Optimal method on dynamic maintenance task scheduling

Energy consumption has been always an important issue in multicore processors which are getting more and more popular in embedded systems. In this paper, we propose an energy-aware task scheduling approach taking advantages of heuristic algorithms based on genetic algorithm. The proposed approach includes both static and dynamic scheduling schemes. The task scheduling is modelled as a genetic algorithm problem which is mainly used when the tasks are ready before run-time; i.e., static task scheduling. The tasks which arrive after beginning task execution are dynamically scheduled using a proposed heuristic algorithm in combination with the genetic algorithm. The experimental results show that the proposed algorithm achieves more energy efficiency in both static and dynamic task scheduling for multicore processors as compared with similar energy-aware scheduling methods.

Dynamic scheduling of maintenance tasks in the petroleum industry: A reinforcement approach

Low-power and short-latency memory access is critical to the performance of chip multiprocessor (CMP) system devices, especially to bridge the performance gap between memory and CPU. Together with increased demand for low-energy consumption and high-speed memory, scratch-pad memory (SPM) has been widely adopted in multiprocessor systems. In this paper, we employ a hybrid SPM, composed of a static random-access memory and a nonvolatile memory (NVM), to replace the cache in CMP. However, there are several challenges related to the CMP that need to be addressed, including how to dynamically assign processors to application tasks and dynamically allocate data to memories. To solve these problems based on this architecture, we propose a novel dynamic data allocation and task scheduling algorithm, i.e., dynamic greedy data allocation and task scheduling (DGDATS). Experiments on DSP benchmarks demonstrate the effectiveness and efficiency of our proposed algorithms; namely, our proposed algorithm can generate a highly efficient dynamic data allocation and task scheduling approach to minimize the total execution cost and produce the least amount of write operations on NVMs. Our extensive simulation study demonstrates that our proposed algorithm exhibits an excellent performance compared with the heuristic allocation (HA) and adaptive genetic algorithm for data allocation (AGADA) algorithms. Based on the CMP systems with hybrid SPMs, DGDATS reduces the total execution cost by 22.18% and 51.37% compared with those of the HA and AGADA algorithms, respectively. Additionally, the average number of write operations on NVM is 19.82% lower than that of HA.

Scheduling is assigning shared resources over time to efficiently complete the tasks over a given period of time. The term is applied separately for tasks and resources correspondingly in task scheduling and resource allocation. Scheduling is a popular topic in operational management and computer science. Effective schedules ensure system efficiency, effective decision making, minimize resource wastage and cost, and enhance overall productivity. It is generally a tedious task to choose the most accurate resources in performing work items and schedules in both computing and business process execution. Especially in real-world dynamic systems where multiple agents involve in scheduling various dynamic tasks is a challenging issue. Reinforcement Learning is an emergent technology which has been able to solve the problem of the optimal task and resource scheduling dynamically. This review paper is about a research study that focused on Reinforcement Learning techniques that have been used for dynamic task scheduling. The paper addresses the results of the study by means of the state-of-the-art on Reinforcement learning techniques used in dynamic task scheduling and a comparative review of those techniques.

Simulation study on the architecture optimization and task scheduling of a parallel flight training simulator

The process of scheduling involves allocating shared resources gradually so that tasks can be completed effectively within the allotted time. In task scheduling and resource allocation, the terms are used independently for tasks and resources, respectively. In computer science and operational management, scheduling is a hot topic. Efficient schedules guarantee system effectiveness, facilitate sound decision-making, reduce resource waste and expenses, and augment total productivity. Selecting the most accurate resources to complete work items and schedules for computing and business process execution is typically a laborious task. Particularly in dynamic real-world systems, where scheduling different dynamic tasks involves multiple tasks, is a difficult problem. Emerging technology known as "nature inspired algorithms" has the ability to dynamically solve the problem of optimal task and resource scheduling. This review paper discusses a study that looked at algorithms inspired by nature and used them to schedule tasks dynamically. The Nature Inspired Algorithms used in dynamic task scheduling and a comparative analysis of those methods are used in this paper to address the study's findings.

Real-time systems often involve time critical control tasks in which their correctness depends not only on the functionality but also on timeliness. In order to guarantee the timely-correctness, real-time scheduling has been studied extensively. The main problem of multiprogramming scheduling on a single processor is that an optimum fixed priority scheduler has a least upper bound to processor utilization, which is around 70 percent for large tasksets, in contrast, full processor utilization can be achieved by dynamically assigning priorities. In this paper DynAmic Real-time Task Scheduling (DARTS) algorithm is proposed, which is based on dynamic utilization and assigns higher priority to a task with the highest utilization with regard to its laxity. Eventually, this method is extended for multiprocessor systems and we demonstrate that not only does DARTS algorithm have better utilization than existing global EDF schedulability tests, but it also has significantly well outputs in total utilization less than 90 percent.

Digital signal processors (DSPs), with their powerful computing abilities, are commonly used for multimedia coding/decoding processes. Therefore, the SOC (System on Chip) industry integrates DSP with ARM (Advanced RISC Machine) for input/output processing, saving power, and building up a multi-core platform used in handheld devices. The computing ability of ARM has been substantially improved with some state of the art technique. The industry currently has regarded the integration of ARM and DSP into SOC as two independent cores to enhance the efficiency. Since one algorithm is added to process the distributed computing work of the dual cores, the efficiency must be doubled. The system will assign the work to the core with higher processing efficiency. Instead of the traditional Static task scheduling, this article proposed a new approach called Dynamic task scheduling, providing 29.88 % higher efficiency than that of Static task scheduling. The reason is that the static partition will finally send the heavy load of computing to DSP; therefore, it is not possible to achieve the enhanced efficiency of the multi-core. However, the dynamic task scheduling will consider the actual loading of each core for computing and communicate with each other; furthermore, it can share the work to assist the process of each core. Besides, the Direct Memory Access is integrated with the multi-core platform to reduce the time-consumption resulted from the communication between the dual cores. The experimental result shows that the dynamic partition operated by the heterogeneous dual core system can use 192 MHz pulse to decode the CIF video signal that the decoding speed can reach 30fps and the efficiency is improved 50 % with DMA (Direct Memory Access) technology incorporated.

A genetic approach to the scheduling of preventive maintenance tasks on a single product manufacturing production line

Bounds on Multiprocessing Timing Anomalies

A mobile cloud computing (MCC) workflow task dynamic scheduling model is designed to improve the level of adaptation of MCC workflow task dynamic scheduling. The group decision model parameter set of MCC workflow task dynamic scheduling is constructed, the preference information of MCC workflow task flow is assembled and clustered using multi-scale and multi-scalar feature analysis methods, and the dynamic feature evolution clustering analysis model of MCC workflow task is constructed, the dynamic information flow regression parameters of MCC workflow task are obtained by partial least squares method, and the fuzzy clustering processing of MCC workflow task dynamic scheduling is realized according to each attribute The fuzzy clustering process of dynamic scheduling of MCC workflow tasks is realized according to the results of weight vector assignment. Finally, the dynamic adaptive scheduling of MCC workflow tasks is realized according to the information clustering results. The experimental findings indicate that the technique has an excellent dynamic distribution and strong comprehensive assessment capability during MCC workflow task scheduling, aiding in the adaptive assignment of MCC workflow tasks.

Objectives: In cloud environment many scheduling algorithms are available to achieve the quality of service, but the scalability of task execution increases, scheduling becomes more complex. So there is a need for better scheduling. This ­paper deals with the survey of dynamic scheduling, different classification and scheduling algorithms currently used in cloud providers. Analysis: This paper focuses on a comparative study of static and dynamic task scheduling currently used and various researchers work on dynamic task scheduling for many applications. Findings: Scheduling methods/ techniques used by various cloud service providers, advantages and limitations of each method are discussed. Scheduling algorithms in cloud setup in each paper is analyzed. Application: Analyzing task scheduling in cloud environment, uniform standard can be applied for all cloud providers. Online scheduling and dynamic scheduling can be further improved Keywords: Cloud Computing, Cloud Setup, Static and Dynamic Scheduling, Task Scheduling Algorithms, Various Metrics

ABSTRACTOne key component of cloud computing that significantly affects performance is task scheduling. Quality of Service (QoS) in networking and the burgeoning information processing economy have spurred interest in the dynamic task scheduling challenge worldwide. Task scheduling is a complicated topic that has been categorized as NP‐hard. It is also more difficult to strike a balance and reap the rewards of every facet of cloud computing because the majority of activities in complex environments are frequently managed using dynamic online task scheduling. This manuscript presents an approach for task scheduling in cloud computing technology for enhancing efficiency in enterprise environments. The proposed algorithm is an Effective Residual Self‐Attention Recurrent Neural Network (ESRNN) Algorithm. The proposed method's primary goal is to improve system reliability, enhance efficiency, and provide better load standard deviation, make span, and throughput. The issue of managing Directed Acyclic Graph (DAG) jobs in a cloud computing context is resolved by the ESRNN algorithm. The performance of the proposed technique is implemented in the MATLAB platform and is compared with various existing approaches. The proposed approach determines better outcomes compared to existing methods such as Data aware, First‐Come‐First‐Served (FCFS) and Round Robin (RR). In the existing method, throughput is 1, 1.2, 1.4 s, and then the proposed method throughput is 1.6 s. Based on the results, it can be concluded that the proposed approach has higher throughput compared to existing techniques.

Mobile Edge Cloud Computing Architecture (MEC-CA) presents key opportunities in performance improvement and energy saving for resource constrained mobile device. The mobile applications such as healthcare application, Augmented Reality can be modeled by task graphs. This work investigates the problem of dynamic application and scheduling tasks (which belong to the same or possibly different applications) in the MCCA environment. Nevertheless, the existing offloading system algorithms did not consider network failure and cloud resource in their MCCA paradigm. More specilically, we suggest the dynamic application partitioning and task scheduling (DAPTS) algorithm such that the application completion time constraint are satisfied while the total energy dissipation of the mobile device and cloud resources is minimized. Performance evaluations result demonstrates that proposed DAPTS outperform minimize the objective function in context of completion time and energy use as compared to the baseline approaches.

In this paper, we are investigating the energy consumption of mobile devices and cloud resources (i.e., virtual machines) while viewing the application partitioning and task scheduling problem in the offloading system. However, offloading system enables mobile device to separate the application tasks into local execution parts and cloud server execution parts. To minimize the energy consumption of mobile devices along with cloud resources in the considered problem we have nominated a new dynamic application partitioning task scheduling (DAPTS) algorithm. Since, DAPTS aims is to efficiently dynamically partitioning the application into tasks and schedule them on the mobile device and cloud resources that one may minimize energy consumption of both mobile devices as well as cloud resources simultaneously. Experimental results show that propose DAPTS outperforms as compared to the baseline approaches.