Aware Task Scheduling Research Articles

Benders decomposition for the energy aware task scheduling of constellations of nanosatellites

This paper describes a novel method for optimizing nanosatellite constellation scheduling based on Benders decomposition. The optimization of nanosatellite constellations is challenging due to the large number of variables and constraints involved, which increases the computational work required to get optimal solutions as the number of participating objects increases. Our strategy overcomes this obstacle by decomposing the complex problem into smaller, more manageable subproblems, rendering the original formulation scalable. The outcomes demonstrate that the Benders decomposition achieves the same optimal solutions obtained with the Gurobi solver applied to the baseline formulation while being significantly faster and more efficient, especially for larger constellation sizes. This method provides a valuable tool for decision-makers in the nanosatellite job scheduling process, highlighting the potential benefits of using decomposition methods such as the Benders decomposition for large-scale or challenging optimization problems in Offline Nanosatellite Task Scheduling (ONTS).

SSKHOA: Hybrid Metaheuristic Algorithm for Resource Aware Task Scheduling in Cloud-fog Computing

Cloud fog computing is a new paradigm that combines cloud computing and fog computing to boost resource efficiency and distributed system performance. Task scheduling is crucial in cloud fog computing because it decides the way computer resources are divided up across tasks. Our study suggests that the Shark Search Krill Herd Optimization (SSKHOA) method be incorporated into cloud fog computing's task scheduling. To enhance both the global and local search capabilities of the optimization process, the SSKHOA algorithm combines the shark search algorithm and the krill herd algorithm. It quickly explores the solution space and finds near-optimal work schedules by modelling the swarm intelligence of krill herds and the predator-prey behavior of sharks. In order to test the efficacy of the SSKHOA algorithm, we created a synthetic cloud fog environment and performed some tests. Traditional task scheduling techniques like LTRA, DRL, and DAPSO were used to evaluate the findings. The experimental results demonstrate that the SSKHOA outperformed the baseline algorithms in terms of task success rate increased 34%, reduced the execution time by 36%, and reduced makespan time by 54% respectively.

Deadline‐cost aware task scheduling algorithm in fog computing networks

SummaryThe Internet of Things (IoT) has provided us with various applications that need processing in real‐time. Fog computing has arisen as a highly effective computing paradigm that provides services closer to the IoT devices and hence fulfills the need for rapid response time and delay requirements of IoT applications. However, the heterogeneity of IoT tasks and the limited resources of fog nodes pose considerable challenges to task scheduling. Therefore, the effective utilization of fog resources is highly significant and necessitates an optimal and intelligent approach to task scheduling. This paper presents the “deadline‐cost aware task scheduling” (DCaTS) algorithm, which is designed for fog‐based IoT applications. The primary objective of this algorithm is to minimize the monetary cost while taking into account the deadline requirements and priority of tasks. The efficacy of DCaTS is assessed by analyzing various parameters, including completion time, monetary cost, response time, and waiting time. The experimental results show that in comparison with traditional policies, the proposed algorithm shows superiority in all the parameters. The proposed algorithm reduces the monetary cost up to 11.07% compared with the second best result. The results also indicate that DCaTS achieves a reduction of 10.05% in completion time, 10% in response time, and 10.12% in waiting time compared with the second best result.

Replication-Based Resource Provisioning and Constrained Aware Task Scheduling Framework for Cloud Workflows

Replication-Based Resource Provisioning and Constrained Aware Task Scheduling Framework for Cloud Workflows

Reliability and Trust Aware Task Scheduler for Cloud-Fog Computing Using Advantage Actor Critic (A2C) Algorithm

Reliability and Trust Aware Task Scheduler for Cloud-Fog Computing Using Advantage Actor Critic (A2C) Algorithm

MPPT aware task scheduling for nanosatellites using MIP-based ReLU proxy models

MPPT aware task scheduling for nanosatellites using MIP-based ReLU proxy models

Bivariate Correlative Modest Adaptive Boost Resource Aware Task Scheduling in Cloud Environment

Cloud computing is a rapidly evolving paradigm that provides accessible and virtualized resources through Internet technology. In this model, Cloud Service Providers (CSPs) offer online access to computing resources for users to execute their application tasks. Task scheduling in cloud computing involves the allocation of computational tasks to available resources within the cloud environment. The primary objectives of task scheduling are to optimize resource utilization, minimize task completion time, and enhance overall system performance. Task scheduling plays a vital role in cloud resource management as it directly impacts the efficiency of cloud data centers. With the increasing number of cloud computing users, task scheduling has become more challenging, requiring the use of suitable scheduling algorithms. To improve task scheduling efficiency, a novel method called Bivariate Correlative Modest Adaptive Boost Resource Optimized Task Scheduling (BCMABROTS) is developed for efficient service provisioning in the cloud environment. The BCMABROTS method begins by collecting the number of incoming user-requested tasks. After that, the resources of virtual machines, such as energy, bandwidth, memory, and CPU, are measured. The ensemble classifier constructs the set of weak learners as the nearest prototype centroid classifier. The weak classifier uses Bivariate Correlation to classify the resource-optimal virtual machine based on the available resources. The results of the weak learners are combined to provide strong classification results. Once the optimal virtual machine is categorized, the task is scheduled for that particular virtual machine by the task assigner. This approach ensures efficient cloud service provisioning with minimal time consumption. Experimental evaluation is carried out to assess factors such as task scheduling efficiency, makespan, throughput and average response time in relation to the number of cloud-requested tasks. The observed performance results confirm that the BCMABROTS method improves the task scheduling efficiency, throughput and minimizes the makespan as well as the average response time than the conventional machine learning methods.

AGWO: Cost Aware Task Scheduling in Cloud Fog Environment Using Hybrid Metaheuristic Algorithm

In IoT concepts, efficient approaches like cloud-fog computing are emerging, enhancing system benefits. The performance and output of such frameworks can be greatly improved by optimized scheduling of Internet of Things (IoT) task requests. This study presents a novel technique for scheduling Internet of Things requests in a cloud-fog environment, based on an adaption of ant grey wolf optimisation (AGWO). By combining the operators of ant colony optimisation (ACO) and grey wolf optimisation (GWO), AGWO aims to improve the speed and quality of ACO's solution discovery. The suggested AGWO approach is evaluated using numerous datasets of varying sizes, both synthetic and real-world. The effectiveness of AGWO is further investigated by comparing it to standard metaheuristic methods. The experimental results demonstrate that AGWO is superior to competing strategies in terms of makespan time and cost while resolving the task scheduling problem.

Hybrid Pelican and Archimedes optimization algorithm fostered energy aware task scheduling in heterogeneous virtualized cloud computing

AbstractEnergy reduction is a key issue of virtualized cloud computing (CC) schemes, because it provide significant benefits, like lower operating costs, improving system effectiveness, securing the environment. Energy‐efficient task scheduling is a feasible mode to attain these objectives. Nevertheless, it is difficult to match cloud resources to user requests in a way that improves performance while meeting a user‐defined deadline for energy consumption reduction. Therefore, hybrid Pelican and Archimedes optimization algorithm fostered energy‐aware task scheduling in heterogeneous virtualized cloud computing (TS‐HVCC‐Hyb‐POA‐AOA) is proposed in this article. A hybrid Pelican and Archimedes optimization algorithm (Hyb‐POA‐AOA) is used to lessen the task duration and the consumption of power in the cloud. For evaluation, the cloud environment uses the provided environment hybrid Pelican and Archimedes optimization algorithm for execute a few common workloads at the simulated data center. The performance metrics, like makespan right skewed analysis, makespan left skewed analysis, energy consumption right skewed analysis, energy consumption left skewed analysis, availability and resource utilization is considered. The performance of the proposed TS‐HVCC‐Hyb‐POA‐AOA method provides 23.69%, 29.50%, and 36.78% higher resource utilization; 38.23%, 31.35%, and 26.19% lower consumption of energy left skewed analysis and 34.52%, 30.28%, and 23.54% lower makespan for right skewed analysis compared with existing methods such as; task scheduling in heterogeneous cloud environment with gray wolf optimization algorithm (TS‐HVCC‐GWOA), hybrid whale optimization approach and differential evolution optimization for multiple objective virtual machine programming at cloud services (TS‐HVCC‐WOA), energy and performance planning in TasterkHeterek virtualized cloud computing using particle swarm optimization algorithm (TS‐HVCC‐PSOA).

An effective fault tolerance aware scheduling using hybrid horse herd optimisation‐reptile search optimisation approach for a cloud computing environment

AbstractIT services can be requested via cloud computing, a model based on services as well as the Internet. It includes all computer systems resources from hardware components to software platforms and software applications in a distributed environment. Scheduling is a key step in processing tasks using remote resources. Serious issues have been brought forward, including the ineffective use of resources and task execution failure. The concurrent provision of fault tolerance and resource optimisation is achallenging task. In the context of cloud computing, this research offers a brand‐new job scheduling and fault‐tolerant system. Tasks submitted by users are taken as an input for the proposed method. Several virtual machines (VM) are initially arranged for scheduling work and execution process. Initially, Horse Herd Optimisation is employed here to allocate the job based on key factors such as deadline and user budget. Once the jobs are assigned to each VM, then each job's deadline is confirmed and transferred to VM which has sufficient capacity. Here, the Reptile Search Optimisation technique is applied to identify the VM error. Any VM that does not have enough capacity is the one that has a problem. When a fault is found, a fault‐tolerant process is instantly started. A replication‐based fault‐tolerant mechanism is used in this manuscript. The proposed approach is tested with several metrics which attains better performance like 80 s response time, turnaround time of 32 s, 17% resource utilisation and a success rate of 92%. Thus the designed model is the best choice for fault‐tolerant aware task scheduling.

Fault aware task scheduling in cloud using min-min and DBSCAN

Cloud computing leverages computing resources by managing these resources globally in a more efficient manner as compared to individual resource services. It requires us to deliver the resources in a heterogeneous environment and also in a highly dynamic nature. Hence, there is always a risk of resource allocation failure that can maximize the delay in task execution. Such adverse impact in the cloud environment also raises questions on quality of service (QoS). Resource management for cloud application and service have bigger challenges and many researchers have proposed several solutions but there is room for improvement. Clustering the resources clustering and mapping them according to task can also be an option to deal with such task failure or mismanaged resource allocation. Density-based spatial clustering of applications with noise (DBSCAN) is a stochastic approach-based algorithm which has the capability to cluster the resources in a cloud environment. The proposed algorithm considers high execution enabled powerful data centers with least fault probability during resource allocation which reduces the probability of fault and increases the tolerance. The simulation is cone using CloudsSim 5.0 tool kit. The results show 25% average improve in execution time, 6.5% improvement in number of task completed and 3.48% improvement in count of task failed as compared to ACO, PSO, BB-BC (Bib ​= ​g bang Big Crunch) and WHO(Whale optimization algorithm).

Frequency aware task scheduling usingDVFSfor energy efficiency in Cloud data centre

AbstractReliable processing capacity and flexible storage space make Cloud computing the most recent favourable technology. Many organizations have converted their conventional processing data centre to a Cloud data centre. Cloud computing provides promising execution and storage, which leads to massive growth in processing demand by Cloud users. It makes the Cloud data centre increase the number of virtual machines (VM) to execute the users tasks. Hence, it causes high frequency disbursed and has increased energy consumption. Many techniques were proposed, which focuses on Cloud energy saving. However, there is still a lack of trade‐off between energy‐efficient task allocation and frequency scaling for a given workload. In this work, we propose a task scheduling algorithm that aims to minimize energy consumption through the frequency scaling technique while improving task execution time. Specifically, our scheduler comprises two modules, which are the scaling frequency module and frequency‐aware task scheduling module. In our first module, we utilize Dynamic Voltage and Frequency Scaling‐Optimal Frequency (DVFS) to determine the optimal frequency and selecting the best server for the incoming tasks. The number of VM is created upon the best server. As for the second module, the VM processing capacity is scaled to the required frequency of the task. We identify it as a required processing capacity for executing the tasks. The experiment result shows that our algorithm has outperformed and efficiently minimized the energy consumption in the Cloud data centre as compared with existing energy‐saving techniques. Meanwhile, the task allocation also has met the system's Quality of Service (QoS). Significantly, leveraging the resource processing frequency is able to gain better trade‐off between performance and energy consumption in the Cloud data centre.

Delay and energy aware task scheduling mechanism for fog-enabled IoT applications: A reinforcement learning approach

With the expansion of the internet of things (IoT) devices and their applications, the demand for executing complex and deadline-aware tasks is growing rapidly. Fog-enabled IoT architecture has evolved to accomplish these tasks at the fog layer. However, fog computing devices have limited power supply and computation resources compared to cloud devices. In delay-sensitive applications of fog-enabled IoT architecture, executing tasks with stringent deadlines while reducing the service latency and energy usage of fog resources is a difficult challenge. This paper presents an effective task scheduling strategy to allocate fog computing resources for IoT requests to meet the deadline of the requests and resource availability. Initially, the scheduling problem is formulated as mixed-integer nonlinear programming (MINLP) to reduce the energy consumption of the fog resources and service time of the tasks subject to the deadline and resource availability constraints. To address the high dimensionality issue of the tasks in a dynamic environment, a fuzzy-based reinforcement learning (FRL) mechanism is employed to reduce the service delay of the tasks and energy usage of the fog nodes. Initially, the tasks are prioritized using fuzzy logic. Then the prioritized tasks are scheduled using the on-policy reinforcement learning technique, which enhances the long-term reward compared to the Q-learning approach. Further, the evaluation outcomes reflect that the proposed task scheduling technique outperforms the existing algorithms with an improvement of up to 23% and 18% regarding service latency and energy consumption, respectively.

An effective technique to schedule priority aware tasks to offload data on edge and cloud servers

Recent advancements in the Internet of Things (IoT) have enhanced the quality of life globally. Billions of devices are brought under the ambit of IoT to make them smarter. IoT-based applications are generating voluminous data and managing this widespread amount of data in real-time through Cloud Technology, which offers high computational and storage facilities. However, sending all data to the cloud can bring serious concerns for applications, which are critical and require instant action without any delay. Edge computing has recently emerged as an effective technology to handle the instant processing of tasks of IoT-based applications locally. Additionally, an important concern in IoT networks is response to emergency tasks on time to increase the performance of large-scale IoT systems. As such, scheduling of tasks becomes vital, where emergency and non-emergency tasks can be prioritized to offload data to the nearby edge and cloud servers respectively and enhance Quality of Service (QoS). The execution order of tasks and allocating resources for computation to avoid delays are two of the most important factors that must be addressed during task scheduling in Edge Computing. With the aforementioned issues, we design a Priority aware Task Scheduling (PaTS) algorithm for sensor networks to schedule priority aware tasks to offload data on edge and cloud servers. The problem is formulated as a multi-objective function and the efficiency of the proposed algorithm is evaluated using the Bio-inspired NSGA-2 technique. The overall improvement for average queue delay, computation time, and energy obtained for 200 tasks is 17.2%, 7.08% and 11.4%, respectively. The results obtained show significant improvement when compared with the benchmark algorithms demonstrating the effectiveness of the proposed solution. Similarly, comparative results for tasks when increased from 200 to 1000 tasks also shows subsequent improvements.

Hybrid heuristic algorithm for cost-efficient QoS aware task scheduling in fog–cloud environment

Fog cloud computing promotes the combination of fog and cloud nodes to satisfy the requests of data accessing from IoT (Internet of Things) devices. To reduce the data delay and enhancing the QoS (Quality of Service), effective task scheduling is one of the major requirements in fog cloud environment. Numerous approaches have been deployed by the researchers for maintaining the QoS requirements. However, the emergence of bursty traffic affects the process of task scheduling due to high service latency. Effective QoS is promoted by minimizing the costs of computation, communication and deadline violation cost. To achieve the main objective of cost minimization, the proposed work is implemented by adopting a novel model called HFSGA (Hybrid Flamingo Search with a Genetic Algorithm) for better task scheduling. Seven basic benchmark optimization test functions are used to compare the performance of HFSGA with other well-known algorithms. Also, Friedman Rank Test is performed to demonstrate the significance of the results. The implemented model presents better outcomes with respect to PDST (Percentage of deadline satisfied task), makespan and cost. When compared to the existing algorithms that include Ant colony optimization (ACO), Particle swarm optimization (PSO), Genetic Algorithm (GA), Min-CCV, Min-V and Round Robin (RR) approach, the proposed work shows better output in satisfying the task scheduling process.

Test Scheduling of SoC by using Dynamic Voltage Frequency Scaling (DVFS) Technique

High temperature gradients in System on Chip (SoC) lowered the performances, reliability and leakage power. In addition, temperature during testing gain more compared to normal operation. Therefore, the investigation of the impact dynamic voltage frequency scaling (DVFS) on the thermal aware test scheduling performance will be the main contribution of this work. The test scheduling algorithm which embeds frequency scaling effect with dynamic voltage supply is tested on ITC’02 benchmark. The formulation of ILP is to minimize the group of the test session in SoC and continued with DVFS formulation. Compared to the conventional thermal-aware scheduling approach based purely on a frequency scaling, this technique provides shorter overall test times and greatly improved flexibility to satisfy strict thermal constraints. The proposed DVFS with thermal aware task scheduling allows to minimize test time more than 46%.

MULTIVARIATE PIECEWISE REGRESSIVE AFRICAN BUFFALO OPTIMIZATION-BASED RESOURCE-AWARE TASK SCHEDULING IN CLOUD

Distributed cloud computing handles a large number of tasks and provides many dynamic virtualized resources that aim to share as a service through the internet. While handling a large volume of tasks, task execution times, throughput, and makespan are the most significant metrics in practical scenarios. So, the scheduling task is essential to achieve accuracy and correctness on task completion. A novel technique called Multivariate Piecewise Regressive African Buffalo Optimization-based Resource Aware Task Scheduling (MPRABO-RATS) is introduced for improving the task scheduling efficiency and minimizing time consumption. First, the cloud user dynamically generates numerous heterogeneous tasks in the cloud environments. After receiving the tasks, the task scheduler in the cloud server finds the resource-optimized virtual machine using the Multivariate Piecewise Regressive African Buffalo Optimization technique. The proposed optimization technique uses the Multivariate Piecewise Regression function for analyzing the different resources availablity such as CPU Time, Memory, Bandwidth, and Energy before the task scheduling. Initially, the population of the virtual machine is defined. After that, the fitness is measured using Multivariate Piecewise Regression. Based on the fitness estimation, the resource-efficient virtual machine is determined. Finally, the task scheduler assigns the tasks to the resource-optimized virtual machine with higher efficiency. Experimental evaluation is carried out in the CloudSim simulator on the factors such as task scheduling Efficiency, Throughput, Makespan, and Memory Consumption with respect to a number of tasks. The observed results indicate that the MPRABO-RATS technique offers an efficient solution in terms of achieving higher task scheduling Efficiency, Throughput, and Minimizing the Makespan as well as Memory Consumption than the conventional scheduling techniques

Workflow simulation and multi-threading aware task scheduling for heterogeneous computing

Efficient application scheduling is critical for achieving high performance in heterogeneous computing systems. This problem has proved to be NP-complete even for the homogeneous case, heading research efforts in obtaining low complexity heuristics that produce good quality schedules. Such an example is HEFT, one of the most efficient list scheduling heuristics in terms of makespan and robustness.In this paper, we propose two task scheduling methods for heterogeneous computing systems that can be integrated to several task scheduling algorithms. First, a method that improves the scheduling time (the time for obtaining the output schedule) of a family of task scheduling algorithms is delivered without sacrificing the schedule length, when the computation costs of the application tasks are unknown. Second, a method that improves the scheduling length (makespan) of several task scheduling algorithms is proposed, by identifying which tasks are going to be executed as single-threaded and which as multi-threaded implementations, as well as the number of the threads used. We showcase both methods by using HEFT popular algorithm, but they can be integrated to other algorithms too, such as HCPT, HPS, PETS and CPOP.The experimental results, which consider 14580 random synthetic graphs and five real world applications, show that by enhancing HEFT algorithm with the two proposed methods, significant makespan gains and high scheduling time gains, are achieved.

Quantum invasive weed optimization-based energy aware task scheduling for cyber–physical system environment

Cyber–physical systems (CPSs) can be treated as an emerging technology that has the ability to handle the physical process and computational view of interlinked systems. At the same time, the high-performing processing capability provides assurance of CPS applications in real time. Besides, task scheduling is considered as the Nondeterministic Polynomial (NP)-hard problem and optimal allocation of tasks is important for the CPS environment. The primary concept of the optimum energy-based scheduling approach searches for the physical host allocation vector to the allotted virtual machine with an aim of reducing energy utilization. The multiple processor packet scheduling technique defined that every task in the system is already divided into processors by the task allocating scheme and every process can execute on the distinct or identical single processor scheduling technique. With this motivation, this paper presents a new quantum invasive weed optimization-based energy-aware scheduling (QIWO-EATS) technique for the CPS environment. The goal of the QIWO-EATS technique is to assign [Formula: see text] autonomous tasks to [Formula: see text] dissimilar resources, and thereby the whole task completion duration gets reduced and resources are completely used. The proposed model has been simulated using the MATLAB tool. The experimental results highlighted the better outcomes of the QIWO-EATS technique over the recent approaches in terms of several evaluation metrics.

An Adaptive Genetic Algorithm-Based Load Balancing-Aware Task Scheduling Technique for Cloud Computing

Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers. Task scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution, and an inefficient task scheduling algorithm results in under-or over-utilization of the resources, which in turn leads to degradation of the services. Therefore, in the proposed work, load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented process. In this paper, we propose an adaptive genetic algorithm-based load balancing (GALB)-aware task scheduling technique that not only results in better utilization of resources but also helps in optimizing the values of key performance indicators such as makespan, performance improvement ratio, and degree of imbalance. The concept of adaptive crossover and mutation is used in this work which results in better adaptation for the fittest individual of the current generation and prevents them from the elimination. CloudSim simulator has been used to carry out the simulations and obtained results establish that the proposed GALB algorithm performs better for all the key indicators and outperforms its peers which are taken into the consideration.