Proposed Scheduling Algorithm Research Articles

A ride time-oriented scheduling algorithm for dial-a-ride problems

This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theory, aims at finding minimal user ride time schedules in worst-case quadratic time. The algorithm can either return feasible routes or it can return incorrect infeasibility declarations, on which feasibility can be recovered through a specifically-designed heuristic. The algorithm is furthermore supplemented by a battery management algorithm that can be used to determine charging decisions for electric and autonomous vehicle fleets. Timing solutions from the proposed scheduling algorithm are obtained on millions of routes extracted from DARP and e-ADARP benchmark instances. They are compared to those obtained from a linear program, as well as to popular scheduling procedures from the DARP literature. The proposed procedure mostly yields optimal solutions, with nearly-optimal solutions occurring in only 27 out of 21.5 million cases on DARP instances. Additionally, it demonstrates an average speed improvement of around 60% compared to a linear program and performs comparably to benchmark scheduling approaches from the DARP literature, while outperforming them in solution quality.

Deterministic Scheduling Algorithm Based on Proportional Conflict and Deadline in Industrial Wireless Networks

<p>Deterministic scheduling technology is of great significance for the real-time and deterministic transmission of industrial wireless network data. In view of the fact that the industrial wireless network data stream itself has priority classification attribute, this paper, based on multi-channel time-division multiple access (TDMA) technology, analyzes link conflict delay and channel contention delay caused by high priority data stream to low priority data stream, and performs scheduling preprocessing on the network, so as to eliminate the network with unreasonable parameters, and feedback to the network manager. For preprocessed networks, the scheduling algorithm prioritizes the allocation of time slots and channel resources for links of high priority data streams, while for data streams belonging to the same priority class, a scheduling scheme based on Maximum Proportional Conflict and Deadline First (MPC-D) is proposed. Under the premise of meeting schedulability conditions, time slots and channel resources are allocated sequentially according to the proportional conflict deadline values of each link, from largest to smallest. The experimental results show that the proposed scheduling algorithm can achieve a high network scheduling success rate.</p> <p> </p>

Dynamic Time Quantum Computation for Improved Round Robin Scheduling Algorithm Using Quartiles and Randomization (IRRQR)

Scheduling is a decision-making process through which large numbers of tasks compete for various system resources. The availability of limited resources makes scheduling a challenge. Among resources, the processor is the most important resource for in-time completion of tasks therefore; developing an efficient processor scheduler is still a topic of interest. In this paper, we have proposed a modified Round Robin scheduling algorithm, in which the dynamic time quantum is computed based on Quartiles and randomness. We have considered average waiting time, average turnaround time, and the number of context switches as performance metrics and compared the proposed scheduling algorithm with existing approaches. The results show that the average waiting time, average turnaround time, and the number of context switches using the proposed algorithm are significantly reduced as compared other algorithms.

Optimizing User-Level Packet Scheduling Performance through Optimal CQI-Based Resource Allocation in LTE

Our research investigates the Dynamic Resource Allocation in the Downlink of OFDMA-based LTE-A networks. It addresses both user-level and system-level packet scheduling performance. At the user-level, a Traffic Differentiator stage segregates packet queues from active users into different service queues based on service types. Users are prioritized within each service queue based on their QoS requirements and wireless channel conditions, utilizing SPSSA. At the system-level, fairness among users is a key consideration. We propose the PITDSA in the TD Scheduler stage, which aims to allocate just enough radio resource to real-time and non-real time services and assign the remaining available resource to background service. In the FD Scheduler stage, we propose an optimal CQI selection algorithm for resource allocation to exploit frequency domain multi-user diversity. We present our simulation results and analyses of all novel algorithms, and the performance of the Optimal CQI selection algorithm is compared with other algorithms. Our proposed scheduling algorithm demonstrates improved QoS for real-time and non-real time services while maintaining a good traded user-level and system-level performance. Future work can focus on optimizing the throughput or the fairness or both, and more advanced and complex techniques can be designed with the same goal.

On multi-path bandwidth scheduling for multiple fixed-slot reservations in high-performance networks

Large-scale applications in various fields generate big data at a high speed, which needs to be transferred through High-Performance Networks (HPNs). Bandwidth scheduling in HPNs plays a pivotal role in providing Quality of Service (QoS) to such applications. In this paper, we formulate two bandwidth scheduling problems: (i) maximize the amount of bandwidths to be allocated for multiple fixed-slot bandwidth reservation requests (FBRRs), referred to as maxAB-MultF, and (ii) maximize the number of requests to be satisfied as well as the degree of user’s satisfaction for multiple prioritized fixed-slot bandwidth reservation requests (PFBRRs), referred to as maxNR-MultPF. We prove both of these problems to be NP-complete, and design two heuristic algorithms, namely, Maximal Reserved Bandwidth Resources (MaxRBR) and Dynamic Resource Occupation (DynRO), for them, respectively. For each problem, we also design two algorithms based on greedy strategy for performance comparison. Extensive simulation results illustrate that MaxRBR for maxAB-MultF improves Reserved Bandwidth Resource Ratio over two other algorithms in comparison by 18% and 15%, respectively, and DynRO for maxNR-MultPF improves over another two algorithms in comparison scheduling success ratio by 10% and 5%, respectively, and user satisfaction by 6.5% and 5.5%, respectively. Considering the rapid expansion of HPNs in both speed and scale, the proposed scheduling algorithms have great potentials to improve the network performance of big data applications that require these two types of services.

Data driven approach for the management of wind and solar energy integrated electrical distribution network with high penetration of electric vehicles

With the increased penetration of fluctuating renewables and growing population of contemporary loads such as electric vehicles, the uncertainties in the generation and demand in the electric power grids are increasing. This makes the efficient operation and management of these systems challenging. Objective of this study is to propose a real-time management system for EV charging, which maximises the renewable energy utilization. An electric power distribution network with an average and peak demands of 1.51 MW, and 3.6 MW respectively, was chosen for the study. The real time power flow through the network components were analyzed using the OpenDSS model. With a wind power density of 574.51 W/m2 and a solar insolation of 4.14 kWh/m2/day, an optimized renewable energy system consisting of a 2.3 MW wind turbine and 2.61 MWp photovoltaic power plant are proposed for the network. Models based on k-Nearest Neighbors algorithms were developed for predicting the performances of these renewable energy systems at the network area. Based on the load profile, power flow analysis, and the predicted generation from solar and wind systems, a demand side management algorithm has been developed for the charge/discharge scheduling of the electric vehicles connected within the network. The basic objective of the algorithm is to maximize the renewable energy utilization by triggering the charging cycle during the periods of excess renewable energy generation. With an annual contribution of renewables is estimated as 12.61 GWh out of which 9.33 GWh is from wind and 3.29 GWh from solar. Wind from wind and solar energy systems, the proposed scheduling algorithm could contribute 71.56 percent of the charging load demand by the EVs.

An Enhanced Load Balancing Algorithm for Cloud Enterprise Resource Planning (ERP) Data in a Multi-Cloud Environment

Businesses and individuals have seen the need to adopt the cloud and mulit-cloud environment for their businesses and storage of data. The load balancing concerns especially in the multi-cloud environment was investigated and a new algorithm proposed. In this research, a proposed new load balancing algorithm is presented and compared with the Round Robin (RR) and Weighted Round Robin (WRR) algorithms. The proposed scheduling algorithm considered several Cloud ERP Data chunks to analyse the data transmission rate or throughput, the transmission delay, data loss and the Cloud ERP Data drop ratio. The proposed algorithm performed better compared to the Round Robin (RR) and Weighted Round Robin (WRR) in a multi cloud environment with data chunks above 150 in terms of throughput. The proposed algorithm again outperformed the RR and WRR with a recorded lower transmission delays and lower data loss.

조선소 병렬 기계 공정에서의 납기 지연 및 셋업 변경 최소화를 위한 강화학습 기반의 생산라인 투입순서 결정

The profile shops in shipyards produce section steels required for block production of ships. Due to the limitations of shipyard’s production capacity, a considerable amount of work is already outsourced. In addition, the need to improve the productivity of the profile shops is growing because the production volume is expected to increase due to the recent boom in the shipbuilding industry. In this study, a scheduling optimization was conducted for a parallel welding line of the profile process, with the aim of minimizing tardiness and the number of set-up changes as objective functions to achieve productivity improvements. In particular, this study applied a dynamic scheduling method to determine the job sequence considering variability of processing time. A Markov decision process model was proposed for the job sequence problem, considering the trade-off relationship between two objective functions. Deep reinforcement learning was also used to learn the optimal scheduling policy. The developed algorithm was evaluated by comparing its performance with priority rules (SSPT, ATCS, MDD, COVERT rule) in test scenarios constructed by the sampling data. As a result, the proposed scheduling algorithms outperformed than the priority rules in terms of set-up ratio, tardiness, and makespan.

Adaptive scheduling algorithm for phased array fire control radar based on comprehensive priority

Aiming at the problem of resource allocation in multi-task scheduling of phased array fire control radar, this paper proposes an adaptive scheduling algorithm based on comprehensive priority, that is, the shooting advantage degree of phased array fire control radar to different targets is calculated by using Analytic Hierarchy Process, and then combined with the priority of the work mode and the task deadline to carry out the comprehensive priority planning of the task. In the performance evaluation algorithm, the index of shooting value rate is proposed to evaluate the performance of the scheduling algorithm for high shooting advantage degree tasks. The simulation results show that, compared with the traditional scheduling algorithm, the proposed scheduling algorithm significantly improves the shooting value rate and the comprehensive scheduling ability of phased array fire control radar.

Optimization Approach to Rolling Stock Scheduling for Timetable Temporarily Changed due to Disaster

When railway operators devise a rolling stock schedule plan, it is desirable that the schedule plan for the new timetable is similar to the previous schedule. In this study, we focus on rolling stock scheduling following a large-scale natural disaster. Generally, partial suspensions occur some sections in the line from the damage caused by the disaster, so a temporary timetable is generated. Subsequently, a rolling stock schedule that is close to the previous one must be created. We propose a two-phase rolling stock scheduling algorithm for the temporarily changed timetable. The proposed scheduling algorithm creates a set of duties based on the multi-commodity network flow problem in the first phase. Following that, the algorithm provides a set of rosters by solving the travelling salesman problem. Finally, we confirm that the proposed algorithm can produce a good solution in terms of evaluation criteria and computational time.

Cloud Task Scheduling Using Modified Penguins Search Optimization Algorithm

The cloud computing has emerged as a novel distributed computing system in past few years. It provides computation and resources over the Internet via dynamic provisioning of services. There are quite a few challenges and issues connected with implementation of cloud computing. This paper considers one of its major problems, i.e. task scheduling. The function of an efficient task scheduling algorithm is that it concentrates not only on attaining the requirements of the user but also in enhancing the efficiency of the cloud computing system. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. This paper proposes a modified Penguins Search Optimization Algorithm (MPeSOA) for efficient cloud task scheduling. The main contribution of our work is to schedule all tasks to available virtual machines so that the makespan is minimized, resource utilization is increased and the degree of imbalance is reduced. The proposed scheduling algorithm was simulated using the CloudSim 4.0 simulator. Experimental results showed that the proposed MPeSOA outperformed three existing meta-heuristics, namely Penguins Search Optimization Algorithm (PeSOA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

Enhanced Subset Greedy Multiuser Scheduling in Clustered Cell-Free Massive MIMO Systems

In this work, we consider the problem of multiuser scheduling for the downlink of cell-free massive multi-input multi-output networks with clustering. In particular, we develop a multiuser scheduling algorithm based on an enhanced greedy method that is deployed with linear precoding and clustering. Closed-form expressions for the sum-rate performance are derived when imperfect channel state information is considered. The proposed scheduling algorithm is then analyzed along with its computational cost and network signaling load. Numerical results show that the proposed scheduling method outperforms the existing methods and in low signal-to-noise ratios, its performance becomes much closer to the optimal approach.

An intelligent scheduling framework for DNN task acceleration in heterogeneous edge networks

With the upgrade of hardware architecture and device capacities, many accelerator-based hardware platforms have been widely deployed in Mobile Edge Computing (MEC) environments. The execution time of many computation-intensive applications (e.g., face recognition and pedestrian detection) can be significantly reduced when deployed on these heterogeneous devices. Moreover, thanks to the popularity of Deep Learning (DL), most terminal applications are integrated with Deep Neural Networks (DNN) and can be divided into interdependent tasks. The structure of these applications can be represented as the Directed Acyclic Graph (DAG). Therefore, it is critical to seek the optimal scheduling order and execution placement of tasks according to the acceleration effects of edge servers and the task dependency. However, conventional scheduling strategies focus on the short-term performance, potentially leading to service quality degradation in the long term. Besides, many studies use Deep Reinforcement Learning (DRL) algorithms to seek a long-term optimal scheduling strategy but ignore the device acceleration and the task dependency. Furthermore, training a well-performed DRL agent is time-consuming, and the large scale of trial-and-error will take up tremendous computation and storage resources. In this paper, we model the scheduling process as a Markov Decision Process (MDP) and design an adaptive scheduling framework for task acceleration. Fully considering the data dependencies, resource conditions, and network conditions, the proposed scheduling algorithm called Meta-AC uses policy gradient combined with meta-learning to minimize the average task delay and the ratio of time-out tasks. As a hierarchical DRL algorithm, Meta-AC uses meta data to learn directed exploration strategies in the high-level agent, improving the learning efficiency from experience samples. Extensive simulations demonstrate the superiority of the proposed method over the counterpart methods.

Deep-Q-Network-Based Packet Scheduling in an IoT Environment.

With the advent of the Internet of Things (IoT) era, a wide array of wireless sensors supporting the IoT have proliferated. As key elements for enabling the IoT, wireless sensor nodes require minimal energy consumption and low device complexity. In particular, energy-efficient resource scheduling is critical in maintaining a network of wireless sensor nodes, since the energy-intensive processing of wireless sensor nodes and their interactions is too complicated to control. In this study, we present a practical deep Q-network (DQN)-based packet scheduling algorithm that coordinates the transmissions of multiple IoT devices. The scheduling algorithm dynamically adjusts the connection interval (CI) and the number of packets transmitted by each node within the interval. Through various experiments, we verify how effectively the proposed scheduler improves energy efficiency and handles the time-varying nature of the network environment. Moreover, we attempt to gain insight into the optimized packet scheduler by analyzing the policy of the DQN scheduler. The experimental results show that the proposed scheduling algorithm can further prolong a network's lifetime in a dynamic network environment in comparison with that in other alternative schemes while ensuring the quality of service (QoS).

Gradient-Based Scheduler for Scientific Workflows in Cloud Computing

It is becoming increasingly attractive to execute workflows in the cloud, as the cloud environment enables scientific applications to utilize elastic computing resources on demand. However, despite being a key to efficiently managing application execution in the cloud, traditional workflow scheduling algorithms face significant challenges in the cloud environment. The gradient-based optimizer (GBO) is a newly proposed evolutionary algorithm with a search engine based on the Newton’s method. It employs a set of vectors to search in the solution space. This study designs a gradient-based scheduler by using GBO for workflow scheduling to minimize the usage costs of workflows under given deadline constraints. Extensive experiments are conducted on well-known scientific workflows of different sizes and types using WorkflowSim. The experimental results show that the proposed scheduling algorithm outperforms five other state-of-the-art algorithms in terms of both the constraint satisfiability and cost optimization, thereby verifying its advantages in addressing workflow scheduling problems.

Scheduling optimization for UAV communication coverage using virtual force-based PSO model

When the ground communication base stations in the target area are severely destroyed, the deployment of Unmanned Aerial Vehicle (UAV) ad hoc networks can provide people with temporary communication services. Therefore, it is necessary to design a multi-UAVs cooperative control strategy to achieve better communication coverage and lower energy consumption. In this paper, we propose a multi-UAVs coverage model based on Adaptive Virtual Force-directed Particle Swarm Optimization (AVF-PSO) strategy. In particular, we first introduce the gravity model into the traditional Particle Swarm Optimization (PSO) algorithm so as to increase the probability of full coverage. Then, the energy consumption is included in the calculation of the fitness function so that maximum coverage and energy consumption can be balanced. Finally, in order to reduce the communication interference between UAVs, we design an adaptive lift control strategy based on the repulsion model to reduce the repeated coverage of multi-UAVs. Experimental results show that the proposed coverage strategy based on gravity model outperforms the existing state-of-the-art approaches. For example, in the target area of any size, the coverage rate and the repeated coverage rate of the proposed multi-UAVs scheduling are improved by 6.9–29.1%, and 2.0–56.1%, respectively. Moreover, the proposed scheduling algorithm is high adaptable to diverse execution environments.© 2022 Published by Elsevier Ltd.

A Proposed Scheduling Algorithm for IoT Applications in a Merged Environment of Edge, Fog, and Cloud

With the rapid increase of Internet of Things (IoT) devices and applications, the ordinary cloud computing paradigm soon becomes outdated. Fog computing paradigm extends services provided by a cloud to the edge of network in order to satisfy requirements of IoT applications such as low latency, locality awareness, low network traffic, mobility support, and so forth. Task scheduling in a Cloud-Fog environment plays a great role to assure diverse computational demands are met. However, the quest for an optimal solution for task scheduling in the such environment is exceedingly hard due to diversity of IoT applications, heterogeneity of computational resources, and multiple criteria. This study approaches the task scheduling problem with aims at improving service quality and load balancing in a merged computing system of Edge-Fog-Cloud. We propose a Multi-Objective Scheduling Algorithm (MOSA) that takes into account the job characteristics and utilization of different computational resources. The proposed solution is evaluated in comparison to other existing policies named LB, WRR, and MPSO. Numerical results show that the proposed algorithm improves the average response time while maintaining load balancing in comparison to three existing policies. Obtained results with the use of real workloads validate the outcomes.

Cloud Task Scheduling using the Squirrel Search Algorithm and Improved Genetic Algorithm

With cloud computing, resources can be networked globally and shared easily between users. A range of heterogeneous needs are met on demand by software, hardware, storage, and networking. Dynamic resource allocation and load distribution pose challenges for cloud servers. In this regard, task scheduling plays a significant role in enhancing the performance of cloud computing. With the increase in the number of users and the capability of cloud computing, cloud data centers are experiencing concerns regarding energy consumption. To leverage cloud resources energy efficiently and provide real-time services to users, a viable cloud task scheduling solution is required. To address these problems, this paper proposes a new hybrid task scheduling algorithm based on squirrel search and improved genetic algorithms for cloud environments. The proposed scheduling algorithm surpasses existing scheduling algorithms across multiple parameters, including makespan, energy consumption, and execution time.

Chatter suppression with productivity improvement by scheduling a [formula omitted] continuous feedrate to match spindle speed variation

Available spindle speed variation (SSV) methods, which are used to suppress regenerative chatter through disrupting the phase relationship between the inner and outer modulation on the machined surface, are usually performed in conjunction with the periodically varying feeds per tooth from the originally given constant feedrate. This article presents a new feedrate scheduling algorithm to match the SSV so that feed per tooth can be maximized during the whole machining. Concretely, a sinusoidal profile of feedrate is scheduled to match the designed SSV, meanwhile, the C3 continuous condition is guaranteed within all kinematic constraints. The feedrate is scheduled as two parts, i.e. a jerk continuous acceleration profile with the constant feedrate and a continuous sinusoidal profile above the constant feedrate. The continuity of the junction between these two parts is achieved through scheduling a gradually varying sinusoidal profile. To guarantee the kinematic constraints, the derivatives of the sinusoidal amplitude variation are obtained by the properties of trigonometric functions. As a result, a constant feed per tooth, which actually corresponds to the maximum of the periodically varying feeds per tooth associated with all existing SSV methods, is obtained, while the speed still periodically varies. It proves that the proposed scheduling algorithm can improve the machining efficiency since the feed per tooth is kept at the maximum level in most machining periods, and this is a great advancement over all existing SSV methods. Experiments confirm that chatter can be well suppressed with the significantly reduced machining time by using the proposed method.

Joint Antenna and Device Scheduling in Full-Duplex MIMO Wireless-Powered Communication Networks

In this article, we study a joint antenna and Internet of Things device (ID) scheduling problem in the full-duplex (FD) multiple-input–multiple-output (MIMO) wireless-powered communication network (WPCN) over time-varying fading channels. We first formulate an optimization problem to maximize the average sum rate of IDs while satisfying their minimum average data rate requirements by jointly scheduling ID selection for uplink data transmission, antenna switching, and beamforming. To deal with the problem, we propose a scheduling algorithm based on Lagrangian duality and the stochastic optimization theory. The proposed scheduling algorithm necessitates solving per-time-slot problems, each of which aims at maximizing the weighted sum of the selected ID’s uplink data rate and the nonselected IDs’ harvested power from the downlink by jointly optimizing ID selection, antenna switching between uplink and downlink, and beamforming at that time slot. To solve the per-time-slot problem, we develop a joint ID selection, antenna switching, and beamforming (Joint-IAB) algorithm based on the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Through simulation, we demonstrate that our scheduling algorithm with the proposed Joint-IAB algorithm provides better performance than the other scheduling algorithms while well satisfying the given minimum average data rate requirements of IDs.