Proposed Scheduling Scheme Research Articles

Dynamic scheduling of bags-of-tasks with sensitive input data and end-to-end deadlines in a hybrid cloud

As organizations with existing on-premise infrastructure investments shift to the hybrid cloud computing paradigm, it is imperative to address the various challenges involved. One of the most important issues is the utilization of novel workload scheduling heuristics in order to effectively harness the security provided by the private cloud and the virtually unlimited resources of the public cloud. In this paper, we propose heuristics for the scheduling of real-time bag-of-tasks jobs that arrive dynamically at a hybrid cloud. The proposed scheduling strategies take into account the end-to-end deadlines of the jobs, as well as the monetary cost required for the utilization of the complementary public cloud resources. Furthermore, they take into consideration that some of the component tasks of the jobs may require input data that are sensitive and thus should not be transferred to the public cloud. The performance of the proposed heuristics is evaluated by simulation. For comparison purposes, two widely used baseline scheduling policies are also examined. In the simulation experiments, we consider jobs with either tight or loose deadlines and with different probabilities that the input data of their component tasks are sensitive.

Resilient Distributed Coordination of Plug-In Electric Vehicles Charging under Cyber-Attack

The coordinated scheduling of plug-in electric vehicle (PEV) charging should be constructed in distributed architecture due to the growing population of PEVs. Since the information and communication technology makes the adversary more permeable, the distributed PEV charging coordination is vulnerable to cyber-attack which may degrade the performance of scheduling and even cause the failure of scheduler task. Considering the tradeoff between system-wide economic efficiency, distribution level limitations and PEV battery degration, this paper investigates the resilient distributed coordination of PEV charging to resist cyber-attack, where the steps of detection, isolation, updating and recovery are designed synthetically. Under the proposed scheduling scheme, the misbehaving PEVs suffering from cyber-attack are gradually marginalized and finally isolated, and the remaining well-behaving PEVs obtain their own optimal charging strategy to minimize the total system cost in distributed architecture. The simulation results verify the effectiveness of theoretical method.

Physical Layer Security for Multiuser Satellite Communication Systems With Threshold-Based Scheduling Scheme

Satellite communication (SatCom) has attracted much attention due to its inherent characteristics. Security issues have gained severe concerns in SatCom since it is susceptible to be illegally eavesdropped by malicious ground stations within large-scale wireless coverage. In this paper, we investigate the physical layer security of a multiuser SatCom system in the presence of multiple eavesdroppers. Particularly, we propose a threshold-based scheduling scheme, where the geographically clustered eavesdroppers with both the colluded and collaborated eavesdropping scenarios are assumed. Specifically, closed-form expression for the secrecy outage probability (SOP) is derived for the passive eavesdropping scenario when the channel state information (CSI) of the eavesdroppers is unavailable. Moreover, we obtain a closed-form expression for the average secrecy capacity (ASC) of the considered system under the proposed user scheduling scheme. In order to get further insights of the proposed scheduling scheme at high signal-to-noise ratios (SNRs), the asymptotic analysis for the SOP and ASC is also demonstrated. Moreover, the reduced percentage with respect to number of user examination is also given, which validates the simplicity and efficiency of our proposed scheme compared to the traditional approaches. Numerical results deduce that with the proposed scheme, a comparable system performance with regard to the maximal selection (MS) scheme can be achieved.

Completion-Time-Driven Scheduling for Uplink NOMA-Enabled Wireless Networks

Efficient scheduling policy is crucial in wireless networks due to delay-sensitivity of many emerging applications. In this work, we consider a joint user pairing and scheduling (UPaS) scheme for multi-carrier non-orthogonal multiple access (MC-NOMA)-enabled wireless networks to reduce the maximum completion time of serving uplink users. The NOMA scheduling problem is shown to be NP-hard and a shortest processing time (SPT)-based strategy to solve the same problem within affordable time and complexity is introduced. The simulation results confirm the efficacy of the proposed scheduling scheme in terms of the maximum completion time in comparison with orthogonal multiple access (OMA) and random NOMA pairing.

Medium Access-Based Scheduling Scheme for Cyber Physical Systems in 5G Networks

The development of the 5G mobile communication standard attempts to meet the future needs of data users. The impact of Cyber Physical Systems (CPS) is crucial in Internet of Things (IoT) and other emerging technologies. The design of medium access mechanisms for CPS such as radio resource scheduling schemes has a significant effect on network performance. Recent literature shows that limited work is available on uplink scheduling schemes, particularly in the 5G domain. Planning a network that can address the modern needs of users entails efficient CPS scheduling mechanisms such that resources are amicably distributed between users of contrasting priorities. The prime focus of this work is to design and develop an uplink radio resource scheduling framework for CPS-based future networks such as 5G. In the designed framework, scarce radio resources are sought to be distributed efficiently according to the service-based needs of users. The proposed scheduling scheme is a service aware (SA) scheduler designed for CPS in accordance with the 5G network peculiarities, intended to achieve higher throughput and reduced latency. The proposed SA scheduler supports multi-bearer traffic and is capable of providing resources in adverse channel conditions in an efficient manner. The SA scheduling mechanism’s performance is evaluated and compared with renowned scheduling algorithms such as blind equal throughput (BET), maximum throughput (MT), and proportional fair (PF) scheduling schemes. The simulation results obtained in a cellular environment demonstrate that the SA scheduler achieves acceptable cell throughput and end-to-end delay results in all scenarios and out-performs other contemporary scheduling schemes.

Locality-Aware Scheduling for Containers in Cloud Computing

The state-of-the-art scheduler of containerized cloud services considers load balance as the only criterion; many other important properties, including application performance, are overlooked. In the era of Big Data, however, applications evolve to be increasingly more data-intensive thus perform poorly when deployed on containerized cloud services. To that end, this paper aims to improve today's cloud service by taking application performance into account for the next-generation container schedulers. More specifically, in this work we build and analyze a new model that respects both load balance and application performance. Unlike prior studies, our model abstracts the dilemma between load balance and application performance into a unified optimization problem and then employs a statistical method to efficiently solve it. The most challenging part is that some sub-problems are extremely complex (for example, NP-hard), and heuristic algorithms have to be devised. Last but not least, we implement a system prototype of the proposed scheduling strategy for containerized cloud services. Experimental results show that our system can significantly boost application performance while preserving high load balance.

Fairness-Based Multiuser Scheduling for Ambient Backscatter Communication Systems

This letter investigates and analyzes multiuser scheduling for ambient backscatter (AmBack) communication systems by taking the fairness issue into account. In contrast to traditional absolute signal-to-noise ratio (SNR)-based scheduling, proportional fairness (PF)-based scheduling is proposed to achieve the PF among AmBack users. Since it is not amendable to a closed-form expression for the outage performance, the asymptotic performance is derived to analyze the multiuser diversity order. For comparison, the cumulative distribution function (CDF)-based scheduling is also considered, where the fair channel access ratio (CAR) for AmBack users can be guaranteed. Simulation results confirm the correctness of analytical results, and demonstrate the effectiveness of the proposed scheduling schemes.

Energy-efficient task scheduling and physiological assessment in disaster management using UAV-assisted networks

Internet of Things (IoT) and unmanned aerial vehicles (UAVs) together can significantly enhance the performance of disaster management systems. UAVs can collect massive heterogeneous data from disaster-affected areas using fifth-generation (5G)/beyond 5G networks and this data can be analyzed to get the information required by the first responders such as marking the boundary of the affected area, identifying the infrastructure damaged and the roads blocked, and the health situation of people living in that area. This paper presents an overview of different platforms (UAVs-based, IoT-based, and IoT, coupled with UAVs) for disaster management. We propose an energy-efficient task scheduling scheme for data collection by UAVs from the ground IoT network. The focus is to optimize the path taken by the UAVs to minimize energy consumption. We also analyze the vital signs data collected by UAVs for people in disaster-affected areas and apply the decision tree classification algorithm to determine their health risk status. The risk status will enable the first responders to decide the areas which need the most immediate help Simulation results compare the effectiveness of our proposed scheduling scheme with the traditional approach used for data collection. Also, we present the results of our predicted risk status compared with the risk status calculated through the National Early Warning Score 2 (NEWS2) method.

Dynamic task scheduling with advance reservation of resources to minimize turnaround time for computational grid

It is a vital necessity of a heterogeneous environment to schedule the resources efficiently among applications to optimize the execution time to provide well-regulated services to the users. The system is said to be effective only if it supplies the resources on time and assures smooth task execution. This paper introduces a dynamic task scheduling with advance reservation (DTSAR) of resources to optimize turnaround time (TAT). A task will be dynamically allocated to the resource in an advance manner that takes meanest execution time. More parameters are considered for performance comparison viz. flow time, average utilization and processing cost. Simulation results acknowledge the preferable achievements of the proposed scheduling schemes on the comparison of another state of art in literature. The experimental assessments and obtained results show that DTSAR is preferable than other considered heuristics in terms of TAT and other QoS parameters.

On the Performance of Multihop Cognitive Wireless Powered D2D Communications in WSNs

This paper studies the performance of multi-hop cognitive wireless powered device-to-device (D2D) communications in wireless sensor networks (WSNs). In our analysis, each sensor node harvests energy from multiple dedicated power beacons and shares the spectrum resources with multiple primary receivers (PRs) using underlay cognitive radio. Additionally, we consider a practical scenario of cognitive wireless powered D2D communications in WSNs, where the knowledge of interference channels is assumed to be imperfect. To improve the network performance, we propose two user scheduling schemes, namely dual-hop scheduling (DHS) and best-path scheduling (BPS) schemes. We then investigate the performance of the proposed scheduling schemes in terms of outage probability and outage floor. Through numerical results, we show that BPS scheme significantly outperforms DHS scheme, which in turns outperforms the state-of-the-art solution. Moreover, the advantages and drawbacks of each scheme are analyzed and discussed comprehensively. We also point out that the inaccurate knowledge of the interference channels significantly affects any performance metric of multi-hop cognitive D2D communications in WSNs such as outage probability, outage floor, and performance loss.

A Three-Stage Coordinated Optimization Scheduling Strategy for a CCHP Microgrid Energy Management System

With renewable generation resources and multiple load demands increasing, the combined cooling, heating, and power (CCHP) microgrid energy management system has attracted much attention due to its high efficiency and low emissions. In order to realize the integration of substation resources and solve the problems of inaccurate, random, volatile and intermittent load forecasting, we propose a three-stage coordinated optimization scheduling strategy for a CCHP microgrid. The strategy contains three stages: a day-ahead economic scheduling stage, an intraday rolling optimization stage, and a real-time adjustment stage. Forecasting data with different accuracy at different time scales were used to carry out multilevel coordination and gradually improve the scheduling plan. A case study was used to verify that the proposed scheduling strategy can mitigate and eliminate the load forecasting error of renewable energy (for power balance and scheduling economy).

Enhanced fair earliest due date first scheduling strategy for multimedia applications in LTE downlink framework

SummaryGuaranteeing a certain delay threshold for delay‐sensitive applications in long term evolution (LTE) cellular communication system is a very challenging mission. By implementing an optimal scheduling strategy, this mission will be achieved. In this article, a novel scheduler is introduced in order to meet a predefined level of service quality by guaranteeing a specific delay threshold for delay‐sensitive applications in LTE cellular systems. The proposed scheduler assigns the available resource blocks (RBs) to active user equipments (UEs) tacking into consideration several attributes. The expiration date of each packet, the channel quality, the average data rate previously achieved by each UE, and the number of dropped packets for each UE compared with the average number of packets totally dropped are all considered in the proposed scheduler working mechanism. Consequently, the proposed scheduling strategy reduces the number of packets dropped for multimedia applications, and at the same time maximizes the overall throughput of the network. Simulation results are provided to study and evaluate the performance of the proposed scheduling strategy. A comparative study is presented between the proposed strategy and the most recent scheduling techniques. The obtained results prove that the proposed scheduling strategy has considerably acceptable and appreciated results compared with the results of the state‐of‐the‐art scheduling techniques.

Strategic demand response framework for energy management in distribution system based on network loss sensitivity

This paper discusses an energy management system–based demand response scheduling strategy in distribution system. The proposed strategy includes customer payment minimization and network loss minimization as responsive load scheduling objectives through centralized approach. Two types of optimization strategies each based on payment minimization and network loss sensitivity are discussed in this paper. Thus, the proposed scheduling strategy can effectively resolve the optimality issue between different objectives of the distribution system scheduling under demand response penetration. The demand response scheduling strategies are simulated using standard IEEE 37 bus distribution test system through different cases of scheduling and optimization scenarios. The simulation results are presented, discussed, and compared with the base test cases without demand response penetration and without optimization strategies under demand response penetration to demonstrate the effectiveness of network loss, sensitivity consideration and optimization strategies in carrying out distribution system scheduling. In addition, sensitivity analysis is performed. The variation of distribution network performance is analyzed for various test cases and scenarios at different penetration levels.

Coordinated Scheduling Strategy for Distributed Generation Considering Uncertainties in Smart Grids

Smart grid with great flexibility requirements will not accommodate high proportion of distributed generation with uncertainty in the future. Hence, it becomes indispensable to research scheduling strategy of distributed generation, aiming to reduce serious curtailment of renewable energy. This paper firstly proposes a virtual power supply, which consists of wind power (WP), photovoltaic (PV) power and pumped storage (PS) power. Based on that, a scheduling strategy structure considering stochastic characteristics caused by WP and PV is designed. After that, according to probability distribution of WP and PV, typical virtual power scenarios are obtained via scenario prediction and scenario reduction method. Furthermore, based on flexibility of virtual power and guidance of time-sharing electricity price mechanism, a coordinated scheduling model with optimization objective that maximizes profit is established. Finally, in simulation, validity of proposed model is verified via comparing with different scheduling models. Besides, profits of system under various peak-valley prices are analyzed, which can provide guidance for electricity pricing. The results illustrate that the proposed scheduling strategy can efficiently balance economy and flexibility in optimizing WP and PV consumption and profits of system are increased with 28% at most.

Energy Transmission Mode Based on All-electric Vessels for Pelagic Islands and Its Coordinated Scheduling Strategy

There is a lack of effective liaison between pelagic island microgrids, leading to inflexible transmission of energy between islands. However, the interconnection mode by submarine cables is not economical due to the long distances between islands. Therefore, an energy supply mode based on allelectric vessels is proposed in this paper. The framework and working principle of the mode are first introduced. A two-stage optimization model is established considering the coordinated operation of island microgrid and the swapping plan of all-electric vessels. The simulation results show that the proposed transmission mode can reduce overall costs and the proposed scheduling strategy can promote renewable energy consumption and reduce operation costs.

Improved Resource Allocation in 5G MTC Networks

Effective resource allocation has always been one of the serious challenges in wireless communication. A considerable number of machine type communication (MTC) devices in 5G with variable quality of service (QoS) aggravates this challenge even further. Existing Resource allocation schemes in MTC are usually considering signal to noise ratio (SNR), which provides preference to MTC devices based on distance rather than their QoS requirements. This paper proposes a resource allocation scheme with dynamic priorities for MTC devices with multiple radio access technologies (RATs). The proposed resource allocation scheme has two main parts namely medium access and resource allocation. The medium access leverages the broadcast nature of wireless signal and MTC devices’ wait time to assign priorities using capillary band in a secure and integral way. At resource allocation, SNR, total induced transmission delay, and transmission-awaiting MTC devices are used to assign resources in the cellular band. The rumination of two-staged dynamic priorities in the proposed scheduling scheme brings significant performance improvements in outage and success probabilities. Compared to SNR-based schemes, the proposed mechanism performs well by expressively improving the outage and success probability by 20% and 30%, respectively.

Optimal Dispatching Strategy of an Electric-Thermal-Gas Coupling Microgrid Considering Consumer Satisfaction

In recent years, with the rapid development of renewable energy, microgrid that behaves as a multi-energy coupling system, has attracted more and more attention. A growing trend needs to be concerned is that the relation among the electric power, thermal and gas in a microgrid system gets closer and closer, which could significantly affect the system dispatching. The existing research on microgrid dispatch mainly focuses on power and load coordination and improving the utilization rate of primary energy. However, there is some limit in the energy form independence and operation mode. Besides, some research had ignored the consumer electricity experience in load optimization. To settle such problem, a dispatching model based on a complicated electric-thermal-gas coupling microgrid is firstly proposed in this paper. Then the mathematical model of the subsystems in the microgrid is established, and the demand side response based on consumer satisfaction is employed to optimize the loads. Finally, the operation cost of the microgrid can be minimized by the proposed economic optimization strategy. Case studies were performed to verify the effectiveness of the proposed scheduling strategy.

Throughput and Fairness Optimal Scheduling for High Speed Cell Edge Users in LTE Networks

Guaranteeing Quality of Service (QoS) to mobile users is the primary aim of cellular broadband system like Long Term Evolution (LTE). Radio resource allocation and scheduling are two important functions in the LTE networks to enhance the quality of service. For increasing the generally user experience, an efficient radio resource allocation and Scheduling algorithm should be used. However, this became a non-trivial task as the demands and requirements of user data changes day-to-day. In these situations, with the limited radio resources, maximum system capacity can be obtained on expense of unfair share of the resources. In this work, high speed cell edge users are considered as they experience poor signal strength and their quality of service degrades when they move away from Evolved-Nodes (e-Nodes). Here, a novel scheduling algorithm has been introduced to extend the cell edge throughput amid during high mobility scenarios. The proposed scheduling scheme will be compared with the conventional schemes like best CQI, RR and PF in terms of throughput and fairness. It is presented that the proposed scheme gives better performance against the conventional ones in the chosen scenario.

Charging scheduling strategy for different electric vehicles with optimization for convenience of drivers, performance of transport system and distribution network

With the popularization of electric vehicles, large-scale electric vehicle charging may negatively impact drivers, the power grid, and traffic conditions. Currently, research conducted on the charging and battery swap of electric vehicles is insufficient. The objective for optimization and the type of electric vehicle proposed by other papers are limited in scope. In order to achieve an overall optimization of the whole system, the driver demands, the road traffic speed, the number of vehicles in the charging station and the charging network load are considered in the development of the charging scheduling strategy for electric vehicles. Such a strategy can further enhance driver convenience in terms of making decisions for charging and battery swap of electric vehicles. Moreover, different types of electric vehicles are taken into account for a more practical proposed scheduling strategy. Utilizing MATLAB and MATPOWER, a simulation platform is established to validate the strategy. Simulation results demonstrate that the proposed scheduling strategy can relieve local traffic jams, smooth network load curve, increase safety and economy of the power network, and decrease the number of charging electric vehicles in station. Ultimately, this plan can simultaneously reduce waiting time of charging and increase the operational efficiency of charging stations.

QoS provisioning for various types of deadline-constrained bulk data transfers between data centers

An increasing number of applications in scientific and other domains have moved or are in active transition to clouds, and the demand for big data transfers between geographically distributed cloud-based data centers is rapidly growing. Many modern backbone networks leverage logically centralized controllers based on software-defined networking (SDN) to provide advance bandwidth reservation for data transfer requests. How to fully utilize the bandwidth resources of the links connecting data centers with guaranteed quality of service for each user request is an important problem for cloud service providers. Most existing work focuses on bandwidth scheduling for a single request for data transfer or multiple requests using the same service model. In this work, we construct rigorous cost models to quantify user satisfaction degree, and formulate a generic problem of bandwidth scheduling for multiple deadline-constrained data transfer requests of different types to maximize the request scheduling success ratio while minimizing the data transfer completion time of each request. We prove this problem to be not only NP-complete but also non-approximable, and hence design a heuristic algorithm. For performance evaluation, we establish a proof-of-concept emulated SDN testbed and also generate large-scale simulation networks. Both experimental and simulation results show that the proposed scheduling scheme significantly outperforms existing methods in terms of user satisfaction degree and scheduling success ratio.