MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

Autonomous sortie scheduling for carrier aircraft fleet under towing mode

Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.

Two-Stage Scheduling Model for Flexible Resources in Active Distribution Networks Based on Probabilistic Risk Perception

Aiming at the problems of increasing uncertainty of low-carbon generation energy in active distribution network(ADN)and the difficulty of security assessment of distribution network,this paper proposes a two-phase scheduling model for flexible resources in ADN based on probabilistic risk perception.First,a full-cycle probabilistic trend sequence is constructed based on the source-load historical data,and in the day-ahead scheduling phase,the response interval of the flexibility resources on the load and storage side is optimized based on the probabilistic trend,with the probability of the security boundary as the security constraint,and with the economy as the objective.Then in the intraday phase,the core security and economic operation boundary of theADNis screened in real time.Fromthere,it quantitatively senses the degree of threat to the core security and economic operation boundary under the current source-load prediction information,and identifies the strictly secure and low/high-risk time periods.Flexibility resources within the response interval are dynamically adjusted in real-time by focusing on high-risk periods to cope with future core risks of the distribution grid.Finally,the improved IEEE 33-node distribution system is simulated to obtain the flexibility resource scheduling scheme on the load and storage side.Thescheduling results are evaluated from the perspectives of risk probability and flexible resource utilization efficiency,and the analysis shows that the scheduling model in this paper can promote the consumption of low-carbon energy from wind and photovoltaic sourceswhile reducing the operational risk of the distribution network.

STK/Scheduler在卫星数传调度中的应用研究

卫星数传调度问题是一类复杂的组合优化问题,即如何为卫星数传任务分配地面资源的问题,它是当前航天领域需要重点研究的问题之一。STK/Scheduler模块是STK工具包中的调度模块,对外提供了二次开发功能。针对卫星数传调度问题,研究了STK/Scheduler模块在该问题中的应用。首先在分析问题的基础上建立了卫星数传任务模型和调度模型,然后对基于STK/Scheduler的卫星数传调度系统进行了设计。最后利用AFIT基准数据进行了验证,结果表明在卫星数传任务规模不太大的情况下,STK/Scheduler为卫星数传调度问题的求解提供了一条捷径。

基于Linux的集群系统中联合调度(Co-Scheduler)模块的设计

在集群系统中,调度模块的设计对整个系统的性能而言是至关重要的。文章针对以Linux操作系统为平台的集群系统,提出了一种联合调度模块的实现方案。该方案在不改动Linux内核的前提下,实现了基于并行作业级的调度,从而大大提高了集群系统的性能和资源的协调利用率。

基于STK/Scheduler的航天任务调度应用研究

STK/Scheduler是与STK（satellite tool kits）完全集成的任务调度软件,通过它可方便的定义任务、资源和各种约束关系。介绍了STK/Scheduler的主要功能,在分析航天任务调度的特点及要素的基础上,建立了调度模型,并利用STK/Scheduler实现了2个典型的卫星任务调度。结果表明：STK/Scheduler能基本满足航天任务调度的需求。

基于NPV Scheduler软件的国外某铅锌矿露天境界优化

基于地质块体模型,采用NPV Scheduler软件对国外某铅锌矿露天境界进行优化,得到了该矿山的最优境界和采剥进度计划,为矿山露天开采和投资提供了技术方案和决策依据。

基于NPV Scheduler软件的露天转地下矿山开采境界研究

基础的L-G图论法未考虑地下开采可能的成本优势,在露天转地下矿山开采境界圈定时存在一定的局限性。本文通过对价格法与储量盈利比较法计算的经济合理剥采比进行分析,结合NPV Scheduler软件研究了露天转地下开采境界圈定方法,并通过工程实例验证了该方法的合理性。研究成果对露天转地下矿山开采境界的确定具有一定的指导意义。

Multiband Scheduler for Future Communication Systems

Operation in multiple frequency bands simultaneously is an important enabler for future wireless communication systems. This article presents a new concept for scheduling transmissions in a wireless radio system operating in multiple frequency bands: the Multiband Scheduler (MBS). The MBS ensures that the operation in multiple bands is transparent to higher network layers. Special attention is paid to achieving low delay and latency when operating the system in the multiband mode. In particular, we propose additions to the ARQ procedures in order to achieve this. Deployment details and assessment results are presented for two multiband deployment scenarios. The first scenario is operation in a spectrum sharing context where multiple bands are used: one dedicated band for basic service and one shared extension band for extended services. In the second scenario we consider multiband operation in a relay environment, where the two bands have different propagation properties and relays provide extra coverage and capacity in the whole cell.

基于TaskScheduler算法的火控计算机并行测试方法

现有的火控计算机测试仪器大多采用串行测试方法,效率较低。针对这一问题,深入分析了某型火控计算机的检测方法,对测试过程进行分解,明确各测试任务流程的相互关系。吸收并行任务调度思想,构造任务资源相关图和相关矩阵对测试任务占用资源情况进行建模。采用TaskScheduler算法,得到了并行的测试任务序列。对比于串行测试序列,较大提高了测试效率。

A New Multi-Objective Model to Optimise Rail Transport Scheduler

The sugarcane transport system plays a critical role in the overall performance of Australia’s sugarcane industry. An inefficient sugarcane transport system interrupts the raw sugarcane harvesting process, delays the delivery of sugarcane to the mill, deteriorates the sugar quality, increases the usage of empty bins, and leads to the additional sugarcane production costs. Due to these negative effects, there is an urgent need for an efficient sugarcane transport schedule that should be developed by the rail schedulers. In this study, a multi-objective model using mixed integer programming (MIP) is developed to produce an industry-oriented scheduling optimiser for sugarcane rail transport system. The exact MIP solver (IBM ILOG-CPLEX) is applied to minimise the makespan and the total operating time as multi-objective functions. Moreover, the so-called Siding neighbourhood search (SNS) algorithm is developed and integrated with Sidings Satisfaction Priorities (SSP) and Rail Conflict Elimination (RCE) algorithms to solve the problem in a more efficient way. In implementation, the sugarcane transport system of Kalamia Sugar Mill that is a coastal locality about 1050 km northwest of Brisbane city is investigated as a real case study. Computational experiments indicate that high-quality solutions are obtainable in industry-scale applications.

Job schedulers for Big data processing in Hadoop environment： testing real-life schedulers using benchmark programs

At present, big data is very popular, because it has proved to be much successful in many fields such as social media, E-commerce transactions, etc. Big data describes the tools and technologies needed to capture, manage, store, distribute, and analyze petabyte or larger-sized datasets having different structures with high speed. Big data can be structured, unstructured, or semi structured. Hadoop is an open source framework that is used to process large amounts of data in an inexpensive and efficient way, and job scheduling is a key factor for achieving high performance in big data processing. This paper gives an overview of big data and highlights the problems and challenges in big data. It then highlights Hadoop Distributed File System （HDFS）, Hadoop MapReduce, and various parameters that affect the performance of job scheduling algorithms in big data such as Job Tracker, Task Tracker, Name Node, Data Node, etc. The primary purpose of this paper is to present a comparative study of job scheduling algorithms along with their experimental results in Hadoop environment. In addition, this paper describes the advantages, disadvantages, features, and drawbacks of various Hadoop job schedulers such as FIFO, Fair, capacity, Deadline Constraints, Delay, LATE, Resource Aware, etc, and provides a comparative study among these schedulers.

基于Globus的有限资源再调度模型R2_Scheduler

网格资源调度是网格管理技术中的关键组成部分,文章在研究Globus网格资源调度模型的基础上提出了一种基于资源动态性的再调度策略R2_Scheduler。资源的动态特性以及跨越多个管理域使得网格管理策略必须根据其可用性作出相应调整,R2_Scheduler通过一个启动因子p与用户进行协商,利用前一次调度的结果,在资源有限的情况下启动任务,可以避免因资源不足造成的请求失败;同时利用资源的局部性特点,把请求资源限制在一个协同调度器的作用域之内,从而减少网络通信负载。

Event-driven Dynamic and Intelligent Scheduling for Agile Manufacturing Based on Immune Mechanism and Expert System

Based on the biological immune concept, immune response mechanism and expert system, a dynamic and intelligent scheduling model toward the disturbance of the production such as machine fault,task insert and cancel etc. Is proposed. The antibody generation method based on the sequence constraints and the coding rule of antibody for the machining procedure is also presented. Using the heuristic antibody generation method based on the physiology immune mechanism, the validity of the scheduling optimization is improved, and based on the immune and expert system under the event-driven constraints, not only Job-shop scheduling problem with multi-objective can be solved, but also the disturbance of the production be handled rapidly. A case of the job-shop scheduling is studied and dynamic optimal solutions with multi-objective function for agile manufacturing are obtained in this paper. And the event-driven dynamic rescheduling result is compared with right-shift rescheduling and total rescheduling.

M/S Controller Area Network(CAN) System Using Shared-Clock Scheduler

The Controller Area Network (CAN) is a well established control network for automotive and automation control applications. Time-Triggered Controller Area Network (TTCAN) is a recent development which introduces a session layer,for message scheduling,to the existing CAN standard,which is a two layer standard comprising of a physical layer and a data link layer. TTCAN facilitates network communication in a time-triggered fashion,by introducing a Time Division Multiple Access style communication scheme. This allows deterministic network behavior,where maximum message latency times can be quantified and guaranteed. In order to solve the problem of determinate time latency and synchronization among several districted units in one auto panel CAN systems,this paper proposed a prototype design implementation for a shared-clock scheduler based on PIC18F458 MCU. This leads to improved CAN system performance and avoid the latency jitters and guarantee a deterministic communication pattern on the bus. The real runtime performance is satisfied.

MapReduce Scheduler by Characterizing Performance Interference

Recently, virtualization has become more and more important in the cloud computing to support efficient flexible resource provisioning. However, performance interference among virtual machines(VMs) has become a challenge which may affect the effectiveness of resource provisioning. In a virtual cluster which runs the Map Reduce applications, the performance interference can also affect the performance of the Map and Reduce tasks and thus cause a performance degradation of the Map Reduce job. Accordingly, this paper presents a Map Reduce scheduling framework to mitigate this performance degradation caused by the performance interference. The framework includes a performance interference prediction module and an interference aware scheduling algorithm. To verify its effectiveness, we have done a set of experiments on a 24-node virtual Map Reduce cluster. The experiments illustrate that the proposed framework can achieve a performance improvement in the virtualized environment compared with other Map Reduce schedulers.

Scheduler Algorithm for Multi-Class Switch with Priority Threshold

The requirement for guaranteed Quality of Service (QoS) have become very essential since there are numerous network base application is available such as video conferencing, data streaming, data transfer and many more. This has led to the multi-class switch architecture to cater for the needs for different QoS requirements. The introduction of threshold in multi-class switch to solve the starvation problems in loss sensitive class has increased the mean delay for delay sensitive class. In this research, a new scheduling architecture is introduced to improve mean delay in delay sensitive class when the threshold is active. The proposed architecture has been simulated under uniform and non-uniform traffic to show performance of the switch in terms of mean delay. The results show that the proposed architecture has achieved better performance as compared to Weighted Fair Queueing (WFQ) and Priority Queue (PQ).

An NMOS output-capacitorless low-dropout regulator with dynamic-strength event-driven charge pump

In this paper,an NMOS output-capacitorless low-dropout regulator(OCL-LDO)featuring dual-loop regulation has been proposed,achieving fast transient response with low power consumption.An event-driven charge pump(CP)loop with the dynamic strength control(DSC),is proposed in this paper,which overcomes trade-offs inherent in conventional structures.The presented design addresses and resolves the large signal stability issue,which has been previously overlooked in the event-driven charge pump structure.This breakthrough allows for the full exploitation of the charge-pump structure's poten-tial,particularly in enhancing transient recovery.Moreover,a dynamic error amplifier is utilized to attain precise regulation of the steady-state output voltage,leading to favorable static characteristics.A prototype chip has been fabricated in 65 nm CMOS technology.The measurement results show that the proposed OCL-LDO achieves a 410 nA low quiescent current(IQ)and can recover within 30 ns under 200 mA/10 ns loading change.

Testing gridded NWS 1-day observed precipitation analysis in a daily irrigation scheduler

One of the inputs required by daily decision support tools for scheduling irrigation is the amount of water supplied by rainfall. In-field measurements of daily precipitation are expensive or laborious, while measurements from gauges within a few kilometers are frequently not representative due to the high spatiotemporal variability of precipitation. Online radarbased precipitation analyses from NOAA’s National Weather Service (NWS) have obvious potential to provide the needed data, but are known to have varying degrees of accuracy with location and conditions. The NWS precipitation analysis is computed on a 4 km × 4 km grid, so differences should also be expected between the product and individual gauge measurements under each grid cell. In order to test the utility of the NWS precipitation analysis in a daily irrigation scheduler, daily data were gathered in July 2012 from 18 weather stations under 2 NWS precapitation analysis grid cells across instru-mented research and production fields in the Mississippi Delta. Differences between individual station measurements and the NWS precipitation analysis are examined, and root-zone daily soil water deficits computed using both station data and the NWS precipitation analysis. Sub-grid spatial variability between gauge locations, and differences in precipitation between gauges and the gridded NWS analysis, are found to be similar to those reported elsewhere. Differences between time series of soil water deficit computed using the two different precipitation data sources are noted, but prove to be of limited impact on the decision to irrigate or not to irrigate. It is also noted that profile-filling rainfalls limit the impact of accumulating error, resetting the modeled soil water to “full”. Given the Delta-local practice of irrigating to replace full evapotranspirational water used, use of the NWS daily precipitation analysis data as input for a daily irrigation scheduler is judged not only acceptable, but also preferable to other sources of daily precipitation data.

A PSL Ontology-based Shop Floor Dynamical Scheduler Design

Due to the complex,uncertainty and dynamics in the modern manufacturing environment,a flexible and robust shop floor scheduler is essential to achieve the production goals.A design framework of a shop floor dynamical scheduler is presented in this paper.The workflow and function modules of the scheduler are discussed in detail.A multi-step adaptive scheduling strategy and a process specification language,which is an ontology-based representation of process plan,are utilized in the proposed scheduler.The scheduler acquires the dispatching rule from the knowledge base and uses the build-in on-line simulator to evaluate the obtained rule.These technologies enable the scheduler to improve its fine-tune ability and effectively transfer process information into other heterogeneous information systems in a shop floor.The effectiveness of the suggested structure will be demonstrated via its application in the scheduling system of a manufacturing enterprise.