Heuristic algorithms for dynamic scheduling of moldable tasks in multicore embedded systems

Dynamic scheduling of parallel tasks is one of the efficient techniques to achieve high performance in multicore systems. Most existing algorithms for dynamic task scheduling assume that a task runs on one of the multiple cores or a fixed number of cores. Existing researches on dynamic task scheduling methods have evaluated their methods in different experimental environments and models. In this paper, the dynamic task scheduling methods are systematically rearranged and evaluated.

Applying Task Scheduling For IOMT-Cloud Business Optimisation Using AI

The IoMT-cloud enables a surplus extent of customers to get disseminated, versatile, and virtualized gear just as programming structure over the Internet. The IoMT-cloud is one of the principal headway used recently, it grants customers to get cloud resources over the internet remotely. Hence, we need to complete a reasonable task scheduling estimation to tolerably and viably meet these requests. The scheduling of task issue is perhaps the most essential issue in the IoMT-cloud since cloud execution depends prevalently upon it. Capable task scheduling administration should meet customer's requirements and improve the resources used to overhaul the introduction of the IoMT-cloud framework. To deal with this issue, in this investigation, we attempt to show the two most notable static and one dynamic task scheduling execution separately, short job first (SJF), first come first serve (FCFS), and round-robin (RR). Likewise, it was advanced using the AI technique known as genetic algorithm (GA). The CloudSim simulation framework is used to measure their impact on total execution time (TET), algorithm complexity, throughput, resource utilization, total waiting time (TWT), availability of assets, total finish time (TFT), cost, and resource utilization. The model proposed is to improve the viability of task scheduling for the IoMT-cloud stage with the best execution rate of 32.47ms. The exploratory results show that GA cuts down the cost of planning and reduces the total time, which is a convincing computation for the IoMT-cloud task scheduling.

Dynamic Task Scheduling Scheme for Processing Real-Time Stream Data in Storm Environments

Owing to the recent advancements in Internet of Things technology, social media, and mobile devices, real-time stream balancing processing systems are commonly used to process vast amounts of data generated in various media. In this paper, we propose a dynamic task scheduling scheme considering task deadlines and node resources. The proposed scheme performs dynamic scheduling using a heterogeneous cluster consisting of various nodes with different performances. Additionally, the loads of the nodes considering the task deadlines are balanced by different task scheduling based on three defined load types. Based on diverse performance evaluations it is shown that the proposed scheme outperforms the conventional schemes.

Dynamically Controlling Offloading Thresholds in Fog Systems

Fog computing is a potential solution to overcome the shortcomings of cloud-based processing of IoT tasks. These drawbacks can include high latency, location awareness, and security—attributed to the distance between IoT devices and cloud-hosted servers. Although fog computing has evolved as a solution to address these challenges, it is known for having limited resources that need to be effectively utilized, or its advantages could be lost. Computational offloading and resource management are critical to be able to benefit from fog computing systems. We introduce a dynamic, online, offloading scheme that involves the execution of delay-sensitive tasks. This paper proposes an architecture of a fog node able to adjust its offloading threshold dynamically (i.e., the criteria by which a fog node decides whether tasks should be offloaded rather than executed locally) using two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC). These algorithms seek to minimize overall delay, maximize throughput, and minimize energy consumption at the fog layer. Compared to other benchmarks, our approach could reduce latency by up to 95%, improve throughput by 71%, and reduce energy consumption by up to 67% in fog nodes.