Performance Evaluation of Task Scheduling Algorithm on LCQ Network

Research on Design Method of Dynamic Shop Floor Scheduling System Based on Human-computer Interaction

This chapter considers a dynamic multi-objective machine scheduling problem in response to continuous arrival of new jobs with deterioration effect, under the assumption that jobs can be rejected and job processing time is controllable by allocating extra resources. By deterioration effect, we mean that each job’s processing time may be subject to change due to the capability deterioration with machine’s usage, i.e., the actual processing time of a job becomes longer if the job starts processing later. The operational cost and the disruption cost need to be optimized simultaneously. To solve these dynamic scheduling problems, a directed search strategy (DSS) is introduced into the elitist non-dominated sorting genetic algorithm (NSGA-II) to enhance its capability of tracking changing optimums while maintaining fast convergence. The DSS consists of a population re-initialization mechanism (PRM) to be adopted upon the arrival of new jobs and an offspring generation mechanism (OGM) during evolutionary optimization. PRM re-initializes the population by repairing the non-dominated solutions obtained before the disturbances occur, modifying randomly generated solutions according to the structural properties, as well as randomly generating solutions. OGM generates offspring individuals by fine-tuning a few randomly selected individuals in the parent population, employing intermediate crossover in combination with Gaussian mutations to generate offspring, and using intermediate crossover together with differential evolution based mutation operator. Both PRM and OGM aim to strike a good balance between exploration and exploitation in solving the dynamic multi-objective scheduling problem. Comparative studies are performed on a variety of problem instances of different sizes and with different changing dynamics. Experimental results demonstrate that the proposed DSS is effective in handling the dynamic scheduling problems under investigation.

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.

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.

The performance of large-scale real-world applications may be enhanced by more efficient utilization of today's powerful parallel computing hardware. The authors describe a novel MIMD (multiple instruction, multiple data) static task allocation methodology created to achieve this goal for a wide range of real-time and deterministic systems. The method performs automatic assignment, mapping, and scheduling of the executable tasks to the available set of processors, assuming synchronous nonbuffered (lock-step) communication between the sending and receiving processors and an arbitrary static message-passing topology. The algorithm incorporates a set of list-based heuristics and graph-theoretical procedures designed to balance computational load with communication requirements. The effectiveness of the method has been verified by applying it to a large number of randomly generated task systems that span a wide range of inherent concurrency as well as to a task system of a real-world simulation of the Space Shuttle main rocket engine. >

Parallel processing has been employed many years in high-performance computing. Parallel processing is mostly used because it provides concurrency, maximizes load balancing, and minimizes system idle time and execution time which depend on various computer architecture. Task scheduling is a parallel processing technique which allocates the tasks to various processors. Mapping of heterogeneous tasks to a heterogeneous processor or core dynamically in a distributed environment is one of the active area of research in the field of parallel computing system. Here in this paper, monarch butterfly optimization (MBO) [1], a nature-inspired metaheuristic algorithm is implemented for a task scheduling problem. A monarch, a North American butterfly is best known for its migratory behavior in the summer season. There are mainly two processes to get the best solution. First, using a migration operator, a new generation is created. Second, they update their position by using a butterfly adjusting operator. The fitness value is evaluated and updated the population with a higher fitness value to satisfy the objective of the problem. Minimizing the cost and time of the scheduling strategy is the main objective of the proposed work.

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.

Attempts to address the production scheduling problem thus far rely on simplifying assumptions, such as static environment and inflexible size of the problem, which compromises the schedule performance in practice due to many unpredictable disruptions to the system. Thus, the study of scheduling in the presence of real-time events, termed dynamic scheduling, continues to attract attention given the agility, flexibility, and timeliness modern production systems must deliver. Additionally, the changing nature of the manufacturing system also raises new challenges to existing scheduling strategies. At the front-end, the development of advanced data creation and exchange frameworks such as the Internet of things and cyber-physical system and their applications to the industrial environment have created an abundance of industrial data, while at the backend, edge and cloud computing technologies greatly enhance the capacity to process that data. Industrial data must be mined and analyzed so that the investment in infrastructure is not wasted, and the production system managed more effectively and in real-time. Many data-driven technologies have been adopted in scheduling research, a promising candidate among them being reinforcement learning (RL) which is able to build a direct mapping from observation of environment to actions that improve its performance. In this thesis, a deep multi-agent reinforcement learning (deep MARL) architecture is proposed to solve the dynamic scheduling problem (DSP). The deep reinforcement learning (DRL) algorithm is used to train the decentralized scheduling agents, to capture the relationship between information on the factory floor and scheduling objectives, with the aim of making real-time decisions for a manufacturing system with frequent unexpected events. Two major aspects of deep MARL application to DSP are addressed in this work, namely the conversion from traditional static scheduling problem (SSP) to dynamic scheduling in a practical context, and the adaptation of existing deep MARL algorithms to solve the scheduling problem in such an environment. Some impractical constraints of traditional studies are removed to create a research context that is closer to actual practice, result in a scheduling problem of variable size and scope. Specialized state and action representations that can handle the ever-changing specification of problem are developed; the criteria of feature selection in dynamic environment are also discussed. Recent progressions in DRL and MARL research are integrated into the proposed approach after selection and adaptation. In addition, various improvements to common deep MARL architecture are proposed, including the lightweight multilayer perceptron (MLP) encoder that is efficient in handling unstructured industrial data, a training scheme under the multi-agent architecture to improve the stability of training and overall performance, and knowledge-based reward-shaping techniques to decompose the joint reward signal into individual utilities to speed up the learning and encourage cooperative behavior between agents. Simulation studies are then conducted for the ablation study and validation. In the first stage, the performance of the proposed approach, either as individual components or as an integrated model, are tested in iterative simulation runs within which a unique instance of production is created. Meanwhile, a set of DRL-based approaches from recent publications are run in parallel. Results suggest that the contribution of each improvement is significant; the integrated architecture also delivers stronger performance than peer DRL-based approaches. For the validation, a set of priority rules that have strong performance in specified context and are widely applied in actual production scheduling are used as the benchmark. Proposed approach also provides performance gain compared to the strongest rule, with a minor increase in computation cost and negligible latency in decision-making.

Cloud computing and big data: Technologies and applications

<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>

Under the federated paradigm of multiprocessor scheduling, a set of processors is reserved for the exclusive use of each task. We consider the federated scheduling of parallel real-time tasks containing conditional (if-then-else) constructs, in which different executions of the task may result in workloads of substantially different magnitude and different character (e.g., degree of parallelism and critical path length). If the task is hard-real-time, then processors must be reserved for it under worst-case assumptions. However, it may be the case that most invocations of the task will have computational demand far below the worst-case characterization, and could have been scheduled correctly upon far fewer processors than had been assigned to it based upon the worst-case characterization of its run-time behavior. Provided we could safely determine during run-time if the worst-case characterization is likely to be realized during some execution and all the processors are therefore going to be needed, for the rest of the time the unneeded processors could be idled in low-energy “sleep” mode, or used for executing non-real time work in the background. In this paper we propose an algorithm for scheduling parallel conditional tasks that permits us to do so.

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.

A robust dynamic scheduling approach based on release time series forecasting for the steelmaking-continuous casting production

Heterogeneous Distributed Computing (HDC) system consists of Workstations (WSs) and Personal Computers (PCs). In HDC system, each WS/PC may have different processor and performance. In order to take advantage of this diversity of processing power of a system, an effective task partitioning, scheduling, and load balancing are needed to get the optimum performance. This paper examines the effectiveness of task partitioning and scheduling strategies for image (raytracing) processing application on HDC system. The static and dynamic/Run-time Task Scheduling (RTS) strategies are shown inadequate for balancing the load of HDC system. Two adaptive tasks scheduling strategies are proposed for HDC image computing system. These adaptive strategies are: i) Master Initiate Sub-task size (MIS) strategy (based on centralized resources management approach), and ii) Worker Initiated Sub-task size (WIS) strategy (based on semi-decentralized resources management approach). The measured results show that the adaptive (MIS & WIS) strategies dramatically improve the performance of HDC raytracing system and remedy the defects of static and RTS strategies. Performances of the all investigated strategies are evaluated on manager/master and workers model of HDC system. In performance comparisons of MIS and WIS strategies, the WIS strategy shows a slightly better speedup and scalability over MIS strategy.

Genetic programming hyper-heuristic-based solution for dynamic energy-efficient scheduling of hybrid flow shop scheduling with machine breakdowns and random job arrivals