Parallel Scheduling Algorithm Research Articles

Efficient Techniques for Residential Appliances Scheduling in Smart Homes for Energy Management Using Multiple Knapsack Problem

The evolution of the smart grid has enabled residential users to manage the ever-growing energy demand in an efficient manner. The smart grid plays an important role in managing this huge energy demand of residential households. A home energy management system enhances the efficiency of the energy infrastructure of smart homes and provides an opportunity for residential users to optimize their energy consumption. Smart homes contribute significantly to reducing electricity consumption costs by scheduling domestic appliances effectively. This residential appliance scheduling problem is the motivation to find an optimal appliance schedule for users that could balance the load profile of the home and helps in minimizing electricity cost (EC) and peak-to-average ratio (PAR). In this paper, we have focused on appliance scheduling on the consumer side. Two novel home energy management models are proposed using multiple scheduling options. The residential appliance scheduling problem is formulated using the multiple knapsack technique. Serial and parallel scheduling algorithms of home appliances namely MKSI (Multiple knapsacks with serial implementation) and MKPI (Multiple knapsacks with parallel implementation) are proposed to reduce electricity cost and PAR. Price-based demand response techniques are incorporated to shift appliances from peak hours to off-peak hours to optimize energy consumption. The proposed algorithms are tested on real-time datasets and evaluated based on time of use pricing tariff and critical peak pricing. The performance of both the algorithms is compared with the unscheduled scenario and existing algorithm. Simulations show that both proposed algorithms are efficient methods for home energy management to minimize PAR and electricity bills of consumers. The proposed MKSI algorithm achieves cost reduction of 20.26% and 42.53% for TOU and CPP, respectively as compared to the unscheduled scenario while PAR is reduced by 45.07% and 39.51% for TOU and CPP, respectively. The proposed MKPI algorithm achieves 22.33% and 46.36% cost reduction compared to the unscheduled case for TOU and CPP while the PAR ratio is reduced by 46.47% and 41.16% for TOU and CPP respectively.

Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach

In this paper, a task scheduling problem for a cloud computing environment is formulated by using the M/M/n queuing model. A priority assignment algorithm is designed to employ a new data structure named the waiting time matrix to assign priority to individual tasks upon arrival. In addition to this, the waiting queue implements a unique concept based on the principle of the Fibonacci heap for extracting the task with the highest priority. This work introduces a parallel algorithm for task scheduling in which the priority assignment to task and building of heap is executed in parallel with respect to the non-preemptive and preemptive nature of tasks. The proposed work is illustrated in a step-by-step manner with an appropriate number of tasks. The performance of the proposed model is compared in terms of overall waiting time and CPU time against some existing techniques like BATS, IDEA, and BATS+BAR to determine the efficacy of our proposed algorithms. Additionally, three distinct scenarios have been considered to demonstrate the competency of the task scheduling method in handling tasks with different priorities. Furthermore, the task scheduling algorithm is also applied in a dynamic cloud computing environment.

Joint computation offloading and parallel scheduling to maximize delay-guarantee in cooperative MEC systems

The growing development of the Internet of Things (IoT) is accelerating the emergence and growth of new IoT services and applications, which will result in massive amounts of data being generated, transmitted and processed in wireless communication networks. Mobile Edge Computing (MEC) is a desired paradigm to timely process the data from IoT for value maximization. In MEC, a number of computing-capable devices are deployed at the network edge near data sources to support edge computing, such that the long network transmission delay in cloud computing paradigm could be avoided. Since an edge device might not always have sufficient resources to process the massive amount of data, computation offloading is significantly important considering the cooperation among edge devices. However, the dynamic traffic characteristics and heterogeneous computing capabilities of edge devices challenge the offloading. In addition, different scheduling schemes might provide different computation delays to the offloaded tasks. Thus, offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay. This paper seeks to guarantee low delay for computation intensive applications by jointly optimizing the offloading and scheduling in such an MEC system. We propose a Delay-Greedy Computation Offloading (DGCO) algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices. A Reinforcement Learning-based Parallel Scheduling (RLPS) algorithm is further designed to schedule offloaded tasks in the multi-core MEC server. With an offloading delay broadcast mechanism, the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization. Finally, the simulation results show that our proposal can bound the end-to-end delay of various tasks. Even under slightly heavy task load, the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%, while that given by benchmarked algorithms is reduced to intolerable value. The simulation results are demonstrated the effectiveness of DGCO-RLPS for delay guarantee in MEC.

A parallel hybrid PSO-GA algorithm for the flexible flow-shop scheduling with transportation

In this paper, a Mixed-Integer Linear Programming (MILP) model to simultaneously schedule jobs and transporters in a flexible flow shop system is suggested. Wherein multiple jobs, finite transporters, and stages with parallel unrelated machines are considered. In addition to the mentioned technicalities, the jobs are able to omit one or more stages, and may not be executable by all the machines, and similarly, transportable by all the transporters. To the best of our knowledge, no study in the literature has featured efficacy of the parallel computing in simultaneous scheduling of jobs and transporters in the flexible flow shop system which remarkably shortens run time if the solution approaches are designed accordingly. To this end, we employ Gurobi solver, Parallel Genetic Algorithm (PGA), Parallel Particle Swarm Optimization (PPSO) and hybrid Parallel PSO-GA Algorithm (PPSOGA) to deal with the problem instances. Furthermore, a parallel version of Ant Colony Optimization (ACO) algorithm adapted from the state-of-the-art literature is developed to verify the performance of our suggested solution methods. Using 60 problem instances generated via uniform distribution, the suggested solution approaches are compared against one another. After assessing the results of the computational experiments, it is deduced that PPSOGA algorithm outperforms PGA, PPSO, Parallel Ant Colony Optimization (PACO) and Gurobi solver in terms of the quality of the solutions. The efficiency and run time of the suggested approaches are then assessed through two prominent statistical tests (i.e., Wald and Analysis of Variance (ANOVA)). Eventually, it comes to spotlight that PPSOGA algorithm is computationally rewarding and dependable.

A Survey of Low-Energy Parallel Scheduling Algorithms

High energy consumption is one of the biggest obstacles to the rapid development of computing systems, and reducing energy consumption is quite urgent and necessary for sustainable computing. Low-energy scheduling based on dynamic voltage and frequency scaling (DVFS) is one of the most commonly used energy optimization techniques. Recent survey works have reviewed some low-energy scheduling algorithms, but there is currently no systematic review in low-energy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">parallel</i> scheduling algorithms. With the increasing complexity of function requirements, many parallel applications have been executed in various sustainable computing systems. In this paper, we survey recent advances in low-energy parallel scheduling algorithms according to three scheduling styles, namely: 1) energy-efficient parallel scheduling algorithms; 2) energy-aware parallel scheduling algorithms; and 3) energy-conscious parallel scheduling algorithms. Low-energy parallel scheduling algorithms basically involve five categories of 1) heuristic algorithms; 2) meta-heuristic algorithms; 3) integer programming algorithms; 4) machine learning algorithms; and 5) game theory algorithms. Further, we introduce the future trends in low-energy parallel scheduling algorithms from the perspectives of new requirements and future developments. By surveying the recent advances and introducing the future trends, we expect to provide researchers with a systematic reference and development directions in low-energy parallel scheduling for sustainable computing systems.

Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling.

An application based on a microservice architecture with a set of independent, fine-grained modular services is desirable, due to its low management cost, simple deployment, and high portability. This type of container technology has been widely used in cloud computing. Several methods have been applied to container-based microservice scheduling, but they come with significant disadvantages, such as high network transmission overhead, ineffective load balancing, and low service reliability. In order to overcome these disadvantages, in this study, we present a multi-objective optimization problem for container-based microservice scheduling. Our approach is based on the particle swarm optimization algorithm, combined parallel computing, and Pareto-optimal theory. The particle swarm optimization algorithm has fast convergence speed, fewer parameters, and many other advantages. First, we detail the various resources of the physical nodes, cluster, local load balancing, failure rate, and other aspects. Then, we discuss our improvement with respect to the relevant parameters. Second, we create a multi-objective optimization model and use a multi-objective optimization parallel particle swarm optimization algorithm for container-based microservice scheduling (MOPPSO-CMS). This algorithm is based on user needs and can effectively balance the performance of the cluster. After comparative experiments, we found that the algorithm can achieve good results, in terms of load balancing, network transmission overhead, and optimization speed.

A parallel genetic algorithm for multi-objective flexible flowshop scheduling in pasta manufacturing

Among the potential road maps to sustainable production, efficient manufacturing scheduling is a promising direction. This paper addresses the lack of knowledge in the scheduling theory by introducing a generalized flexible flow shop model with unrelated parallel machines in each stage. A mixed-integer programming formulation is proposed for such model, solved by a two-phase genetic algorithm (GA), tackling job sequencing and machine allocation in each phase. The algorithm is parallelized with a specialized island model, where the evaluated chromosomes of all generations are preserved to provide the final Pareto-Optimal solutions. The feasibility of our method is demonstrated with a small example from literature, followed with the investigation of the premature convergence issue. Afterwards, the algorithm is applied to a real-sized instance from a Belgium pasta manufacturer. We illustrate how the algorithm converges over iterations to trade-off near-optimal solutions (with 8.50% shorter makespan, 5.24% cheaper energy cost and 6.02% lower labor cost), and how the evaluated candidates distribute in the objective space. A comparison with a NSGA-II implementation is further performed using hypothesis testing, having 5.43%, 0.95% and 2.07% improvement in three sub-objectives mentioned above. Although this paper focuses on scheduling issues, the proposed GA can serve as an efficient method for other multi-objective optimization problems.

Energy Aware Parallel Scheduling Techniques for Network-on-Chip Based Systems

Minimizing execution time, energy consumption, and network load through scheduling algorithms is challenging for multi-processor-on-chip (MPSoC) based network-on-chip (NoC) systems. MPSoC based systems are prevalent in high performance computing systems. With the increase in computing capabilities of computing hardware, application requirements have increased many folds, particularly for real world scientific applications. Scheduling large scientific workflows consisting hundreds and thousands of tasks consume significant amount of time and resources. In this article, energy aware parallel scheduling techniques are presented primarily aimed at reducing the algorithm execution time while considering network load. Experimental results reveal that the proposed parallel scheduling algorithms achieve significant reduction in execution time.

Architecture Design of Teenager Mental Health Evaluation System Based on High Concurrency and High Availability

In order to solve the problem of high concurrency and stability of real-time collection and analysis of teenager mental health data, this paper proposes a high concurrency and high availability system architecture based on Docker container, choreography and deployment technology and artificial fish swarm algorithm. To solve the problem of low efficiency of Kubernetes default scheduling algorithm, this paper designs a parallel scheduling algorithm using artificial fish swarm algorithm. The experimental results show that the parallel scheduling algorithm is effective. For the complexity of container redundant operation, this paper designs a microservice rank scheme. Combined with AFS-FCM algorithm, the microservice rank of the system platform is determined, so that the redundant equipment can be automatically optimized for high availability.

Research on parallel inspection model and key technology of distribution transformer

In this paper, a parallel inspection model based on pipeline and multitask is designed innovatively for the large-scale sampling inspection of distribution transformer before putting into operation. According to the characteristics of inspection resource sharing, the key technologies such as resource allocation and task scheduling are optimized, and the resource optimization allocation method and task parallel scheduling algorithm are proposed. Compared with the former, the optimized parallel inspection model improves the inspection efficiency and resource utilization to a large extent, and has important application value in the modern factoryinspection mode.

A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process

PurposeThe purpose of this paper is to present a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations. In this problem, each product is produced by assembling a set of several different parts. At first, the parts are processed in a flexible job shop system, and then at the second stage, the parts are assembled and products are produced.Design/methodology/approachAs the problem is non-deterministic polynomial-time-hard, a new hybrid particle swarm optimization and parallel variable neighborhood search (HPSOPVNS) algorithm is proposed. In this hybrid algorithm, particle swarm optimization (PSO) algorithm is used for global exploration of search space and parallel variable neighborhood search (PVNS) algorithm for local search at vicinity of solutions obtained in each iteration. For parameter tuning of the metaheuristic algorithms, Taguchi approach is used. Also, a statistical test is proposed to compare the ability of metaheuristics at finding the best solution in the medium and large sizes.FindingsNumerical experiments are used to evaluate and validate the performance and effectiveness of HPSOPVNS algorithm with hybrid particle swarm optimization with a variable neighborhood search (HPSOVNS) algorithm, PSO algorithm and hybrid genetic algorithm and Tabu search (HGATS). The computational results show that the HPSOPVNS algorithm achieves better performance than competing algorithms.Practical implicationsScheduling of manufacturing parts and planning of assembly operations are two steps in production systems that have been studied independently. However, with regard to many manufacturing industries having assembly lines after manufacturing stage, it is necessary to deal with a combination of these problems that is considered in this paper.Originality/valueThis paper proposed a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations.

A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

With the rapid economic development, manufacturing enterprises are increasingly using an efficient workshop production scheduling system in an attempt to enhance their competitive position. The classical workshop production scheduling problem is far from the actual production situation, so it is difficult to apply it to production practice. In recent years, the research on machine scheduling has become a hot topic in the fields of manufacturing systems. This paper considers the batch processing machine (BPM) scheduling problem for scheduling independent jobs with arbitrary sizes. A novel fast parallel batch scheduling algorithm is put forward to minimize the makespan in this paper. Each of the machines with different capacities can only handle jobs with sizes less than the capacity of the machine. Multiple jobs can be processed as a batch simultaneously on one machine only if their total size does not exceed the machine capacity. The processing time of a batch is determined by the longest of all the jobs processed in the batch. A novel and fast 4.5-approximation algorithm is developed for the above scheduling problem. For the special case of all the jobs having the same processing times, a simple and fast 2-approximation algorithm is achieved. The experimental results show that fast algorithms further improve the competitive ratio. Compared to the optimal solutions generated by CPLEX, fast algorithms are capable of generating a feasible solution within a very short time. Fast algorithms have less computational costs.

Optimal channel selection in parallel scheduling of heterogeneous network based on competitive channel non-cooperative game model

In order to improve the parallel scheduling performance and channel balance of heterogeneous network, an optimal channel selection algorithm for parallel scheduling of heterogeneous network based on competitive channel non-cooperative game model is proposed in this paper. In this algorithm, the channel model of heterogeneous network is constructed, and the impulse response frequency doubling technology is adopted to spread the heterogeneous network channels. The simulation results show that this proposed method can improve equalisation and anti-interference ability of channels in optimal channel selection in parallel scheduling of heterogeneous network and it provides the maximum channel transmission bit error rate of 0.35 and channel transmission bit error rate of 0 at signal-to-noise ratio of 50 dB, which indicates that the bit error rate of the receiving end is small. With this method, the end-to-end time delay in parallel scheduling of heterogeneous network is reduced and the channel impulse response time is about 0.4~0.6 seconds, which improves the stability and accuracy of parallel scheduling of heterogeneous network.

Research on Ship Data Big Data Parallel Scheduling Algorithm Based on Cloud Computing

Li, X., and Guo, J.-J., 2019. Research on ship data big data parallel scheduling algorithm based on cloud computing. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 535–539. Coconut Creek (Florida), ISSN 0749-0208.The traditional big data parallel scheduling algorithm has a small number of ships and the scheduling accuracy is very poor. In order to solve this problem, a new ship big data parallel scheduling algorithm based on cloud computing is studied, and the computing framework of the algorithm is constructed. The computing framework includes infrastructure services, software services and platform services. The scheduling process is discussed. The horizontal and vertical coordinates of each ship in the dimensional network are scheduled. A set of energy-aware dual-adaptation dynamic genetic scheduling strategy is constructed. The scheduling strategy is based on the traditional genetic algorithm, and sets the dual optimization targets with weights, which are the execution time of the task and the energy consumption of the infrastructure. By adjusting the optimization weight, the optimization center of gravity can be controlled. The scheduling strategy also fully considers the dynamics of the cloud computing environment and the traditional algorithm. The experimental results show that the cloud computing-based ship big data parallel scheduling algorithm can simultaneously schedule multiple ships, and the scheduling accuracy is very high. The algorithm has broad market development space and is worthy of promotion.

Optimization of multitask parallel mobile edge computing strategy based on deep learning architecture

As a mainstream computing and storage strategy for mobile communications, Internet of Things and large data applications, mobile edge computing strategy mainly benefits from the deployment and allocation of small base stations. Mobile edge computing mainly helps users to complete complex, intensive and sensitive computing tasks. However, the algorithm has many problems in practical application, such as complex user needs, complex user mobility, numerous services and applications. Therefore, under the above background, it is of great significance to solve the computational pressure of current mobile edge algorithm and optimize its algorithm architecture. This paper creatively proposes a deep learning architecture based on tightly connected network, and transplants it into mobile edge algorithm to realize the payload sharing process of edge computing, so as to establish an efficient network model. At the same time, we creatively propose a multi-task parallel scheduling algorithm, which realizes the mobile edge algorithm in the face of complex computing and algorithm efficiency. Finally, the above algorithms are simulated and tested. The experimental results show that under the same task, the time consumed by the proposed algorithm is 3.5–4, while the time consumed by the traditional algorithm is 4.5–8, and the corresponding time is standardized time, so the practice shows that the algorithm has obvious overall efficiency advantages.

Hybrid dual-objective parallel genetic algorithm for heterogeneous multiprocessor scheduling

Scheduling is a process of mapping resources to tasks and it’s objective is either one or more. This paper focuses on scheduling in heterogeneous multiprocessor systems. Here the resources are processing elements and tasks are the jobs submitted to the processor. The main objectives of multiprocessor scheduling are reducing schedule length, reducing the overall energy consumption, reducing the temperature, reducing failure rates and so on. A Hybrid dual-objective parallel genetic algorithm is applied in the proposed work. Makespan and energy consumption are the two objectives considered. The proposed algorithm determines the global optimal solutions by generating the initial population using some heuristics and then performing parallel genetic operations on it. The main aim of employing parallelism is to find a global optimum solution by avoiding premature convergence in a local optimum and to reduce the running time of the algorithm. Hill climbing is also used in addition, to avoid local optimum solutions. The proposed algorithm balances the tradeoff between energy consumption and makespan according to the inclinations of the users by following weighted sum methodology. Our experimental results demonstrate that the proposed algorithm outperforms the other existing algorithms in terms of both makespan and energy consumption by incurring less running time.

Automated Scheduling for Optimal Parallelization to Reduce the Duration of Vehicle Software Updates

The needs-oriented expansion and maintenance of vehicle functions through software updates will increase drastically due to current megatrends such as autonomous driving or connected car services. Both, shorter innovation cycles and increasing software volumes resulting from these megatrends, increase update frequency and duration equally. Consequently, a reduction of update duration will become even more important in the future. Popular literature has shown that in a vehicle-internal network of up to 100 electronic control units (ECUs), the parallelization of individual ECU updates has great potential to reduce the total update duration. However, the methods presented so far do not show an optimal parallel scheduling algorithm that uses the full potential of the vehicle-internal network. In addition, some of the approaches are also limited to certain bus systems or do not have an automated parallelization strategy based on ECU requirements. The approach introduced here uses a high abstraction model for optimal parallel ECU software update scheduling. For this purpose, we developed two scheduling algorithms, one as a mixed-integer linear program and the other as a hybrid algorithm. We verified our approach in a real testing environment with up to 20 ECUs regarding update duration and computation time. Evaluation shows that the reprogramming time can be reduced by up to 77% compared to sequential update processing through optimal parallelization. Furthermore, the model is very easy to adapt to different and new vehicle generations due to its high abstraction.

A Parallel Complex Coloring Algorithm for Scheduling of Input-Queued Switches

This paper explores the scheduling problem of input-queued switches, based on a new algebraic method of edge coloring called complex coloring. The proposed scheduling algorithm possesses three important features inherent from complex coloring: parallelizability, optimality and rearrangeability. Parallelizability makes the algorithm running very fast in a distributed manner, optimality ensures that the algorithm always returns a proper connection pattern with the minimum number of required colors, and rearrangeability allows partially re-scheduling the existing connection patterns if the traffic patterns only changes slightly. The amortized time complexity of the proposed parallel scheduling algorithm, in terms of the time to compute a matching in a timeslot, is $O({\log}N)$ , where $N$ is the switch size. As for the scalability of input-queued switches, due to its low complexity, our algorithm can achieve nearly 100 percent throughput and provide acceptable queuing delay when $N$ is large. Furthermore, the complex coloring method naturally provides an adaptive solution to non-uniform input traffic pattern. Thus, the proposed parallel scheduling algorithm is highly robust in the face of traffic fluctuations.

A parallel Lagrange algorithm for order acceptance and scheduling in cluster supply chains

In a single supply chain scenario, orders are likely to be refused for lack of insufficient capacity and production time. In this paper, cluster supply chains (a kind of multiple supply chains, short for CSC) is introduced to avoid this potential operational risk via across-chain cooperation, which is not considered in any previous work. First, the framework of order selection in cluster supply chain (CSC) is presented based on four order categories (direct order, reserve order, across-chain order and rejected order), followed by that the model without and with across-chain cooperation in cluster supply chains are proposed to aid operational managers to make joint decision regarding order acceptance and scheduling under maximizing the overall profit. Considering the complexity of cluster supply chains structure and a mass of data from actual operations, a parallel Lagrange heuristic algorithm is devised to solve the Mixed-Integer Non-Linear Program (MINLP) problem. Meanwhile, Benders algorithm is utilized to compare with it for evaluating performance. The result proves the parallel Lagrange heuristic algorithm outperforms Benders approach, the former can efficiently solve large-scale-data problem instances at relatively short time. The outcomes also reveal that, by designing the different combination of the factor of rejected order and that of across-chain order, it can be better trade-off between order due-date and cost while better aligning with the long-term business strategy in cluster supply chains.

Spatial query based virtual reality GIS analysis platform

Abstract A virtual reality GIS analysis platform based on spatial query is proposed in this paper. The proposed platform serves 3D digital city and supports integrated VRGIS functions including 3D spatial analysis functions and 3D visualization functions for spatial process. The 3D analysis and visualization of the concerned city massive information are conducted on the platform. The amount of information that can be visualized through this platform is overwhelming, and the GIS-based navigational scheme allows great flexibility in accessing different available data sources. A multi-level mixed three-dimensional space index is built for the virtual scene. Multi-task parallel scheduling and pre-scheduling algorithms are designed to support the distribution and query of spatial data. Experiment result indicates that the designed query algorithm improves the performance of the proposed system.