Conventional Scheduling Algorithms Research Articles

A Two-Hop MIMO Relay Architecture Using LTE and Millimeter Wave Bands in High-Speed Trains

In this paper, a novel two-hop relay architecture using mobile relay nodes (MRN) technology for high-speed trains is presented. In this architecture, a long-term evolution advanced (LTE-A) link in sub-6 GHz bands is established between the Donor-eNB (DeNB) and the MRNs mounted on the roof of the train and another in millimeter Wave (mmWave) bands between the MRN and the user equipment (UE) terminals inside the train. Moreover, the multiple-input multiple-output (MIMO) channel is exploited between the DeNB and the UEs to enhance channel capacity. Different relay structures—several relays in a train carriage and a single relay with multiple antennas—and different types of relays—amplify-and-forward and decode-and-forward (DF)—are studied. Ultimately, performance analysis is studied and discussed by means of a downlink-level simulator with accurate channel models. Furthermore, multiuser transmission and conventional user scheduling algorithms are added to obtain a more realistic simulator. A new algorithm for one of the structures and the DF relay is proposed. This paper will examine how to provide broadband data access to train passengers in high-speed environments by validating a new architecture, which takes advantage of MIMO techniques, mobile relays, and millimeter bands.

Improved scheduling algorithm for signal processing in asynchronous distributed ultrasonic total-focusing-method system.

Compared to the conventional ultrasonic phased-array system, a large-element phased-array system employing the total focusing method (TFM) can yield improved image resolution and accuracy, providing more flexible scanning methods and image merging functionality. In order to meet various forms of ultrasonic multi-group scanning, an architecture for multi-group scan integration called the “asynchronous distributed ultrasonic TFM system” is proposed, and a novel scheduling algorithm called “the sum of start time and processing time adjacent (SSPA) algorithm” is presented. The architecture adds a focus and group scheduler (FGS) and signal processing scheduler (SPS) to the traditional ultrasonic phased array system and constructs a signal processing arbitration (SPA) with several signal processing modules (SPMs). The FGS provides the focus parameters, pixel memory range, and number of pixels in each group. The SPS controls the SPA for the ultrasonic scanning data obtained from the elements, with SPM-sharing output data; hence, the optimal priority order and SPM assignment are realized, enabling switching of reading operations among the first-in−first-out memories for signal processing and minimal time-slot waiting. The SSPA algorithm is used to solve the job-shop scheduling problem with start time, which considers the processing time and start time, in order to reduce the time slot after each scheduling using adjacent operations. Therefore, the architecture enhances the flexibility of the multi-group scan, and this algorithm decreases the makespan, achieving higher efficiency compared to conventional scheduling algorithms. The reliability and validity of the algorithm are substantiated after its implementation using FPGA technology. The SPM utilization rate and the real-time performance of the ultrasonic TFM are improved. Thus, the proposed algorithm and architecture have considerable potential application in multi-sensor systems.

Energy- and labor-aware flexible job shop scheduling under dynamic electricity pricing: A many-objective optimization investigation

Energy-aware production scheduling is a promising way to adapt the factories’ energy consumption behavior to the volatile electricity prices in the demand response initiative of smart grids. However, it may not be economical by simply scheduling production loads to the periods with lower electricity prices, as these periods often have higher labor wage, e.g., nights and weekends. Based on this gap, this paper proposes a many-objective integrated energy- and labor-aware flexible job shop scheduling model. Many objectives refer to the number of optimization objectives surpasses three (i.e., five objectives: makespan, total energy cost, total labor cost, maximal workload, and total workload), whereas the existing energy-aware production scheduling research is limited within three objectives. To enable energy awareness in the conventional production scheduling algorithms, a state-based shop floor wide energy model is proposed. To enable labor awareness, the number and type of human workers are matched to the scheduled production loads, with varying labor wage over shifts. As one of the most complex shop floor configurations, the partial flexible job shop further considers job recirculation and operation sequence-dependent machine setup times. The recently-proposed nondominated sorting genetic algorithm-III (NSGA-III) is tailored for this many-objective optimization problem (MaOP), including scheduling solution encoding and decoding, crossover, mutation, and solution evaluation using the energy- and labor-aware discrete-event simulation framework. Through numerical experiments under real-time pricing (RTP) and time-of-use pricing (ToUP), insights are statistically obtained on the relation among these five production objectives; the effectiveness and efficiency of NSGA-III in solving a MaOP are also demonstrated. This proposed scheduling method can be used to automated and enhance the decision making of factory managers in jointly allocating machine, human worker, and energy resources on the shop floor, such that the production cost is minimized even under time-varying electricity and labor prices.

Scheduling computation and communication on a software-defined photonic Network-on-Chip architecture for high-performance real-time systems

Abstract This paper presents a novel scheduling approach that efficiently schedules the computational and the communicational resources of a Software-Defined Photonic Network-on-Chip (SD-PNoC) Architecture designed for high-performance real-time systems. The proposed scheduling approach extends the conventional cyclic executive scheduling algorithm from the one that schedules solely the computational resources into a general one that schedules both the computational and the communicational resources together in a synchronized manner. The proposed SD-PNoC architecture employs Software Defined Network (SDN) approach by using application specific conflict-free and contention-free communication patterns to solve the routing and wavelength assignment (RWA) problem exists in photonic networks. The implementations of the collective communication primitives such as broadcast and all-to-broadcast are also presented to demonstrate the system as a whole.

Secure workflow scheduling in cloud environment using modified particle swarm optimization with scout adaptation

In the distributed data-intensive computing environment, securely assigning tasks to appropriate machines is a big job scheduling problem. The complexity of this problem increases with the number of jobs and their job times. Several meta-heuristic algorithms including particle swarm optimization (PSO) technique and variable neighborhood particle swarm optimization (VNPSO) technique are employed to solve the problem to a certain extent. This paper proposes a modified PSO with scout adaptation (MPSO-SA) algorithm, which uses a cyclic term called mutation operator, to solve the job scheduling problem in the cloud environment. The comparative study between the proposed MPSO-SA scheduling mechanism and the conventional scheduling algorithms show that the proposed method decreases the probability of security risk on scheduling the jobs.

An Online Algorithm for Task Offloading in Heterogeneous Mobile Clouds

Mobile cloud computing is emerging as a promising approach to enrich user experiences at the mobile device end. Computation offloading in a heterogeneous mobile cloud environment has recently drawn increasing attention in research. The computation offloading decision making and tasks scheduling among heterogeneous shared resources in mobile clouds are becoming challenging problems in terms of providing global optimal task response time and energy efficiency. In this article, we address these two problems together in a heterogeneous mobile cloud environment as an optimization problem. Different from conventional distributed computing system scheduling problems, our joint offloading and scheduling optimization problem considers unique contexts of mobile clouds such as wireless network connections and mobile device mobility, which makes the problem more complex. We propose a context-aware mixed integer programming model to provide off-line optimal solutions for making the offloading decisions and scheduling the offloaded tasks among the shared computing resources in heterogeneous mobile clouds. The objective is to minimize the global task completion time (i.e., makespan). To solve the problem in real time, we further propose a deterministic online algorithm—the Online Code Offloading and Scheduling (OCOS) algorithm—based on the rent/buy problem and prove the algorithm is 2-competitive. Performance evaluation results show that the OCOS algorithm can generate schedules that have around two times shorter makespan than conventional independent task scheduling algorithms. Also, it can save around 30% more on makespan of task execution schedules than conventional offloading strategies, and scales well as the number of users grows.

An opportunistic scheduling algorithm using aged CSI in massive MIMO systems

In time-division duplex (TDD) massive multiple-input multiple-output (MIMO) systems, the spatial multiplexing gain cannot be fully achieved since available pilot resources at each channel coherence time interval are limited. In this paper, we propose an Opportunistic user Scheduling algorithm, termed OpSAC, that uses Aged Channel state information (CSI) to increase spatial multiplexing gain without incurring additional pilot overhead. Assuming the base station (BS) employs two popular precoders of maximum ratio transmission (MRT) and zero-forcing (ZF), we first derive their closed-form lower bounds on the achievable sum-rate under channel aging. According to the analysis results, we develop a heuristic solution that estimates the amount of channel variation for each user by using correlation of CSI samples, and exploits channel conditions to opportunistically schedule more users by using aged CSI, thereby enhancing spectral efficiency. Through numerical analysis and simulation we confirm that the proposed lower bounds are very tight, and show the impact of channel aging on the performance of massive MIMO systems. In addition, it is shown that OpSAC achieves near-optimal performance and considerably outperforms the conventional user scheduling algorithm that uses current CSI only.

An Adaptive Dwell Scheduling Algorithm for Digital Array Radar Based on Pulse Interleaving

According to the characteristics of signal processing in Digital Array Radar (DAR), a dwell scheduling algorithm is proposed. Based on the pulse interleaving technique, the dwell scheduling algorithm can achieve the sparse-aperture cognitive ISAR imaging of precision tracking targets simultaneously during implementing searching and tracking tasks, which gets the full exploitation of transmitting, waiting and receiving durations of radar dwells. Simulation results demonstrate that, compared with the conventional dwell scheduling algorithms, the proposed algorithm can improve the scheduling performance of DAR.

A review of virtual machine (VM) resource scheduling algorithms in cloud computing environment

Cloud Computing (CC) is the latest cost-effective computing model with the purpose of distributes on-demand access to services on pay-per-usage basis. The CC technology offers many benefits to together cloud customers and service providers. The major objective of this work is to offer a new cloud services with the purpose of maximizes the computational cost profit via the use of its datacenter resources during Virtualization Machine (VM) technology and effectual scheduling algorithms with the limits of Service-Level Agreement (SLA) to cloud users and restricted power usage. Among its increasing application and popularization, IT companies are quickly organizing distributed data centers globally, introducing new problems in terms of scheduling of resources under different VM. This perspective brings out certain vital factors used for effectual scheduling of resources given that an extensive type of characteristics, range in background level of service agreements and that too among user-dependent flexibility. This survey work presents a detailed survey of the conventional resource scheduling methods focusing on the proficient features and issues of CC technology. Review studies of several VM resource scheduling algorithms are focusing on key performance of mounting the speed and capability of the resource allocation. This survey paper also studies the major issues to the conventional VM resource scheduling algorithms and Swarm Intelligence (SI) based VM resource scheduling algorithms in CC environment. Based on the literature review, the issues faced by the CC in terms of high energy consumption of distributed data centers, high computation cost, resources consumption, customer satisfaction, service provider success etc. are recognized in CC environment.

Statistical CSIT Aided User Scheduling for Broadcast MU-MISO System

Recent studies show that the statistical channel state information (SCSI) helps to largely increase the capacity of communication systems when the instantaneous perfect CSI is unavailable. In this paper, we consider multiuser multiple-input-single-output broadcast channels where the transmitter has the knowledge of SCSI. The major issue of concern in our paper is to improve the average group-rate of the whole system by scheduling users over different time slots. With SCSI at the transmitter side, we are able to precode signals and, hence, compute the theoretical achievable group-rate of arbitrary user groups. Based on the group-rates, we propose tier-2 Munkres user scheduling algorithm (T2-MUSA) that leads to higher average group-rate than existing algorithms with generally better fairness. The optimality of the proposed algorithm in energy-fair user scheduling space is proved and we derive a lower bound of a special case to verify the validity of our simulations. In addition, many conventional user scheduling algorithms maintain queue stability by solving a weighted sum-rate (WSR) problem, using queue lengths to represent weight coefficients. Inspired by T2-MUSA, we propose a QoS-based MUSA (QB-MUSA) aimed at stabilizing queue lengths and maximizing throughput. In results, we show that QB-MUSA exhibits higher throughput than the conventional WSR-based algorithm.

QoS provisioning of a task-scheduling algorithm for lightweight devices

Task scheduling is a main objective in operating system design. Many operating systems have been designed and applied to various systems. With the upcoming Internet of things, the requirements for the operating system will vary according to the application. We propose a round-robin-based scheduling algorithm to satisfy the quality of service (QoS) of real-time tasks. Many conventional scheduling algorithms have dealt with the real-time issue, but they still require improvement. Moreover, most assume the burst time is known beforehand, an unrealistic assumption in practical implementations. Thus, we present a heuristic method to predict the burst time of each task and a round-robin-based weighted scheduling algorithm providing QoS to real-time tasks for lightweight devices. The performance of the proposed scheduling algorithm is verified on an embedded system. Experimental results proved the QoS of this algorithm was satisfied.

Scheduling Performance Analysis on Parameter Sweep Applications in Cloud Environments

Recently, cloud computing has becoming a promising networking infrastructure paradigm which enable us to deploy large-scale applications in a cost-effective manner. However, many existing cloud platforms are designed for supporting commercial applications instead of large-scale scientific computing workloads. As a result, the effectiveness of running such kind of applications on cloud platforms is still an opening issue, especially when the performance penalties introduced by virtualization technology is taken into consideration. In this work, we take effects on analyzing the scheduling performance of cloud platforms for Parameter Sweep Applications (PSA), which is one of most used program models in large-scale scientific computing applications. All the experiments are conducted on our integrated performance evaluation middleware. The experimental results indicate that many conventional scheduling algorithms which are effective in classic distributed systems (i.e. grid, cluster) need to take into account the negative effects introduced by virtualization technology. In addition, the experimental results also indicate some useful hints for improving the scheduling performance of PSA workloads in cloud environments.

Online Resource Scheduling Under Concave Pricing for Cloud Computing

With the booming cloud computing industry, computational resources are readily and elastically available to the customers. In order to attract customers with various demands, most Infrastructure-as-a-service (IaaS) cloud service providers offer several pricing strategies such as pay as you go, pay less per unit when you use more (so called volume discount), and pay even less when you reserve. The diverse pricing schemes among different IaaS service providers or even in the same provider form a complex economic landscape that nurtures the market of cloud brokers. By strategically scheduling multiple customers’ resource requests, a cloud broker can fully take advantage of the discounts offered by cloud service providers. In this paper, we focus on how a broker can help a group of customers to fully utilize the volume discount pricing strategy offered by cloud service providers through cost-efficient online resource scheduling. We present a randomized online stack-centric scheduling algorithm (ROSA) and theoretically prove the lower bound of its competitive ratio. Three special cases of the offline concave cost scheduling problem and the corresponding optimal algorithms are introduced. Our simulation shows that ROSA achieves a competitive ratio close to the theoretical lower bound under the special cases. Trace-driven simulation using Google cluster data demonstrates that ROSA is superior to the conventional online scheduling algorithms in terms of cost saving.

Trust-Driven and PSO-SFLA based job scheduling algorithm on Cloud

AbstractWith the advent of big data era, Cloud Computing has drawn widespread interests from industrial and academia. Job scheduling algorithm plays a crucial role in the paradigm of Cloud Computing. The well-designed job scheduling algorithms can provide fast, high quality and safe services. However, the conventional job scheduling algorithms are focusing on the improvement of efficiency, these obscure the important issue of trustworthiness in Cloud. This paper proposes a job scheduling algorithm with the consideration of efficiency and trustworthiness in Cloud. The intuition of the proposed algorithm is based on Particle Swarm Optimization (PSO) and Shuffled Frog Leaping Algorithm (SFLA). In this way, the proposed algorithm can avoid to obtain local optimal results. Also, the trust model is introduced to improve the trust of resources. The comprehensive simulations have been conducted via CloudSim. The experimental results have demonstrated that the proposed algorithm improve the trustworthiness than th...

Improving energy-efficiency of large-scale workflows in heterogeneous systems

With the rapid growth of grid computing, more and more data-intensive applications have been deployed in grid environments, which in turn increase the energy consumption in high-performance computing platforms. To address the issue of energy consumption optimisation when scheduling data-intensive workflows, a novel heuristic policy called 'minimal energy consumption path' is proposed. By using this heuristic, we devise two energy-aware algorithms which are deprived from two classical scheduling algorithms. Extensive experiments are conducted to investigate the performance of the proposed algorithms, and the results show that they can significantly reduce the data-accessing energy consumption. Also, the proposed algorithms show better adaptivity than conventional scheduling algorithms, especially when the system is in presence of large-scale workflows which involve highly intensive data-accessing operations.

Priority-based quality of service guaranteed radio resource allocation in long term evolution network

In this paper, radio resource allocation to users demanding priority for data transmission in the downlink of long term evolution LTE system is carried out. The proposed algorithm defines priority and non-priority user classes and considers minimum data rate requirement of each user. In this scenario, algorithm is implemented in a way to satisfy the priority users followed by allocating the radio resource to the remaining users. The performance analysis of existing conventional scheduling algorithms such as round robin RR, best channel quality indicator BCQI and proportional fair PF is carried out, followed by implementation of the proposed algorithm. The simulation results show that compared to the conventional scheduling algorithms, the proposed algorithm makes efficient utilisation of the scarce radio spectrum and the users with priority have higher chance of achieving their minimum data rate requirement which corresponds to their quality of service QoS requirements being met irrespective of their channel conditions.

Improving energy-efficiency of large-scale workflows in heterogeneous systems

With the rapid growth of grid computing, more and more data-intensive applications have been deployed in grid environments, which in turn increase the energy consumption in high-performance computing platforms. To address the issue of energy consumption optimisation when scheduling data-intensive workflows, a novel heuristic policy called 'minimal energy consumption path' is proposed. By using this heuristic, we devise two energy-aware algorithms which are deprived from two classical scheduling algorithms. Extensive experiments are conducted to investigate the performance of the proposed algorithms, and the results show that they can significantly reduce the data-accessing energy consumption. Also, the proposed algorithms show better adaptivity than conventional scheduling algorithms, especially when the system is in presence of large-scale workflows which involve highly intensive data-accessing operations.

An Optimized Scheduling Algorithm for High Level Synthesis (HLS) to Reduce the Control Steps

In Static List Scheduling (SLS), initially the ALAP (As Late As Possible) algorithm is performed, followed by ASAP (As Soon As Possible) algorithm. The time interval for control steps and successor selection are the vital drawback of ASAP algorithm. Hence, in this paper, the optimized scheduling algorithm is proposed for High Level Synthesis (HLS), where the ALAP in SLS is followed by Force Directed Scheduling (FDS). The proposed scheduling algorithm is implemented in Icarus Verilog and targeted in ZC7020 FPGA board. The proposed optimized scheduling algorithm is evaluated in terms of control steps and resource utilization. The evaluation is carried out for various benchmarks such as, EW Filter, IIR filter, FIR filter, AR filter and Discrete Cosine Transform (DCT). The achieved results in terms of the control steps and resource utilization (i.e. LUT and Flip-flop) shows the proposed optimized scheduling algorithm outperforms the conventional Static List Scheduling (SLS) algorithm.

Towards 5G: Context Aware Resource Allocation for Energy Saving

With the objective of providing high quality of service (QoS), 5G system will need to be context-aware that uses context information in a real-time mode depends on network, devices, applications, and the environment of users'. In order to continue enjoying the benefits provided by future technologies such as 5G, we need to find solutions for reducing energy consumption. One promising solution is taking advantage of the context information available in today's networks. In this paper, we take a step towards 5G by utilizing context information in the scheduling process as conventional packet scheduling algorithms are mainly designed for increasing throughput but not for the energy saving. We investigate a Context Aware Scheduling (CAS) algorithm which considers the context information of users along with conventional metrics for scheduling. An information model of context awareness along with a context aware framework for resource management is also presented in this paper. CAS is simulated applying a system level simulator and the results obtained show that considerable amount of energy is saved by utilizing the context information compare to conventional scheduling algorithms.

LsPS: A Job Size-Based Scheduler for Efficient Task Assignments in Hadoop

The MapReduce paradigm and its open source implementation Hadoop are emerging as an important standard for large-scale data-intensive processing in both industry and academia. A MapReduce cluster is typically shared among multiple users with different types of workloads. When a flock of jobs are concurrently submitted to a MapReduce cluster, they compete for the shared resources and the overall system performance in terms of job response times, might be seriously degraded. Therefore, one challenging issue is the ability of efficient scheduling in such a shared MapReduce environment. However, we find that conventional scheduling algorithms supported by Hadoop cannot always guarantee good average response times under different workloads. To address this issue, we propose a new Hadoop scheduler, which leverages the knowledge of workload patterns to reduce average job response times by dynamically tuning the resource shares among users and the scheduling algorithms for each user. Both simulation and real experimental results from Amazon EC2 cluster show that our scheduler reduces the average MapReduce job response time under a variety of system workloads compared to the existing FIFO and Fair schedulers.