Adaptive energy minimization of embedded heterogeneous systems using regression-based learning

SummaryIn the modern Internet of Things (IoT) era, several applications generate a vast amount of data and that needs to be handled appropriately. The conventional cloud computing system delivers us with enormous resources to manage such voluminous data. Despite that, the growing demands of IoT applications on minimal energy consumption, minimal latency, the privacy of data, data processing based on location, and maximum Quality of Service(QoS) impels the advent of fog computing. As the devices in the fog layer are heterogeneous, distributed, and resource‐constrained, how the fog resources are effectively utilized for executing latency and time‐sensitive applications is a primary challenge. The data generated by the IoT devices are voluminous and produce overhead in network bandwidth during transmission and slow down the response time. This article addresses the task scheduling problem in fog computing to minimize the makespan and energy consumption. The proposed work consists of two phases. In the first phase, the tasks are ordered based on the heuristic method, heterogeneous earliest finish time (HEFT). Then the ordered tasks are scheduled by applying the improved gaining sharing knowledge (IGSK) based algorithm. To reduce the energy consumption dynamic voltage frequency scaling (DVFS) is applied in the proposed scheme. To evaluate the proposed scheduling method the simulations are performed on different workflows with varying sizes. The performance evaluation results exhibit that the proposed work outperforms the task scheduling with similar methods in terms of makespan and energy consumption.

Modern information and communication technology along with the broad adaptation of computing paradigms, namely cloud computing has significant impact on environment due to huge amount of CO 2 emission. In the recent decade, a significant growth in the research domain of “green” and low power consumption network has been observed. With the recent advancements of compute intensive machine learning frameworks like deep learning, the energy consumption by GPUs is a challenging issue. In all of these cases, Dynamic Voltage Frequency Scaling (DVFS) has gained significant research interests and shown encouraging outcome. Dynamic Voltage and Frequency Scaling (DVFS) is utilized to reduce the energy consumption in data centers without impacting the Quality of Service. On the other side, spatio-temporal dataset is huge in size and requires compute and time intensive algorithms and techniques for different spatio-temporal query processing. In all aspects, the correlations among energy, DVFS, consolidation, and performance are the key-enablers of energy-efficient management. In this chapter, we explore different existing DVFS techniques and challenges to analyse and process spatio-temporal query in cloud based paradigm. We have considered movement datasets for demonstrating energy consumption and utility of dynamic voltage and frequency scaling.KeywordsDynamic voltage and frequency scalingSpatio-temporal QueryCloud datacenterScheduling algorithmPowerEnergy

A smart energy and reliability aware scheduling algorithm for workflow execution in DVFS-enabled cloud environment

Power awareness has become a critical issue in real-time scheduling of embedded systems. In the context of control applications, the goal of high control performance and low energy consumption are at odds with each other. While dynamic voltage/frequency scaling (DVS) has proved to be promising in energy saving while preserving task schedulability, traditional DVS algorithms use either open loop or ad hoc solutions, and hence cannot perform well for dynamic systems where the workload varies significantly. By targeting these systems, a novel scheme, namely DVS-FS, which combines DVS and feedback scheduling, is suggested. The objective is to save CPU energy as much as possible, while still providing control performance guarantees, which largely depends on successful schedule of the control task set. DVS-FS exploits feedback control methodology, and facilitates tradeoffs between energy consumption and control performance through controlling the CPU utilization at a considerably high level. Simulation experiments demonstrate that DVS-FS can easily reduce significant energy consumption at the expense of only minor control performance degradation.

Cloud computing is the combination of grid computing, distributed computing and utility computing. Cloud computing provides various types of services (servers and storage facility) in the on demand basis. The main goal of the cloud computing is to preserve and organize the very huge data center or data forms. The data forms are composed of thousands of servers that absorb the giant (ample) amount of electricity in the word of energy. The decreasing the energy consumption in datacenter is the major challenge in cloud computing now a day. This research article is going to address the problem of high energy consumption at datacenter. Concentrate on virtual machine scheduling in cloud datacenter with Dynamic Voltage Frequency Scaling (DVFS) approach. We have combined shortest job first and Round Robin algorithms with Vibrant Quantum. This combination of algorithm is considered as shortest round vibrant queue (SRVQ) algorithm. SRVQ reduces the waiting time of the scheduling process and minimize the starvation. The DVFS and SRVQ worked together and produced fruitful results in the final experiments. This work reduced the server’s energy consumption in the cloud data center. In the final results, our proposed framework exhibits 45% of energy efficiency compare to other previously proposed algorithms. 33% of QoS performance were enhanced by our framework.

The high energy consumption associated with acceleration requires electric vehicles (EVs) to accelerate to a chosen speed optimally, particularly in urban driving cycles. Existing methods deal with the minimization of acceleration energy without considering the acceleration duration. This paper focuses on solving a multiobjective optimization problem (MOOP) with two conflicting objectives: minimization of acceleration duration and minimization of energy consumption. Two approaches were used to reach a desired speed: using a single acceleration value and using multiple acceleration values. For each approach, demonstrative speed changes were chosen, and the problem was solved using multiobjective genetic algorithms (MOGAs). The results (Pareto-optimal fronts) obtained by these two approaches were compared using suitable performance metrics. To validate the reliability of MOGA results, statistical analysis was carried out. Furthermore, a nonparametric study, i.e., the Wilcoxon signed-rank test, was conducted to compare the effectiveness of both approaches. It was found that multiple accelerations were more effective in minimizing the duration and energy consumption than a single acceleration. For the same duration, multiple accelerations reduced energy consumption by up to 2%. Sensitivity analysis for both approaches with electric motor model parameters was conducted. The simulation results of EV acceleration using the preferred optimal solution based on driving comfort and the Pareto front's knee suggested a strong implication toward driving assistance.

Energy consumption and cycle time are two contradictory optimization objectives for the robotic assembly line balancing problem (RALBP). Indeed, minimizing the cycle time leads to choosing the fastest robots, while minimizing the energy consumption leads to choosing the robots with the smallest powers. In the context of RALBP, cycle time minimization has been extensively studied while energy minimization has been much less considered. Studies dealing with simultaneous minimization of the two later are even scarcer. A bi‐objective RALBP considering simultaneous minimization of cycle time and energy consumption is studied in this paper. The energy consumption is calculated based on recent papers from the literature. It includes energy consumed during both operation time and idle time. In this paper, a pseudo‐polynomial case is solved thanks to an exact algorithm called split. This latter enumerates all Pareto‐optimal solutions corresponding to a given giant sequence of operations. Split is then used as a decoder in a metaheuristic operating in a reduced search space where giant sequences encode solutions. An experimental study is performed on instances taken from the literature to test the suggested encoding–decoding scheme. It shows that the suggested approach yields competitive results compared to the literature.

The aim is to achieve schedulability of tasks in a real time system with fault tolerance and energy minimization. The fault tolerance can be achieved by maintaining enough time redundancy so that task can be re-executed in presence of fault. During re-execution, check pointing policy gives reliability in a system. First, calculate an optimal number of checkpoints to reduce the redundancy and save the system to complete re-execution. Energy minimization can be achieved by DVFS (Dynamic Voltage Frequency & Scaling). In this paper, existing non-preemptive EDF scheduling algorithm has been modified for fault tolerance and energy minimization. We adjust the voltage level according to available storage energy in the system & find feasibility test on each task. The worst case execution time is associated with voltage level. The approach is developed to enhance task schedulability and minimize energy consumption in presence of fault. At the end of the paper, experimental results shows that proposed algorithm is better than existing algorithm.

Nowadays, the expanding computational capabilities of the cloud system rely on the minimisation of the consumed power to make them sustainable and economically productive. Power management of cloud data centres received a great attention from industry and academia as it consumes high energy and thus increases the operational cost. One of the core approaches for the conservation of energy in the cloud data centre is the task scheduling. This task allocation in a heterogeneous environment is a well known NP-hard problem due to which researchers pay attention for proposing various heuristic techniques for the problem. In this paper, a technique is proposed based on dynamic voltage frequency scaling (DVFS) for optimising the energy consumption in the cloud environment. The basic idea is to address the trade-off between energy consumption and makespan of the system. Here, we formally introduce a model that includes various subsystems and assess the implementation of the algorithm in the heterogeneous environment.

With the development of streaming media, soft real-time system in today’s life could participate in the use of more extensive areas. The use frequency was also increasing. Consequently, modern processors were equipped with software control mechanisms such as DVFS (Dynamic Voltage Frequency Scaling) to allow operating systems to meet required performance while reducing power consumption. Therefore, we propose a task scheduling algorithm combined DVFS technology and time deterministic cyclic scheduling to achieve energy saving effect. First, the algorithm needed to minimize the preemption between tasks to reduce latency, so we created a buffer to save periodic tasks to avoid preemption. Second, to reduce the computational cost of the scheduling scheme, a scheduling template were designed to perform tasks. In this paper, the scheduling of periodic tasks, task scheduling would be designed when the task scheduling template would be fixed length. Besides, this algorithm supported that task could adopt appropriate voltage and frequency through DVFS technology in idle time under the condition of satisfying task dependence. Experimental analysis showed that the proposed algorithm could effectively reduce the system energy consumption while ensuring the completion of the task.

Reducing energy consumption without affecting computational performance is a significant research driver in computer engineering. The Partitioned Global Address Space (PGAS) programming model provides a global address space for ease-of-use while providing locality-awareness for efficient execution. For symmetric multiprocessor (SMP) clusters, PGAS locality-awareness offers opportunities for intelligent energy management due to the inherent latencies in the interconnection network fabric. In this paper, the second in a series that explores PGAS power optimization, we present a detailed examination of how data locality-awareness can be exploited to improve the energy consumption of workload sharing on SMP clusters. We present a systematic methodology for analyzing a given algorithm's potential for locality-aware power optimization. We show how to automate the code modifications for an iterative stencil loop algorithm using a profiler that analyzes the algorithms' non-local access patterns and optimizes the code for thread-local data pre-fetch. We explore the use of dynamic voltage frequency scaling (DVFS) in conjunction with time-based optimizations and provide an extensible and precise power measurement framework for generating the experimental results. We compare optimized and CPU-default approaches to DVFS application at the program-execution and processor architecture level. We show how the latency of the cluster's interconnection network can impact the opportunities for power optimization by comparing the programs' energy/time behavior when the cluster nodes are connected by Ethernet or Infiniband network fabrics. For our representative iterative stencil loop algorithm, locality aware power optimizations show significant improvements in energy efficiency over native code running in default processor power modes.

Emerging trends in applications with the requirement of considerable computational performance and decreasing time-to-market have urged the need of multiprocessor systems. With the increase in number of processor there is an increased demand to efficiently control the energy and power budget of such embedded systems as well. Dynamic voltage frequency scaling (DVFS) strategies permits to control this budget by actively changing the power consumption profile of the system. These techniques exploit the execution times variation of Real-time applications for dynamically adjusting the voltage and frequency of processors in order to reduce power and energy consumption. This paper presents one DVFS strategy called Dynamic slack reclamation (DSR) for real time applications. It is based on detecting early completion of tasks. DSR determines the amount of dynamic slack (if any) by comparing the worst-case execution requirement of a tasks job with its actual execution requirements. Experimental results will be extracted from the simulation of MPSoC architectures using the SoCLib platform; they show that DSR approach gives better power consumption performance.

This paper proposes to take the relationship between delay and workload into account in the power optimization of microprocessors in mobile embedded systems. Since the components outside a device continuously change their values or properties, the workload to be handled by the systems becomes dynamic and variable. This variable workload is formulated as a staircase function of the delay taken at the previous iteration in this paper and applied to the power optimization of DVFS (dynamic voltage-frequency scaling). In doing so, a graph representation of all possible workload/mode changes during the lifetime of a device, Workload Transition Graph (WTG), is proposed. Then, the power optimization problem is transformed into finding a cycle (closed walk) in WTG which minimizes the average power consumption over it. Out of the obtained optimal cycle of WTG, one can derive the optimal power management policy of the target device. It is shown that the proposed policy is valid for both continuous and discrete DVFS models. The effectiveness of the proposed power optimization policy is demonstrated with the simulation results of synthetic and real-life examples.

In the past few years, with the rapid development of heterogeneous computing systems (HCS), the issue of energy consumption has attracted a great deal of attention. How to reduce energy consumption is currently a critical issue in designing HCS. In response to this challenge, many energy-aware scheduling algorithms have been developed primarily using the dynamic voltage-frequency scaling (DVFS) capability which has been incorporated into recent commodity processors. However, these techniques are unsatisfactory in minimizing both schedule length and energy consumption. Furthermore, most algorithms schedule tasks according to their average-case execution times and do not consider task execution times with probability distributions in the real-world. In realizing this, we study the problem of scheduling a bag-of-tasks (BoT) application, made of a collection of independent stochastic tasks with normal distributions of task execution times, on a heterogeneous platform with deadline and energy consumption budget constraints. We build execution time and energy consumption models for stochastic tasks on a single processor. We derive the expected value and variance of schedule length on HCS by Clark's equations. We formulate our stochastic task scheduling problem as a linear programming problem, in which we maximize the weighted probability of combined schedule length and energy consumption metric under deadline and energy consumption budget constraints. We propose a heuristic energy-aware stochastic task scheduling algorithm called ESTS to solve this problem. Our algorithm can achieve high scheduling performance for BoT applications with low time complexity $O(n(M+\log n))$ , where $n$ is the number of tasks and $M$ is the total number of processor frequencies. Our extensive simulations for performance evaluation based on randomly generated stochastic applications and real-world applications clearly demonstrate that our proposed heuristic algorithm can improve the weighted probability that both the deadline and the energy consumption budget constraints can be met, and has the capability of balancing between schedule length and energy consumption.

The borehole heat exchanger (BHE) is an efficient and economic tool for extracting geothermal energy within ground source heat pumps (GSHPs). The layout and cross-section tube configuration of the BHE is one of the crucial bottlenecks that strongly influenced the effectiveness of the BHEs, which recently attracted numerous attention.Hence, the interest in retrofitting cost-efficient configurations of BHEs is recently regarded as an improvement pathfor maximizing geothermal energy production. In this study, novel geothermal borehole heat exchangers with elliptic and oval double U-tube (2U) configurations are investigated and compared to a traditional circular 2U BHE.This paper explains the development of traditional circular 2U-tube BHEs that makes an optimally designed model for maximizing the heat extraction rates and minimizing the energy consumption characteristics and BHE number. A 3-D numerical model was developed and validated by relevant field experiments (Tianjin geothermal project). The numerical study was performed on four cases corresponding to different widths of elliptic and oval 2U BHEs configurations. Furthermore, a sensitivity analysis was performed to parametrically investigate the impacts of key design and operational parameters on heat extraction performance. Additionally, a 3-D numerical model coupled with a finite line heat source model was also developed to perform case studies for energy consumption analysis for two goals of optimization. The results show that the oval 2U BHE (type II) outperformed the other investigated 2U BHE configurations, which increased the maximal and mean heat transfer rate by up to 54.0% and 12.30%, respectively, compared to circular 2U BHE. While the elliptic 2U-tube BHE (type II) was minimal pressure drop and energy consumption rates, which showed a pressure drop of 36.20% lower than the circular 2U BHE. In the case study findings, the utilization of elliptic 2U-tube BHE (type II) can not only reduce the BHE number by 6.70% but also minimize the energy consumption of the water pump and the entire system by 36.9% and 8.2%, compared to that of traditional circular 2U-tube BHE, respectively.