Dynamic Scheduling Scheme Research Articles

A genetic algorithm-based tasks scheduling in multicore processors considering energy consumption

Energy consumption has been always an important issue in multicore processors which are getting more and more popular in embedded systems. In this paper, we propose an energy-aware task scheduling approach taking advantages of heuristic algorithms based on genetic algorithm. The proposed approach includes both static and dynamic scheduling schemes. The task scheduling is modelled as a genetic algorithm problem which is mainly used when the tasks are ready before run-time; i.e., static task scheduling. The tasks which arrive after beginning task execution are dynamically scheduled using a proposed heuristic algorithm in combination with the genetic algorithm. The experimental results show that the proposed algorithm achieves more energy efficiency in both static and dynamic task scheduling for multicore processors as compared with similar energy-aware scheduling methods.

Dynamic Network Access Scheduling and Stabilization of TPCS Networked Control System

The Networked Control Systems (NCS) is a significant arena in the current complex technical scenario. NCS provides the user a wide range of scope in mitigation of its underlying issues in order to benefit the versatility of the network. Two Inverted Pendulums Coupled by a Spring (TPCS) system is one of the finest platforms to study NCS and its issues. The objective considered here is to stabilize both the pendulums at desired vertical position while the “Medium Access Constraint” of NCS is addressed. The proposed Dynamic Medium Access Scheduling strategy aims at analyzing the issue so that it can be applied in practical scenarios. In this paper (i) TPCS is modelled as a switched system (ii) Dynamic Medium Access Scheduling protocols are discussed, selected and applied to TPCS and (iii) the best of the two: Weighted Fastest Decay and Guaranteed Dwell-Time methods to give a design solution is summarized. This is aided with computer simulations and the results show that the scheduling polices are effective in controlling TPCS-NCS.

Application Conditions of Bounded Rationality and a Microgrid Energy Management Control Strategy Combining Real-Time Power Price and Demand-Side Response

Microgrid energy management is a typical discrete non-linear optimization problem that is usually solved by off-line optimization, day-ahead demand-side management, and long-term dynamic optimization scheduling strategy. However, due to the intermittent distributed generation and time-varying load in microgrids, more attention should be paid to the real-time optimal scheduling of the overall operation of energy to ensure the dynamic balance of supply and demand in microgrids. Combining demand-side response with real-time power price, this paper applies the strategy to microgrid energy management and proposes a distributed energy real-time management model of microgrid based on demand-side response function. A deep adaptive dynamic programming optimization algorithm is also proposed for the model. The real-time interaction between microgrid operators and users is realized. The closed-loop feedback control structure of the proposed model ensures the real-time optimization control strategy. Therefore, the proposed energy management model and control strategy can realize intra-day dispatching in microgrids. The real-time performance and effectiveness of the proposed energy management model and control strategy are also verified by numerical simulation. Finally, since the proposed model is approximate, whether the solution obtained by the algorithm is the optimal or satisfactory solution of the optimization strategy set is a lack of theoretical support. Therefore, according to the approximation theorem of bounded rationality, the application conditions of the model in power markets are proposed. It is proved that the proposed model meets the application conditions, and is a specific application of bounded rationality approaching complete rationality in the power market. It is also proved that the best solution is involved in the satisfactory solution set of the model. Thus, the control strategy is a rational and feasible optimal management control strategy, which provides a theoretical basis for its further implementation.

AggreFlow: Achieving Power Efficiency, Load Balancing, and Quality of Service in Data Center Networks

Power-efficient Data Center Networks (DCNs) have been proposed to save power of DCNs using OpenFlow. In these DCNs, the OpenFlow controller adaptively turns on/off links and OpenFlow switches to form a minimum-power subnet that satisfies the traffic demand. As the subnet changes, flows are dynamically routed and rerouted to the routes composed of active switches and links. However, existing flow scheduling schemes could cause undesired results: (1) power inefficiency: due to unbalanced traffic allocation on active routes, extra switches and links may be activated to cater to bursty traffic surges on congested routes, and (2) Quality of Service (QoS) fluctuation: because of the limited flow entry processing ability, switches may not be able to timely install/delete/update flow entries to properly route/reroute flows. In this paper, we propose AggreFlow, a dynamic flow scheduling scheme that achieves power efficiency and QoS improvement using three techniques: Flow-set Routing, Lazy Rerouting, and Adaptive Rerouting. Flow-set Routing achieves load balancing with a small number of flow entry operations by routing flows in a coarse-grained flow-set fashion. Lazy Rerouting spreads rerouting operations over a relatively long period of time, reducing the burstiness of entry operation on switches. Adaptive Rerouting selectively reroutes flow-sets to maintain load balancing. We built an NS3 based fat-tree network simulation platform to evaluate AggreFlow's performance. The simulation results show that AggreFlow reduces power consumption by about 18%, yet achieving load balancing and improved QoS (low packet loss rate and reducing the number of processing entries for flow scheduling by 98%), compared with baseline schemes.

Research on DCN Traffic Scheduling Based on Private Cloud Platform

In the virtual switching of multi-tenant network under private cloud, the traffic load in peak period is too large to ensure the balance and bandwidth quality of service, while in the low period, the traffic load is too low to waste computing resources. Based on the current SDN, using Openflow to send traffic table to the data plane to realize dispatching and classifying the data flow according to the granularity of the data flow, and considering the factors such as QoS and resource utilization. This paper studies the hybrid scheduling strategy and priority based dynamic scheduling strategy to ensure the load balance and service quality in the peak period, in the low period, the utilization and throughput of computing resources are maintained.

Spectrum Management Schemes for Internet of Remote Things (IoRT) Devices in 5G Networks via GEO Satellite

The rapid growth of not just mobile devices but also Internet of Things (IoT) devices has introduced a new paradigm in mobile networks. This evolution and the continuous need to provide spectrum efficient, high data rates, low latency, and low energy consumption radio access networks have led to the emergence of fifth generation (5G) networks. Due to technical and economical limitations, the satellite air interface is expected to complement the 5G terrestrial air interface in the provision of 5G services including IoT communications. More importantly, it is on this premise that the 5G satellite air interface is expected to provide network access to IoT devices in rural and remote areas termed Internet of Remote Things (IoRT). While this remains an interesting solution, several radio resource management issues exist. One of them, spectrum management, in the 5G satellite as it affects IoRT communications, remains unclear. Hence, the aim of this paper is to investigate and recommend the spectrum management scheme that will be most suitable not only for Human-to-Human communications but also Machine-to-Machine communications in 5G satellite networks. In order to conduct this investigation, a new dynamic scheduling scheme that will be suitable for such a scenario is proposed in this paper. The investigation is conducted through simulations, using throughput, delay, spectral efficiency, and fairness index as the performance metrics.

Digital-Twin-Based Job Shop Scheduling Toward Smart Manufacturing

Job shop scheduling always plays an important role in the manufacturing process and is one of the decisive factors influencing manufacturing efficiency. In the actual process of production scheduling, there exist some uncertain events, information asymmetry, and abnormal disturbance, which would cause the execution deviation and affect the efficiency and quality of scheduling execution. Traditional scheduling methods are not sufficient to solve the challenges well. Due to the rise of digital twin, which has the characters of virtual reality interaction, real-time mapping, and symbiotic evolution, a new job shop scheduling method based on digital twin is proposed to reduce the scheduling deviation. In this article, the architecture and working principle of the new job shop scheduling mode are introduced. Then, scheduling resource parameter updating methods and dynamic interactive scheduling strategies are proposed to achieve real-time and precise scheduling. Finally, a prototype system is designed to verify the validity of this new job shop scheduling mode.

Dynamic scheduling strategy for bicycle-sharing in cities

Dynamic scheduling strategy for bicycle-sharing in cities

A Study of Dynamic Scheduling Strategy of Intelligent Machining System

Rail Guided Vehicle (RGV) is used in the field of automated logistics system and automated warehouse. It has the characteristics of high speed, high reliability and low cost. The combination of RGV and Computer Number Controller (CNC) can greatly improve production efficiency. This paper designs an optimal dynamic scheduling strategy and a shortest walking path of RGV for an intelligent machining system composed of one RGV and eight CNC. Firstly, based on the shortest path algorithm and the principle of "nearest priority", this paper designs three optimal RGV dynamic scheduling models to adapt the three different cases. In these models, the optimization objective is the total number of clinkers produced by eight CNC, the decision variable is the number of clinkers produced by each CNC, and the constraints are determined by the time when each CNC issues instructions. Secondly, according to the different properties of the three models, the corresponding solutions have been given. Meanwhile, in order to describe the RGV scheduling strategy and the operational efficiency of the system, this paper uses the cost-time and average time spent for generating a clinker to measure the operational efficiency of the system. If their ratio were closer to 1, the work efficiency is higher. If the ratio were closer to 0, the work efficiency is lower. Finally, in order to test the practicability and validity about the three models, we import the known data into the three models to find how to manage the CNC and when to carry out the downloading and uploading operations by MATLAB simulation. The practicability of the optimized dynamic scheduling model and the corresponding calculation are verified.

An Effective Method for Dynamic Global Optimal Scheduling of RGV Based on Probability Function

In this study, an effective dynamic scheduling strategy based on quasi-global optimizing search method is designed to plan the working sequence of RGV. By using ‘0-1’ variable, the working state of CNC is simplified and the whole processing procedure could be simulated by Matlab. Also, probability function is applied to decide the scheduling sequences, thus avoiding local optimal solutions. Then, the dynamic global optimizing scheduling strategy is completed. Besides, the efficiency of the whole system is calculated and the corresponding results are compared with local optimizing scheduling. This dynamic scheduling strategy could improve the efficiency of the whole system during the processing materials.

Dynamic Schedule Method Based on Rolling Horizon Procedure for a Discrete Manufacturing Shop with Uncertain Processing Time

To solve the rescheduling problem of discrete manufacturing shop with uncertain processing time, an improved ant algorithm based on the rolling horizon procedure is proposed. The dynamic scheduling strategy is based on a hybrid scrolling mechanism driven by events and cycles. Analysing disturbances in the process, we divide them into explicit and implicit disturbances. The length deviation tolerance (LDT) of processing time is designed and proposed to filter out redundant rescheduling. The dynamic scheduling algorithm, based on improved ant colony algorithms, is considered with the resource constraint of the shop, and with the use of a customized state transition rule, it helps to overcome the drawbacks of long ant path searching prone to stagnation. Using simulation, the performance of the dynamic scheduling strategy and scheduling algorithm are analysed and verified, and better rolling scheduling policy parameters are obtained.

A Study of Dynamic Scheduling Strategy of Intelligent Machining System

A Study of Dynamic Scheduling Strategy of Intelligent Machining System

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

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

ProMo: A Probabilistic Model for Dynamic Load-Balanced Scheduling of Data Flows in Cloud Systems

An important issue in cloud computing is the balanced flow of big data centers, which usually transfer huge amounts of data. Thus, it is crucial to achieve dynamic, load-balanced data flow distributions that can take into account the possible change of states in the network. A number of scheduling techniques for achieving load balancing have therefore been proposed. To the best of my knowledge, there is no tool that can be used independently for different algorithms, in order to model the proposed system (network topology, linking and scheduling algorithm) and use its own probability-based parameters to test it for good balancing and scheduling performance. In this paper, a new, Probabilistic Model (ProMo) for data flows is proposed, which can be used independently with a number of techniques to test the most important parameters that determine good load balancing and scheduling performance in the network. In this work, ProMo is only used for testing with two well-known dynamic data flow scheduling schemes, and the experimental results verify the fact that it is indeed suitable for testing the performance of load-balanced scheduling algorithms.

A real-time decentralized algorithm for task scheduling in multi-agent system with continuous damage

In this paper, a common model of task scheduling problems in agent rescue scenario is proposed, in which tasks with continuous dynamic damage are introduced to capture the emerging applications of using rescue robots and other resources to enhance human disaster rescue capability. Beyond this, we mainly focus on finding the optimal task scheduling strategy. We design a heuristic algorithm based on greedy strategy to obtain the optimal dynamic scheduling strategy of agents. Compared with solving global integer programming directly, the computational time is greatly reduced. The proof of the greedy strategy’s validity is also demonstrated under some specific damage functions. By comparing with the two strategies commonly used in real life, it is proved that our strategy is optimal. For practical application, we design an automatic negotiation framework, which realizes the real-time decentralized automated negotiation of agents. Then, using Game Description Language (GDL) as a tool, an automated negotiation algorithm is implemented, which enables agents to adjust the plan dispersedly. Experiments show that the algorithm is more efficient than the centralized algorithm in the case of limited communication.

RGV Dynamic Scheduling Strategy Based on Network Cellular Automaton and Marko Model

In an intelligent processing system, if a Rail Guide Vehicle (RGV), also known as automated guide vehicle, is provided with the optimal dynamic scheduling strategy, the work efficiency and economic profit of the workshop can be greatly improved. This paper considers the variable number as well as the types of Computer Number Controller (CNC) and regards the real problem as the Job Shop Scheduling Problem (JSSP). To solve JSSP, this paper use Network Cellular Automaton (NCA) to abstract the flexible workshop system and apply the rule of one-step trial-and-error for the core link between the network cellular automaton and the Markov Transition Probability Matrix. Further this system is simulated using Markov chain in probabilities to expand the solution space with the optimal solution among according to the Monte Carlo experimental principle. Finally, the result can jump out of the local optimal solution and approximate the global optimal dynamic scheduling strategy by trial-and-error rule and Transition Probability Matrix. To verify the NCA-Markov model, this paper use an intelligent flexible workshop which produces two-process materials. By simulating this system with the model, the efficiency of the intelligent processing system can be greatly improved.

Scheduling strategy for computational-intensive data flow in generalized cluster environments

A classical problem in the field of distributed computation and parallel processing concerns reasonable allocation of resources among computational-intensive data flows. We introduce the generalized cluster in this paper for processing large-scale scientific computations and to further explore a consecutive cooperation game-based dynamic scheduling strategy. We construct an abstract generalized cluster environment and summarize the types of data flows. We then convert the multi-objective scheduling problem into a multi-objective expectation function-based continuous cooperation game model and discuss its strategy and a solution for its kernel. We also propose a dynamic scheduling mechanism to address the instability of generalized clusters to ensure a reasonable, real-time adjusting allocation scheme by monitoring and compensating appropriately. Finally, we apply our method to a real world application to demonstrate our successful scheduling strategy that achieves superior results for overall cost, cost-performance index, and mean run time when compared with other methods.

A fault‐tolerant dynamic scheduling method on hierarchical mobile edge cloud computing

AbstractMobile edge cloud computing has been a promising computing paradigm, where mobile users could offload their application workloads to low‐latency local edge cloud resources. However, compared with remote public cloud resources, conventional local edge cloud resources are limited in computation capacity, especially when serve large number of mobile applications. To deal with this problem, we present a hierarchical edge cloud architecture to integrate the local edge clouds and public clouds so as to improve the performance and scalability of scheduling problem for mobile applications. Besides, to achieve a trade‐off between the cost and system delay, a fault‐tolerant dynamic resource scheduling method is proposed to address the scheduling problem in mobile edge cloud computing. The optimization problem could be formulated to minimize the application cost with the user‐defined deadline satisfied. Specifically, firstly, a game‐theoretic scheduling mechanism is adopted for resource provisioning and scheduling for multiprovider mobile applications. Then, a mobility‐aware dynamic scheduling strategy is presented to update the scheduling with the consideration of mobility of mobile users. Moreover, a failure recovery mechanism is proposed to deal with the uncertainties during the execution of mobile applications. Finally, experiments are designed and conducted to validate the effectiveness of our proposal. The experimental results show that our method could achieve a trade‐off between the cost and system delay.

Data-driven dynamic resource scheduling for network slicing: A Deep reinforcement learning approach

Network slicing is designed to support a variety of emerging applications with diverse performance and flexibility requirements, by dividing the physical network into multiple logical networks. These applications along with a massive number of mobile phones produce large amounts of data, bringing tremendous challenges for network slicing performance. From another perspective, this huge amount of data also offers a new opportunity for the management of network slicing resources. Leveraging the knowledge and insights retrieved from the data, we develop a novel Machine Learning-based scheme for dynamic resource scheduling for networks slicing, aiming to achieve automatic and efficient resource optimisation and End-to-End (E2E) service reliability. However, it is difficult to obtain the user-related data, which is crucial to understand the user behaviour and requests, due to the privacy issue. Therefore, Deep Reinforcement Learning (DRL) is leveraged to extract knowledge from experience by interacting with the network and enable dynamic adjustment of the resources allocated to various slices in order to maximise the resource utilisation while guaranteeing the Quality-of-Service (QoS). The experiment results demonstrate that the proposed resource scheduling scheme can dynamically allocate resources for multiple slices and meet the corresponding QoS requirements.

Event-Driven Sensor Scheduling for Mission-Critical Control Applications

In this paper, we consider a mission-critical control system, where an unstable dynamic plant is monitored by a number of distributed sensors connected to the controller over the wireless fading channels. We focus on the dynamic sensor scheduling to stabilize the unstable dynamic plant. The dynamic sensor scheduling is modeled as a non-convex drift-plus-penalty minimization problem. To improve the scheduling efficiency, the proposed scheme adapts to both the fading channel state as well as the dynamic plant state. To overcome the non-convexity in the minimization problem, we propose a novel transformation technique for the scheduling variables and the objective function (using the Lyapunov drift). Based on that, we can derive a low complexity dynamic sensor scheduling scheme and also obtain a closed-form stability analysis (despite the non-convexity) of the mission-critical control system via a randomized state-independent policy. Compared with various baselines, the proposed scheme has higher power efficiency and superior scalability performance.