Distributed Scheduling Algorithm Research Articles

Research on Distributed Scheduling Algorithm for Virtual City Power Plants Based on Blockchain Technology

Research on Distributed Scheduling Algorithm for Virtual City Power Plants Based on Blockchain Technology

Distributed Dispatching Optimization based on Consistency Theory for Smart Grid

Abstract The energy management issue of a smart grid is the key to the efficient and stable operation of a smart grid. Due to the large number of distributed energy sources connected, the centralized dispatch method is no longer applicable, and the smart grid uses distributed algorithms for dispatch. In this paper, based on modeling and analysis of the smart grid dispatch problem, the distributed dispatch algorithm is designed by combining the consistency theory in multi-intelligent body systems. First, the distributed scheduling algorithm is designed under ideal communication conditions. Then, the correctness of the algorithm is verified in the MATLAB simulation platform under two conditions without considering the generator set output constraint and considering the generator set output constraint. The analysis of the study simulation results shows that the optimal solution of the scheduling problem is obtained when the marginal costs of the generation equipment converge consistently.

A Q-learning memetic algorithm for energy-efficient heterogeneous distributed assembly permutation flowshop scheduling considering priorities

Most studies on distributed assembly permutation flowshop scheduling do not consider product priorities and factory heterogeneity. This causes delays in critical products and cannot reflect the real-world production situation. This paper focuses on the energy-efficient heterogeneous distributed assembly permutation flowshop scheduling considering priorities (EHDAPFS-P) to minimize total tardiness and total energy consumption simultaneously. Unlike traditional models, factory heterogeneity and product priorities are considered to better reflect the production environment and customer satisfaction in real-world situations. Then, a Q-learning memetic algorithm (QLMA) is proposed to solve this problem: (i) a high-quality initial population is obtained using a hybrid initialization strategy that combines four problem-specific heuristics; (ii) six efficient neighborhood structures are tailored to guide the population to converge to the promising areas; (iii) the most useful neighborhood structure is selected among the six structures using the Q-learning algorithm to accelerate the convergence, thus maximizing the cumulative and future improvements according to the population state; and (iv) an energy-saving strategy is developed to optimize the total energy consumption without deteriorating the total tardiness. The proposed QLMA is compared with seven state-of-the-art algorithms on 261 benchmark instances to demonstrate its superiority or at least competitiveness.

Resiliency enhancement of distribution network with distributed scheduling algorithm

Resiliency enhancement of distribution network with distributed scheduling algorithm

A performance-impact based multi-task distributed scheduling algorithm with task removal inference and deadlock avoidance

A performance-impact based multi-task distributed scheduling algorithm with task removal inference and deadlock avoidance

Coordinative Optimization Between Multiple Data Center Operators and a System Operator Based on Two-Level Distributed Scheduling Algorithm

Data Centers (DCs) have been playing a significant role in demand response (DR) programs in recent years due to their considerable DR capability. DCs are in the charge of the data center operator (DCO), who is responsible for making a reasonable allocation of computing tasks to provide DR resources. To relieve the transmission pressure of power systems, the system operator (SO) encourages DCOs to participate in the DR programs. To maximize the total welfare, a detailed DR scheduling model of DCOs and SO is proposed for the coordinative optimization. Considering the privacy issue of DCOs, a two-level distributed scheduling algorithm based on the alternating direction multiplier method (ADMM) is designed for privacy protection and distributed autonomy. Simulation results show that the proposed coordinative optimization algorithm can effectively realize the maximization of total social welfare with data privacy protection. For a power system with multiple DCOs, reasonable scheduling of DCO’s DR resources can reduce the peak-valley difference of system loads reliably and economically.

Distributed Scheduling for Cooperative Navigation Based on Uncertainty Evolution

Cooperative navigation is a promising solution for many emerging applications of the Internet of Robotic Things to provide accurate location information via relative measurement, information exchange, and information fusion. However, these operations cause large communication overhead and thus are impractical for applications in resource-constrained environment. To alleviate this problem, a distributed scheduling algorithm based on uncertainty evolution under the framework of belief propagation (BP) is proposed in this paper. Specifically, we first derive a computationally efficient upper bound to characterize the approximated reduction of position uncertainty through cooperation. Then, a novel distributed cooperative navigation algorithm with candidate selection scheme is proposed for robots to guarantee satisfactory performance while reducing communication rate. Numerical simulations show that the proposed algorithm can achieve higher navigation accuracy and more efficient communication with limited on-board resources.

Distributed Scheduling With Centralized Coordination for Scalable Wireless Mesh Networking

A coordinated carrier sense multiple access (C-CSMA) scheme is developed to schedule packet transmissions in multihop wireless networks. It integrates distributed scheduling and centralized coordination at different time scales. At a small time scale, a distributed scheduling algorithm, which is incorporated into CSMA, runs on each node to determine key parameters for CSMA. These parameters are optimized by ensuring multiple network attributes, i.e., average link date rate, node transmission probability, and the conditional probability of a link being selected for transmission, approaching their corresponding optimal values at a large time scale, e.g., a few seconds. Such optimal values are obtained from a centralized algorithm running on a portal node. The algorithm collects topology, link, and traffic information from the network at a large time scale and determines the attributes above to fulfill the optimal tradeoff between throughput and fairness. Since the distributed algorithm is shepherded by the centralized algorithm, C-CSMA has high scalability: first, it achieves long-term optimal performance with low information collection overhead; second, it schedules packet transmissions in a distributed way and responds quickly to changes in networks. It is proved that network attributes resulting from the distributed scheduling algorithm converge to optimal values provided by the centralized algorithm. Simulation experiments demonstrate that the convergence speed is fast. Moreover, extensive simulation results show that C-CSMA achieves much higher throughput, better fairness, and lower delay than existing scheduling schemes under different carrier sensing thresholds and traffic conditions.

Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning

Performance of vehicle-to-vehicle (V2V) communications depends highly on the employed scheduling approach. While centralized network schedulers offer high V2V communication reliability, their operation is conventionally restricted to areas with full cellular network coverage. In contrast, in out-of-cellular-coverage areas, comparatively inefficient distributed radio resource management is used. To exploit the benefits of the centralized approach for enhancing the reliability of V2V communications on roads lacking cellular coverage, we propose VRLS (Vehicular Reinforcement Learning Scheduler), a centralized scheduler that proactively assigns resources for out-of-coverage V2V communications \textit{before} vehicles leave the cellular network coverage. By training in simulated vehicular environments, VRLS can learn a scheduling policy that is robust and adaptable to environmental changes, thus eliminating the need for targeted (re-)training in complex real-life environments. We evaluate the performance of VRLS under varying mobility, network load, wireless channel, and resource configurations. VRLS outperforms the state-of-the-art distributed scheduling algorithm in zones without cellular network coverage by reducing the packet error rate by half in highly loaded conditions and achieving near-maximum reliability in low-load scenarios.

Low-complexity scheduling algorithms with constant queue length and throughput guarantees

Distributed scheduling algorithms based on carrier sense multiple access (CSMA) are optimal in terms of the throughput and the steady-state queue lengths. However, they take a prohibitively long time to reach the steady-state, often exponential in the network size. Therefore for large networks that operate over a finite time horizon, apart from the guarantees on the steady-state queue lengths, performance guarantees on the short-term (i.e., transient) queuing behaviour are also required. To that end, we propose distributed scheduling algorithms that are guaranteed to have O(1) expected queue lengths not just in the steady-state but at every time instant, where O(⋅) is with respect to the network size. Further, our algorithms have O(1) complexity and support a constant fraction of the maximum throughput for typical wireless topologies. The central idea of our algorithms is to resolve collisions among pairs of conflicting nodes by assigning a master–follower hierarchy. The master–follower hierarchy can either be chosen randomly or based on the topology of the conflict graph, leading to different performance guarantees.In addition to these hierarchical collision resolution algorithms, which are primarily designed for the conflict graph-based interference model, we also propose an Aloha-based algorithm for the K-neighbour collision tolerance interference model, which is a generalization of the conflict graph model. We show that the proposed Aloha-based algorithm supports a constant fraction of the maximum throughput for typical wireless topologies.

Distributed Bandwidth Allocation Strategy for QoE Fairness of Multiple Video Streams in Bottleneck Links

In QoE fairness optimization of multiple video streams, a distributed video stream fairness scheduling strategy based on federated deep reinforcement learning is designed to address the problem of low bandwidth utilization due to unfair bandwidth allocation and the problematic convergence of distributed algorithms in cooperative control of multiple video streams. The proposed strategy predicts a reasonable bandwidth allocation weight for the current video stream according to its player state and the global characteristics provided by the server. Then the congestion control protocol allocates the proportion of available bandwidth, matching its bandwidth allocation weight to each video stream in the bottleneck link. The strategy trains a local predictive model on each client and periodically performs federated aggregation to generate the optimal global scheme. In addition, the proposed strategy constructs global parameters containing information about the overall state of the video system to improve the performance of the distributed scheduling algorithm. The experimental results show that the introduction of global parameters can improve the algorithm’s QoE fairness and overall QoE efficiency by 10% and 8%, respectively. The QoE fairness and overall QoE efficiency are improved by 8% and 7%, respectively, compared with the latest scheme.

A Graph Attention Mechanism-Based Multiagent Reinforcement-Learning Method for Task Scheduling in Edge Computing

Multi-access edge computing (MEC) enables end devices with limited computing power to provide effective solutions while dealing with tasks that are computationally challenging. When each end device in an MEC scenario generates multiple tasks, how to reasonably and effectively schedule these tasks is a large-scale discrete action space problem. In addition, how to exploit the objectively existing spatial structure relationships in the given scenario is also an important factor to be considered in task-scheduling algorithms. In this work, we consider indivisible, time-sensitive tasks under this scenario and formalize the task-scheduling problem to minimize the long-term losses. We propose a multiagent collaborative deep reinforcement learning (DRL)-based distributed scheduling algorithm based on graph attention neural networks (GATs) to solve task-scheduling problems in the MEC scenario. Each end device creates a graph representation agent to extract potential spatial features in the scenario and a scheduling agent to extract the timing-related features of the tasks and make scheduling decisions using a gated recurrent unit (GRU). The simulation results show that, compared with several baseline algorithms, our proposed algorithm can take advantage of the spatial positional relationship of devices in the environment, significantly reduce the average delay and drop rate, and improve link utilization.

Energy Efficient Mobile Harvesting Scheme for Clustered SDWSN with Beamforming Technique

Software Defined Wireless Sensor Networks (SDWSN) provides a centralized scheduling algorithm to decrease energy consumption compared to WSN. The sensor nodes have a finite battery capacity in the SDWSN that reduces the lifetime of the nodes. To harvest energy for energy depleted nodes without interfering with the eventful data transfer in the clustered SDWSN, an energy efficient mobile harvesting scheme with the Multiple Input Single Output (MISO) beamforming technique is proposed. The mobile harvesting scheme transfer the energy to the energy starving node and the beamforming algorithm which transmits the energy in the desired direction increases the lifetime of the nodes. The Secondary Cluster Head (SCH) node in a cluster is selected based on Particle Swarm Optimization (PSO) with the distance between mobile harvester and sensor node, the energy consumption rate of a node and lifetime of a nodes constraints for harvesting energy without interrupting data transfer to sink node in clustered SDWSN. The distributed scheduling algorithm and efficient path planning decrease the energy consumption of mobile harvester in the network. The optimization problem is formulated with Signal to Noise Ratio (SNR) and transmit power constraints to increase the harvested energy for sensor nodes in the network. The simulation results show the enhanced performance of the proposed algorithm compared to the existing algorithm in terms of residual energy, end to end delay, throughput, travel distance, service time and charging delay.

A Non-Cooperative Game-Based Distributed Beam Scheduling Framework for 5G Millimeter-Wave Cellular Networks

This paper studies the problem of distributed beam scheduling for 5G millimeter-Wave (mm-Wave) cellular networks where base stations (BSs) belonging to different operators share the same spectrum without centralized coordination among them. Our goal is to design efficient distributed scheduling algorithms to maximize the network utility, which is a function of the achieved throughput by the user equipment (UEs), subject to the average and instantaneous power consumption constraints of the BSs. We propose a Media Access Control (MAC) and a power allocation/adaptation mechanism utilizing the Lyapunov stochastic optimization framework and non-cooperative games. In particular, we first decompose the original utility maximization problem into two sub-optimization problems for each time frame, which are a convex optimization problem and a non-convex optimization problem, respectively. By formulating the distributed scheduling problem as a non-cooperative game where each BS is a player attempting to optimize its own utility, we provide a distributed solution to the non-convex sub-optimization problem via finding the Nash Equilibrium (NE) of the game whose weights are determined optimally by the Lyapunov optimization framework. Finally, we conduct simulation under various network settings to show the effectiveness of the proposed game-based beam scheduling algorithm in comparison to that of several reference schemes.

An effective hybrid collaborative algorithm for energy-efficient distributed permutation flow-shop inverse scheduling

Distributed scheduling problem, a novel model of intelligent manufacturing, urgently needs new scheduling methods to meet the dynamic market demand. The inverse scheduling in a distributed shop greatly impacts both its energy consumption and productivity. This paper proposes the energy-efficient distributed permutation flow-shop inverse scheduling problem to minimize adjustment and energy consumption simultaneously. This model contains some realistic constraints, controllable processing times and energy consumption factors. To solve the problem effectively, an effective hybrid collaborative algorithm with cooperative search scheme is designed. The heuristic method and random method are improved to initialize the population. In order to balance the global exploration and local development ability of the algorithm, a double-population cooperative search link based on learning mechanism is presented. Moreover, a dual-mode local search based on switching mechanism is addressed. The influence of key parameters on the performance of the algorithm is proved by using the ANOVA of design-of-experiment. Finally, the developed algorithm has been compared with other approaches for distributed inverse scheduling and assessed by satisfactory results on different set of distributed inverse scheduling problems.

Cache-aware scheduling of scientific workflows in a multisite cloud

Many scientific experiments today are performed using scientific workflows, which become more and more data-intensive. We consider the efficient execution of such workflows in a multisite cloud, leveraging heterogeneous resources available at multiple geo-distributed data centers. Since it is common for workflow users to reuse code or data from previous workflows, a promising approach for efficient workflow execution is to cache intermediate data in order to avoid re-executing entire workflows. However, caching intermediate data and scheduling workflows to exploit such caching in a multisite cloud is complex. In particular, workflow scheduling must be cache-aware, in order to decide whether reusing cache data or re-executing workflows entirely. In this paper, we propose a solution for cache-aware scheduling of scientific workflows in a multisite cloud. Our solution includes a distributed and parallel architecture and new algorithms for adaptive caching, cache site selection, and dynamic workflow scheduling. We implemented our solution in the OpenAlea workflow system, together with cache-aware distributed scheduling algorithms. Our experimental evaluation in a three-site cloud with a real application in plant phenotyping shows that our solution can yield major performance gains, reducing total time up to 42% with 60% of the same input data for each new execution.

PackStealLB: A scalable distributed load balancer based on work stealing and workload discretization

The scalability of high-performance, parallel iterative applications is directly affected by how well they use the available computing resources. These applications are subject to load imbalance due to the nature and dynamics of their computations. It is common that high performance systems employ periodic load balancing to tackle this issue. Dynamic load balancing algorithms redistribute the application’s workload using heuristics to circumvent the NP-hard complexity of the problem However, scheduling heuristics must be fast to avoid hindering application performance when distributing the workload on large and distributed environments. In this work, we present a technique for low overhead, high quality scheduling decisions for parallel iterative applications. The technique relies on combined application workload information paired with distributed scheduling algorithms. An initial distributed step among scheduling agents group application tasks in packs of similar load to minimize messages among them. This information is used by our scheduling algorithm, PackStealLB, for its distributed-memory work stealing heuristic. Experimental results showed that PackStealLB is able to improve the performance of a molecular dynamics benchmark by up to 41%, outperforming other scheduling algorithms in most scenarios over almost one thousand cores.

Competitive Age of Information in Dynamic IoT Networks

In the past decades, Dynamic Internet of Things (D-IoT) networks have played a conspicuously more important role in many real-life areas, including disaster relief, environment monitoring, public safety, and so on, to rapidly collect information from the environment and help people to make the decision. Meanwhile, due to the widespread implementation of dynamic IoT networks, there exists an enormous demand on designing suitable models and efficient algorithms for fundamental operations in dynamic IoT networks, to achieve the high throughput and reliable low-latency communication demands in 6G networks. In this article, we first present a general dynamic model to comprehensively depict most of the dynamic phenomena in IoT networks. Then, based on the proposed dynamic model, a distributed scheduling algorithm is proposed to competitively optimize the Age-of-Information (AoI) problem in the context of a D-IoT network. We say our scheduling algorithm is competitive: the throughput of the base station approximates the optimal solution with constant competitive ratio; and, the latency for a packet received by the base station is only constant times larger than the optimal latency. Rigorous theoretical analysis and extensive simulations are presented to verify the high throughput and reliable low-latency communications in our proposed algorithm.

Distributed Reliable and Energy-Efficient Scheduling for LR-WPANs

Pervasiveness of wireless networks drives the heterogeneity and density of devices in a vast diversity of environments. To achieve high reliability and low energy consumption while enabling pervasiveness is inherently a resource allocation problem. In low-rate wireless personal area networks, multi-frequency time-division multiple access methods are identified as compelling solutions to resource allocation via scheduling transmissions in time and frequency. This work presents the TREE (TRaffic-aware Energy Efficient) algorithm, an adaptive and distributed scheduling algorithm, designed to provide high reliability in terms of packet reception ratio while optimizing the energy consumption of each device. This algorithm schedules communications according to the packets in the queue and short-memory performance. Decisions are made locally, and low-interference scheduling emerges at the network level. TREE is an adaptive threshold-based model that allocates more network resources (e.g., timeslots) when the communication queue size crosses a threshold and frees resources if the resource was underutilized. Implemented over IEEE-802.15.4-TSCH and extensively tested in simulation and on real deployments up to 81 devices, the algorithm is compared to MSF, Alice, and Orchestra, the state of the art in time-slotted channel hopping scheduling. Results highlight a high reliability regarding packet reception ratio and a lower energy consumption compared to the state of the art.

Accurate Graph Filtering in Wireless Sensor Networks

Wireless sensor networks (WSNs) are considered as a major technology enabling the Internet-of-Things (IoT) paradigm. The recent emerging graph signal processing field can also contribute to enabling the IoT by providing key tools, such as graph filters (GFs), for processing the data associated with the sensor devices. GFs can be performed over WSNs in a distributed manner by means of a certain number of communication exchanges among the nodes. But, WSNs are often affected by interferences and noise, which leads to view these networks as directed, random and time-varying graph topologies. Most of the existing works neglect this problem by considering an unrealistic assumption that claims the same probability of link activation in both directions when sending a packet between two neighboring nodes. This work focuses on the problem of operating graph filtering in random asymmetric WSNs. We show first that graph filtering with finite impulse response GFs (node-invariant and node-variant) requires having equal connectivity probabilities for all the links in order to have an unbiased filtering, which cannot be achieved in practice in random WSNs. After this, we characterize the graph filtering error and present an efficient strategy to conduct graph filtering tasks over random WSNs with node-variant GFs by maximizing accuracy, that is, ensuring a small bias-variance tradeoff. In order to enforce the desired accuracy, we optimize the filter coefficients and design a cross-layer distributed scheduling algorithm (CDSA) at the MAC layer. Extensive numerical experiments are presented to show the efficiency of the proposed solution as well as the CDSA for the denoising application.