Dynamic Scheduling for Emergency Tasks on Distributed Imaging Satellites with Task Merging

Enhanced wireless communication improves the connectivity of vehicular networks in which vehicles are utilized as infrastructures for communication and computation. Thus, a new concept “Vehicular Edge Computing (VEC)” is formed. As VEC utilizes a collaborative multitude of near-user edge resources (i.e. vehicles) in the Internet of Vehicles, the capability of these joint resources becomes heterogeneous especially in their movements. Therefore, one critical problem is how to efficiently schedule each task under such mobile environments. For the reason, we propose a hybrid dynamic scheduling scheme (HDSS) that has the ability to optimize the task scheduling dynamically based on the changeable system environments. HDSS provides a decision function (DF) to select a better-performed scheduling algorithm from two provided candidates: the queue-based dynamic scheduling (QDS) algorithm and the time-based dynamic scheduling (TDS). QDS coincides with the Join-the-Shortest Queue scheme, which decides the scheduling by sorting out a server with the shortest queue-length; nevertheless, TDS is novel scheme that is designed to implement task allocation by estimating the waiting time of each server in order to select a server with the fastest response. Finally, this research generates formal models of each scheduling algorithm and the hybrid scheduling scheme to conduct performance evaluation with a fluid flow approximation technique. The analysis results in a superior performance of HDSS in the unstable VEC environments.

The scheduling of tasks with deadlines on clusters is a key issue for offering quality-of-service (QoS) assurance. A critical challenge in real-time task scheduling is to handle various types of applications. This paper investigates the scheduling problem for processing a set of tasks comprising both divisible and indivisible real-time tasks on cluster systems. Indivisible tasks are characterized by the property that they need to be processed on their entirety on a single processor while divisible tasks can be distributed across several processing nodes by exploiting the underlying data parallelism. We propose a dynamic (on-line) real-time scheduling algorithm referred to as Hybrid Loads Push-Pull Scheduling (HLPPS) algorithm for handling a set of tasks comprising both divisible and indivisible real-time tasks on cluster systems. HLPPS is shown to efficiently exploit the parallelism in divisible tasks without undermining the schedulability of indivisible tasks and thereby optimize the overall performance. We consider two distinct network platforms - tightly coupled and loosely coupled clusters in designing the strategy. We conduct extensive performance evaluation studies to quantify the performance of the proposed algorithm under a variety of scenarios.

Scheduling plays a significant role in improving observation effectiveness of imaging satellites.Although extensive satellite scheduling algorithms have been proposed, none of them focuses on emergency scheduling in dynamic environment.In this paper, a novel dynamic emergency scheduling model of multiple imaging satellites is established.To improve user's satisfaction ratio and resource utilization, we propose a novel task dynamic merging strategy, developing an alternative task set establishment (ATSE) algorithm used for dynamic merging.Besides, a novel dynamic emergency scheduling algorithm called DM-DES is presented, which considers the dynamic merging.To demonstrate the superiority of our DM-DES, we conduct extensive experiments by simulations to compare DM-DES with an existing algorithm--RBHA as well as a baseline algorithm--DES.The experimental results indicate that DM-DES improves the scheduling quality of others and is suitable for emergency scheduling.

Objective: To develop a task scheduling algorithm that efficiently approximates solutions for the multi-objective task scheduling problem in a cloud environment, optimizing resource utilization, execution time, cost, and overall system performance. Method: A Modified Sunflower Optimization Algorithm for Task Scheduling (MSOTS) is proposed to improve efficiency in cloud environments. The traditional sunflower optimization algorithm is enhanced with Levy flight, crossover, and mutation operations to achieve a better balance between exploration and exploitation while preventing entrapment in local minima. These enhancements help improve convergence speed and solution quality. CloudSim is utilized for comprehensive performance evaluation, comparing MSOTS with existing algorithms in terms of execution time, cost, and resource utilization. The randomly generated dataset (500 tasks and 50 VMs) is used for analyze the performance of the MSOTS. Findings: The results demonstrate that MSOA significantly reduces makespan and cost while enhancing resource utilization. Specifically, the proposed method reduces makespan by 22.43% compared to PSO and 14.95% compared to SOA. Additionally, cost is reduced by 14.47% and 10.38% compared to PSO and SOA, respectively, while resource utilization increases by 3.89% and 2.51% over these methods. These findings indicate that MSOA effectively improves task scheduling performance in terms of makespan, cost efficiency, and resource utilization, making it a promising approach for cloud computing environments. Novelty: The MSOA is integrated with single-point crossover, swap mutation, and Lévy flight to update the solution, which prevents entrapment in local minima and premature convergence and enhances task scheduling efficiency, optimizes resource allocation, minimizes execution time, and reduces overall costs in cloud computing environments. Keywords: Meta-heuristic, Sunflower optimization, Levy flight, Crossover, Mutation

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. Keywords: Cloud Environment, Heterogeneous Cloud, Locality-Load-Prediction Aware Scheduling, Multi-Objective, Task Scheduling

In this paper classic static and dynamic scheduling strategy is analyzed first, and then communication network of schedule ability judgment basis is given. An improved dynamic EDF scheduling algorithm is proposed in order to improve the scheduling task of real-time. The scheduling strategy is to change task priority according to the transmission error over deadline task when applying dynamic EDF scheduling strategy. True Time tool is used to build CAN network control system simulation platform. Dynamic EDF scheduling algorithm and improved scheduling algorithm are simulated respectively. The effectiveness of improved scheduling algorithm is verified by the simulation Keywords-Network control system; Scheduling Algorithm; True Time toolbox result.

<span>The distributed fog node network and variable workloads make task distribution difficult in fog computing. Optimizing computing resources for dynamic workloads with heuristic and metaheuristic algorithms has shown potential. To address changing workloads, these algorithms enable real-time decision-making. This systematic review examines heuristic, meta-heuristic, and real-time dynamic job scheduling strategies in fog computing. Static methods like heuristic and meta-heuristic algorithms can help modify dynamic task scheduling in fog computing situations. This paper covers a current study area that stresses real-time approaches, meta-heuristics, and fog computing environments' dynamic nature. It also helps build reliable and scalable fog computing systems by spotting dynamic task scheduling trends, patterns, and issues. This study summarizes and analyzes the latest fog computing research on task-scheduling algorithms and their pros and cons to adequately address their issues. Fog computing task scheduling strategies are detailed and classified using a technical taxonomy. This work promises to improve system performance, resource utilization, and fog computing settings. The work also identifies fog computing job scheduling innovations and improvements. It reveals the strengths and weaknesses of present techniques, paving the way for fog computing research to address unresolved difficulties and anticipate future challenges.</span>

In cloud computing, optimizing task scheduling is crucial for improving overall system performance and resource utilization. To minimize cloud service costs and prevent resource wastage, advanced techniques must be employed to efficiently allocate cloud resources for executing tasks. This research presents a novel multi-objective task scheduling method, BSSA, which combines the Backtracking Search Optimization Algorithm (BSA) and the Sparrow Search Algorithm (SSA). BSA enhances SSA’s convergence accuracy and global optimization ability in later iterations, improving task scheduling results. The proposed BSSA is evaluated and compared against traditional SSA and other algorithms using a set of 8 benchmark test functions. Moreover, BSSA is tested for task scheduling in cloud environments and compared with various metaheuristic scheduling algorithms. Experimental results demonstrate the superiority of the proposed BSSA, validating its effectiveness and efficiency in cloud task scheduling.

There are many tasks in cloud computing that should be executed by available resources to acquire high performance, reduce task completion time, increase utilisation of resources etc. Since task scheduling problem in cloud computing is an NP-hard problem, designing an efficient scheduling strategy to achieve the intentions listed above is challenging. Task scheduling is the process of allocating tasks to available resources such that performance metrics are improved. This paper proposes a dynamic scheduling algorithm that uses hill climbing algorithm. It tries to minimise completion time of tasks while maximising throughput and utilisation of resources. This algorithm allocates independent tasks to available resources to achieve load balance. The simulation results show that the algorithm can achieve load balance and reduces completion time of tasks.

This paper focuses on multi-objective dynamic flexible job shop scheduling problem (MODFJSP) with machine breakdown. First, a multi-objective dynamic scheduling model is established, with objectives to minimize makespan and total machine workload. Second, according to the processing status of faulty machine, a hybrid rescheduling strategy including transfer rescheduling strategy and complete rescheduling strategy is proposed to react to stochastic machine breakdown. The performance of two rescheduling strategies is analyzed in terms of the scheduling efficiency and its stability, from the delay extent and initial scheduling deviation, respectively. Besides, the optimal adaptation conditions of both scheduling strategies are obtained. Furthermore, the non-dominated sorting genetic algorithm (NSGA-II) is employed to solve the constructed model. Experimental results demonstrate the effectiveness of the proposed strategies on reducing the impact of machine breakdown in real scheduling.

Existing real-time multicore schedulers use either global or partitioned scheduling technique to schedule real-time tasks. Partitioned scheduling is a static approach in which, a task is mapped to a per-processor ready queue prior to scheduling it and it cannot migrate. Partitioned scheduling makes ineffective use of the available processing power and incurs high overhead when real-time tasks are dynamic in nature. Global scheduling is a dynamic scheduling approach, where the processors share a single ready-queue to execute the highest priority tasks. Global scheduling allows task migration which results in high scheduling overhead. In this paper, we present a dynamic partitioning based scheduling of real-time tasks, called DP scheduling. In DP scheduling, jobs of tasks are assigned to cores when they are released and remain in the same core till they finish execution. The partitioning in DP scheduling is done based on the slack time and priority of jobs. If a job cannot be allocated to any core, then it is split, and executed on more than one core. DP scheduling technique attempts to retain good features of both global and partitioned scheduling without compromising on resource utilization, and at the same time, also tries to minimize the scheduling overhead. We have tested DP scheduling technique with EDF scheduling policy at each core, and we term this scheduling algorithm as DP-EDF. The performance of DP-EDF scheduling algorithm has been evaluated using simulation study and its implementation in LITMUS-RT on a 64-bit intel processor with eight logical cores. Both simulation and experimental results show that DP-EDF scheduling algorithm has better performance in terms of resource utilization, and comparable or better performance in terms of scheduling overhead in comparison to contemporary scheduling algorithms.

In heterogeneous environments, dynamic scheduling algorithms are a powerful tool towards performance improvement of scientific applications via load balancing. However, these scheduling techniques employ heuristics that require prior knowledge about workload via profiling resulting in higher overhead as problem sizes and number of processors increase. In addition, load imbalance may appear only at run-time, making profiling work tedious and sometimes even obsolete. Recently, the integration of dynamic loop scheduling algorithms into a number of scientific applications has been proven effective. This paper reports on performance improvements obtained by integrating the Adaptive Weighted Factoring, a recently proposed dynamic loop scheduling technique that addresses these concerns, into two scientific applications: computational field simulation on unstructured grids, and N-Body simulations. Reported experimental results confirm the benefits of using this methodology, and emphasize its high potential for future integration into other scientific applications that exhibit substantial performance degradation due to load imbalance.

Cyber-physical systems (CPS) based on cloud computing provides resources over the Internet and allow a variety of applications to be deployed to provide services for various industries. We proposed IoT-based healthcare cyber-physical system that provides effective resource utilization at fog and cloud levels with minimum execution cost. In addition, we also consider data from social media networking and drug review for the analysis. Furthermore, two different feature extraction approaches were applied based on data collection. Homogeneity score-based K-means clustering is used as a feature extraction and selection method for sensor data features, while text mining and sentiment analysis approach is used for social media networking and drug review data feature extraction. We proposed efficient resource utilization and cost-effective task scheduling at the Fog level and multi-objective heuristic approach Ant colony optimization task scheduling (MOHACO-TS) at cloud level. Both task scheduling algorithms focus on executing maximum task tasks in minimum time with effective resource utilization. We consider five different datasets and existing task scheduling and classification approaches for performance evaluation of the proposed IoT-HCPS framework. From the results, it is evident that the proposed work IoT-HCPS outperformed the exisitng techniques and algorithms.

Mobile edge computing enables the execution of compute-intensive applications, e.g. deep learning applications, on the end devices with limited computation resources. However, the deep learning applications bring the performance bottleneck in mobile edge computing, due to the movements of a large amount of data incurred by the large number of layers and millions of weights. In this paper, the computing model for parallel deep learning applications in mobile edge computing is proposed, by considering the occupancy allocation of processors, cost of context switch, and multi-processors in edge server and remote cloud. The problem of minimizing the completion time for deep learning applications is formulated, and the NP-hardness of the problem is proved. To solve the problem, an integrated algorithm by merging and scheduling is proposed. Moreover, a real-world distributed platform is developed for evaluating the proposed algorithm. Experimental results show that, the completion time of deep learning application for the proposed algorithm is decreased by 63% and 75%, respectively, without extra control costs, compared with the existing algorithms.

Hadoop uses a reliable, efficient and scalable way to process data. It provides a good solution for dealing with big data. The task scheduler is the core component of Hadoop, and it is responsible for the managing and allocating the cluster resources. Therefore, scheduling algorithm directly affects the overall performance of Hadoop platform and utilization of cluster resource. Based on this, the improved accelerate particle swarm algorithm (IAPSO) is introduced to the cloud environment, and to solve the cloud task scheduling problem in this article. When we use particle swarm algorithm for task scheduling, the tasks are considered as particles, the resource pool is seen as the search space, and the process of finding the optimal solution is considered as a process of task scheduling. If all the sub tasks find the appropriate resources, then stop the iteration and allocate sub asks to the resource nodes. Finally, we simulate the experiment by using CloudSim software. When a single type of task is committed, our algorithm and the other three algorithms can also be used to complete the task scheduling process, and our algorithm is more efficient. But in practice, the cloud computing environment is facing multiuser, and the types of tasks are also varied. With the increase in the number of tasks, the advantage of the other three algorithms decreases gradually, but algorithm in this paper has been exhibited higher efficiency. In addition, with the increase of the number of nodes, task completed time of the algorithm in this paper is significantly less than the other three algorithms, and it has a steady downward trend. Therefore, IAPSO algorithm which is proposed in this paper is applied to solve task scheduling problem in the cloud environment, and it can effectively improve the efficiency of task scheduling.