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).

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。

Performance Improvement through Novel Adaptive Node and Container Aware Scheduler with Resource Availability Control in Hadoop YARN

The default scheduler of Apache Hadoop demonstrates operational inefficiencies when connecting external sources and processing transformation jobs.This paper has proposed a novel scheduler for enhancement of the performance of the Hadoop Yet Another Resource Negotiator(YARN)scheduler,called the Adaptive Node and Container Aware Scheduler(ANACRAC),that aligns cluster resources to the demands of the applications in the real world.The approach performs to leverage the user-provided configurations as a unique design to apportion nodes,or containers within the nodes,to application thresholds.Additionally,it provides the flexibility to the applications for selecting and choosing which node’s resources they want to manage and adds limits to prevent threshold breaches by adding additional jobs as needed.Node or container awareness can be utilized individually or in combination to increase efficiency.On top of this,the resource availability within the node and containers can also be investigated.This paper also focuses on the elasticity of the containers and self-adaptiveness depending on the job type.The results proved that 15%–20%performance improvement was achieved compared with the node and container awareness feature of the ANACRAC.It has been validated that this ANACRAC scheduler demonstrates a 70%–90%performance improvement compared with the default Fair scheduler.Experimental results also demonstrated the success of the enhancement and a performance improvement in the range of 60%to 200%when applications were connected with external interfaces and high workloads.

A Modified Firefly Optimization Algorithm-Based Fuzzy Packet Scheduler for MANET

In Mobile ad hoc Networks(MANETs),the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput with high packet loss.In this paper,a Modified Firefly Optimization Algorithm improved Fuzzy Scheduler-based Packet Scheduling(MFPA-FSPS)Mechanism is proposed for sustaining Quality of Service(QoS)in the network.This MFPA-FSPS mechanism included a Fuzzy-based priority scheduler by inheriting the merits of the Sugeno Fuzzy inference system that potentially and adaptively estimated packets’priority for guaranteeing optimal network performance.It further used the modified Firefly Optimization Algorithm to optimize the rules uti-lized by the fuzzy inference engine to achieve the potential packet scheduling pro-cess.This adoption of a fuzzy inference engine used dynamic optimization that guaranteed excellent scheduling of the necessitated packets at an appropriate time with minimized waiting time.The statistical validation of the proposed MFPA-FSPS conducted using a one-way Analysis of Variance(ANOVA)test confirmed its predominance over the benchmarked schemes used for investigation.

采用NPV Scheduler软件对某大型露天矿山进行境界优化

某大型矿床随着开采的进行以及补充勘探的深入,加上市场的变化,使得原来的境界设计条件发生了一些变化,因此需要根据最新的边界条件对境界进行优化设计。采用NPV Scheduler软件用L-G图论法对该铜矿的最终开采境界进行了优化圈定,使得最终境界更符合当前采选成本和市场条件。

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。

An On-Line Scheduler over Hard Real-Time Communication System

By thorough research on the prominent periodic and aperiodic scheduling algorithms,anon-line hard real-time scheduler is presented,which is applicable to the scheduling of packets over a link.This scheduler,based on both Rate Monotonic,pinwheel scheduling algorithm Sr and Polling Serverscheduling algorithms,can rapidly judge the schedulability and then automatically generate a bus tablefor the scheduling algorithm to schedule the packets as the periodic packets.The implementation of thescheduler is simple and easy to use,and it is effective for the utilization of bus link.The orderly executionof the bus table can not only guarantee the performance of the hard real time but also avoid the blockageand interruption of the message transmission.So the scheduler perfectly meets the demand of hard real-time communication system on the field bus domain.

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。

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.

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.

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.

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.

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).

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 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.

Continuous query scheduler based on operators clustering

Data stream management system (DSMS) provides convenient solutions to the problem of processing continuous queries on data streams.Previous approaches for scheduling these queries and their operators assume that each operator runs in separate thread or all operators combine in one query plan and run in a single thread.Both approaches suffer from severe drawbacks concerning the thread overhead and the stalls due to expensive operators.To overcome these drawbacks,a novel approach called clustered operators scheduling (COS) is proposed that adaptively clusters operators of the query plan into a number of groups based on their selectivity and computing cost using S-mean clustering.Experimental evaluation is provided to demonstrate the potential benefits of COS scheduling over the other scheduling strategies.COS can provide adaptive,flexible,reliable,scalable and robust design for continuous query processor.

Extended Balanced Scheduler with Clustering and Replication for Data Intensive Scientific Workflow Applications in Cloud Computing

Cloud computing is an advance computing model using which several applications,data and countless IT services are provided over the Internet.Task scheduling plays a crucial role in cloud computing systems.The issue of task scheduling can be viewed as the finding or searching an optimal mapping/assignment of set of subtasks of different tasks over the available set of resources so that we can achieve the desired goals for tasks.With the enlargement of users of cloud the tasks need to be scheduled.Cloud’s performance depends on the task scheduling algorithms used.Numerous algorithms have been submitted in the past to solve the task scheduling problem for heterogeneous network of computers.The existing research work proposes different methods for data intensive applications which are energy and deadline aware task scheduling method.As scientific workflow is combination of fine grain and coarse grain task.Every task scheduled to VM has system overhead.If multiple fine grain task are executing in scientific workflow,it increase the scheduling overhead.To overcome the scheduling overhead,multiple small tasks has been combined to large task,which decrease the scheduling overhead and improve the execution time of the workflow.Horizontal clustering has been used to cluster the fine grained task further replication technique has been combined.The proposed scheduling algorithm improves the performance metrics such as execution time and cost.Further this research can be extended with improved clustering technique and replication methods.

D-scheduler:A scheduler in time-triggered distributed system through decoupling dependencies between tasks and messages

Time-triggered architecture,as a mainstream design of the distributed real-time system,has been successfully applied in the aerospace,automotive and mechanical industries.However,time-triggered scheduling is a challenging NP-hard problem.There are few studies that could quickly solve the scheduling problem of large distributed time-triggered systems.To solve this problem,a communication affinity parameter is defined in this paper to describe the degree of bias of the shaper task towards sending or receiving messages.Based on this,an innovative task-message decoupling model named D-scheduler is built to reduce the computation complexity of the scheduling problem in large-scale systems.Additionally,we provide mathematical proof that our model is a convex optimization that is easy to solve with existing computational tools.Our experiments substantiate the efficacy of the D-scheduler.It dramatically reduces the scheduling complexity of large-scale real-time systems with a small loss of solving space compared to the federal scheduler.

Tax-Scheduler:An interactive visualization system for staff shifting and scheduling at tax authorities

Given a large number of applications and complex processing procedures,how to efficiently shift and schedule tax officers to provide good services to taxpayers is now receiving more attention from tax authorities.The availability of historical application data makes it possible for tax managers to shift and schedule staff with data support,but it is unclear how to properly leverage the historical data.To investigate the problem,this study adopts a user-centered design approach.We first collect user requirements by conducting interviews with tax managers and characterize their requirements of shifting and scheduling into time series prediction and resource scheduling problems.Then,we propose Tax-Scheduler,an interactive visualization system with a time-series prediction algorithm and genetic algorithm to support staff shifting and scheduling in the tax scenarios.To evaluate the effectiveness of the system and understand how non-technical tax managers react to the system with advanced algorithms and visualizations,we conduct user interviews with tax managers and distill several implications for future system design.