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In the current computing environment, the significance of distributed heterogeneous systems has gained prominence. The research on scheduling problems in distributed systems that consider energy consumption has garnered substantial attention due to its potential to enhance system stability, achieve energy savings, and contribute to environmental preservation. However, efficient scheduling in such systems necessitates not only the consideration of energy consumption but also the ability to adapt to the dynamic nature of the system. To tackle these challenges, we propose an online energy-aware scheduling algorithm for deadline-constrained applications in distributed heterogeneous systems, leveraging dynamic voltage and frequency scaling (DVFS) techniques. First, the algorithm models the continuously arriving applications and heterogeneous processors and proposes a novel task-sorting method to prioritize tasks, ensuring that more applications are completed within their respective deadlines. Second, the algorithm controls the selection range of processors based on the task's subdeadline and assigns the task to the processor with the minimum energy consumption. Through experiments conducted with randomly generated applications, our approach consistently exhibits superior performance when compared to similar scheduling algorithms. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing has been widely adopted in practical applications due to its strong calculating ability and high parallel feature. Although cloud computing can achieve significant cost reduction and flexibility enhancement, it results in a serious task scheduling problem. As one of the key techniques for automate management of cloud resources, task scheduling plays an important role in improving system utilization and supporting load balancing. In this article, we focus on the scheduling problem of independent tasks in cloud environment with heterogeneous and distributed resources. First, with models of resources and tasks, we present an exact formulation based on linear programming to fully search solution space and produce optimal allocation schemes for tasks. Then, inspired from the differential evolution method, we propose a population‐based approach to allocate tasks to their suitable resources such that the total time cost would be minimized. Experiments with multi‐task sets are conducted to show the convergence and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Avionics systems, which determine the performance, stability, and safety of aircraft, are a crucial part of aircraft. With the rapid development of the aviation industry, there are many serious problems in the process of the traditional simulation and verification of avionics systems, especially in the aspects of poor reusability of the hardware and software, poor real-time data interaction, and the high cost of development. In order to solve these problems, a simulation and verification platform for avionics systems based on the Future Airborne Capability Environment (FACE) architecture has been designed by using component and memory database technology. First, a general architecture is designed by referencing the FACE architecture, which allows flexible access to software and hardware resources of avionics systems. Second, the key technologies involved in the platform are described in detail, including scheduling management, communication management, and configuration management, which provide technical support for the simulation and verification of avionics systems. Finally, the simulation and verification environment of avionics systems is established, which realizes data interaction and management of various models, and improves the efficiency of the development and implementation of avionics systems. [ABSTRACT FROM AUTHOR]

Abstract Non-preemptive tasks with strict periods are usually adopted in practical multi-core real-time systems when continual sampling and processing of data are required. Systems designers need to provide a proper scheduling strategy such that the tasks' deadlines will be met even under the worst-case conditions. In this paper, we study the scheduling problem of non-preemptive tasks with strict periods in multi-core real-time systems. We first derive a necessary and sufficient condition to determine whether a new task is schedulable upon a multi-core platform without changing the allocations of the existing tasks. Then, with a game theory analogy, we design a recursive method to calculate the maximum permissible execution time for a given task, and propose a new schedulability condition used when the start time and processor assignments of the existing tasks can be modified. Finally, based on the conditions derived previously, we present a task assignment algorithm, which not only provides valid allocations for all tasks, but also obtains the minimum number of processors required by the system. Simulation experiments with randomly generated task sets have been conducted to show the high efficiency and reliability of the proposed approach. [ABSTRACT FROM AUTHOR]

Heterogeneous multi-processor architecture which achieves rich functionalities with different types of processors, is widely used to provide powerful calculating capability while keeping energy consumption under control. Although this architecture can improve system flexibility for adapting to future requirement variations, it results in a complex multi-task scheduling problem for system designers to produce a reasonable schedule that satisfies all deadline, data dependency, and energy limitation constraints. In this paper, we concentrate on the energy consumption-constrained scheduling problem of workflows in heterogeneous multi-processor embedded systems. First, we model the workflows and energy consumption of processors, and formulate the energy consumption-constrained scheduling problem as an optimization one whose objective is to shorten the schedule length of workflows as much as possible. Then, with an improved energy per-assignment strategy, we propose a novel energy difference coefficient-based scheduling algorithm to produce an approximately optimal allocation of processors, frequencies, and start times for each task while guaranteeing that the data dependency and energy limitation constraints are satisfied. Finally, experiments on both randomly-generated and real-world workflows are conducted to verify the reliability and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Non-preemptive tasks with strict periods are usually adopted in practical real-time systems where missing deadlines may lead to catastrophic situations. Their schedulability analysis plays a crucial role in guiding the design and development of such real-time systems. In this paper, we study the schedulability analysis problem of partitioned non-preemptive scheduling for strictly periodic tasks on multiprocessors. We propose a set of schedulability conditions, which determines whether a new task can be scheduled on a processor without changing the offsets of the existing tasks and identifies all valid start time offsets for the new task if it is schedulable. Based on these conditions, we present a task assignment algorithm, which is not optimal, but provides an upper bound on the number of cores required by a periodic task set. We illustrate this algorithm with a practical example and conduct stimulation experiments with randomly generated task sets to evaluate the performance of our approach from several aspects. [ABSTRACT FROM AUTHOR]

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

• A review and classification of multiobjective workflow scheduling. • A formulation of the joint optimization of makespan and execution cost for workflows executing on the IaaS clouds. • A new list multiobjective scheduling approach with high effectiveness and efficiency for workflow scheduling. • Extensive experiments on real-life workflows and randomly synthetic workflows with several existing approaches. Highly scalable resource supply capacity of cloud computing has greatly improved the execution speed of workflow applications, however, traditional workflow scheduling algorithms which focus on the optimization of makespan (execution time) of workflows, become inappropriate for the design of large-scale workflow systems. Workflow scheduling in cloud computing is particularly a multiobjective optimization problem, in which many critical issues besides the execution time of workflows should be taken into account. Although many heuristics and meta-heuristics have been proposed to solve this problem, most of them cannot produce satisfactory cost-makespan tradeoffs and have a long time overhead. In this paper, we propose an efficient heuristic named CMSWC (Cost and Makespan Scheduling of Workflows in the Cloud) to solve the workflow scheduling problem, by simultaneously minimizing cost and makespan of workflows. CMSCW follows a two-phase list scheduling philosophy: ranking and mapping. Furthermore, CMSCW incorporates with three designs specifically for the multiobjective challenges: (i) The mapping phase is designed to avoid exploring useless resources for tasks, which significantly narrows down the search space. (ii) A new method is proposed to select non-dominated solutions, by combining the quick non-dominated sorting approach and Shift-Based Density Estimation (SDE) based crowding distance. (iii) Several elitist study strategies are designed to make solutions close to the true Pareto front as well as avoid trapping into local optimum. Extensive experiments on real-life workflows demonstrate that our approach can generate better cost-makespan tradeoff fronts than that of several state-of-the-art approaches. [ABSTRACT FROM AUTHOR]