Energy-aware Task Scheduling Research Articles

REALISM: Reliability-aware energy management in multi-level mixed-criticality systems with service level degradation

Mixed-criticality embedded systems, as the next-generation of safety-critical systems, are increasingly employed in the industry due to consolidating functionalities with varying criticality levels onto the same computing platform. Technology scaling, battery-supplied design, and heavy computation in mixed-criticality systems necessitate employing energy management techniques. Due to the degrading effects of these techniques on the system's reliability, minimizing energy consumption and meeting tasks’ timing constraints without sacrificing the reliability requirement is a vital challenge in designing mixed-criticality systems. In this paper, we propose REALISM; a novel Reliability- and Energy-Aware task mapping and scheduling approach in multi-core mixed-criticality systems with three levels of criticality. To guarantee the desired QoS of tasks from lower levels of criticality, our approach exploits the degraded WCET for these imprecise mixed-criticality tasks in critical modes. In design time, tasks are mapped to cores based on dynamic switching between different system utilizations in different modes. In each core, task instances are scheduled according to their virtual deadlines, which are calculated based on the system's overrun tolerance limit (TL) and the desired QoS, by time reservation. Then our heuristic DVFS techniques are applied in order to reduce system energy consumption. Our simulation results show that the REALISM provides on average ~41.5% (up to 52.7%) energy savings compared to the approach without energy management with 100% QoS degradation in lower levels of criticality. Also, with guaranteeing the system's desired QoS, REALISM provides on average ~38% (up to 46.2%) energy savings compared to the approach without energy management.

Autoregressive Dragonfly Optimization for Multiobjective Task Scheduling (ADO-MTS) in Mobile Cloud Computing

Mobile Cloud computing is a recognized computing platform and is being considered as a business model due to its potential growth in offering required services to the users. However, it poses a number of challenges, among which the consumption of energy in the data centers is the key issue. Hence, an energy aware task scheduling technique, called Autoregressive Dragonfly Optimization-based Multi-objective Task Scheduling (ADO-MTS), is designed that schedules the tasks to the suitable cloud resources. The scheduling of the tasks is either in the public cloud or Mobile Cloud (MC) such that the utilization of energy is reduced. Accordingly, an optimization algorithm, Autoregressive Dragonfly Optimization (ADO), is developed combining Conditional Autoregressive Value at Risk (CAViaR) with Dragonfly Algorithm (DA). Moreover, a multi-objective model concerning energy consumption, makespan, and resource utilization, is designed for the optimal allocation of resources to the tasks. Three measures, such as resource utilization, makespan, and energy, are used to evaluate the performance of the proposed ADO-MTS technique. The results show that the ADO-MTS technique has achieved the maximum performance by increasing the resource utilization and minimizing the makespan and energy consumption as compared to other existing techniques.

Energy Utilization Task Scheduling for MapReduce in Heterogeneous Clusters

Nowadays, energy costs are the most important factor in cloud computing. Therefore, the implementation of energy-aware task scheduling methods is of utmost importance. A task scheduling framework considering deadlines, data locality and resource utilization is proposed to save on energy costs in heterogeneous clusters. The framework consists of task list construction, task scheduling and slot list updating. In terms of deadline constraints, number of job slots allocated and possible processing times of jobs, a new job sequence is proposed to construct an reasonable task list. Tasks are scheduled to promising slots from their rack-local servers, cluster-local servers and remote servers in the produced task scheduling, which greatly improves data locality. After the assignment among tasks and slots, an update of available slots in clusters is proposed not only to find available slots but also to improve server resource utilization using fuzzy logic with the available number of slots according to current CPU, memory and bandwidth utilization. Experimental results show that the proposed heuristic results in lower energy consumption than the adapted existing algorithms with a variable total number of slots.

A genetic algorithm-based tasks scheduling in multicore processors considering energy consumption

Energy consumption has been always an important issue in multicore processors which are getting more and more popular in embedded systems. In this paper, we propose an energy-aware task scheduling approach taking advantages of heuristic algorithms based on genetic algorithm. The proposed approach includes both static and dynamic scheduling schemes. The task scheduling is modelled as a genetic algorithm problem which is mainly used when the tasks are ready before run-time; i.e., static task scheduling. The tasks which arrive after beginning task execution are dynamically scheduled using a proposed heuristic algorithm in combination with the genetic algorithm. The experimental results show that the proposed algorithm achieves more energy efficiency in both static and dynamic task scheduling for multicore processors as compared with similar energy-aware scheduling methods.

A reformed task scheduling algorithm for heterogeneous distributed systems with energy consumption constraints

As the scale increases and performance improves, the energy consumption of high-performance computer systems is rapidly increasing. The energy-aware task scheduling for high-performance computer systems has become a hot spot for major supercomputing centers and data centers. In this paper, we study the task scheduling problem to minimize the schedule length of parallel applications while satisfying the energy constraints in heterogeneous distributed systems. The existing approaches mainly allocate unassigned tasks with minimal energy consumption which cannot achieve optimistic scheduling length in most cases. Based on this situation, we propose a reformed scheduling method with energy consumption constraint algorithm, which is based on an energy consumption level to pre-allocate energy consumption for unassigned tasks. The experimental results show that compared with the existing algorithms, our new algorithm can achieve better scheduling length under the energy consumption constraints.

Energy-aware scheduling using slack reclamation for cluster systems

Reducing energy consumption in processors has become a critical issue in cluster systems. The dynamic voltage and frequency scaling (DVFS) is one of the effective techniques to minimize energy consumption in processors when executing parallel applications. These applications may consist of many interrelated tasks that may be computed when their precedence constraints are satisfied. The scheduling of these precedence-constrained tasks on to processors, to optimize makespan or energy consumption or both, has been studied as an NP-complete problem. Thus, many algorithms have been given by the researchers and newer algorithms keep coming in. In the parallel applications, there exist some tasks which may have slacks due to the dependencies between tasks. The EASLA (energy aware service level agreement) is one of the well-known energy-aware task scheduling algorithms which utilize the underused slack of tasks. In this paper, we present an improved version of its sub-algorithm, NCM (Not Changing Makespan), for heterogeneous cluster systems that is used to downscale frequencies when schedule length does not change. The presented algorithm also makes use of a fast and low complexity algorithm called PEFT (Predict Earliest Finish Time) to compute the schedule length of the application. We performed experiments for randomly generated graphs, and the results illustrate that the improved version of the NCM algorithm achieves better energy saving compared to the NCM algorithm.

An Energy Efficient Task Scheduling Strategy in a Cloud Computing System and its Performance Evaluation using a Two-Dimensional Continuous Time Markov Chain Model

With ongoing energy shortages and rises in greenhouse emissions worldwide, increasing academic attention is being turned towards ways to improve the efficiency and sustainability of cloud computing. In this paper, we present a performance analysis and a system optimization of a cloud computing system with an energy efficient task scheduling strategy directed towards satisfying the service level agreement of cloud users while at the same time improving the energy efficiency in cloud computing system. In this paper, we propose a novel energy-aware task scheduling strategy based on a sleep-delay timer and a waking-up threshold. To capture the stochastic behavior of tasks with the proposed strategy, we establish a synchronous vacation queueing system combining vacation-delay and N-policy. Taking into account the total number of tasks and the state of the physical machine (PM), we construct a two-dimensional continuous-time Markov chain (CTMC), and produce an infinitesimal generator. Moreover, by using the geometric-matrix solution method, we analyze the queueing model in the steady state, and then, we derive the system performance measures in terms of the average sojourn time and the energy conservation level. Furthermore, we conduct system experiments to investigate the proposed strategy and validate the system model according to performance measures. Statistical results show that there is a compromise between the different performance measures when setting strategy parameters. By combining different performance measures, we develop a cost function for the system optimization. Finally, by dynamically adjusting the crossover probability and the mutation probability, and initializing the individuals with chaotic equations, we present an improved genetic algorithm to jointly optimize the sleep parameter, the sleep-delay parameter and the waking-up threshold.

Energy-aware task scheduling in mobile cloud computing

The limited energy supply, computing, storage and transmission capabilities of mobile devices pose a number of challenges for improving the quality of service (QoS) of various mobile applications, which has stimulated the emergence of many enhanced mobile computing paradigms, such as mobile cloud computing (MCC), fog computing, mobile edge computing (MEC), etc. The mobile devices need to partition mobile applications into related tasks and decide which tasks should be offloaded to remote computing facilities provided by cloud computing, fog nodes etc. It is very important yet tough to decide which tasks to be uploaded and where they are scheduled since this could greatly impact the applications’ timeliness and mobile devices’ lifetime. In this paper, we model the task scheduling problem at the end-user mobile device as an energy consumption optimization problem, while taking into account task dependency, data transmission and other constraint conditions such as task deadline and cost. We further present several heuristic algorithms to solve it. A series of simulation experiments are conducted to evaluate the performance of the algorithms and the results show that our proposed algorithms outperform the state-of-the-art algorithms in energy efficiency as well as response time.

An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

<p>Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads.</p>

Energy-efficient hadoop for big data analytics and computing: A systematic review and research insights

As the demands for big data analytics keep growing rapidly in scientific applications and online services, MapReduce and its open-source implementation Hadoop gained popularity in both academia and enterprises. Hadoop provides a highly feasible solution for building big data analytics platforms. However, defects of Hadoop are also exposed in many aspects including data management, resource management, scheduling policies, etc. These issues usually cause high energy consumption when running MapReduce jobs in Hadoop clusters. In this paper, we review the studies on improving energy efficiency of Hadoop clusters and summarize them in five categories including the energy-aware cluster node management, energy-aware data management, energy-aware resource allocation, energy-aware task scheduling and other energy-saving schemes. For each category, we briefly illustrate its rationale and comparatively analyze the relevant works regarding their advantages and limitations. Moreover, we present our insights and figure out possible research directions including energy-efficient cluster partitioning, data-oriented resource classification and provisioning, resource provisioning based on optimal utilization, EE and locality aware task scheduling, optimizing job profiling with machine learning, elastic power-saving Hadoop with containerization and efficient big data analytics on Hadoop. On one hand, the summary of studies on energy-efficient Hadoop presented in this paper provides useful guidance for the developers and users to better utilize Hadoop. On the other hand, the insights and research trends discussed in this work may inspire the relevant research on improving the energy efficiency of Hadoop in big data analytics.

An Energy-Aware Task Scheduling in the Cloud Computing Using a Hybrid Cultural and Ant Colony Optimization Algorithm

In a cloud environment, computing resources are available to users, and they pay only for the used resources. Task scheduling is considered as the most important issue in cloud computing which affects time and energy consumption. Task scheduling algorithms may use different procedures to distribute precedence to subtasks which produce different makespan in a heterogeneous computing system. Also, energy consumption can be different for each resource that is assigned to a task. Many heuristic algorithms have been proposed to solve task scheduling as an NP-hard problem. Most of these studies have been used to minimize the makespan. Both makespan and energy consumption are considered in this paper and a task scheduling method using a combination of cultural and ant colony optimization algorithm is presented in order to optimize these purposes. The basic idea of the proposed method is to use the advantages of both algorithms while avoiding the disadvantages. The experimental results using C# language in cloud azure environment show that the proposed algorithm outperforms previous algorithms in terms of energy consumption and makespan.

Energy Efficient Scheduling Methods for Computational Grids and Clouds

This paper presents an overview of techniques developed to improve energy efficiency of grid and cloud computing. Power consumption models and energy usage proles are presented together with energy efficiency measuring methods. Modeling of computing dynamics is discussed from the viewpoint of system identication theory, indicating basic experiment design problems and challenges. Novel approaches to cluster and network-wide energy usage optimization are surveyed, including multi-level power and software control systems, energy-aware task scheduling, resource allocation algorithms and frameworks for backbone networks management. Software-development techniques and tools are also presented as a new promising way to reduce power consumption at the computing node level. Finally, energy-aware control mechanisms are presented. In addition, this paper introduces the example of batch scheduler based on ETC matrix approach.

Energy-aware task scheduling by a true online reinforcement learning in wireless sensor networks

Wireless sensor networks (WSNs) are an attractive platform for various pervasive computing applications. A typical WSN application is composed of different tasks. In this paper, an energy-aware task scheduling method is proposed to achieve better energy consumption/performance trade-off. The proposed method exploits a true online reinforcement learning algorithm. This method is compared with the existing approaches exploiting online learning: distributed independent reinforcement learning (DIRL), reinforcement learning (RL), cooperative reinforcement learning (CRL) and exponential weight for exploration and exploitation (Exp3), in terms of tracking quality/energy consumption trade-off. Simulation results show that our proposed method outperforms existing methods for the target tracking application.

DReAM

Accurate per-task energy estimation in multicore systems would allow performing per-task energy-aware task scheduling and energy-aware billing in data centers, among other applications. Per-task energy estimation is challenged by the interaction between tasks in shared resources, which impacts tasks’ energy consumption in uncontrolled ways. Some accurate mechanisms have been devised recently to estimate per-task energy consumed on-chip in multicores, but there is a lack of such mechanisms for DRAM memories. This article makes the case for accurate per-task DRAM energy metering in multicores, which opens new paths to energy/performance optimizations. In particular, the contributions of this article are (i) an ideal per-task energy metering model for DRAM memories; (ii) DReAM, an accurate yet low cost implementation of the ideal model (less than 5% accuracy error when 16 tasks share memory); and (iii) a comparison with standard methods (even distribution and access-count based) proving that DReAM is much more accurate than these other methods.

Energy-Aware Task Scheduling by a True Online Reinforcement Learning in Wireless Sensor Networks

Wireless sensor networks (WSNs) are an attractive platform for various pervasive computing applications. A typical WSN application is composed of different tasks. In this paper, an energy-aware task scheduling method is proposed to achieve better energy consumption/performance trade-off. The proposed method exploits a true online reinforcement learning algorithm. This method is compared with the existing approaches exploiting online learning: distributed independent reinforcement learning (DIRL), reinforcement learning (RL), cooperative reinforcement learning (CRL) and exponential weight for exploration and exploitation (Exp3), in terms of tracking quality/energy consumption trade-off. Simulation results show that our proposed method outperforms existing methods for the target tracking application.

Holistic multiobjective planning of datacenters powered by renewable energy

Energy efficiency is a major concern to datacenter operators, because the large amounts of energy used by parallel computing infrastructures increases costs and affects the electricity grid. Datacenter power consumption can be reduced by applying intelligent control techniques to dynamically adjust power demand, but this is hampered by conflicting objectives. For instance, the workload can be controlled to adjust power, but at the expense of service quality. Or, the cooling infrastructure demand can be manipulated without affecting workloads, but at the risk of shifting the datacenter temperature outside the acceptable limits. This paper proposes a multiobjective, evolutionary approach to solving the problem of energy-aware task scheduling in datacenters. Our approach takes into account three problem objectives (power consumption, temperature, and quality of service) when both computing and cooling infrastructures are holistically controlled. We report the two best solutions to each of these problem objectives, as well as the selected trade-off solutions between them.

SEATS: smart energy-aware task scheduling in real-time cloud computing

Mitigating energy consumption in Clouds reduces operational costs for providers. Power management policies which aim to reduce total energy consumed in data-centers pose challenges in both hardware technologies and resource management policies. We introduce an optimal utilization level of a host to execute a certain number of instructions to minimize energy consumption of the host. We also propose a virtual machine (VM) scheduling algorithm based on the unsurpassed utilization level to come up with optimal energy consumption while meeting a given QoS. In other words, our proposed algorithm regulates allocated computing resources of VMs on a host which results in reaching an optimal energy level in the host. The simulation results show that our proposed method not only reduces total energy consumption of a Cloud by 60 %, but also has a profound impact on turnaround times of real-time tasks by 94 %. It also increases the acceptance rate of arrival tasks by 96 %. Moreover, it takes a leading part in accepting lengthy tasks which have short deadlines.

Energy-Aware task scheduler for self-powered sensor nodes: From model to firmware

The self-powering paradigm surely represents a challenging issue in the Wireless Sensor Network (WSN) field. The chance of supplying the sensor node with environmental energy is attractive not only to make it autonomous and reduce the human intervention for battery substitution but also to improve the overall WSN flexibility and applicability. From this same perspective, a relevant role is played by the node task scheduling, which is required to face strong constraints due to energy availability limitations. On purpose, some of the authors have recently proposed an Energy-Aware approach (namely Energy-Aware Lazy Scheduling Algorithm, EA-LSA), which shows a certain versatility in efficiently exploiting the harvested energy, reducing the starving occurrences in non-ideal conditions, with respect to the no-EA counterpart.In this work a task scheduler based on the EA-LSA technique has been implemented on a low-cost and commercially available platform, from Texas Instruments, with the aim to propose an open and easy-to-use HW/SW reference for the community working in the field. The firmware, including the hardware abstraction and the scheduling routine layers, has been developed from scratch. Moreover, a few tasks have been developed so that the device activity could be simulated in a realistic scenario.To monitor the harvesting process an emulated energy harvester has been devised and included in the overall framework. The different task energy consumptions have been measured by means of an energy audit tool, specifically set up in order to properly gauge the very low current consumptions of the device. Experiments performed under different power income levels, allowed to positively conclude about the effectiveness of the implemented Energy-Aware scheduler, its flexibility in terms of environmental energy exploitation, and therefore its suitability for involvement in real-world WSNs.

On optimizing system energy of voltage–frequency island based 3-D multi-core SoCs under thermal constraints

Three dimensional (3-D) multi-core SoC has been recognized as a promising solution for implementing complex applications with lower system energy. Recently, voltage–frequency island (VFI)-based design paradigm was widely adopted for energy optimization. However, the existing work commonly targeted 2-D platform, which cannot handle the exacerbated thermal issues and the increased solution space from 3-D integration. In this paper, we propose an optimization framework targeting VFI-based 3-D multi-core SoCs to minimize system energy meanwhile still meeting task deadline and thermal constraints. Our framework conducts at an earlier design phase in which designers have the freedom to determine the core stacks and map them into the hardware platform. Besides energy-aware task scheduling, we also conduct core stacking and task adjusting to balance the powers across the chip for thermal optimization. Moreover, by treating each core stack as a unity, the complicated problem of core mapping and VFI partitioning in 3-D platform can be simplified as a 2-D one. Experimental results demonstrate that on average our framework can achieve an energy reduction of 15.8% over the prior thermal balancing algorithm [17] (X. Zhou, J. Yang, Y. Xu, et al. Thermal-aware task scheduling for 3D multicore processors, IEEE Trans. Parallel Distrib. Syst. (TPDS), 21(1) (2010), 60–71.). Moreover, on average a reduction of 4.8°C in peak temperature is achieved by our framework, compared with the state-of-the-art energy optimization scheme [8] (U.Y. Ogras, R. Marculescu, P. Choudhary, et al. Voltage–frequency island partitioning for GALS-based networks-on-chip, in: ACM/IEEE Design Automation Conference (DAC), 2007, pp. 110–115.).

Energy-aware task scheduling in heterogeneous computing environments

Efficient application scheduling is critical for achieving high performance in heterogeneous computing (HC) environments. Because of such importance, there are many researches on this problem and v...