Analysis and modeling of task scheduling in wireless sensor network based on divisible load theory

In this paper, we propose a novel task scheduling algorithm (Divisible Task scheduling Algorithm for Wireless sensor networks (DTAW)) based on divisible load theory in heterogeneous wireless sensor networks to complete the tasks within the shortest possible time and reduce the sensors' energy‐consuming. In DTAW, the tasks are distributed to the wireless sensor network by the (SINK) on the basis of the processing and communication capacity of each sensor. After receiving the subtasks, the intracluster sensors carry out its tasks simultaneously and send the results to cluster head sequentially. By removing communication interference between each sensor, reduced task completion time and improved network resource utilization are achieved. Each cluster head simultaneously finishes sending fused data to the SINK after fusing the data obtained from intracluster sensors. In this way, the overlap between the task performing and communication phase would be much better. Simulation results are presented to demonstrate the impacts of different network parameters on the makespan and energy consumption. The results show that the algorithm enables to reasonably distribute tasks to each sensor and then effectively reduces the time‐consuming and energy‐consuming. Copyright © 2012 John Wiley & Sons, Ltd.     

Multicriteria decision‐making techniques for avoiding similar task scheduling conflict in cloud computing

An efficient task scheduling approach shows promising way to achieve better resource utilization in cloud computing. Various task scheduling approaches with optimization and decision‐making techniques have been discussed up to now. These approaches ignored scheduling conflict among the similar tasks. The conflict often leads to miss the deadlines of the tasks. The work studies the implementation of the MCDM (multicriteria decision‐making) techniques in backfilling algorithm to execute deadline‐based tasks in cloud computing. In general, the tasks are selected as backfill tasks, whose role is to provide ideal resources to other tasks in the backfilling approach. The selection of the backfill task is challenging one, when there are similar tasks. It creates conflict in the scheduling. In cloud computing, the deadline‐based tasks have multiple parameters such as arrival time, number of VMs (virtual machines), start time, duration of execution, and deadline. In this work, we present the deadline‐based task scheduling algorithm as an MCDM problem and discuss the MCDM techniques: AHP (Analytical Hierarchy Process), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to avoid similar task scheduling conflicts. We simulate the backfilling algorithm along with three MCDM mechanisms to avoid scheduling conflicts among the similar tasks. The synthetic workloads are considered to study the performance of the proposed scheduling algorithm. The mechanism suggests an efficient VM allocation and its utilization for deadline‐based tasks in the cloud environment.     

一种雷达信号处理多任务调度算法

雷达信号处理(RSP)系统的实时性一直是系统设计者需要重点考虑的内容之一。为提高雷达系统的实时性,本文提出了一种基于多模式雷达的RSP任务模型,并根据DP-Wrap算法和流水处理的思想提出了一种高效的任务调度算法。研究了在本模型下影响系统最小处理时间的因素,给出了系统最小处理时间的精确表达式,并在此基础上进行数值仿真,给出了根据RSP任务选择处理器个数的依据。     

Processor Utilization Bounds for Real-Time Systems With Precedence Constraints

This paper presents a novel approach to computing tight upper bounds on the processor utilization for general real-time systems where tasks are composed of subtasks and precedence constraints may exist among subtasks of the same task. By careful analysis of preemption effects among tasks, the problem is formulated as a set of linear programming (LP) problems. Observations are made to reduce the number of LP problem instances required to be solved, which greatly improves the computation time of the utilization bounds. Furthermore, additional constraints are allowed to be included under certain circumstances to improve the quality of the bounds.     

协作制造环境下子任务调度的优化方法

协作制造模式为分布式生产设备的高效利用提供了共享合作平台,如何将生产任务高效调度到各设备中是一个复杂的优化问题.基于对任务结构和过程的分析提出子任务调度模型,使不同位置和功能的设备能协作处理一批任务.基于对生产代价和时延的建模,采用遗传算法实现3种优化调度策略,优化目标分别为设备负载均衡、最小化总生产时延和最小化总生产开销.仿真结果表明这3种策略能分别实现对应的优化目标.     

基于两步分布协同拍卖的多机协同任务分配

针对多异构机载平台对不同类型的地面目标执行攻击任务的协同任务分配问题,以平台载弹量以及摧毁任务目标的需弹量建立平台与任务之间的关系,以各平台的任务序列以及执行任务时的武器使用量序列作为决策变量,在基地-任务航路矩阵和任务-任务航路矩阵的基础上,综合考虑平台武器约束、平台航程约束、任务需弹量等约束,建立多机协同任务分配模型。设计了两步分布协同拍卖算法,通过多次生成任务的拍卖招标顺序和基地的拍卖竞标顺序,实现了多机协同任务分配问题的优化求解。仿真结果表明,所建模型和求解算法能够有效合理地解决多机协同对地攻击的任务分配问题。     

High‐Speed Hardware Architectures for ARIA with Composite Field Arithmetic and Area‐Throughput Trade‐Offs

This paper presents two types of high‐speed hardware architectures for the block cipher ARIA. First, the loop architectures for feedback modes are presented. Area‐throughput trade‐offs are evaluated depending on the S‐box implementation by using look‐up tables or combinational logic which involves composite field arithmetic. The sub‐pipelined architectures for non‐feedback modes are also described. With loop unrolling, inner and outer round pipelining techniques, and S‐box implementation using composite field arithmetic over GF(2⁴)², throughputs of 16 Gbps to 43 Gbps are achievable in a 0.25 μm CMOS technology. This is the first sub‐pipelined architecture of ARIA for high throughput to date.     

MDP-Based Budget Allocation for Efficient Cooperative Task Solving

In order to facilitate crowdsourcing-based task solving,complex tasks are decomposed into interdependent subtasks that can be executed cooperatively by individual workers.Aiming to maximize the quality of the final solution subject to the self-interested worker's utility maximization,a key challenge is to allocate the limited budget among the subtasks as the rewards for workers having various levels of abilities.This study is the first attempt to show the value of Markov decision processes (MDPs) for the problem of optimizing the quality of the final solution by dynamically determining the budget allocation on sequentially dependent subtasks under the budget constraints and the uncertainty of the workers' abilities.Our simulation-based approach verifies that compared to some offiine methods where workers' abilities are fully known,our proposed MDP-based payment planning is more efficient at optimizing the final quality under the same limited budget.     

A non‐cooperative game model for reliability‐based task scheduling in cloud computing

Cloud computing is a newly emerging distributed system. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes, so that the tasks can get a balanced allocation or each task's execution cost decreases to the minimum, or the overall system performance is optimal. Unlike task scheduling based on time or cost before, aiming at the special reliability requirements in cloud computing, we propose a non‐cooperative game model for reliability‐based task scheduling approach. This model takes the steady‐state availability that computing nodes provide as the target, takes the task slicing strategy of the schedulers as the game strategy, then finds the Nash equilibrium solution. We also design a task scheduling algorithm based on this model. It can be seen from the experiments that our task scheduling algorithm is better than the so‐called balanced scheduling algorithm.     

An adaptive admittance controller for collaborative drilling with a robot based on subtask classification via deep learning

In this paper, we propose a supervised learning approach based on an Artificial Neural Network (ANN) model for real-time classification of subtasks in a physical human–robot interaction (pHRI) task involving contact with a stiff environment. In this regard, we consider three subtasks for a given pHRI task: Idle, Driving, and Contact. Based on this classification, the parameters of an admittance controller that regulates the interaction between human and robot are adjusted adaptively in real time to make the robot more transparent to the operator (i.e. less resistant) during the Driving phase and more stable during the Contact phase. The Idle phase is primarily used to detect the initiation of task. Experimental results have shown that the ANN model can learn to detect the subtasks under different admittance controller conditions with an accuracy of 98% for 12 participants. Finally, we show that the admittance adaptation based on the proposed subtask classifier leads to 20% lower human effort (i.e. higher transparency) in the Driving phase and 25% lower oscillation amplitude (i.e. higher stability) during drilling in the Contact phase compared to an admittance controller with fixed parameters.     

一种云计算环境下任务调度策略

文章提出的问题是在云计算环境下任务调度策略。该策略的目标是将任务分配到计算单元上达到任务完成总时间最少和资源充分利用。基于此目标文章提出利用遗传算法对任务完成时间进行优化,并为处于空闲状态计算单元动态调整任务分配以改善资源利用率。利用CloudSim仿真平台验证该方法的有效性。     

The Philips Research system for continuous-speech recognition

This paper gives an overview of the Philips Research system for continuous-speech recognition. The recognition architecture is based on an integrated statistical approach. The system has been successfully applied to various tasks in American English and German, ranging from small vocabulary tasks to very large vocabulary tasks and from recognition only to speech understanding. Here, we concentrate on phoneme-based continuous-speech recognition for large vocabulary recognition as used for dictation, which covers a significant part of our research work on speech recognition. We describe this task and report on experimental results. In order to allow a comparison with the performance of other systems, a section with an evaluation on the standard North American Business news (NAB²) task (dictation of American English newspaper text) is supplied.     

面向多模异构任务的无人机集群自主协同优化

针对无人机集群同时遂行多个异构模式、异构价值、异构需求任务时的自主协同优化问题,构建了集群遂行多模异构任务协同优化模型,提出了一种基于重叠式联盟博弈的分布式协作算法。通过综合考虑任务模式、任务价值、任务需求,以及集群中不同无人机成员的资源情况,基于不同任务类型下联盟内任务成功率和效能计算,优化无人机任务选择和资源分配并实现算法收敛和系统稳定,以及优化的分布式多机协同。仿真结果表明,所提方法能有效提高系统效用和任务成功率,并能在不同环境下实现面向异构任务目标的高效协同。     

Multifactor task allocation problem in wireless sensor and actuator networks

Task allocation is an essential part of many military applications of WSAN such as intelligent minefield.The key problem of task allocation decision in these systems for the optimal scheme on task allocation is how to obtain the node-target assignment.Combing with the factors of target’s parameters and node’s own status,the various influence factors of task efficiency were analyzed and the positive and negative indicators were processed by range normalization method separately.The linear-weighted task efficiency function was proposed in the applications of WSAN such as intelligent minefield.It can be utilized as the evaluation index of assignment schemes since it reflected the integrated impact of target’s threat and node’s value on the system more comprehensively.Task efficiency function can be constructed flexibly based on the demand of different multifactor task allocation application.The essence of the problem was how to make node-target assignment to achieve the maximum task efficiency of the whole system.This problem turned into assignment problem and can be solved.Finally,an application was implemented to demonstrate this scheme.The results show that the model is suitable for small-scale multifactor task allocation problem in intelligent minefield system.     

Task scheduling mechanisms in cloud computing: A systematic review

Today, cloud computing has developed as one of the important emergent technologies in communication and Internet. It offers on demand, pay per use access to infrastructure, platforms, and applications. Due to the increase in its popularity, the huge number of requests need to be handled in an efficient manner. Task scheduling as one of the challenges in the cloud computing supports the requests for assigning a particular resource so as to perform effectively. In the resource management, task scheduling is performed where there is the dependency between tasks. Many approaches and case studies have been developed for the scheduling of these tasks. Up to now, a systematic literature review (SLR) has not been presented to discover and evaluate the task scheduling approaches in the cloud computing environment. To overcome, this paper presents an SLR‐based analysis on the task scheduling approaches that classify into (a) single cloud environments that evaluate cost‐aware, energy‐aware, multi‐objective, and QoS‐aware approaches in task scheduling; (b) multicloud environment that evaluates cost‐aware, multi‐objective, and QoS‐aware task scheduling; and (c) mobile cloud environment that is energy‐aware and QoS‐aware task scheduling. The analytical discussions are provided to show the advantages and limitations of the existing approaches.     

A genetic algorithm‐based task scheduling for cloud resource crowd‐funding model

With the rapid development of cloud computing, the number of cloud users is growing exponentially. Data centers have come under great pressure, and the problem of power consumption has become increasingly prominent. However, many idle resources that are geographically distributed in the network can be used as resource providers for cloud tasks. These distributed resources may not be able to support the resource‐intensive applications alone because of their limited capacity; however, the capacity will be considerably increased if they can cooperate with each other and share resources. Therefore, in this paper, a new resource‐providing model called “crowd‐funding” is proposed. In the crowd‐funding model, idle resources can be collected to form a virtual resource pool for providing cloud services. Based on this model, a new task scheduling algorithm is proposed, RC‐GA (genetic algorithm for task scheduling based on a resource crowd‐funding model). For crowd‐funding, the resources come from different heterogeneous devices, so the resource stability should be considered different. The scheduling targets of the RC‐GA are designed to increase the stability of task execution and reduce power consumption at the same time. In addition, to reduce random errors in the evolution process, the roulette wheel selection operator of the genetic algorithm is improved. The experiment shows that the RC‐GA can achieve good results.     

A Modelling of Context-Aware Elderly Healthcare Eco-System-(CA-EHS) Using Signal Analysis and Machine Learning Approach

In cloud computing, more often times cloud assets are underutilized because of poor allocation of task in virtual machine (VM). There exist inconsistent factors affecting the scheduling tasks to VMs. In this paper, an effective scheduling with multi-objective VM selection in cloud data centers is proposed. The proposed multi-objective VM selection and optimized scheduling is described as follows. Initially the input tasks are gathered in a task queue and tasks computational time and trust parameters are measured in the task manager. Then the tasks are prioritized based on the computed measures. Finally, the tasks are scheduled to the VMs in host manager. Here, multi-objectives are considered for VM selection. The objectives such as power usage, load volume, and resource wastage are evaluated for the VMs and the entropy is calculated for the measured objectives and based on the entropy value krill herd optimization algorithm prioritized tasks are scheduled to the VMs. The experimental results prove that the proposed entropy based krill herd optimization scheduling outperforms the existing general krill herd optimization, cuckoo search optimization, cloud list scheduling, minimum completion cloud, cloud task partitioning scheduling and round robin techniques.

     

Constraint transformation for IC physical design

In a top-down design methodology, design tasks are divided into simpler subtasks across levels of a hierarchy as an effective divide-and-conquer technique. For every task, tolerances are defined on all performance characteristics to take into account parasitics, mismatches, and other nondeterministic process parameter variations. Constraint transformation is a process used to translate performance specifications into subtask requirements. This paper introduces the problem of constraint transformation and describes some formal solutions for analog circuit applications. Examples illustrate the methodology and show the suitability of this approach in industrial-strength applications     

Dynamic Task-Based Anycasting in Mobile Ad Hoc Networks

Mobile ad hoc networks (MANETs) have received significant attention in the recent past owing to the proliferation in the numbers of tetherless portable devices, and rapid growth in popularity of wireless networking. Most of the MANET research community has remained focused on developing lower layer mechanisms such as channel access and routing for making MANETs operational. However, little focus has been applied on higher layer issues, such as application modeling in dynamic MANET environments. In this paper, we present a novel distributed application framework based on task graphs that enables a large class of resource discovery based applications on MANETs. A distributed application is represented as a complex task comprised of smaller sub-tasks that need to be performed on different classes of computing devices with specialized roles. Execution of a particular task on a MANET involves several logical patterns of data flow between classes of such specialized devices. These data flow patterns induce dependencies between the different classes of devices that need to cooperate to execute the application. Such dependencies yield a task graph (TG) representation of the application.We focus on the problem of executing distributed tasks on a MANET by means of dynamic selection of specific devices that are needed to complete the tasks. In this paper, we present simple and efficient algorithms for dynamic discovery and selection (instantiation) of suitable devices in a MANET from among a number of them providing the same functionality. This is carried out with respect to the proposed task graph representation of the application, and we call this process Dynamic Task-Based Anycasting. Our algorithm periodically monitors the logical associations between the selected devices, and in the event of a disruption in the application owing to failures in any component in the network, it adapts to the situation and dynamically rediscovers the affected parts of the task graph, if possible. We propose metrics for evaluating the performance of these algorithms and report simulation results for a variety of application scenarios differing in complexity, traffic, and device mobility patterns. From our simulation studies, we observed that our protocol was able to instantiate and re-instantiate TG nodes quickly and yielded high effective throughput at low to medium degrees of mobility and not much below 70% effective throughput for high mobility scenarios.     

Watermarking Hardware Based on Wavelet Coefficients Quantization Method

Progress in the digital multimedia technologies during the last decade has offered many facilities in the transmission, reproduction, and manipulation of data. However, this advancement has also brought the problem such as copyright protection for content providers. Digital watermarking is a proposed solution for copyright protection for multimedia. The goal of hardware assisted watermarking is to achieve low power usage, real-time performance, reliability, and ease of integration with existing consumer electronic devices. An efficient architecture for transform domain watermarking using quantization approach is proposed here. This architecture is optimized using pipelining. The main objective of this paper is to propose a very-large-scale integration (VLSI) architecture for robust and blind image watermarking chip. Watermarking architectures with and without pipeline are synthesized using Xilinx’s ISE for a field-programmable gate array (FPGA), and then semi-custom integrated chip is designed using UMC 0.18 μm technology standard cell library for both these architectures. The proposed optimized pipelined watermarking encoder core requires 0.027 mm² area and 0.074 mW power.