Priority Scheduling Research Articles

Digitalization of process management in the agro-industrial complex

The article is devoted to the consideration of the current problem of developing heuristic approaches in operational scheduling of production processes. The purpose of the study is related to the development of methods and algorithms for generating schedules for an arbitrary number of request vectors. The problem was formulated and formalized using scheduling schemes and priority rules. For numerical experimentation, a model of a program for generating a railway schedule and a test task were developed, including 4573 applications for weekly schedules of a railway network containing 100 stations, 128 hauls between stations, 100 passenger routes of different frequencies, 471 trains per week. The developed program model, in addition to carrying out calculations using the presented algorithms, visualizes the results of numerical experimentation in the form of spiral representations of station and haul schedules. The performance of the developed methods and algorithms has been confirmed. Conclusions and discussion of the study results are presented.

A Unified Blocking Analysis for Parallel Tasks With Spin Locks Under Global Fixed Priority Scheduling

Spin locks are widely used in embedded systems to coordinate mutually exclusive accesses to shared resources from different tasks. Although the design and analysis of locking protocols have been intensively studied for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sequential</i> real-time tasks, there have been few works on this topic for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">parallel</i> real-time tasks. In this paper, we study the analysis of parallel real-time tasks modeled by directed acyclic graphs (DAGs) under global fixed priority scheduling using both preemptable and non-preemptable spin locks to protect accesses to shared resources in three commonly used request serving orders (unordered, FIFO-order and priority-order). In particular, we develop a general schedulability analysis framework where the blocking time caused by resource contention is formally defined, so that the blocking analysis can be performed independently and easy to combine with the traditional interference analysis techniques. Moreover, we present a unified blocking analysis technique where the blocking time is analyzed in a scalable manner based on a linear-programming (LP) approach, making our method flexible and extendable. We conduct comprehensive experiments to evaluate our method with other the-state-of-the-art approaches for scheduling real-time parallel tasks using semaphores and spin locks.

The Performance Analysis of SPMA Protocol in High Speed Mobile Ad Hoc Network

Aiming at the performance analysis of statistical priority-based multiple access (SPMA) protocol, the time slot transmission probability modeling method based on preemptive M/G/1 queue is proposed. The equation of time slot transmission probability is deduced through mathematical modeling, and the solution method under given system parameters is given. Then, the successful reception probability of the message is derived, and the general relationship between the successful reception probability and the duty cycle is established, so as to obtain the threshold setting method of the message with the highest priority. To solve the contradiction between channel utilization maximization and priority scheduling mechanism between nodes, the strategy of minimizing re-served resources among priorities is adopted to set the low priority threshold with the minimum threshold difference, which solves the SPMA protocol threshold setting problem. The simulation scenario of high speed mobile ad hoc network is established. The performance curves of SPMA protocol under typical simulation parameters are obtained, including throughput, probability of successful transmission and end-to-end transmission delay, etc. The effectiveness of the proposed method is verified comprehensively.

Energy efficient non-preemptive scheduling of imprecise mixed-criticality real-time tasks

Previous studies focus on the classical mixed-criticality task model with preemptive dynamic priority scheduling in which all low-criticality (LO) tasks are dropped in the high-criticality mode (HI-mode). This paper focuses on the imprecise mixed-criticality (IMC) tasks model in which LO tasks reduce their execution budgets instead of being dropped. So far, the energy minimization problem of non-preemptive dynamic priority scheduling has received little attention. In this paper, we first develop schedulability tests of non-preemptive earliest deadline first with virtual deadline (NP-EDFVD) for IMC tasks, which is the first attempt for non-preemptive dynamic priority scheduling in single processor IMC systems. Subsequently, the uniform single speed (USS) algorithm based on the schedulability tests of NP-EDFVD is proposed to reduce energy consumption while protecting the IMC correctness. Finally, the experimental results show that the USS algorithm saves an average of 25.62% energy compared to NP-EDFVD.

Learning-Based Off-Chain Transaction Scheduling in Prioritized Payment Channel Networks

Payment channel network (PCN) is one of the promising solutions for scalable blockchains since it shows great potential in improving blockchain network throughput. However, the growing number of transactions and the payment-channel sharing of concurrent transactions can lead to channel congestion. Although many studies have proposed different solutions to solve this problem, they ignore a fact that applications may have different transaction rate requirements at different times. In this paper, we propose a priority-aware PCN to meet the requirements of those transactions. Senders in priority-aware PCNs can specify the priority of their transactions by paying a corresponding forwarding fee on each hop along the transaction path. However, capacity competition occurs on the shared hops. Moreover, we propose a multi-agent DQN-based priority assignment algorithm to address the competition issue and design a PCN simulator for performance evaluation. Simulation results show that our solution can guarantee a high throughput of transactions and assign priorities appropriately to balance the transaction rate and forwarding fee cost. The experimental results demonstrate that the priority scheduling scheme can achieve higher transaction throughput and success ratio than other scheduling methods in a congested PCN environment.

A collaborative scheduling method for cloud computing heterogeneous workflows based on deep reinforcement learning

Aiming at the problem of low overall service quality caused by the disordered collaboration of heterogeneous workflows and discontinuous task execution in cloud computing scenarios, this paper proposes a collaborative scheduling method for heterogeneous workflows in cloud computing based on deep reinforcement learning. The method optimizes workflow makespan, cost, fairness and continuity in cloud computing under the constraints of task execution continuity. First, the structure and time sequence features are extracted for the dynamic scheduling process, and a reasonable scheduling decision support feature set is constructed. Second, a time-step adaptive scheduling mechanism is designed to simplify redundant information in the scheduling process and enables the agent to achieve efficient learning. In addition, using equilibrium, priority and preference scheduling strategies, an immediate-lag compound reward mechanism and a scheduling-switching hybrid action are designed to achieve a unification of the agent’s learning objectives and actual scheduling requirements. Finally, by constructing a simulation platform and conducting comparative experiments with four other algorithms, the results show that the proposed method has advantages in collaborative optimization of high-dimensional objectives under task continuity constraints. Including the task loading strategy can optimize the makespan performance by 16.6% and improve the fairness index by 5.3%.

An On-Board Task Scheduling Method Based on Evolutionary Optimization Algorithm

In order to meet the requirements of task scheduling in on-board distributed computing environment, an evolutionary optimization scheduling method for on-board tasks was proposed based on the on-board dynamic heterogeneous computing resource model and task model. In this method, task scheduling priority coding was used to adapt to dynamic changes of computing resources. Heuristic critical path for comprehensive index balance was adopted to implement the evaluation, so as to achieve a balance between task makespan, power consumption and reliability. Multi-group neighborhood search strategy was applied to avoid the algorithm falling into local optimization and simplify the complexity of scheduling algorithm. Fault-tolerant strategy of convergence monitoring and stop loss reconfiguration were designed for scheduling unit fault tolerance. Scheduling strategy based on active and backup redundant subtasks was used for computing unit fault tolerance. The simulation results showed the efficiency of this method to deal with the scenario of on-board distributed computing resources and implement the optimal scheduling of on-board tasks.

Artificial intelligence-enabled probabilistic load demand scheduling with dynamic pricing involving renewable resource

Residential demand response is one of the key enabling technologies which plays an important role in managing the load demand of prosumers. However, the load scheduling problem becomes quite challenging due to the involvement of dynamic parameters and renewable energy resources. This work has proposed a bi-level load scheduling mechanism with dynamic electricity pricing integrated with renewable energy and storage system to overcome this problem. The first level involves the formulation of load scheduling and optimization problems as optimal stopping problems with the objective of energy consumption and delay cost minimization. This problem involved the real-time electricity pricing signal, customers load scheduling priority, machine learning (ML) based forecasted load demand, and renewable & storage unit profiles, which is solved using mathematical programming with branch-and-cut & branch-and-bound algorithms. Since the first-level optimization problem is formulated as a stopping problem, the optimal time slots are obtained using a one-step lookahead rule to schedule the load with the ability to handle the uncertainties. The second level is used to further model the load scheduling problem through the dynamic electricity pricing signal. The cost minimization objective function is then solved using the genetic algorithm (GA), where the input parameters are obtained from the first-level optimization solution. Furthermore, the impact of load prioritization in terms of time factor and electricity price is also modeled to allow the end-users to control their load. Analytical and simulation results are conducted using solar-home electricity data, Ausgrid, AUS to validate the proposed model. Results show that the proposed model can handle uncertainties involved in the load scheduling process along with a cost-effective solution in terms of cost and discomfort reduction. Furthermore, the bi-level process ensures cost minimization with end-user satisfaction regarding the dynamic electricity price signal.

OCTRA‐5G: Osmotic computing based task scheduling and resource allocation framework for 5G

SummaryLong term evolution (LTE) mobile technology provides high data rate and low latency. 5G Technology is capable of handling the increasing number of IoT devices and provides ultra‐low latency, higher throughput, and higher reliability. Mobile edge computing (MEC) a key 5G technology strengthens the real‐time processing ability, releases the load on the Core Network, and helps in the real‐time processing of data, fulfilling the promise of high data rate and low latency. MEC is used to manage services efficiently to the near user resource. Using Osmotic Computing the services are efficiently scheduled and migrated. The work presented in this article proposes OCTRA‐5G Framework to effectively schedule services and allocate resources using Osmotic Computing (OC) by segregating the services into microservices and macroservices. The results are validated on the sets of 10, 20, and 30 gNBs (base stations) through simulation. OCTRA‐5G is tested on First Come First Serve (FCFS), Priority Scheduling (PS), and Shortest Job First (SJF) algorithm. FCFS provides less time complexity and higher throughput. The results presented using numerical simulations shows better performance by an average of 66.921% with OC than without OC.

IoT based smart parking model using Arduino UNO with FCFS priority scheduling

Develop a suitable method to handle parking problem in the crowded big city, as per the demand and number of parking slots available, by giving priority to the users with help of smart parking system. This study proposed Internet of Things-(IoT) based smart parking model using the Arduino UNO model in implementation. This proposed model helps users to allocate booking users vehicles parking free available slot as per time to time availability in college campuses during technical fest in different parking slots and locations. In this working model, Passive Infrared, with ultrasonic Sensors are used which helps to deducting the available parking location and allow us to find out for giving priority based First Come First Serve based-(FCFS) scheduling of parking allotments as per slot is vacant count. This model examine the way of the proposed system with utilizing scenarios of various possible ways with FCFS which show the significance of the parking model using IoT platform. The parking availability detection is capable to find solutions for an empty vehicle parking area for monitoring and reduces users search time as per an automated smart parking model using the internet of things, sensing devices and Arduino UNO(“Uno” means one in Italian languages) in giving priority to users with scheduling model is actual findings with Smart systems. This study proposed an automated parking system to function on a real-time basis to detect all empty parking places based on the internet of things platform, with help of sensors. Arduino UNO platform brings automation to this system on per priority basis to all the users with scheduling techniques. The priority given to users with an automation system is a novelty compared with an existing system. This smart parking is working on real-time basis inputs based on that user demands priority is varying to book parking slots with first come first serve preference.

Application of effective water-energy management based on digital twins technology in sustainable cities construction

This work aims to effectively resolve the problem of waterlogging in cities and manage water resources in sustainable cities. Digital Twins (DTs) technology was applied to the Urban Drainage System (UDS) and solves the modeling and scheduling problems of emergency drainage through the core model construction method of DTs. Firstly, the components of the UDS that are necessary in the process of building the model were listed according to the entity elements of the five-dimensional (5D) DTs model. Then, this work analyzed the essential data of the DTs of UDS and the Data Collection method according to the data elements of the 5D DTs model. Finally, the Multi-Level Dynamic Priority and Importance Scheduling (MDPIS) algorithm was proposed based on the Fixed Priority Scheduling (FPS) algorithm, which was verified by the simulation experiment. The experimental results indicated that the MDPIS algorithm showed significant performance in the rainfall scene with large fluctuations compared with the FPS algorithm. Specifically, the average improvement ratio was the highest, reaching 49.81%; the overall improvement rate was constant at about 48%. The operation results showed an apparent correlation between the catchment parameters and the overflow loss of the pumping station. The improved MDPIS algorithm can effectively utilize the water storage capacity and drainage capacity of the pumping station and reduce overflow losses during rainfall by dynamically adjusting the priority to solve the problem of urban inland inundation. The DTs-based UDS proposed here can effectively mitigate the overflow loss and improve the working efficiency of the pumping station cluster, promoting the development of Substainable Cities.

Lane-Changing Model of Intelligent Connected Vehicle Considering the Factor of Turn Signal

Unsafe lane-changing behaviors can easily lead to traffic accidents. Drivers usually turn on their turn signals to signal surrounding vehicles before changing lanes. At present, there is a lack of consideration of the impact of turn signals on the lane-changing behavior of intelligent vehicles. Therefore, based on the cellular automata theory, this paper improves the lane-changing rules in the STNS model and proposes a vehicle safe lane-changing model. The model considers the priority scheduling problem of different vehicles’ driving behavior when changing lanes, the influence of driver’s subjective factors on the driving speed when changing lanes, and the relationship between vehicle speed and safe lane-changing distance. After discussion and analysis, the model can reduce the number of lane changes of vehicles, increase the average speed of vehicles, and increase the traffic flow. It provides theoretical support for the safe lane-changing behavior of intelligent networked vehicles in the new era.

Simulation of Priority Round Robin Scheduling Algorithm

In this journal, simulation of priority round robin scheduling algorithm is presented. To imitate the processes of operating system operation, simulation can be used. By simulation, model is used, namely models that represent the characteristics or behaviour of systems. Process scheduling is one important operation in operating system. OS-SIM can be used to model and simulate the operations of process scheduling. Some scheduling algorithms are available in modern operating systems, like First come First Serve (FCFS), Shortest Job First (SJF), Round Robin (RR), Priority Scheduling or combination of these algorithms. One important scheduling algorithm for real-time or embedded system is priority round robin scheduling algorithm. Priority round robin scheduling algorithm is a preemptive algorithm. Each process is given time quantum. Each process has a priority. Here time quantum 3 is given. The higher the time quantum, the more the context switching. By the use of OS-SIM, simulation can be understood easily and thoroughly. The statistics, will be calculated automatically by the system by the simulator, like the number of context switching, average waiting time, average turn around time, and average responds time. With one example, by using quantum=3. The average turn around time is 18.25 ms. The Average Waiting Time is 12 ms. The Average Responds time is 2.75 ms. The total burst time is 25 ms.

Design of topic priority based on industrial publish-subscribe system

As the size of the industrial systems increases, more and more topic messages need to be published and subscribed to in industrial scenarios such as microgrids and manufacturing industries. A priority scheduling algorithm based on the hotness of message topics is proposed to address the problem of efficient distribution of messages with different topics in microgrids. It is an algorithm that gives different priority permissions according to the number of topics, so that in the face of a large number of different topic messages, the data messages with a higher number or urgency can be transmitted first, solving the problem of port blocking when the server distributes the messages. At the same time, to prevent low priority data from sending timeout or waiting for a long time, a time period is set up, a low priority transmission mechanism is set up within a specific time period to ensure that the system can send data to customers in a timely and accurate manner. The results show that the priority design in this paper can well solve the power scheduling problem of multi-target data transmission and ensure the transmission efficiency of messages in the microgrid system.

A Collaborative Optimization Method of Flight Slots Considering Fairness Among Airports

With the rapid development of civil aviation transportation, an increasing number of airport groups are formed. However, the existing literature on fairness mostly focuses on the fairness among airlines. There is no research on the realization of scheduling fairness among airports with overlapping resources in the airport group. The goal of this paper is to comprehensively consider efficiency and fairness in slot scheduling, where fairness should include both interairline and interairport fairness. Subsequently, we developed a collaborative optimization model for airport group that takes into account the above three objectives and then uses the ε-constraint method to solve it. In addition to considering the basic operational constraints of the airport, the model also sets different adjustment boundaries to achieve the scheduling priorities specified by IATA. Applying the model to the Yangtze River Delta airport group, the results show that improved flight schedules can significantly reduce flight congestion, and a relatively fair scheduling result can be obtained by weighing airlines’ fairness and airports’ fairness. The model can improve the transparency of slot scheduling decisions and can be used as an auxiliary tool to help make slot scheduling decisions.

Speculative Container Scheduling for Deep Learning Applications in a Kubernetes Cluster

In the past decade, we have witnessed a dramatically increasing volume of data collected from varied sources. The explosion of data has transformed the world as more information is available for collection and analysis than ever before. To maximize the utilization, various machine and deep learning models have been developed, e.g. CNN [1] and RNN [2], to study data and extract valuable information from different perspectives. While data-driven applications improve countless products, training models for hyperparameter tuning is still a time-consuming and resource-intensive process. Cloud computing provides infrastructure support for the training of deep learning applications. The cloud service providers, such as Amazon Web Services [3], create an isolated virtual environment (virtual machines and containers) for clients, who share physical resources, e.g., CPU and memory. On the cloud, resource management schemes are implemented to enable better sharing among users and boost the system-wide performance. However, general scheduling approaches, such as spread priority and balanced resource schedulers, do not work well with deep learning workloads. In this project, we propose SpeCon, a novel container scheduler that is optimized for shortlived deep learning applications. Based on virtualized containers, such as Kubernetes [4] and Docker [5], SpeCon analyzes the common characteristics of training processes. We design a suite of algorithms to monitor the progress of the training and speculatively migrate the slow-growing models to release resources for fast-growing ones. Specifically, the extensive experiments demonstrate that SpeCon improves the completion time of an individual job by up to 41.5%, 14.8% system-wide and 24.7% in terms of makespan.

A Unified BRB-Based Framework for Real-Time Health Status Prediction in High-Speed Trains

The health status of the running gear in high-speed trains changes dynamically with time in a complete life cycle. Running gear systems composed of many coupled components are complex, and health statuses of which are difficult to predict in real time through a traditional health status prediction scheme. Lately, belief rule base (BRB), which is able to combine quantitative information and expert knowledge, has shown excellent expression in modeling complex systems. In the procedure of health status prediction, expert expertise can sufficiently enhance the accuracy and efficiency of this model. Therefore, this paper puts forwards a real-time health status prediction framework based on a multi-layer BRB with priority scheduling strategies for running gears. In the first-layer BRB, a time-series prediction model of multiple module BRB considering complete features is established. In the second-layer model, grey relation analysis (GRA) is employed in priority scheduling strategies of features. The third-layer BRB is used for assessing the health status of running gears by combining the features. In addition, the initial parameters of all module BRB given by experts may not be precise. Accordingly, the initial parameters in the BRB are updated by the recursive algorithm online. Finally, the proposed method is tested on the testing platform in running gears. The results make it clear the proposed method can predict the health status of running gears with much accuracy in real time.

Energy-Aware Nonpreemptive Scheduling of Mixed-Criticality Real-Time Task Systems

Energy-aware real-time scheduling for mixed-criticality (MC) systems with different criticality levels has drawn many researchers’ attentions. However, most of the studies focus on the preemptive MC task model and few studies consider the nonpreemptive MC task model, in which all jobs cannot be preempted until completion. In this article, we address the energy minimization problem for MC systems with nonpreemptive dynamic priority scheduling. First, we develop schedulability test of nonpreemptive earliest deadline first (NP-EDF) in single processor MC systems. Second, we extend the results to nonpreemptive earliest deadline first with virtual deadline (NP-EDFVD), which is the first attempt for nonpreemptive dynamic priority scheduling in single processor MC systems. Third, the energy-aware nonpreemptive scheduling algorithm (EANPS) based on NP-EDFVD is proposed to solve the energy minimization problem for MC systems with nonpreemptive dynamic priority scheduling. Finally, an industrial use-case and extensive simulations are used to validate the performance of the proposed algorithm, and the experimental results show that the EANPS algorithm consumes average 25.72% less energy than that of NP-EDFVD.

A New Hybrid TLPD Algorithm for Task Scheduling in Cloud Computing

In this paper we have designed the hybrid approach of combination of credit based task length &amp; priority algorithm and credit based deadline algorithm as well as compare the results with FCFS, SJF and task length &amp; priority scheduling algorithms. When we use the credit based task length &amp; priority scheduling algorithm to schedule the task without knowing the deadline of the task, it will cause the dead of the least deadline task. The deadline credit is also included so that assigning number of resources to the tasks in such a way that there will be maximum resource utilization and minimum processing time achieved. This paper presents the simulation results of the proposed methodology implemented with the help of Cloudsim and Net beansIDE8.0 and analysis of results.

Priority-Based Resource Allocation and Energy Harvesting for WBAN Smart Health

With the emergence of new viral infections and the rapid spread of chronic diseases in recent years, the demand for integrated short-range wireless technologies is becoming a major bottleneck. Implementation of advanced medical telemonitoring and telecare systems for on-body sensors needs frequent recharging or battery replacement. This paper discusses a priority-based resource allocation scheme and smart channel assignment in a wireless body area network capable of energy harvesting. We investigate our transmission scheme in regular communication, where the access point transmits energy and command while the sensor simultaneously sends the information to the access point. A priority scheduling nonpreemptive algorithm to keep the process running for all the users to achieve the maximum reliability of access by the decision-maker or hub during critical situations of users has been proposed. During an emergency or critical situation, the process does not stop until the decision-maker or the hub takes a final decision. The objective of the proposed scheme is to get all the user processes executed with minimum average waiting time and no starvation. By allocating a higher priority to emergency and on data traffic signals such as critical and high-level signals, the proposed transmission scheme avoids inconsistent collisions. The results demonstrate that the proposed scheme significantly improves the quality of the network service in terms of data transmission for higher priority users.