Efficient Scheduling Scheme Research Articles

Hybrid meta-heuristic techniques based efficient charging scheduling scheme for multiple Mobile wireless chargers based wireless rechargeable sensor networks

Hybrid meta-heuristic techniques based efficient charging scheduling scheme for multiple Mobile wireless chargers based wireless rechargeable sensor networks

Research and Implementation of Scheduling Strategy in Kubernetes for Computer Science Laboratory in Universities

How to design efficient scheduling strategy for different environments is a hot topic in cloud computing. In the private cloud of computer science labs in universities, there are several kinds of tasks with different resource requirements, constraints, and lifecycles such as IT infrastructure tasks, course design tasks submitted by undergraduate students, deep learning tasks and and so forth. Taking the actual needs of our laboratory as an instance, these tasks are analyzed, and scheduled respectively by different scheduling strategies. The Batch Scheduler is designed to process tasks in rush time to improve system throughput. Dynamic scheduling algorithm is proposed to tackle long-term lifecycle tasks such as deep learning tasks which are hungry for GPU resources and have dynamically changing priorities. Experiments show that the scheduling strategies proposed in this paper improve resource utilization and efficiency.

TSN Switch Queue Length Prediction Based on an Improved LSTM Network

With the high‐speed development of network technology, time‐sensitive networks (TSNs) are experiencing a phase of significant traffic growth. At the same time, they have to ensure that highly critical time‐sensitive information can be transmitted in a timely and accurate manner. In the future, TSNs will have to further improve network throughput to meet the increasing traffic demand based on the guaranteed transmission delay. Therefore, an efficient route scheduling scheme is necessary to achieve network load balance and improve network throughput. A time‐sensitive software‐defined network (TSSDN) can address the highly distributed industrial Internet network infrastructure, which cannot be accomplished by traditional industrial communication technologies, and it can achieve distributed intelligent dynamic route scheduling of the network through global network monitoring. The prerequisite for intelligent dynamic scheduling is that the queue length of future switches can be accurately predicted so that dynamic route planning for flow can be performed based on the prediction results. To address the queue length prediction problem, we propose a TSN switch queue length prediction model based on the TSSDN architecture. The prediction process has three steps: network topology dimension reduction, feature selection, and training prediction. The principal component analysis (PCA) algorithm is used to reduce the dimensionality of the network topology to eliminate unnecessary redundancy and overlap of relevant information. Feature selection requires comprehensive consideration of the influencing factors that affect the switch queue length, such as time and network topology. The training prediction is performed with the help of our enhanced long short‐term memory (LSTM) network. The input‐output structure of the network is changed based on the extracted features to improve the prediction accuracy, thus predicting the network congestion caused by bursty traffic. Finally, the results of the simulation demonstrate that our proposed TSN switch queue length prediction model based on the improved LSTM network algorithm doubles the prediction accuracy compared to the original model because it considers more influencing factors as features in the neural network for training and learning.

An improved optimization method for materials distribution based on spatiotemporal clustering in automobile assembly lines

The development of smart factories has put forward more flexible logistics needs for automobile assembly system, and efficient scheduling strategies to meet these requirements still demand prompt solution. Thus, this paper focuses on the problem of materials distribution with automated guided vehicles (AGVs) in automobile assembly lines. The mathematical model is established in the light of actual situation with mixed time windows and an improved genetic algorithm (GA) is developed. Considering the demand characteristics both in time and space, material demand points are clustered based on their spatiotemporal distance to generate the initial population. Then, selection, crossover and mutation operators of GA are also ameliorated as necessary to minimize the total travel cost. Finally, practical examples are carried out to demonstrate the effectiveness of this methodology.

A Fair and Efficient Packet Scheduling Scheme for IEEE 802.16 Broadband Wireless Access Systems

A Fair and Efficient Packet Scheduling Scheme for IEEE 802.16 Broadband Wireless Access Systems

SEIZE: Runtime Inspection for Parallel Dataflow Systems

Many Data-Intensive Scalable Computing (DISC) Systems provide easy-to-use functional APIs, and efficient scheduling and execution strategies allowing users to build concise data-parallel programs. In these systems, data transformations are concealed by exposed APIs, and intermediate execution states are masked under dataflow transitions. Consequently, many crucial features and optimizations (e.g., debugging, data provenance, runtime skew detection), which require runtime datafow states, are not well-supported. Inspired by our experience in implementing features and optimizations over DISC systems, we present SEIZE, a unified framework that enables dataflow inspection-wiretapping the data-path with listening logic-in MapReduce-style programming model. We generalize our lessons learned by providing a set of primitives defining dataflow inspection, orchestration options for different inspection granularities, and operator decomposition and dataflow punctuation strategy for dataflow intervention. We demonstrate the generality and flexibility of the approach by deploying SEIZE in both Apache Spark and Apache Flink, and by implementing a prototype runtime query optimizer for Spark. Our experiments show that, the overhead introduced by the inspection logic is most of the time negligible (less than 5 percent in Spark and 10 percent in Flink).

Delay-throughput tradeoff in satellite data relay networks with prioritized user satellites

The efficient antenna scheduling strategy for data relay satellites (DRSs) is essential to optimize the throughput or delay of the satellite data relay network. However, these two objectives conflict with each other since the user satellites (USs) with higher priorities take up more transmission time of DRSs' antennas for greater throughput but the USs storing more packets cause a severer waiting delay to the whole network. To balance the conflicting metrics for meeting the delay-throughput integrated requirements, we formulate the antenna scheduling as a stochastic non-convex fractional programming, which is challenging to be solved. For the tractability, we equivalently transform the fractional programming to a parametric problem and implement the Lyapunov drift to guarantee the constraint of mean rate stability. By proposing a delay and throughput tradeoff based antenna scheduling algorithm, we further transform the parametric problem to a solvable weight matching problem. Simulation results reveal the feasible region of the preference control parameter for integrated QoS cases and its variation relationship with network delay and throughput.

An Efficient Resource Scheduling Strategy for V2X Microservice Deployment in Edge Servers

The fast development of connected vehicles with support for various V2X (vehicle-to-everything) applications carries high demand for quality of edge services, which concerns microservice deployment and edge computing. We herein propose an efficient resource scheduling strategy to containerize microservice deployment for better performance. Firstly, we quantify three crucial factors (resource utilization, resource utilization balancing, and microservice dependencies) in resource scheduling. Then, we propose a multi-objective model to achieve equilibrium in these factors and a multiple fitness genetic algorithm (MFGA) for the balance between resource utilization, resource utilization balancing, and calling distance, where a container dynamic migration strategy in the crossover and mutation process of the algorithm is provided. The simulated results from Container-CloudSim showed the effectiveness of our MFGA.

Multi-objective Energy Aware Scheduling of Deadline Constrained Workflows in Clouds using Hybrid Approach

Cloud computing is undoubtedly one of the most significant advances in the domain of information technology. It facilitates elastic and on-demand provisioning of high performance computing capabilities employing pay-per-use model that has snowballed its adoption by scientists and engineers over the past few years. They often exploit workflows to represent their massive applications. Workflow scheduling in cloud has been devoted considerable investigation by researchers owing to its NP-complete nature of problem. Most of the previous studies targeted optimization of schedule length and execution cost within given deadlines/budget restrictions, or both. However, enormous energy consumption in the cloud data centers is not only negatively impacting the environment but also resulting in increased operational costs and thus cannot be ignored. Efficient scheduling strategies can significantly lessen the energy usage while complying with the user’s Quality of Service limitations. This research study proposes a Hybrid Approach for Energy aware scheduling of Deadline constrained workflows (HAED) using Intelligent Water Drops algorithm and Genetic Algorithm, which provides non-dominated solutions to the user. In particular, it focuses on multiple objectives i.e. reduction of schedule length, execution cost and energy usage within deadlines specified by the user. Its performance has been assessed on four scientific workflows from diverse domains using hypervolume and set coverage. The results achieved with the simulations demonstrate that the solutions produced by HAED are of better quality in terms of accuracy and diversity than non-dominated sorting genetic algorithm and hybrid particle swarm optimization.

A Graph-Based Fault-Tolerant Approach to Modeling QoS for IoT-Based Surveillance Applications

Node scheduling provides an effective way to prolong the network lifetime of Internet-of-Things (IoT) networks comprising of energy-constrained sensor nodes. Barrier scheduling is a special type of node scheduling scheme that targets IoT-based surveillance applications. An efficient barrier scheduling scheme must address the key Quality-of-Service (QoS) requirements of smart surveillance applications, such as coverage, connectivity, and energy efficiency. Moreover, such a scheme must be capable of dynamically adapting its execution strategy in the event of node failures caused due to faults arising out of unexpected battery depletion. This article proposes a fault-tolerant barrier scheduling scheme that satisfies the key QoS requirements of surveillance applications in the event of such faults. The approach is based on a novel fully weighted dynamic graph model. This article suggests two novel heuristics to guarantee fault tolerance and recovery. Extensive simulation studies are conducted to evaluate and compare the performance and effectiveness of this scheme with other such approaches.

Online EV Charge Scheduling Based on Time-of-Use Pricing and Peak Load Minimization: Properties and Efficient Algorithms

Electric vehicles (EVs) endow great potentials for future transportation systems, while efficient charge scheduling strategies are crucial for improving profits and mass adoption of EVs. Two critical and open issues concerning EV charging are how to minimize the total charging cost (Objective 1) and how to minimize the peak load (Objective 2). Although extensive efforts have been made to model EV charging problems, little information is available about model properties and efficient algorithms for dynamic charging problems. This paper aims to fill these gaps. For Objective 1, we demonstrate that the greedy-choice property applies, which means that a globally optimal solution can be achieved by making locally optimal greedy choices, whereas it does not apply to Objective 2. We propose a non-myopic charging strategy accounting for future demands to achieve global optimality for Objective 2. The problem is addressed by a heuristic algorithm combining a multi-commodity network flow model with customized bisection search algorithm in a rolling horizon framework. To expedite the solution efficiency, we derive the upper bound and lower bound in the bisection search based on the relationship between charging volume and parking time. We also explore the impact of demand levels and peak arrival ratios on the system performance. Results show that with prediction, the peak load can converge to a globally optimal solution, and that an optimal look-ahead time exists beyond which any prediction is ineffective. The proposed algorithm outperforms the state-of-the-art algorithms, and is robust to the variations of demand and peak arrival ratios.

Energy-Efficient Time-Domain Equilibrium Scheduling and Optimization Scheme for Energy Harvesting-Powered D2D Communication

Energy Harvesting- (EH-) powered Device-to-Device (D2D) Communication underlaying Cellular Network (EH-DCCN) has been deemed as one of the basic building blocks of Internet of Things due to its green energy efficiency and adjacent communication. But available energy will be one of the biggest obstacles when implementing EH-DCCN due to the immaturity of EH technology and the volatility of environmental energy resources. To improve energy utilization, this study investigates an efficient scheduling and power allocation scheme about transmission load equilibrium in the time domain. Accordingly, a short-term Sum Energy Efficiency (stSEE) maximization problem for EH-powered D2D communication is modelled, while ensuring a fundamental transmission rate requirement of cellular users. Consequently, the optimization problem is a nonconvex mixed integer nonlinear programming problem. Thus, we propose a two-layer convex approximation iteration algorithm which can obtain a feasible quasioptimal solution for the stSEE problem. Simultaneously, a two-step heuristic algorithm in a slot-by-slot fashion is also developed to acquire a suboptimal solution without requiring statistical knowledge of channel and energy arrival processes. Simulated analysis indicates that the short-term scheduling strategy can obtain better performances in terms of energy efficiency and transmission rate than conventional real-time scheduling scheme. Besides, the maximum scheduled number of EH-D2D pairs underlaying one cellular user under different EH efficiency is analysed, which can give us a theoretical reference about the deployment of future EH-DCCN.

Distributed Stable Global Broadcasting for SINR-Based Multi-Channel Wireless Multi-Hop Networks

Recently, the problem of Stable Global Broadcasting (SGB) with continuous packet injections at a source node has attracted considerable attention and much work has been carried out. However, existing SGB algorithms are all centralized and under the graph-based interference model. How to achieve efficient distributed SGB under the more realistic Signal-to-Interference-plus-Noise-Ratio (SINR) interference model is still an open issue. In this paper, we focus on the design of distributed SGB algorithms for multi-channel wireless multi-hop networks under the SINR model. We first present an efficient Backbone-based Multi-channel Concurrent Scheduling (BMCS) strategy and prove an upper bound of $1/2$ packets/slot for the broadcast capacity (i.e., the maximum supportable packet injection rate for all possible SGB algorithms). By iterating the BMCS strategy in different ways, we present two distributed SGB algorithms, one for deterministic packet injection model corresponding to the broadcast capacity (called SGB-DPI) and another for stochastic packet injection model (called SGB-SPI). We prove that: 1) both SGB-DPI and SGB-SPI meet the queue stability and latency stability constraints for providing stable global broadcasting services, and 2) SGB-DPI is throughput-optimal. We evaluate the performance of our proposed algorithms through simulations. The simulation results validate our theoretical analysis and also show that even under the stochastic packet injection model, SGB-DPI has a comparable and even better throughput performance than SGB-SPI.

Energy-Efficient Resource Provisioning Strategy for Reduced Power Consumption in Edge Computing

Edge computing is an emerging paradigm that settles some servers on the near-user side and allows some real-time requests from users to be directly returned to the user after being processed by these servers settled on the near-user side. In this paper, we focus on saving the energy of the system to provide an efficient scheduling strategy in edge computing. Our objective is to reduce the power consumption for the providers of the edge nodes while meeting the resources and delay constraints. We propose a two-stage scheduling strategy which includes the scheduling and resource provisioning. In the scheduling stage, we first propose an efficient scheme based on the branch and bound method. In order to reduce complexity, we propose a heuristic algorithm that guarantees users’ deadlines. In the resource provisioning stage, we first approach the problem by virtualizing the edge nodes into master and slave nodes based on the sleep power consumption mode. After that, we propose a scheduling strategy through balancing the resources of virtual nodes that reduce the power consumption and guarantees the user’s delay as well. We use iFogSim to simulate our strategy. The simulation results show that our strategy can effectively reduce the power consumption of the edge system. In the test of idle tasks, the highest energy consumption was 27.9% lower than the original algorithm.

An Energy Efficient Adaptive Scheduling Scheme (EASS) for Mesh Grid Wireless Sensor Networks

The technology-driven shift in distributed network systems, Internet technology and energy grid digitization have changed the traditional spoke-hub distribution models to the more flexible and giant network-to-networks paradigm known as Energy Cloud. This energy cloud paradigm has the ability to adapt to the large-scale distributed energy resources with dynamic demand–response associated with smart grid and smart homes applications, which allows customers/users to have connectivity of electrical devices with supply grid into one giant energy cloud network. If efficient policies are not adapted in the design and operations of such networks, sensor devices may sense and forward redundant data packets. Hence, valuable energy of the node will be depleted quickly and as a result; the network will be down before accomplishing its intended task. To address the issue of energy-efficiency in energy-cloud at embedded networks, this paper proposes a novel scheme, “Energy Efficient Adaptive Scheduling Scheme (EASS) for Mesh Grid Wireless Sensor Networks”. In EASS, a sensor node configures and schedules its functions/roles according to the contents of sensed data packets and frequency of generated traffic. Like energy cloud, tasks are uniformly distributed on all nodes in mesh-grid in four states – each state of a node is responsible for configuring its components on a specific energy level – which aims to avoid link disconnection and vanish redundant data packets generation. Simulation results show that EASS increases energy-efficiency by 50% due to the four-states model, 62.5% due to alive nodes, 92.60% due to minimized Cluster-Heads overhead and 38.11% due to the reduction in dead nodes ratio.

Secure and Optimized Cloud-Based Cyber-Physical Systems with Memory-Aware Scheduling Scheme

Computer based algorithms are used for monitoring and controlling a computers where human, physical, digital and analog interaction occurs. This scheme termed as cyber-physical systems (CPS) is being deployed in a wide array of applications due to its easy deployment feature and connectivity. CPS based on cloud computing technology offers wider application range with enormous amount of storage and computing resources. However, despite the wide application and deployment of CPS in combining the key technologies like big data analytics, cloud computing and IoT, its energy consumption is large. Optimization of this energy is a major field of research and is of due importance. For this purpose, in cloud environment, virtual machines (VMs) are used for hosting the applications and the resources are managed thereby optimizing the energy consumption. The security issues and the quality of service (QoS) requirements of the system is also met by optimizing the system design. The scheduling issue of the system is addressed by implementation of efficient memory-aware scheduling strategy and algorithms. The proposed technique is tested for performance and the results of simulation is presented.

Congestion-Balanced and Welfare-Maximized Charging Strategies for Electric Vehicles

With the increase of the number of electric vehicles (EVs), it is of vital importance to develop the efficient and effective charging scheduling schemes for all the EVs. In this article, we aim to maximize the social welfare of all the EVs, charging stations (CSs) and power plant (PP), by taking into account the changing demand of each EV, the changing price, the capacity and the congestion balance between different CSs. To this end, two efficient scheduling algorithms, i.e., Centralized Charging Strategy (CCS) and Distributed Charging Strategy (DCS) are proposed. CCS has a slightly better performance than the DCS, as it takes all the information and make the decision in the central control unit. On the other hand, DCS dose not require the private information from EVs and can make decentralized decision. Extensive simulation are conducted to verify the effectiveness of the proposed algorithms, in terms of the performance, congestion balance, and computing complexity.

Personality- and Value-Aware Scheduling of User Requests in Cloud for Profit Maximization

The main goal of a cloud provider is to make profits by providing services to users. Existing profit optimization strategies employ homogeneous user models in which user personality is ignored, resulting in fewer profits and particularly notably lower user satisfaction that in turn, leads to fewer users and reduced profits. In this article, we propose efficient personality-aware request scheduling schemes to maximize the profit of the cloud provider under the constraint of user satisfaction. Specifically, we first model the service requests at the granularity of individual personality and propose a personalized user satisfaction prediction model based on questionnaires. Subsequently, we design a personality-guided integer linear programming (ILP)-based request scheduling algorithm to maximize the profit under the constraint of user satisfaction, which is followed by an approximate but lightweight value assessment and cross entropy (VACE)-based profit improvement scheme. The VACE-based scheme is especially tailored for applications with high scheduling resolution. Extensive simulation results show that our satisfaction prediction model can achieve the accuracy of up to 83 percent, and our profit optimization schemes can improve the profit by at least 3.96 percent as compared to the benchmarking methods while still obtaining a speedup of at least 1.68x.

A QoE-Aware Service-Enhancement Strategy for Edge Artificial Intelligence Applications

Due to the high complexity of artificial intelligence (AI) algorithms, performing the AI tasks on the resource-limited Internet-of-Things (IoT) devices has been proved to be inadvisable. Edge computing provides an effective computing paradigm for executing AI tasks, where large numbers of AI tasks can be offloaded to the edge servers. Most of the existing works focus on achieving efficient computing offload through improving the Quality of Service (QoS), such as reducing the average server-side delay. However, we show that those efforts are inefficient due to the heterogeneous impact of delays on users’ Quality of Experience (QoE). Inspired by the observations, in this article, we reconsider the scheduling method from an orthometric perspective, i.e., improving the QoE by designing a QoE-aware service-enhancement strategy for edge AI applications. Besides, multiple AI algorithms are utilized in our service model to execute the same type of tasks concurrently, thus meeting users’ heterogeneity requirements of accuracy and delays. Specifically, for the online arriving AI tasks, we optimize the task allocation and scheduling strategy according to the QoE sensitivity of each task. The model can be formulated as the mixed-integer nonlinear programming problem, which is known to be NP-hard. Hence, we then propose an efficient two-phase scheduling strategy for this problem. The results of comprehensive emulations validate that our model can effectively improve the average QoE of users and achieve a higher task completion ratio.

A Hybrid Fault-Tolerant Scheduling for Deadline-Constrained Tasks in Cloud Systems

Among multiple fault-tolerant strategies, resubmission, and replication are fundamental and widely recognized in distributed computing systems. In recent years, many algorithms based on replication or resubmission have been proposed. However, few of them consider these two techniques together, especially in Cloud systems. In this article, we propose a Hybrid Fault-Tolerant Scheduling Algorithm (HFTSA) for independent tasks with deadlines by integrating the above techniques in virtualized Cloud systems. During the task scheduling process, HFTSA selects fault-tolerant strategies from resubmission and replication for each accepted task based on the characteristics of both task and Cloud resources and then reserves suitable resources. During the task execution process, HFTSA adopts an online adjustment scheme for fault-tolerant strategies of some tasks if necessary while providing an online scheduling scheme for faults. Moreover, an elastic resource provisioning mechanism is designed and incorporated into HFTSA to dynamically adjust the provided resources to improve resource utilization. Experiments on a real cloud platform and a simulated platform are conducted to verify the effectiveness of the proposed HFTSA. The results demonstrate that HFTSA can provide an efficient fault-tolerant scheduling strategy for deadline-constrained tasks with high resource utilization and performs better than corresponding competitors.