companies, such as Google, Amazon, ONLIVE, Giaki, and eBay, are truly concerned about the impact of energy consumption. These cloud computing companies use huge data centers, consisting of virtual computers that are positioned worldwide and necessitate exceptionally high-power costs to preserve. The increased requirement for energy consumption in IT firms has posed many challenges for cloud computing companies pertinent to power expenses. Energy utilization is reliant upon numerous aspects, for example, the service level agreement, techniques for choosing the virtual machine, the applied optimization strategies and policies, and kinds of workload. The present paper tries to provide an answer to challenges related to energy-saving through the assistance of both dynamic voltage and frequency scaling techniques for gaming data centers. Also, to evaluate both the dynamic voltage and frequency scaling techniques compared to non-power-aware and static threshold detection techniques. The findings will facilitate service suppliers in how to encounter the quality of service and experience limitations by fulfilling the service level agreements. For this purpose, the CloudSim platform is applied for the application of a situation in which game traces are employed as a workload for analyzing the procedure. The findings evidenced that an assortment of good quality techniques can benefit gaming servers to conserve energy expenditures and sustain the best quality of service for consumers located universally. The originality of this research presents a prospect to examine which procedure performs good (for example, dynamic, static, or non-power aware). The findings validate that less energy is utilized by applying a dynamic voltage and frequency method along with fewer service level agreement violations, and better quality of service and experience, in contrast with static threshold consolidation or non-power aware technique.

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.

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 VM opening algorithms for real-time workflows in heterogeneous clouds

Minimizing energy consumption is a critical challenge for real-time workflows, particularly in heterogeneous cloud computing systems. State-of-the-art algorithms aim to minimize the energy consumed for processing such applications by choosing virtual machines (VMs) to shut down from all opened VMs (i.e., VM merging). However, such VM merging through an “on-to-close” approach usually incurs high computational complexity. This paper proposes an energy-efficient VM opening (EEVO) algorithm that is capable of choosing VMs to turn on from all closed VMs while satisfying the real-time constraint of applications. Considering that there are slacks that can be eliminated or reduced between adjacently scheduled tasks after using the EEVO algorithm, a dynamic scaling down EEVO algorithm (DEEVO) is further proposed. DEEVO is implemented by scaling down the frequency of VMs executing each task based on the dynamic voltage and frequency scaling (DVFS) technique. Experimental results demonstrate that, with the above-mentioned improvements, DEEVO achieves lower energy consumption for real-time workflows than state-of-the-art algorithms do. In addition, DEEVO outperforms state-of-the-art algorithms in the computational efficiency of accomplishing task scheduling.

Convex optimization of random dynamic voltage and frequency scaling against power attacks

Convex optimization is explored in this paper to maximize the security against power analysis attacks while minimizing the power and performance overheads for a cryptographic circuit employs a random dynamic voltage and frequency scaling (RDVFS) technique. To realize this optimization goal, firstly, non-convex functions are used for modeling all the critical parameters related to the RDVFS technique to create a non-convex objective function. Subsequently, the non-convex objective function is appropriately converted into a convex objective function in order to grab the global optimum value. By transforming the acquired convex objective function into the Lagrange dual function and using Karush–Kuhn–Tucker (KKT) conditions to solve this optimization problem, the best RDVFS strategy can be uncovered. As shown in the result, after employing convex optimization for a cryptographic circuit with a RDVFS technique, the corresponding figure-of-merit (FOM) is enhanced by 43.4%. Moreover, in comparison with the conventional machine learning technique, the proposed convex optimization technique improves the overall FOM related with RDVFS technique by 17.6% while reducing 85.3% computational time.

Distribution slack allocation algorithm for energy aware task scheduling in cloud datacenters

Regarding the ever-increasing development of data and computational centers due to the contribution of high-performance computing systems in such sectors, energy consumption has always been of great importance due to CO2 emissions that can result in adverse effects on the environment. In recent years, the notions such as “energy” and also “Green Computing” have played crucial roles when scheduling parallel tasks in datacenters. The duplication and clustering strategies, as well as Dynamic Voltage and Frequency Scaling (DVFS) techniques, have focused on the reduction of the energy consumption and the optimization of the performance parameters. Concerning scheduling Directed Acyclic Graph (DAG) of a datacenter processors equipped with the technique of DVFS, this paper proposes an energy- and time-aware algorithm based on dual-phase scheduling, called EATSDCDD, to apply the combination of the strategies for duplication and clustering along with the distribution of slack-time among the tasks of a cluster. DVFS and control procedures in the proposed green system are mapped into Petri net-based models, which contribute to designing a multiple decision process. In the first phase, we use an intelligent combined approach of the duplication and clustering strategies to run the immediate tasks of DAG along with monitoring the throughput by concentrating on the reduction of makespan and the energy consumed in the processors. The main idea of the proposed algorithm involves the achievement of a maximum reduction in energy consumption in the second phase. To this end, the slack time was distributed among non-critical dependent tasks. Additionally, we cover the issues of negotiation between consumers and service providers at the rate of μ based on Green Service Level Agreement (GSLA) to achieve a higher saving of the energy. Eventually, a set of data established for conducting the examinations and also different parameters of the constructed random DAG are assessed to examine the efficiency of our proposed algorithm. The obtained results confirms that our algorithm outperforms compared the other algorithms considered in this study.

Dynamic frequency scheduling for CubeSat's on-board and data handling subsystem

&lt;p&gt;CubeSat is a small-sized satellite that provides a cheaper option for the manufacturer to have a fully operational satellite. Due to its size, CubeSat can only generate limited power, and this will restrict its functionality. This research aims to improve CubeSat’s power consumption by implementing the dynamic voltage and frequency scaling (DVFS) technique to on-board and data handling subsystem (OBDH). DVFS will find the best operating frequency to execute all of OBDH’s task. This paper explains how we determined the task set, representing all routine tasks performed by OBDH during normal operation mode. We have simulated the task set using two DVFS algorithms, static earliest deadline first (EDF) and cycle conserving edf (CC EDF). The result shows that both scheduling algorithms give a similar result to our task set. However, when the scheduler is configured as non-preemptive, the simulator failed to schedule the critical task. It means that the system fails to work as intended. Therefore, we conclude that we need to implement mixed-criticality scheduling to prevent critical tasks from being aborted by the system.&lt;/p&gt;

Quality and energy optimized scheduling technique for executing scientific workload in cloud computing environment

&lt;p class="Abstract"&gt;Modern BigData data-intensive and scientific workload execution is challenging. The major issues are reliable processing, performance efficiency and energy efficacy perquisite of BigData processing framework. This work assume self-aware MC architectures that autonomously adjust or optimize their performance to accommodate users quality of service (QoS) performance requirement, job execution performance, energy efficiency, and resource accessibility. Extensive workload scheduling has been presented to minimize energy consumption in cloud computing (CC) environment. However, the existing workload scheduling model induces higher amount of interaction cost between inter-processors communications. Further, due to poor resource utilization, routing inefficiency these existing model induces higher energy cost and fails to meet workload QoS prerequisite. For overcoming research challenges, this paper presented quality and energy optimized scheduling (QEOS) technique for executing data-intensive workload by employing dynamic voltage and frequency scaling (DVFS) technique. Experiment outcome shows QEOS model attains good trade-off between system performance and energy consumption in multi-core cloud computing (CC) architectures when compared with existing model.&lt;/p&gt;

Multi‐tier MEC offloading strategy based on dynamic channel characteristics

Comparing with the cloud computing, mobile edge computing (MEC) can further decrease the latency and improve the stability of the networks. However, it is challenging for the edge servers to deal with the large computation task due to the limited computing capacity. In this study, we design a novel three-layer network architecture consisting of mobile devices, edge cloudlets, and helper cloudlets, where the computing data can be partially processed at the edge cloudlet and helper cloudlet. Based on this, a joint communication, offloading, and computation resource allocation problem is formulated to minimise the computation cost and energy consumption. Due to its difficulty to directly solve the formulated problem, we first propose an offloading scheme to obtain the closed-form solutions for the optimal offloading data size. Next, we decompose the optimisation problem into two subproblems: (i) for the cloud execution, we dynamically adjust the data transmission rate according to the stochastic channel condition, (ii) for the mobile execution, the energy consumption can be further reduced by applying the dynamic voltage and frequency scaling technique. Finally, the numerical results demonstrate the efficiency of the proposed scheme, and show the performance gains in terms of delay, computation cost and energy consumption.

Cost Optimization of Partial Computation Offloading and Pricing in Vehicular Networks

For vehicles with limited computation resources offloading their computational tasks to a mobile edge computing (MEC) server has been studied in the past as an effective means for improving their computational capabilities. However, most of these studies do not consider, in a meaningful way, the economic aspects related to both the computation offloading of the vehicles and the MEC service providers. In order to fill this gap, in this paper, a new cost based optimization methodology which jointly considers the cost of partial offloading vs. the pricing of the MEC server is proposed and its performance is analyzed. In particular, we first formally establish the cost model for vehicles and then, by setting a service price, the revenue model for MEC server. Secondly, optimal vehicle offloading strategies are identified and through a cost minimization partial computation offloading algorithm vehicles can configure, in an optimal way, the local CPU frequency and task partition based on the service price. Thirdly, by considering its computation resource limitations, the resource allocation and pricing mechanism for the MEC server is presented. It is shown that, through the development of an appropriate pricing algorithm, the MEC server can obtain the service price which maximizes its revenue while at the same time satisfying the server’s resource constraints. Numerical results have verified that the proposed scheme is indeed more cost effective as compared to local execution with dynamic voltage scaling (DVS) technique, full computation offloading and other partial computation offloading schemes. Furthermore, various performance evaluation results obtained by means of computer simulations have shown that the proposed pricing scheme achieves higher revenue as compared to other previously known fixed and random pricing schemes.

VoltJockey

Based on the concept of hardware separation, ARM introduced TrustZone to build a trusted execution environment for applications. It has been quite successful in defending against various software attacks and forcing attackers to explore vulnerabilities in interface designs and side channels. In this article, we propose an innovative software-controlled hardware fault-based attack, VoltJockey, on multi-core processors that adopt dynamic voltage and frequency scaling (DVFS) techniques for energy efficiency. We deliberately manipulate the processor voltage via DVFS to induce hardware faults into the victim cores, and therefore breaking TrustZone. The entire attack process is based on software without any involvement of hardware, which makes VoltJockey stealthy and hard to prevent.