Energy-aware Scheduling Algorithm Research Articles

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.

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

AbstractThis paper presents an analysis of the performance of the Energy Aware Scheduling Algorithm (EASA) in a 5G green communication system. 5G green communication systems rely on EASA to manage resource sharing. The aim of the proposed model is to improve the efficiency and energy consumption of resource sharing in 5G green communication systems. The main objective is to address the challenges of achieving optimal resource utilization and minimizing energy consumption in these systems. To achieve this goal, the study proposes a novel energy‐aware scheduling model that takes into consideration the specific characteristics of 5G green communication systems. This model incorporates intelligent techniques for optimizing resource allocation and scheduling decisions, while also considering energy consumption constraints. The methodology used involves a combination of mathematical analysis and simulation studies. The mathematical analysis is used to formulate the optimization problem and design the scheduling model, while the simulations are used to evaluate its performance in various scenarios. The proposed EASM reached a 91.58% false discovery rate, a 64.33% false omission rate, a 90.62% prevalence threshold, and a 91.23% critical success index. The results demonstrate the effectiveness of the proposed model in terms of reducing energy consumption while maintaining a high level of resource utilization.

Online fault tolerant energy-aware algorithm for CubeSats

Energy and reliability constraints play an important role in performances of embedded multiprocessor systems. This paper proposes an online fault tolerant energy-aware scheduling algorithm called OneOffEnergy. It is designed for energy-constrained applications, such as CubeSats, with aim to make these small satellites more robust and to reduce energy consumption. We propose to take advantage of multiprocessor architecture and put together all CubeSat processors on one board. This multiprocessor system makes use of our devised algorithm, which schedules all tasks aboard a CubeSat, detects faults and takes appropriate measures in order to deliver correct results subject to limited energy resources.OneOffEnergy operates in two processor modes (Run and Standby) to save energy and considers three system modes (normal, safe and critical) related to the task priorities depending on the current energy stored in the battery. In order to show the wide algorithm usability, OneOffEnergy was not only evaluated for CubeSats but also in the context of another energy-constrained application.It is shown that the implementation of OneOffEnergy aboard a CubeSat reduces energy consumption. The results demonstrate that putting processors in Standby mode brings energy savings and OneOffEnergy exhibits better performances in terms of the system operation under energy constraints when compared to simpler algorithms. Finally, it was found that OneOffEnergy also performs well in a harsh environment.

Intelligent energy-efficient scheduling with ant colony techniques for heterogeneous edge computing

Energy efficiency is a significant issue in heterogeneous edge computing systems for a large number of latency-sensitive applications. This article presents an efficient technique to minimize energy overhead of time-constrained applications modeled by DAGs in heterogeneous edge computing. The technique is divided into three stages. First, we design a new method to compute task priority and propose the ant-colony based energy-aware scheduling algorithm to get a preliminary scheduling result. Second, taking the slack time between tasks and their deadlines into consideration, we propose the downward proportionally reclaiming slack algorithm to further cut down energy overhead by the DVFS technique. Third, taking the slack time between tasks into consideration, we propose the upward and downward proportionally reclaiming slack algorithm to cut down energy overhead by the DVFS technique again. Simulated results indicate that the presented technique is highly efficient in reducing energy overhead compared with state-of-the-art techniques using benchmarks of distinct characteristics.

Energy-Aware Cloud Task Scheduling algorithm in heterogeneous multi-cloud environment

Cloud computing is growing massively in the world of information technology. As a result of which the number of servers and virtual machines is increasing day by day. It leads to huge energy consumption and carbon emission. In this regard, task scheduling has drawn the attention of cloud service providers to reduce energy consumption and increase cloud utilization. This is a crucial issue in a heterogeneous cloud environment. In this paper, energy consumption issues of cloud datacenters are addressed and an Energy-Aware Cloud Task Scheduling (EACTS) algorithm is proposed. It is taking the concept from traditional min-min, max-min, and sufferage heuristics and merge with the energy model. These heuristics are implemented on the heterogeneous cloud environment. EACTS energy model focuses on the estimation of energy consumption in cloud datacenters. The proposed algorithms estimated the makespan, cloud utilization, and energy consumption for a benchmark dataset. The experimental results obtained from EACTS algorithm provides a valid arbitration between energy efficiency and makespan. It has shown the comparison between above mentioned algorithms for various scheduling parameters.

Energy-aware scheduling for dependent tasks in heterogeneous multiprocessor systems

Heterogeneous multiprocessor platform has been widely adopted as an effective approach to providing strong calculation capability while keeping complexity and energy consumption under control in large-scale systems. Although this platform is able to achieve efficient cost reduction and flexibility enhancement in the design and development process of real-time applications, it brings a serious and complex multi-task scheduling problem, especially for dependent tasks with energy consumption constraints. All tasks should be scheduled according to appropriate strategies such that their dependence requirements and energy consumption limitations would be satisfied even in the worst-case situations. In this work, we focus on the energy-aware scheduling problem of dependent tasks in heterogeneous multiprocessor systems. First, we model the dependent tasks and heterogeneous processors, and formulate the energy-aware scheduling problem as a constrained optimization one with an objective of minimizing the schedule length of tasks. Then, by adopting an efficient task prioritization strategy and a weight-based energy distribution strategy, we propose a list-based energy-aware scheduling algorithm to seek an approximate optimal start time and processor allocation for each task, guaranteeing that all tasks would be executed efficiently while meeting the dependence and energy requirements. Experiments with randomly generated tasks are conducted to evaluate the performances of the proposed approach in terms of schedule length, optimal solution ratio, and execution time.

Structure-Free General Data Aggregation Scheduling for Multihop Battery-Free Wireless Networks

With advances in wireless power transfer techniques, battery-free wireless sensor networks (BF-WSNs) which can support long-term applications, has been attracting increasing interests in recent years. Unfortunately, the problem of minimum latency aggregation scheduling (MLAS) is not well studied in BF-WSNs. Existing works always have a rigid assumption that there is only one single query which is targeted at the whole network. Aiming at making the work more practical and general, we investigate the general MLAS problem in BF-WSNs, which is targeted at any subset of nodes in the network and aimed for an arbitrary number of aggregation queries. First, the general MLAS problem when there is one single query is studied. To control the number of nodes participating in the aggregation process, a node selection algorithm is proposed to cover and connect the whole target nodes. Then, a latency and energy aware scheduling algorithm is proposed to integrate the construction of aggregation tree with the chosen nodes, and the computation of a conflict-free schedule simultaneously, relying on non-predetermined structures. Second, the general MLAS problem when there is a group of aggregation queries is studied. Through designing some special structures to avoid collisions between both current and existing aggregation schedules, an algorithm without any waiting time is proposed. Additionally, the algorithm under physical interference model and dynamic energy arrival model are also presented. The theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency and energy efficiency.

An energy-aware scheduling algorithm under maximum power consumption constraints

This research investigates the production scheduling problems under maximum power consumption constraints. Probabilistic models are developed to model dispatching-dependent and stochastic machine energy consumption. A multi-objective scheduling algorithm called the energy-aware scheduling optimization method is proposed in this study to enhance both production and energy efficiency. The explicit consideration of the probabilistic energy consumption constraint and the following factors makes this work distinct from other existing studies in the literature: 1) dispatching-dependent energy consumption of machines, 2) stochastic energy consumption of machines, 3) parallel machines with different production rates and energy consumption pattern, and 4) maximum power consumption constraints. The proposed three-stage algorithm can quickly generate near-optimal solutions and outperforms other algorithms in terms of energy efficiency, makespan, and computation time. While minimizing the total energy consumption in the first and second stages, the proposed algorithm generates a detailed production schedule under the probabilistic constraint of peak energy consumption in the third stage. Numerical results show the superiority of the scheduling solution with regard to quality and computational time in real problems instances from manufacturing industry. While the scheduling solution is optimal in total energy consumption, the makespan is within 0.6 % of the optimal on average.

Energy-Aware Scheduling of Workflow Using a Heuristic Method on Green Cloud

Energy consumption has been one of the main concerns to support the rapid growth of cloud data centers, as it not only increases the cost of electricity to service providers but also plays an important role in increasing greenhouse gas emissions and thus environmental pollution, and has a negative impact on system reliability and availability. As a result, energy consumption and efficiency metrics have become a vital issue for parallel scheduling applications based on tasks performed at cloud data centers. In this paper, we present a time and energy-aware two-phase scheduling algorithm called best heuristic scheduling (BHS) for directed acyclic graph (DAG) scheduling on cloud data center processors. In the first phase, the algorithm allocates resources to tasks by sorting, based on four heuristic methods and a grasshopper algorithm. It then selects the most appropriate method to perform each task, based on the importance factor determined by the end-user or service provider to achieve a solution designed at the right time. In the second phase, BHS minimizes the makespan and energy consumption according to the importance factor determined by the end-user or service provider and taking into account the start time, setup time, end time, and energy profile of virtual machines. Finally, a test dataset is developed to evaluate the proposed BHS algorithm compared to the multiheuristic resource allocation algorithm (MHRA). The results show that the proposed algorithm facilitates 19.71% more energy storage than the MHRA algorithm. Furthermore, the makespan is reduced by 56.12% in heterogeneous environments.

KRAFT: Kalman Filter based Energy-aware LTE and Wi-Fi Joint Throughput Optimization

Resource block allocation in LTE is a complex metric consisting of available bandwidth, required data rate and channel state conditions. A Wi-Fi access point, co-operating with LTE eNB can service a single User Equipment (UE) through multiple interfaces (multihoming) for improving throughput. The challenge is to inter-operate the greedy LTE scheduler with deferral-based Wi-Fi band allocation. A joint scheduling therefore is a complex problem requiring a robust estimation method which not only optimize per-user throughput but also can support a minimum average system throughput condition. Kalman filter provides resource prediction while minimizing the mean square error. In this paper, we propose KRAFT, a novel LTE and Wi-Fi joint scheduler using Kalman filter. We periodically measure the discrete SINR values, available bandwidth and resource requirements to predict the system throughput optimized scheduling among Wi-Fi and LTE networks. Also, an energy-aware scheduling algorithm is proposed as a tie breaker between all the other approaches.

An energy-aware scheduling algorithm for budget-constrained scientific workflows based on multi-objective reinforcement learning

Since scientific workflow scheduling becomes a major energy contributor in clouds, much attention has been paid to reduce the energy consumed by workflows. This paper considers a multi-objective workflow scheduling problem with the budget constraint. Most existing works of budget-constrained workflow scheduling cannot always satisfy the budget constraint and guarantee the feasibility of solutions. Instead, they discuss the success rate in the experiments. Only a few works can always figure out feasible solutions. These methods work, but they are too complicated. In workflow scheduling, it has been a trend to consider more than one objective. However, the weight selection is usually ignored in these works. The inappropriate weights will reduce the quality of solutions. In this paper, we propose an energy-aware multi-objective reinforcement learning (EnMORL) algorithm. We design a much simpler method to ensure the feasibility of solutions. This method is based on the remaining cheapest budget. Reinforcement learning based on the Chebyshev scalarization function is a new framework, which is effective in solving the weight selection problem. Therefore, we design EnMORL based on it. Our goal is to minimize the makespan and energy consumption of the workflow. Finally, we compare EnMORL with two state-of-the-art multi-objective meta-heuristics in the case of four different workflows. The results show that our proposed EnMORL outperforms these existing methods.

EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters

EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters

An energy-aware scheduling algorithm for big data applications in Spark

Energy consumption is explosive increasing with the fast growth of big data applications. High carbon emissions from big data platforms have serious impacts on environment. In this paper, we propose an energy-aware scheduling algorithm for Spark (EASAS) to reduce energy consumption while satisfying the service level agreement (SLA). First, we present a new energy consumption model based on Spark framework. Then a strategy table for the relationship between tasks and executors is designed to record the execution time and energy consumption of tasks. The task scheduling in Spark is conducted and optimized based on the strategy table. The proposed strategy overcomes the defect of the default scheduling strategy FIFO and FAIR which cannot perceive energy consumption with the characteristics of energy consumption perception and dynamic optimization scheduling. Compared against FIFO and FAIR, Our EASAS effectively reduces on average about 25–40% of the total energy consumption of Spark applications under deadline constrains.

Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment

Energy consumption in cloud data centers is increasing as the use of such services increases. It is necessary to propose new methods of decreasing energy consumption. Green cloud computing helps to reduce energy consumption and significantly decreases both operating costs and greenhouse gas emissions. Scheduling the enormous number of user-submitted workflow tasks is an important aspect of cloud computing. Resources in cloud data centers should compute these tasks using energy efficient techniques. This paper proposed a new energy-aware scheduling algorithm for time-constrained workflow tasks using the DVFS method in which the host reduces the operating frequency using different voltage levels. The goal of this research is to reduce energy consumption and SLA violations and improve resource utilization. The simulation results show that the proposed method performs more efficiently when evaluating metrics such as energy utilization, average execution time, average resource utilization and average SLA violation.

Cost and Energy Aware Scheduling Algorithm for Scientific Workflows with Deadline Constraint in Clouds

Cloud computing is a suitable platform to execute the deadline-constrained scientific workflows which are typical big data applications and often require many hours to finish. Moreover, the problem of energy consumption has become one of the major concerns in clouds. In this paper, we present a cost and energy aware scheduling (CEAS) algorithm for cloud scheduler to minimize the execution cost of workflow and reduce the energy consumption while meeting the deadline constraint. The CEAS algorithm consists of five sub-algorithms. First, we use the VM selection algorithm which applies the concept of cost utility to map tasks to their optimal virtual machine (VM) types by the sub-makespan constraint. Then, two tasks merging methods are employed to reduce execution cost and energy consumption of workflow. Further, In order to reuse the idle VM instances which have been leased, the VM reuse policy is also proposed. Finally, the scheme of slack time reclamation is utilized to save energy of leased VM instances. According to the time complexity analysis, we conclude that the time complexity of each sub-algorithm is polynomial. The CEAS algorithm is evaluated using Cloudsim and four real-world scientific workflow applications, which demonstrates that it outperforms the related well-known approaches.

Energy-aware interference management for ultra-dense multi-tier HetNets: Architecture and technologies

In order to improve spectral efficiency in future ultra-dense heterogeneous networks (HetNets), small cells and macro cells should be ultra-dense when deployed and dynamically overlaid. In this condition, overlaid deployment, state transition, and load migration will cause energy consumption and complex interference. Managing interference in ultra-dense multi-tier HetNets is challenging; interference can burden the network significantly when network conditions change over time. Because of the distribution of small cells and overload conditions in these ultra-dense networks, we must take into account the interference relationship among the base stations and distribution and loading conditions. An interference management scheme based on energy-aware architecture is proposed for ultra-dense multi-tier HetNets in this article. A survey is presented on energy-aware scheduling algorithms. We aim to study energy efficiency issues using graph theory and clustering. HetNets are divided into numerous interference areas, which correspond to coverage areas of each base station. Any conflict among users’ resources depends on if the interference user is located in a base station interference area, hence the use of a reinforcement-learning algorithm to optimize ongoing interference management. Given the complexity of a multi-layer network, conflict graph theory can easily identify network dynamics because it focuses on users’ received interference. Conflict graph theory can also increase resource reusability and efficiency. The proposed scheme can allocate the frequency spectrum equitably, reduce system interference, and improve throughput performance.

Multiple MapReduce Jobs in Distributed Scheduler for Big Data Applications

The majority of large-scale data intensive applications executed by data centers are based on MapReduce or its open-source implementation, Hadoop. Such applications are executed on large clusters requiring large amounts of energy, making the energy costs a considerable fraction of the data center’s overall costs. Therefore minimizing the energy consumption when executing each MapReduce job is a critical concern for data centers. In this paper, we propose a framework for improving the energy efficiency of MapReduce applications, while satisfying the service level agreement (SLA).We first model the problemof energy-aware scheduling of a single MapReduce job as an Integer Program. We then propose two heuristic algorithms, called energy-aware MapReduce scheduling algorithms (EMRSA-I and EMRSA-II), that find the assignments of map and reduce tasks to the machine slots in order to minimize the energy consumed when executing the application. Our algorithm able to find near optimal job schedules consuming approximately 40 percent less energy on average than the schedules obtained by a common practice scheduler that minimizes the makespan.

A new high-performance open CNC system and its energy-aware scheduling algorithm

Computer numerical control (CNC) systems are shifting to a direction of open architecture which has better flexibility, adaptability, versatility, and expansibility. Existing CNC systems tend to have a high level energy consumption. This paper introduces a new open CNC system based on the low-power embedded platform, named open and high-performance CNC (OHP-CNC). OHP-CNC is able to achieve high precision, high efficiency, and low power consumption by making use of international standards, open components such as hardware and software, and an energy-aware real-time scheduling algorithm. The proposed algorithm for mixed tasks, including periodic and aperiodic tasks, is divided into two phases. Firstly, the slack time and utilization are calculated on each processor and tasks are assigned to the processor according to the load. Secondly, because there is a trade-off between the energy-saving and the response times of the aperiodic task, the scheduling server is used to schedule aperiodic tasks in order to meet the response time constraints of aperiodic tasks. Meanwhile, periodic tasks recycle the slack time with dynamic voltage scaling technology to achieve low power consumption. Experiment results show that the energy-aware real-time algorithm yields high-performance and effective machining processes.

Locality-Load-Prediction Aware Multi-Objective Task Scheduling in the Heterogeneous Cloud Environment

Objectives: Current state-of-the-art task scheduling algorithms were mainly focused on deadline, load and energy factors in centralized cloud context. So, the proposed research objective focuses on dynamic and decentralized context. Methods/Statistical Analysis: Multi-objective task scheduling has become an important criterion for the dynamic and decentralized nature of cloud environment. Moreover, existing research works assumes that the resource load, energy and task execution time are known due its homogeneous nature. In order to improve the cloud consumer’s satisfaction, a novel Locality-Load-Prediction Aware Multi-objective Task Scheduling (LLPAMTS) algorithm is proposed to eventually distribute the tasks according to dynamic nature of cloud virtual machines. Findings: Proposed LLPAMTS algorithm will effectively schedule the tasks in an optimized manner by VM Scheduler component. This scheduling algorithm exploits the various monitoring parameters like locality, load and prediction parameters. It outperforms the existing deadline, load and energy aware scheduling algorithms in terms of task transfer time, task waiting time, task execution time, and task completion time. Applications/Improvements: The proposed LLPAMTS algorithm provides an average of 5 to 10% less task completion time compared to the existing deadline, load and energy aware scheduling algorithms.

A comparative study of energy-aware scheduling algorithms for computational grids

Recent advances in High Performance Computing (HPC) have required the attention of scientific community regarding aspects that do not concern only performance. In order to enhance computational capacity, modern parallel and distributed architectures are designed with more processing units, causing an increase in energy consumption. Currently, one of the most representative HPC platforms are computational grids, which are used in many scientific and academic projects. In this work, we propose four energy-aware scheduling algorithms to efficiently manage the energy consumption in computational grids, trying to mitigate performance loss. Our algorithms propose an efficient management of idle resources and a clever use of active ones. We have evaluated our algorithms using the SimGrid framework and an energy consumption estimation method we proposed for Bag-of-Tasks-type (BoT) applications. We compared our algorithms against five others developed to work with computational grids. In a set of experimental scenarios, our results show that by using our algorithms it is possible to achieve up to 75.90% of reduction in the energy consumption combined with 5.28% of performance loss compared with the best algorithm in performance.