Energy-efficient Task Scheduling Research Articles

HunterPlus: AI based energy-efficient task scheduling for cloud–fog computing environments

Cloud computing is a mainstay of modern technology, offering cost-effective and scalable solutions to a variety of different problems. The massive shift of organization resource needs from local systems to cloud-based systems has greatly increased the costs incurred by cloud providers in expanding, maintaining, and supplying server, storage, network, and processing hardware. Due to the large scale at which cloud providers operate, even small performance degradation issues can cause energy or resource usage costs to rise dramatically. One way in which cloud providers may improve cost reduction is by reducing energy consumption. The use of intelligent task-scheduling algorithms to allocate user-deployed jobs to servers can reduce the amount of energy consumed. Conventional task scheduling algorithms involve both heuristic and metaheuristic methods. Recently, the application of Artificial Intelligence (AI) to optimize task scheduling has seen significant progress, including the Gated Graph Convolution Network (GGCN). This paper proposes a new approach called HunterPlus which examine the effect of extending the GGCN’s Gated Recurrent Unit to a Bidirectional Gated Recurrent Unit. The paper also studies the utilization of Convolutional Neural Networks (CNNs) in optimizing cloud-fog task scheduling. Experimental results show that the CNN scheduler outperforms the GGCN-based models in both energy consumption per task and job completion rate metrics by at least 17 and 10.4 percent, respectively.

Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog Environment

Inadequate resources and facilities with zero latency affect the efficiencies of task scheduling (TS) and resource allocation (RA) in the fog paradigm. Only the incoming tasks can be completed within the deadline if the resource availability in the cloud and fog is symmetrically matched with them. A container-based TS algorithm (CBTSA) determines the symmetry relationship of the task/workload with the fog node (FN) or the cloud to decide the scheduling workloads (whether in the fog or a cloud). Furthermore, by allocating and de-allocating resources, the RA algorithm reduces workload delays while increasing resource utilization. However, the unbounded cloud resources and the computational difficulty of finding resource usage have not been considered in CBTSA. Hence, this article proposes an enhanced CBTSA with intelligent RA (ECBTSA-IRA), which symmetrically balances energy efficiency, cost, and the performance-effectiveness of TS and RA. Initially, this algorithm determines whether the workloads are accepted for scheduling. An energy-cost–makespan-aware scheduling algorithm is proposed that uses a directed acyclic graph (DAG) to represent the dependency of tasks in the workload as a graph. Workloads are prioritized and selected for the node to process the prioritized workload. The selected node for processing the workload might be a FN or cloud and is decided by an optimum efficiency factor that trades off the schedule length, cost, and energy. Moreover, a Markov decision process (MDP) was adopted to allocate the best resources using the reinforcement learning scheme. Finally, the investigational findings reveal the efficacy of the presented algorithms compared to the existing CBTSA in terms of various performance metrics.

Task Scheduling Techniques for Energy Efficiency in the Cloud

Energy efficiency is a key goal in cloud datacentre since it saves money and complies with green computing standards. When energy efficiency is taken into account, task scheduling becomes much more complicated and crucial. Execution overhead and scalability are major concerns in current research on energy-efficient task scheduling. Machine learning has been widely utilized to solve the problem of energy-efficient task scheduling, however, it is usually used to anticipate resource usage rather than selecting the schedule. The bulk of machine learning approaches are used to anticipate resource consumption, and heuristic or metaheuristic algorithms utilize these predictions to choose which computer resource should be assigned to a certain activity. As per the knowledge and research, none of the algorithms have independently used machine learning to make an energy-efficient scheduling decision. Heuristic or meta-heuristic approaches, as well as approximation algorithms, are frequently used to solve NP-complete problems. In this paper, we discuss various studies that have been used to solve the problem of task scheduling which belongs to a class of NP-hard. We have proposed a model to achieve the objective of reduced energy consumption and CO2 emission in a cloud environment. In the future, the model shall be implemented in MATLAB and would be assessed on various parameters like makespan, execution time, resource utilization, QoS, Energy utilization, etc.

Deadline aware and energy-efficient scheduling algorithm for fine-grained tasks in mobile edge computing

These days, more and more web are converting into mobile devices such as the internet of things (IoT). These applications are distributed in nature and installed on mobile and servers to achieve many business goals. With this motivation, mobile edge computing (MEC) is an evolving paradigm that reduces the energy consumption of mobile devices through the offload process. However, currently all studies only concentrated on mobile energy and widely overlooked the energy usage of the computing node resources and application deadlines during offloading and scheduling in the MEC network. This research suggests a new mobile edge cloud (mob-cloud) architecture that minimises the nodes energy and meets the deadline requirements of applications. This research formulates this problem as the convex optimisation problem under linear integer programming. The mob-cloud consists of different types of virtual machines which are implementing at the edge computing. This study introduces a novel deadline and energy-efficient task scheduling (DEETS) algorithm framework consisting of task sequencing, resource searching, and mobility-aware scheduling. Furthermore, DEETS keeps tradeoff performances between resource energy usage and task deadline. Simulation results show that the proposed DEETS outperformed all contemporary methods regarding energy usage and deadlines of applications by 50%.

Energy efficient VM placement and task scheduling techniques to achieve green cloud environment

Energy efficient VM placement and task scheduling techniques to achieve green cloud environment

Energy efficient VM placement and task scheduling techniques to achieve green cloud environment

Continuous increase in the demand for cloud services has resulted in increased workloads. Effective distribution of these augmented tasks on a limited infrastructure for providing the services is a challenging issue. This necessitates servers to run continuously raising the power usage in the data centres. High power consumption results in releasing major amount of CO2 gas to the atmosphere, polluting our natural environment. So, there is a need to think of green cloud computing to reduce this effect of harmful gas to our environment. Designing an energy efficient data centre is a perennial challenge. Hence in this paper, optimal virtual machine placement strategy using knapsack dynamic programming technique and an efficient precedence-based task distribution approach are proposed to alleviate the power consumption and achieve the quality of service maintaining the service level agreements. The implementation results obtained have proved its power efficiency with better quality of service.

Energy efficient routing and task scheduling for autonomous transfer vehicles in intra logistics

Autonomous Transfer Vehicles (ATVs) are becoming increasingly prevalent in intra logistics. Industry 4.0 is bringing us closer to the efficient routing and scheduling of autonomous multi robot systems which perform transportation tasks. In this study, an energy efficient routing and scheduling system is proposed to minimize the total energy that the vehicles spend. Not only travelled distance but also the load of the vehicle is considered between two points. The routes of vehicles are obtained by using the proposed Hybrid Simulated Annealing Algorithm. An algorithm for the initial solution is also proposed for determining of the minimum number of vehicles for pickup and delivery requests. The performance of the algorithm is compared with the best solutions of the test problems in the literature. Besides, the proposed energy efficient routing and task scheduling model is compared with the classical distance model for routing and scheduling with backhauls. An analysis of trade-offs between energy and distance is proposed for intra logistics.

ESPS: Energy Saving Power Spectrum-Aware Scheduling to Leverage Differences in Power Ratings of Physical Hosts in Datacenters

Cloud Computing has seen massive growth over the past couple of decades, leading to exponential growth in energy consumption at data centres. Data centres consuming high amounts of energy leave a carbon footprint of the same scale, hence Cloud Service Providers (CSPs) have been looking for energy-efficient solutions to task scheduling in cloud to reduce the amount of carbon dioxide emission. Saving energy not only helps reduce the carbon footprint datacentres have on the environment, but also helps cover the costs of running multiple datacentres on the CSP’s end. In this paper, we propose an energy saving task scheduling heuristic for heterogeneous cloud systems which selects the optimal physical host containing virtual machines with the additional consideration of the utilization of any incoming task on that particular virtual machine. We compare the energy efficiency of our proposed heuristic with recent algorithms including ECTC, MaxUtil, Random, and FCFS on several benchmark and synthetic datasets to display its superiority in energy-efficient task scheduling in heterogeneous cloud environments. Our proposed heuristic, namely Energy Saving Power Spectrum-Aware Scheduling (ESPS) minimizes energy consumption in a heterogeneous cloud environment by about 38.65%, 33.59%, 53.02%, and 46.96% when compared to FCFS, MaxUtil, Random and ECTC respectively.

Task scheduling and resource allocation of seasonal requests of users in cloud using NMKA and CM-GA techniques

PurposeThe purpose of this paper is to provide energy-efficient task scheduling and resource allocation (RA) in cloud data centers (CDC).Design/methodology/approachTask scheduling and RA is proposed in this paper for cloud environment, which schedules the user’s seasonal requests and allocates resources in an optimized manner. The proposed study does the following operations: data collection, feature extraction, feature reduction and RA. Initially, the online streaming data of seasonal requests of multiple users were gathered. After that, the features are extracted based on user requests along with the cloud server, and the extracted features are lessened using modified principal component analysis. For RA, the split data of the user request is identified and that data is pre-processed by computing closed frequent itemset along with entropy values. After that, the user requests are scheduled using the normalized K-means algorithm (NKMA) centered on the entropy values. Finally, the apt resources are allotted to that scheduled task using the Cauchy mutation-genetic algorithm (CM-GA). The investigational outcomes exhibit that the proposed study outruns other existing algorithms in respect to response time, execution time, clustering accuracy, precision and recall.FindingsThe proposed NKMA and CM-GA technique’s performance is analyzed by comparing them with the existing techniques. The NKMA performance is analyzed with KMA and Fuzzy C-means regarding Prc (Precision), Rca (Recall), F ms (f measure), Acr (Accuracy)and Ct (Clustering Time). The performance is compared to about 500 numbers of tasks. For all tasks, the NKMA provides the highest values for Prc, Rca, Fms and Acr, takes the lowest time (Ct) for clustering the data. Then, the CM-GA optimization for optimally allocating the resource in the cloud is contrasted with the GA and particle swarm optimization with respect to Rt (Response Time), Pt (Process Time), Awt (Average Waiting Time), Atat (Average Turnaround Time), Lcy (Latency) and Tp (Throughput). For all number of tasks, the proposed CM-GA gives the lowest values for Rt, Pt, Awt, Atat and Lcy and also provides the highest values for Tp. So, from the results, it is known that the proposed technique for seasonal requests RA works well and the method optimally allocates the resources in the cloud.Originality/valueThe proposed approach provides energy-efficient task scheduling and RA and it paves the way for the development of effective CDC.

Deadline Aware Energy-Efficient Task Scheduling Model for a Virtualized Server

The demand of cloud-based services is increasing day by day. To fulfill the seamless computing demand of consumers, a large number of servers are installed in the datacenters. These servers consume high energy. Reducing the energy consumption of servers with the same performance of service is a vital challenge. Energy-efficient task scheduling is one of the method to reduce energy consumption while preserving the task constraints. This paper presents a deadline and energy-aware scheduling (DEAS) model for a virtualized server to achieve energy efficiency while executing deadline conscious tasks. These tasks are independent in nature and arrives dynamically. The presented DEAS model follows the heuristic approach where the first instance guarantee ratio (GR) is maximized to raise the energy efficiency of the server per unit of work performed. Task slack time is utilized in an innovative way to improve the GR of the server. In this way, the average energy consumption (energy consumption per task) of the server per unit of work done is minimized. The energy efficiency of the server in its idle state is further achieved by applying core-level granularity of dynamic voltage and frequency scaling (DVFS) technology. The DEAS model is evaluated through extensive simulation experiments using the CloudSim simulator. Results show that DEAS model performs better than existing models on the account of considered performance metrics, i.e., GR, total energy consumption, energy consumption per task, and resource utilization.

Energy Efficient and Reliability Aware Workflow Task Scheduling in Cloud Environment

With the rising demand for cloud services, the high energy consumption of cloud data centers is a significant problem that needs to be handled. The Dynamic Voltage and Frequency Scaling approach has been identified as one of the efficient techniques to conserve energy, particularly while scheduling real-world scientific workflows. Moreover, scientific workflows demand high-availability of the system. The computational systems in the cloud data centers are not failure-free and further frequency scaling impacts negatively on the reliability of the system by increasing the transient fault rate. A trade-off is required between energy conservation and the reliability of the computational machine. In this paper, we propose an energy-efficient and reliability aware workflow task scheduling in a cloud environment (EERS) algorithm, which conserves energy and maximizes the system reliability. The EERS comprises five sub-algorithms. First, we apply a task rank calculation algorithm to preserve the task dependencies. Second, a task clustering algorithm to reduce the communication cost which reduces energy consumption. The third is the sub-target time distribution algorithm to define the sub_makespan for each task. Further, we propose a cluster-VM mapping algorithm that reduces energy and maximizes system reliability and finally, a slack algorithm to reclaim slack associated with the non-critical tasks. The performance of the EERS evaluated on the WorkflowSim simulator using two real-world scientific workloads CyberShake and Montage. The results indicate that it surpasses the related existing approaches.

Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing

• We build a system architecture that contains master node, task sequencing, initial scheduling, and energy-efficient task reassignment components for process the tasks under their deadlines. • We design a scheduling algorithm to reduce energy consumption in the heterogeneous virtualized cloud. • This study proposes an energy-efficient task priority framework to create a fair balance between task scheduling and energy saving. • The simulation results demonstrate that our proposed method helps to reduce significant energy consumption with the deadline constraint satisfied compared with the existing solution methods. In virtualized cloud computing systems, energy reduction is a serious concern since it can offer many major advantages, such as reducing running costs, increasing system efficiency, and protecting the environment. At the same time, an energy-efficient task scheduling strategy is a viable way to meet these goals. Unfortunately, mapping cloud resources to user requests to achieve good performance by minimizing the energy consumption of cloud resources within a user-defined deadline is a huge challenge. This paper proposes Energy and Performance-Efficient Task Scheduling Algorithm (EPETS) in a heterogeneous virtualized cloud to resolve the issue of energy consumption. There are two stages in the proposed algorithm: initial scheduling helps to reduce execution time and satisfy task deadlines without considering energy consumption, and the second stage task reassignment scheduling to find the best execution location within the deadline limit with less energy consumption. Moreover, to make a reasonable balance between task scheduling and energy saving, we suggest an energy-efficient task priority system. The simulation results show that, compared to current energy-efficient scheduling methods of RC-GA, AMTS, and E-PAGA, the proposed solution helps to reduce significant energy consumption and improve performance by 5 % – 20 % with deadline constraint satisfied.

Energy Efficient Task Scheduling in Fog Environment using Deep Reinforcement Learning Approach

Abstract The users of cloud span to several types of tasks for various purposes, such as users who need to accomplish tasks that utilize cloud based on as Infrastructure as a Service. These tasks are usually of high latency. There are some tasks that require immediate response. That is, ultra-low latency tasks like IoT device requirements. It is not feasible to always depend on a far cloud datacenter. Unlike traditional cloud computing, it is possible to place edge and fog nodes can be placed close to the IoT devices provides noticeable reduction in latency. The emerging fog computing technology is characterized by ultra-low latency response, which benefits several time-sensitive services and applications. Nodes in fog are deployed in less centralized. In a fog layer, the computing equipment dedicates parts of its limited resources to process IoT application tasks. Therefore, efficient utilization of computing resources is essential and requires an optimal and intelligent strategy for task scheduling. This paper focuses on scheduling IoT tasks in a fog-based environment with the aim to minimizing energy, cost, and service delay. Towards this end, a deep reinforcement learning based algorithm named Clipped Double Deep Q-learning using target networks and experience replay techniques is proposed. To ensure there is no lag in using resources optimally, a parallel queueing is utilized. One of the important factors in cloud (and fog) computing research is to address the problem of long waiting time of the task in the virtual machine queue. This paper proposes a dual queue method.

Energy Reduction Through Memory Aware Real-Time Scheduling on Virtual Machine in Multi-Cores Server

Not only weighty energy usage pose issues for the environment, but it also raises server maintenance costs in data centers. The massive task with the various power control functions in computer components was made to minimize energy consumption. Increasing consumption of energy in data server environments means that data centers will have high maintenance costs. Various geo-distributed data centers are starting to grow in an age of data proliferation and information growth. Energy management for servers is now demanded for technological, environmental, and economic reasons. In this environment, the main memory is a major energy consumer, not less than the processor. At the same time, an energy-efficient task scheduling strategy is a viable way to meet these goals. Unfortunately, mapping Virtual Machine (VM) resources to the Main Memory (MM) demands to achieve good performance by minimizing the energy consumption within a certain limit is a huge challenge. This paper simulates energy-efficient task scheduling algorithms in a heterogeneous virtualized environment using real-time virtual machine scheduling to resolve the issue of energy consumption. Using a simulator Real-Time system SIMulator (RTSIM), several hardware-based scheduling algorithms are implemented to observe VM memory scheduling efficiency to save memory energy. The simulation results show that, compared to current energy-efficient scheduling methods Rate Monotonic (RM), Earliest-Deadline-First (EDF), and Least-Laxity-First (LLF), helps to reduce energy consumption and improve performance. It is also observed that memory-aware energy management architecture reduces energy and memory consumption efficiently by using EDF scheduling algorithms. In particular, EDF saves approximately 58.3 percent of memory energy than conventional systems that cannot benefit from memory-aware energy management algorithms. The energy efficiency of the algorithms continues to improve as the level of server consolidation rises. We also implemented the EDF scheduling algorithm in Xen’s Credit scheduler to see if the simulation outcomes can be simulated on physical systems. Results of simulation and deployment are equated, and comparable outcomes are achieved. We also identified that shared memory between virtual machines deliberately affects memory’s energy consumption based on the implementation.

NOMA-based energy-efficient task scheduling in vehicular edge computing networks: A self-imitation learning-based approach

Mobile Edge Computing (MEC) is promising to alleviate the computation and storage burdens for terminals in wireless networks. The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries. In addition, Orthogonal Multiple Access (OMA) technique cannot utilize limited spectrum resources fully and efficiently. Therefore, Non-Orthogonal Multiple Access (NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important, especially in highly-dynamic vehicular edge computing networks. The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers. Self-Imitation Learning (SIL)-based Deep Reinforcement Learning (DRL) has emerged as a promising machine learning technique to break through obstacles in various research fields, especially in time-varying networks. In this paper, we first introduce related MEC technologies in vehicular networks. Then, we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL, with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers. Numerical results demonstrate that the proposed algorithm outperforms other methods.

Urgent point aware energy-efficient scheduling of tasks with hard deadline on virtualized cloud system

Abstract Cloud computing platform has emerged to be a promising computing paradigm of recent time. Various applications from different domains having rigid deadline constraints are deployed in the cloud system for their respective benefits. Energy-efficient execution of these applications, meeting their deadline constraints is a challenge. Most of the existing research on the energy-efficient scheduling of these applications in the cloud domain consider a linear relationship between the energy consumption and the resource utilization of the system, and they focus on maximizing the utilization of resources to reduce the active number of computing nodes to minimize energy consumption. In this paper, we first devise a power consumption model for the cloud system which considers both the static and dynamic components of it and assumes a nonlinear relationship with utilization. Then we introduce the concept of urgent points in case of tasks having deadline in the context of a heterogeneous cloud computing environment. Then we propose two energy-efficient scheduling approaches, named UPS and UPS-ES designed based on the urgent points of the tasks and two threshold values of the host utilization. Extensive simulation experiments are conducted both for synthetic tasksets and Google cloud tracelogs. The results are compared with a state of the art scheduling policy and found that our policies perform significantly better than them, with an energy reduction of up to 42% while the deadline constraints of all the tasks are met.

Energy-efficient task scheduling and physiological assessment in disaster management using UAV-assisted networks

Abstract Internet of Things (IoT) and unmanned aerial vehicles (UAVs) together can significantly enhance the performance of disaster management systems. UAVs can collect massive heterogeneous data from disaster-affected areas using fifth-generation (5G)/beyond 5G networks and this data can be analyzed to get the information required by the first responders such as marking the boundary of the affected area, identifying the infrastructure damaged and the roads blocked, and the health situation of people living in that area. This paper presents an overview of different platforms (UAVs-based, IoT-based, and IoT, coupled with UAVs) for disaster management. We propose an energy-efficient task scheduling scheme for data collection by UAVs from the ground IoT network. The focus is to optimize the path taken by the UAVs to minimize energy consumption. We also analyze the vital signs data collected by UAVs for people in disaster-affected areas and apply the decision tree classification algorithm to determine their health risk status. The risk status will enable the first responders to decide the areas which need the most immediate help Simulation results compare the effectiveness of our proposed scheduling scheme with the traditional approach used for data collection. Also, we present the results of our predicted risk status compared with the risk status calculated through the National Early Warning Score 2 (NEWS2) method.

A Multi‐queue Approach of Energy Efficient Task Scheduling for Sensor Hubs

With the emergence of Internet of things (IoT), sensor hubs, which integrate data from different sensors, play increasingly important role. Energy efficiency is one of the most important issues for sensor hubs. To attack this challenge, this paper proposes a task scheduling scheme for sensor hubs to improve their energy efficiency. A multi-queue-based framework is designed, and its theoretical model and the corresponding mathematical analyses are presented. By optimizing the model with Lyapunov optimization techniques, an algorithm of energy efficient task scheduling in sensor hubs is proposed. Finally, simulation experiments based on real-life data extracted from Internet of vehicles (IoV) environments are conducted to validate the efficacy of the approach.

An artificial neural network based approach for energy efficient task scheduling in cloud data centers

Abstract Energy efficiency is considered as a crucial objective in cloud data centers as it reduces cost and meets the standard set in green computing. Task scheduling an important problem becomes more complex and critical under energy efficiency consideration. Key issues in recent research on energy efficient task scheduling are execution overhead and scalability. Machine learning has been widely employed for energy efficient task scheduling problem but mostly used to predict resource consumption only instead of deciding the schedule itself. However, we used the neural network to decide which resource should be assigned to given task independently. In this paper, we proposed an energy efficient independent task scheduler using supervised neural networks with the aim to reduce makespan, energy consumption, execution overhead and number of active racks. Proposed artificial neural network-based scheduler takes incoming task and current cloud environment state as input and predict the best computing resource for given task as output which compiles our aim. We used genetic algorithm to generate a huge dataset (∼18 million training instances) and trained our neural network on this dataset using back propagation algorithm with 99.9% accuracy. We simulated experiments on heavily loaded and lightly loaded cloud environment and compared with well-known approaches: Genetic algorithm, MinMIN-MINMin heuristic and Linear regression based energy efficient task schedulers. Results clearly indicate that proposed work outperforms considered algorithms. In heavily (lightly) loaded environment, it improves makespan by 59% (64%), energy consumption by 45% (71%), execution overhead by 88% (43%) respectively and number of active racks by 70%.

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.