Existing Scheduling Algorithms Research Articles

(1 + ϵ)2-and Polynomial-Time Approximation Algorithms for Network Lifetime Maximization With Relay Hop Bounded Connected Target Coverage in WSNs

Scheduling sensor activity to prolong the network lifetime while guaranteeing coverage and connectivity is a fundamental and critical issue in handling wireless sensor networks. Although many efforts have been made in this area, none of the prior works considers the relay hop bound constraint. As a result, the existing scheduling algorithms can provide only connectivity with uncontrollable latency to the sinks and can't be applied to delay-sensitive applications. In this paper, we are the first one coping with the scheduling problem for network lifetime maximization under the requirements of full target coverage and connectivity with bounded relay hop. We first propose an exact LP (i.e., Linear Programming) formulation to determine the optimal solution. Then, to reduce the time complexity, we develop a (1 + E)2-approximation algorithm based on the partitioning and shifting technique. Furthermore, we propose an approximate LP formulation whose variable size is polynomial to the number of targets and sensors and whose performance ratio is M, where M is the maximum number of targets covered by a sensor. The experiment results show that when the number of sensors is sufficiently large, our algorithms extend the network lifetime up to 3.68 times compared to the existing approaches. Moreover, the proposed algorithms shorten time complexity significantly. Specifically, our algorithms' time complexity is always less than 20% that of the benchmarks.

Multi-objective heuristics algorithm for dynamic resource scheduling in the cloud computing environment

Cloud infrastructure provides resources needed for tasks for resource scheduling. This work uses a genetic algorithm based on encoded chromosome (GEC-DRP) to manage dynamic resource scheduling. However, the existing scheduling algorithm estimates the number of required physical machines (PM) needed for the client in the future. This developed scheduling algorithm schedules the tasks on cloud by calculating the number of virtual machines needed in the near future along with their predicted CPU and memory requirements, which is the main contribution of the work. K-means algorithm clusters the tasks based on CPU and memory usage as parameters. The future arrival of tasks for every cluster is predicted and accordingly, the required number of VMs is created. The incoming requests known as tasks are scheduled on the appropriate VM using the genetic algorithm (GA). Based on the workload prediction results, a cost-optimized resource scheduling strategy in cloud computing environment is proposed aiming at minimizing the total cost of rental virtual machines from the central cloud. Finally, a genetic algorithm is used to solve the resource scheduling strategy. The developed algorithms are evaluated by the workload prediction accuracy, the total cost of the cluster and the algorithm’s consuming time for solving the resource scheduling problems through the experiments. Finally, the effective of workload prediction algorithm based on SES and cost-optimized resource scheduling strategy is verified by simulation.

An Energy-Efficient Hybrid Scheduling Algorithm for Task Scheduling in the Cloud Computing Environments

The main intent of the cloud computing to provide utilities to the demands of the users that are booming day by day. To meet the requirements, existing scheduling algorithms focus on the improving the performance and neglecting the energy consumed to fulfill those demands. Hence, we propose a new Hybrid Scheduling Algorithm (HS) which is based on the Genetic Algorithm (GA) and Flower Pollination based Algorithm (FPA) for cloud environments. The proposed scheduling algorithm has surpassed in terms of performance across various parameters, i.e. completion time, resource utilization, cost of computation, and energy consumption for both cloud environments than the existing scheduling algorithms (GA and FPA). The simulation results revealed that HS has demonstrated maximum resource utilization with minimum energy consumption in less completion time for the execution of the tasks as compared to the existing scheduling algorithms in both environments. The simulation results have shown that HS has utilization of the resources, 36% better than GA and 16% better than FPA in homogeneous environment whereas in heterogenous environment, HS has performed 12% better than GA and 3.8% better than FPA. The performance of HS has an improvement of 2.6% from FPA and 6.9% from the GA for completion time in homogeneous environment whereas the completion time of the HS is reduced by 17.8% from FPA and 33.7% from GA in heterogeneous environment. For energy consumption, HS has improved 22% than FPA and 11% from GA in the homogeneous environment and HS is 4% better than FPA and 14% from GA in heterogeneous environment.

Task schedule algorithm based on double-objective particle swarm optimization under cloud computing

In cloud computing environment, how to reasonably carry out the resource scheduling and reduce the task execution time and task execution energy consumption has become a hot issue in the researches. In this paper, the task processing time and energy consumption were used as the optimization objectives, and a Double-Objective Particle Swarm Optimization (DOPSO) was used to perform the optimal scheduling of task completion time and energy consumption. Finally, the results obtained in DOPSO were simulated in CloudSim, and results showed that compared with existing scheduling algorithms, the proposed algorithm (DOPSO) had obvious advantages in the integrated scheduling performance.

NITCO: an intelligent agent technique for optimising of resource utilisation in cloud

Efficient task scheduling is significant to meet the quality of service (QoS) requirements in cloud computing. Cloud is a large pool of virtual access resources to perform thousands of computational and storage operations. Task scheduling is an NP-hard problem, unsuitable matching leads to performance degradation and violation of service level agreement (SLA). The growing complexity of cloud services needs an extension of existing scheduling algorithms. In this paper, the scheduling problem has been explored based on growing application trends. Cloud dynamic resource provisioning can satisfy users' requirements if execution of tasks performed: identifying of task requirements; workflow of application scheduling using a sufficient amount of resources. In this research work, we present an intelligent agent technique for optimising resource utilisation named NITCO. NITCO considers the above mentioned challenge, identification of task requirements and configuration of resource. The performance of proposed NITCO has been evaluated on simulated cloud environment and comparison of results show that NITCO performed better in terms of execution cost, execution time, VM utilisation and SLA violation while it delivers quality of service.

Dynamic Scheduling for Emergency Tasks in Space Data Relay Network

As a platform for data transmission, space data relay network (SDRN), which has high coverage and continuous communications, provides data relay services for the spacecrafts and satellites by allocating antenna resource. However, the diversity between emergency tasks and common tasks for antenna requirements may result in the conflicts of resource occupation. This leads to the loss of emergency information and the low scheduling efficiency. In this paper, a real-time dynamic scheduling (RTDS) scheme is proposed to ensure the transmission of emergency tasks and improve the scheduling efficiency by scheduling sequence adjustment and task management. Firstly, the relative SDRN system models are given and a dynamic scheduling mechanism is designed to deal with the disturbance factors during the scheduling process. Then, based on the dynamic scheduling mechanism, the RTDS scheme is implemented in three stages, which include initial scheduling, dynamic scheduling for emergency tasks, and final scheduling. The simulation results demonstrate that the proposed RTDS scheme can guarantee the timely transmission of emergency tasks and enhance the scheduling efficiency compared with the existing scheduling algorithms.

WITHDRAWN: An optimal SLA based task scheduling aid of hybrid fuzzy TOPSIS-PSO algorithm in cloud environment

Cloud computing is an important technology for bulk data storage, resource mobilization and online access to computer services. In the cloud computing platform there are plenty of resources, but the foremost challenge is to allocate tasks. The vital issue in scheduling is how the entire task could be allocated with maximized resource utilization to a resultant virtual machine, existing scheduling algorithms mainly focused on minimization of the task execution time while ignoring the SLA (Service Level Agreement) and QoS (Quality of Service) assurance. This paper thrives to accomplish the total task with eminent resource utilization, minimum migration cost, and minimum utilization of energy. For attaining these objectives, the proposed method utilizes the hybrid algorithm such as Fuzzy-TOPSIS and particle swarm optimization (PSO) approach. Initially the available task and the no. of VMs (virtual machines) are optimized by PSO algorithm. In the cloud, the multi-objective SLA based task scheduling problem is solved by the Fuzzy TOPSIS which uses the weighted sum of energy, cost and execution time as an objective function. Fuzzy TOPSIS method is work positively for many applications and impacts liberally on real world decision making concerns. The proposed approach is investigated based on the distinct evaluation metrics. Our proposed algorithm outperforms the existing approaches and achieves better results in terms of QoS parameters. The implementation had done using JAVA with CloudSim simulator.

Real-time neural network scheduling of emergency medical mask production during COVID-19

During the outbreak of the novel coronavirus pneumonia (COVID-19), there is a huge demand for medical masks. A mask manufacturer often receives a large amount of orders that must be processed within a short response time. It is of critical importance for the manufacturer to schedule and reschedule mask production tasks as efficiently as possible. However, when the number of tasks is large, most existing scheduling algorithms require very long computational time and, therefore, cannot meet the needs of emergency response. In this paper, we propose an end-to-end neural network, which takes a sequence of production tasks as inputs and produces a schedule of tasks in a real-time manner. The network is trained by reinforcement learning using the negative total tardiness as the reward signal. We applied the proposed approach to schedule emergency production tasks for a medical mask manufacturer during the peak of COVID-19 in China. Computational results show that the neural network scheduler can solve problem instances with hundreds of tasks within seconds. The objective function value obtained by the neural network scheduler is significantly better than those of existing constructive heuristics, and is close to those of the state-of-the-art metaheuristics whose computational time is unaffordable in practice.

Real-Time Scheduling and Analysis of OpenMP DAG Tasks Supporting Nested Parallelism

OpenMP is a promising framework to develop parallel real-time software on multi-cores. Although similar to the DAG task model, OpenMP task systems are significantly more difficult to analyze due to constraints posed by OpenMP specifications. One of the most interesting features in OpenMP is the support for nested parallelism, enjoying benefits in enhancing performance transparency of parallel libraries and promoting reuse of black-box code. Previous researches on DAG task scheduling mainly restrict to only one level of parallelism. The problem whether OpenMP tasks with multiple levels of parallelism are suitable to real-time systems remains open. In this paper, we study the real-time scheduling and analysis of OpenMP task systems supporting nested parallelism. First, we show that under existing scheduling algorithms in OpenMP implementations, nested parallelism indeed may lead to extremely bad timing behaviors where the parallel workload is sequentially executed completely. To solve this problem, we propose a new scheduling algorithm and develop two sound response time bounds by considering the trade-off between simplicity and analysis precision. Experiments demonstrate the efficiency of our methods.

Hybrid electro search with genetic algorithm for task scheduling in cloud computing

Cloud computing is on-demand Internet-based computing, which is a highly scalable service adopted by different working and non-working classes of people around the globe. Task scheduling one of the critical applications used by end-users and cloud service providers. The significant challenging in the task scheduler is to find an optimal resource for the given input task. In this paper, we proposed Hybrid Electro Search with a genetic algorithm (HESGA) to improve the behavior of task scheduling by considering parameters such as makespan, load balancing, utilization of resources, and cost of the multi-cloud. The proposed method combined the advantage of a genetic algorithm and an electro search algorithm. The genetic algorithm provides the best local optimal solutions, whereas the Electro search algorithm provides the best global optima solutions. The proposed algorithm outperforms than existing scheduling algorithms such as Hybrid Particle Swarm Optimization Genetic Algorithm (HPSOGA), GA, ES, and ACO.

Task Scheduling in Heterogeneous Computing Systems Based on Machine Learning Approach

Task scheduling problem of heterogeneous computing system (HCS), which with increasing popularity, nowadays has become a research hotspot in this domain. The task scheduling problem of HCS, which can be described essentially as assigning tasks to the proper processor for executing, has been shown to be NP-complete. However, the existing scheduling algorithm suffers from an inherent limitation of lacking global view. Here, we reported a novel task scheduling algorithm based on Multi-Logistic Regression theory (called MLRS) in heterogeneous computing environment. First, we collected the best scheduling plans as the historical training set, and then a scheduling model was established by which we could predict the following schedule action. Through the analysis of experimental results, it is interpreted that the proposed algorithm has better optimization effect and robustness.

Integrated optimization of disruption management and scheduling for reducing carbon emission in manufacturing

Currently disruption management and Job-shop scheduling are optimized separately, which may ignore the influence of carbon emission on machine allocation, processing sequence in scheduling. In order to reduce carbon emission in manufacturing process, a multi-objective optimization model of disruption management and rescheduling strategy are proposed. Because there are many parameters to be optimized in the proposed method, an improve quantum bacterial optimization algorithm (IQBFO) based on prospect theory is designed to solve the proposed model. Four response indexes of 16 kinds of rescheduling scenarios are simulated and analyzed by using the IQBFO and comparing with the existing scheduling algorithms. The validity of proposed disruption management model and method on reducing carbon emission in manufacturing processes is verified by statistical analysis of the experimental results.

Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm.

In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

Biobjective Task Scheduling for Distributed Green Data Centers

The industry of data centers is the fifth largest energy consumer in the world. Distributed green data centers (DGDCs) consume 300 billion kWh per year to provide different types of heterogeneous services to global users. Users around the world bring revenue to DGDC providers according to actual quality of service (QoS) of their tasks. Their tasks are delivered to DGDCs through multiple Internet service providers (ISPs) with different bandwidth capacities and unit bandwidth price. In addition, prices of power grid, wind, and solar energy in different GDCs vary with their geographical locations. Therefore, it is highly challenging to schedule tasks among DGDCs in a high-profit and high-QoS way. This work designs a multiobjective optimization method for DGDCs to maximize the profit of DGDC providers and minimize the average task loss possibility of all applications by jointly determining the split of tasks among multiple ISPs and task service rates of each GDC. A problem is formulated and solved with a simulated-annealing-based biobjective differential evolution (SBDE) algorithm to obtain an approximate Pareto-optimal set. The method of minimum Manhattan distance is adopted to select a knee solution that specifies the Pareto-optimal task service rates and task split among ISPs for DGDCs in each time slot. Real-life data-based experiments demonstrate that the proposed method achieves lower task loss of all applications and larger profit than several existing scheduling algorithms. Note to Practitioners-This work aims to maximize the profit and minimize the task loss for DGDCs powered by renewable energy and smart grid by jointly determining the split of tasks among multiple ISPs. Existing task scheduling algorithms fail to jointly consider and optimize the profit of DGDC providers and QoS of tasks. Therefore, they fail to intelligently schedule tasks of heterogeneous applications and allocate infrastructure resources within their response time bounds. In this work, a new method that tackles drawbacks of existing algorithms is proposed. It is achieved by adopting the proposed SBDE algorithm that solves a multiobjective optimization problem. Simulation experiments demonstrate that compared with three typical task scheduling approaches, it increases profit and decreases task loss. It can be readily and easily integrated and implemented in real-life industrial DGDCs. The future work needs to investigate the real-time green energy prediction with historical data and further combine prediction and task scheduling together to achieve greener and even net-zero-energy data centers.

An adaptive scheduling approach based on integrated best-worst and VIKOR for cloud computing

Cloud computing is a new distributed environment to provide on-demand services through the internet and has become popular due to this specific feature. By increasing the count of user requests and different criteria in applying the cloud resources, there exist challenges for managing these requests and their optimal allocation. Some of these challenges consist of considering the dynamic nature of the cloud environment, finding the values of the different criteria and the scheduling tasks by considering the user’s Quality of Service (QoS) preferences, load balancing, etc. Static load balancers distribute requests based on pre-known server capability ratios and these models do not produce optimal throughput when the server capabilities change overtime. None of the other scheduling algorithms is able to provide an adaptive approach to consider both of the load balancing and optimizing the QoS requirements. To solve this problem, this paper proposes a new adaptive approach based on a combination of the concept of the best-worst multi criteria decision-making method (BWM), and compromise ranking method (VIKOR). The VIKOR method is implemented as a decision maker to specify the task priorities. The proposed approach is validated with numerical experiments and is compared with the existing scheduling algorithms over different performance metrics. The simulation results indicate that the proposed approach improves the performance metrics like throughput, makespan, waiting time, virtual machine (VM) utilization and VM usage cost for all considered experimental scenarios in comparison with its counterparts.

Minimizing Energy Consumption Scheduling Algorithm of Workflows With Cost Budget Constraint on Heterogeneous Cloud Computing Systems

Cloud computing is a promising platform to conduct large-scale workflow applications according to the pay-per-use model. Minimizing the energy consumption of precedence constrained workflows with cost budget constraints has become one of the popular topics in cloud data centers. Most existing scheduling algorithms mainly consider execution time or cost of a given workflow application under a budget constraint; however, these algorithms do not adequately consider energy saving. A reducing energy consumption strategy using a critical task remapping (RMREC) algorithm is proposed in this study. This algorithm is decomposed into two phases: energy consumption reduction and critical task remapping. In the first phase, the adjustable cost budget and spare cost are determined on the basis of cost budget, critical task path, and adjustable budget factor. All workflow tasks are further allocated to virtual machines (VMs) with the lowest energy consumption to achieve preliminary mapping between tasks and VMs while satisfying the adjustable cost budget constraint. In the second phase, critical tasks are remapped to VMs according to spare cost to decrease energy consumption caused by task migration. Experiments on two types of workflow applications with different scales demonstrate that the presented RMREC algorithm effectively reduces energy consumption without violating cost budget constraints compared with existing algorithms.

Edge computing task scheduling strategy based on load balancing

With the rapid development and wide application of the Internet of Everything, in order to cope with the increasing amount of data and computational scale of mobile terminal processing, and the imbalance of existing scheduling algorithms and low resource utilization, this paper proposes a task scheduling algorithm based on business priority. The algorithm firstly divides the service according to the priority of the service. Secondly, the standard deviation of the computing task group is used to determine the proportion of long and short services, and the dynamic selection model is established. Finally, according to the idea of secondary allocation, the task of heavy load is assigned to the scheduling strategy of light load resources to execute, and the service redistribution model is established. The simulation results show that compared with the typical algorithm, the proposed algorithm achieves the result of comprehensive consideration of Makespan and load balancing to improve system efficiency.

ISleep: thermal entropy aware intelligent sleep scheduling algorithm for wireless sensor network

The optimal operation of Wireless Sensor Network (WSN) requires a sustainable topology. A dead node may result in a breach of end-to-end connectivity in the network. The thermal sensitivity of any sensor affects the lifetime of the node. Every sensor node has a typical operating temperature zone. Beyond the operating temperature threshold, the system reduces the performances and consequently forms a disconnected network. Network entropy represents the evaluation of the anisotropy of the temperature distribution profile of the system that measures the network stability. In this work, an intelligent sleep scheduling algorithm ‘iSleep’ is proposed based on available neighboring nodes, associated network entropy, and traffic flow pattern. iSleep enables the sensor nodes to auto-control the sleep state of the nodes and maintain network connectivity for a longer amount of time. The proposed algorithm is tested for numerous representative networks on MATLAB 2016a and the Cooja simulator on Instant Contiki-2.7 with two energy models of Tmote Sky and Zolertia Z1. It is found that the proposed algorithm outperforms the existing scheduling algorithms in terms of the lifetime of a network for both the energy models.

Dynamic PSO for Task Scheduling Optimization in Cloud Computing

Task scheduling is still a challenge in cloud computing as no existing scheduling algorithms are not effectively provisioning and scheduling the resources in the cloud. Existing authors considered only metrics like makespan, execution time and turnaround time etc. and the previous authors concentrated only to optimize the above mentioned metrics. But no existing authors were considered about the effective provisioning of the resources in the cloud i.e, compute, storage and network capacities and still many resources in the cloud were underutilized. In this paper, we want to propose an algorithm which can effectively utilize the resources in the cloud by extending Particle Swarm Optimization by addressing the metrics Bandwidth utilization and Memory utilization particularly. We have simulated this algorithm by using cloudsim and compared the modified Dynamic PSO with the PSO algorithm and it outperforms in terms of Bandwidth and Memory utilization and the makespan is also optimized.

Extending resources for avoiding overloads of mixed‐criticality tasks in cyber‐physical systems

With the increasing number of services and industries including nuclear, chemical, aerospace, and automotive sectors in cyber-physical systems (CPSs), systems are being severely overloaded. CPSs comprises mixed-critical tasks which are of either safety-critical (high) or non-safety critical (low). In traditional task scheduling, most of the existing scheduling algorithms provide poor performance for high-criticality tasks when the system experiences overload and do not show explicit separation among different criticality tasks to take advantage of using cloud resources. Here, we propose a framework to schedule the mixed-criticality tasks by analyzing their deadlines and execution times which leverage the performance of parallel processing through OpenMP. The proposed framework introduces a machine learning-based prediction for a task offloading in the cloud. Moreover, it illustrates to execute a selected number of low-criticality tasks in the cloud while the high-criticality tasks are run on the local processors during the system overload. As a result, the high-criticality tasks meet all their deadlines and the system achieves a significant improvement in the overall execution time and better throughput. In addition, the experimental results employing OpenMP show the effectiveness of using the partitioned scheduling over the global scheduling method upon multiprocessor systems to achieve the tasks isolation.