Scheduling In Networks Research Articles

VNF Scheduling and Sampling Rate Maximization in Energy Harvesting IoT Networks

VNF Scheduling and Sampling Rate Maximization in Energy Harvesting IoT Networks

A Joint Distributed Optimization Framework for Voltage Control and Emergency Energy Storage Vehicle Scheduling in Community Distribution Networks

A Joint Distributed Optimization Framework for Voltage Control and Emergency Energy Storage Vehicle Scheduling in Community Distribution Networks

On the time complexity of achieving optimal throughput in time division multiple access communication networks

<abstract><p>The fundamental problem of finding transmission schedules for achieving optimal throughput in time division multiple access (TDMA) communication networks is known to be NP-hard. Let $ \mathcal{N} $ be a scheduled $ k $-time slot TDMA network with $ n $ stations and $ m $ links. We showed that an optimal link schedule for $ \mathcal{N} $ can be computed recursively with a recursion tree of logarithmic depth $ \mathcal{O}(\ln m) $ in expectation. Additionally, we showed that optimal link schedules for those TDMA networks, with recursion trees of depth meeting the expectation, can be found in time $ \mathcal{O}(m^{2+\ln k}) $. Likewise, we discuss analogous results for computing optimal station schedules of TDMA networks.</p></abstract>

Learning-assisted User Scheduling and Beamforming for mmWave Vehicular Networks

Learning-assisted User Scheduling and Beamforming for mmWave Vehicular Networks

Optimal Scheduling of Multi Vector Energy Networks Considering Resiliency Improvement

Optimal Scheduling of Multi Vector Energy Networks Considering Resiliency Improvement

Delay-Aware Link Scheduling in IAB Networks with Dynamic User Demands

Delay-Aware Link Scheduling in IAB Networks with Dynamic User Demands

Frequency-Constrained Optimal Restoration Scheduling in Active Distribution Networks with Dynamic Boundaries for Networked Microgrids

Frequency-Constrained Optimal Restoration Scheduling in Active Distribution Networks with Dynamic Boundaries for Networked Microgrids

DATA: Throughput and Deadline-Aware Genetic Approach for Task Scheduling in Fog Networks

DATA: Throughput and Deadline-Aware Genetic Approach for Task Scheduling in Fog Networks

Fairness Scheduling in User-Centric Cell-Free Massive MIMO Wireless Networks

Fairness Scheduling in User-Centric Cell-Free Massive MIMO Wireless Networks

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach

Mobile power sources (MPSs) have been gradually deployed in microgrids as critical resources to coordinate with repair crews (RCs) towards resilience enhancement owing to their flexibility and mobility in handling the complex coupled power-transport systems. However, previous work solves the coordinated dispatch problem of MPSs and RCs in a centralized manner with the assumption that the communication network is still fully functioning after the event. However, there is growing evidence that certain extreme events will damage or degrade communication infrastructure, which makes centralized decision making impractical. To fill this gap, this paper formulates the resilience-driven dispatch problem of MPSs and RCs in a decentralized framework. To solve this problem, a hierarchical multi-agent reinforcement learning method featuring a two-level framework is proposed, where the high-level action is used to switch decision-making between power and transport networks, and the low-level action constructed via a hybrid policy is used to compute continuous scheduling and discrete routing decisions in power and transport networks, respectively. The proposed method also uses an embedded function encapsulating system dynamics to enhance learning stability and scalability. Case studies based on IEEE 33-bus and 69-bus power networks are conducted to validate the effectiveness of the proposed method in load restoration.

Railway wireless train communication network scheduling based on GA-BP algorithm

Railway wireless train communication network scheduling based on GA-BP algorithm

Railway wireless train communication network scheduling based on GA-BP algorithm

Railway wireless train communication network scheduling based on GA-BP algorithm

Job-Deadline-Guarantee-Based Joint Flow Scheduling and Routing Scheme in Data Center Networks.

Many emerging Internet of Things (IoT) applications deployed on cloud platforms have strict latency requirements or deadline constraints, and thus meeting the deadlines is crucial to ensure the quality of service for users and the revenue for service providers in these delay-stringent IoT applications. Efficient flow scheduling in data center networks (DCNs) plays a major role in reducing the execution time of jobs and has garnered significant attention in recent years. However, only few studies have attempted to combine job-level flow scheduling and routing to guarantee meeting the deadlines of multi-stage jobs. In this paper, an efficient heuristic joint flow scheduling and routing (JFSR) scheme is proposed. First, targeting maximizing the number of jobs for which the deadlines have been met, we formulate the joint flow scheduling and routing optimization problem for multiple multi-stage jobs. Second, due to its mathematical intractability, this problem is decomposed into two sub-problems: inter-coflow scheduling and intra-coflow scheduling. In the first sub-problem, coflows from different jobs are scheduled according to their relative remaining times; in the second sub-problem, an iterative coflow scheduling and routing (ICSR) algorithm is designed to alternately optimize the routing path and bandwidth allocation for each scheduled coflow. Finally, simulation results demonstrate that the proposed JFSR scheme can significantly increase the number of jobs for which the deadlines have been met in DCNs.

OPTIMAL MODEL FOR EVALUATING THE TOTAL DURATION OF A BUSINESS PROCESS

When assessing the performance of business processes, it should be noted that one of the most important operational indicators and evaluation criteria of any company should be the indicator of the duration of the process completion cycle. Cycle duration is the amount of time that passes from the start of a task to the moment it is completed. Time is a critical metric for completing any task as it relates to customer satisfaction and the cost of completing that service. Based on this, the main goal of this work is to find the critical path of a business process and build a mathematical model to determine the objective function. To achieve the set goals, the problem of finding the maximum path in a directed graph constructed according to the list of works of the information system implementation project is considered. In accordance with the list of works, the relationships between them were determined, a network schedule for project planning was constructed, and the main relationships and patterns were identified. Studies of the constructed mathematical model were carried out using the MS Excel software application. The critical path of the business process of implementing an information system was found and conclusions were drawn on the total duration of the project. Thus, operations lying on the critical path require additional slack planning.

Adaptive refined random orthogonal matching pursuit algorithm for FBMC/OQAM MIMO framework

The fifth generation of wireless communication is anticipated to provide improved quality of service and enhanced data rates to the end users. One such technology that stands out as a potential transmission scheme for 5G systems is Filter Bank Multicarrier using Offset Quadrature Amplitude Modulation (FBMC/OQAM) with an effective channel estimation technique for improved performance. However, due to the inherent imaginary interference, channel estimation methods relying on preamble structures in FBMC/OQAM systems exhibit sub-optimal performance, particularly within Multiple-Input Multiple-Output (MIMO) setups. For channel estimation schemes based on compressed sensing, the inherent sparsity of wireless channels can be exploited for accurate channel reconstruction and overall performance improvement.We propose a novel compressed sensing based algorithm namely, Adaptive Refined Random Orthogonal Matching Pursuit (ARROMP), for MIMO-FBMC system with Coordinated MultiPoint (CoMP) scheduling. This algorithm adaptively selects a support set by utilizing a double threshold for the minimization of mean squared error and for accurate channel reconstruction. The proposed algorithm's performance is compared with existing Orthogonal Matching Pursuit (OMP) schemes such as random OMP, refined random OMP, and least square-based estimation. The numerical simulations suggest that the proposed adaptive algorithm provides performance improvement in terms of reduced Mean Squared Error (MSE) of channel reconstruction and Bit Error Rate (BER). Moreover, the proposed ARROMP algorithm for MIMO-FBMC is rigorously tested with CoMP scheduling for a cellular network using frequency division duplex mode. The proposed system presents significant improvements in throughput and spectral efficiency for all types of cell users, including cell-edge users. The simulation results validate the improved performance of the proposed algorithm with CoMP scheduling over the existing single-cell system with no coordination.

Joint user plane function instance and base station scheduling in mobile networks

AbstractTo guarantee a high data transmission rate in heterogeneous mobile networks, sufficient small base stations (SBSs) and user plane function (UPF) instances should be active. However, the excessive operation of SBSs and UPF instances can increase the operating expenditure (OPEX) for the network operator. To balance the data rate and OPEX, we propose a joint UPF instance–SBS scheduling algorithm (J‐UBSA). In the proposed J‐UBSA, a controller periodically determines the appropriate number of active SBSs and UPF instances based on estimated variations in session requests. The decision‐making process is formulated as a constrained Markov decision process and converted into a linear programming model to obtain the optimal solution using a traditional algorithm with low complexity. Evaluation results demonstrate that J‐UBSA can substantially reduce the OPEX while guaranteeing a suitable average data rate for mobile devices.

TSN Network Scheduling—Challenges and Approaches

Time-Sensitive Networking (TSN) is a set of Ethernet standards aimed to improve determinism in packet delivery for converged networks. The main goal is to provide mechanisms that enable low and predictable transmission latency and high availability for demanding applications such as real-time audio/video streaming, automotive, and industrial control. To provide the required guarantees, TSN integrates different traffic shaping mechanisms including 802.1Qbv, 802.1Qch, and 802.1Qcr, allowing for the coexistence of different traffic classes with different priorities on the same network. Achieving the required quality of service (QoS) level needs proper selection and configuration of shaping mechanisms, which is difficult due to the diversity in the requirements of the coexisting streams under the presence of potential end-system-induced jitter. This paper discusses the suitability of the TSN traffic shaping mechanisms for the different traffic types, analyzes the TSN network configuration problem, i.e., finds the optimal path and shaper configurations for all TSN elements in the network to provide the required QoS, discusses the goals, constraints, and challenges of time-aware scheduling, and elaborates on the evaluation criteria of both the network-wide schedules and the scheduling algorithms that derive the configurations to present a common ground for comparison between the different approaches. Finally, we analyze the evolution of the scheduling task, identify shortcomings, and suggest future research directions.

Analysis of the Theory and Traffic Scheduling for Transit Network by Genetic Algorithm-Based Optimization Technique

Analysis of the Theory and Traffic Scheduling for Transit Network by Genetic Algorithm-Based Optimization Technique

A sustainable battery scheduling and echelon utilization framework for electric bus network with photovoltaic charging infrastructure

Battery capacity degradation in battery electric buses (BEBs) poses a significant operational challenge for transit agencies. This study presents a sustainable battery scheduling and echelon utilization framework considering battery capacity fading and charging infrastructure integrated with solar photovoltaic (PV) and energy storage systems. The framework aims to minimize the sum of bus charging, battery replacement, and carbon emission costs during the BEB lifespan. We first present a power battery replacement problem for a single bus fleet, then extend it to a joint power battery replacement and fleet-depot matching problem. Finally, we propose a power battery replacement and fleet-depot problem by introducing solar PV and energy storage systems. Dynamic programming, Lagrange relaxation, and a two-step approach are adopted to solve the three problems. A case study involving six bus depots in Beijing demonstrates that optimal battery replacement schedules can significantly lower charging costs. Moreover, integrating solar PV and energy storage is shown to considerably reduce both charging costs and carbon emissions.

Markov decision process based value chain calculation of water distribution network scheduling

Precise value of scheduling decisions forms the cornerstone of water distribution network (WDN) scheduling optimization, which aims at conserving energy and enhancing network operational efficiency. This article proposes a computational methodology for evaluating the value chain of scheduling decisions in WDN. The scheduling process is modeled as a Markov decision process with immediate reward function, action and state space. Due to the periodicity of water supply and sequential nature of scheduling, the calculation quantifies cumulative value of scheduling decisions by incorporating state transition probability with expected value. The effectiveness and applicability of the proposed evaluation method are demonstrated using scheduling data from a real world WDN. The method provides rational values on scheduling period and strategies, offering practical feedback for scheduling decisions.