scheduling of tasks

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Abstract: Task scheduling plays the key role systems in cloud computing. Scheduling of tasks cannot be

Abstract: Task scheduling plays the key role systems in cloud computing. Scheduling of tasks cannot be

Abstract: Task scheduling plays the key role systems in cloud computing. Scheduling of tasks cannot be done on the basis of single criteria but under a lot of the regulations and rules that we can term as an agreement between users and providers of cloud. This agreement is nothing else the quality of service that the user wants from the providers. Providing good quality of services to the users according to the agreement is a decisive task of the providers at the same time there are a large number of tasks running at the provider’s side. The task scheduling problem can be viewed as the finding or searching an optimal assignment or mapping set of the subtasks of different tasks over the available set of the resources (computers or processor machines) so that we can achieve the desired goals for tasks.
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STRENGTHENING ANTI JAM GPS SYSTEM WITH ADAPTIVE PHASE ONLY NULLING USING 
EVOLUTIONARY ALGORITHMS

STRENGTHENING ANTI JAM GPS SYSTEM WITH ADAPTIVE PHASE ONLY NULLING USING EVOLUTIONARY ALGORITHMS

This article proposes a method called residual fair sharing algorithm for scheduling of tasks in a heterogeneous grid environment. The proposed Residual Fair Sharing scheduling scheme is fairer and exploit the available multiprocessor Grid resources with less sensitive to processor capacity variations than max-min sharing scheme. The simulations have been conducted by thousands of tasks of varying size and workload variance, submitted to a multiprocessor computing system comprising of hundreds of processors of varying capacity. The experimental study revealed that the residual sharing algorithm performs better with respect to max min algorithm especially; there is a noticeable reduction in the time complexity between the two algorithms. However, in all conditions, the proposed Residual algorithm is more effective and outperforms max min algorithm.
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Schedulability Analysis of Distributed Real Time Applications under Dependence and Several Latency Constraints

Schedulability Analysis of Distributed Real Time Applications under Dependence and Several Latency Constraints

This paper focuses on the analysis of real-time non preemptive mul- tiprocessor scheduling with precedence and several latency con- straints. It aims to specify a schedulability condition which enables a designer to check a priori -without executing or simulating- if its scheduling of tasks will hold the precedences between tasks as well as several latency constraints imposed on determined pairs of tasks. It is shown that the required analysis is closely linked to the topological structure of the application graph. More precisely, it depends on the configuration of tasks paths subject to latency con- straints. As a result of the study, a sufficient schedulability con- dition is introduced for precedences and latency constraints in the hardest configuration in term of complexity with an optimal num- ber of processors in term of applications parallelism. In addition, the proposed conditions provides a practical lower bounds for gen- eral cases. Performances results and comparisons with an optimal approach demonstrate the effectiveness of the proposed approach.
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Survey of Task Scheduling Algorithms in Cloud Computing

Survey of Task Scheduling Algorithms in Cloud Computing

Task Scheduling plays a key role to improve flexibility and reliability of systems in cloud. The main reason behind scheduling tasks to the resources in accordance with the given time bound. Cloud consists of a number of resources that are different with one other via some means and cost of performing tasks in cloud. Using resources of cloud is different so scheduling of tasks in cloud is different from the traditional methods of scheduling and so scheduling of tasks in cloud need better attention to be paid because services of cloud depends on them[4].
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ANALYSIS OF INFORMATION SYSTEM QUALITY OF SERVICE ON BSI ACADEMYS ENVIRONMENT 
USING WEBQUAL METHODS, IMPORTANCE PERFORMANCE ANALYSIS AND FISHBONE

ANALYSIS OF INFORMATION SYSTEM QUALITY OF SERVICE ON BSI ACADEMYS ENVIRONMENT USING WEBQUAL METHODS, IMPORTANCE PERFORMANCE ANALYSIS AND FISHBONE

In first scenario, 15 nodes divided into two large groups and two small groups were deployed in the region. Due to less number of nodes and group of nodes, there were few choices for allocation. Therefore, performance gains are not significant. In second scenario, large numbers of nodes were deployed into small groups, so there were several choices for allocation. NRA allocated task to nodes with better connection quality. Furthermore, when large numbers of tasks were submitted, they were evenly distributed to nodes in order to balance the data transfer load in the network. PRA allocated tasks to nodes based on transmission power and did not consider network conditions. Therefore, tasks were allocated to same group of nodes because they were accessible at minimum transmission power. This increased data transfers and degraded communication performance and connection quality between nodes in that region. Thus completion time of tasks allocated in that region was increased.
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Tuples: A New Scheduling Algorithm

Tuples: A New Scheduling Algorithm

Many scheduling algorithms have been developed. The goal of the scheduling problem is to maximize or minimize an objective function such as turnaround time (the amount of time between starting a task and its finish ), makespan (the maximum completion time), response time (amount of time from submission of a task to its first response), CPU utilization (keeping the CPU as busy as possible), throughput (number of processes completed per a unit of time ), or waiting time (the amount of time the task is waiting in the ready queue) [3].

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A New Algorithm for Scheduling Parallel Tasks in Utility Grids using Reserve Resources for Tasks with Deadline

A New Algorithm for Scheduling Parallel Tasks in Utility Grids using Reserve Resources for Tasks with Deadline

Abstract — In recent years, Grid computing systems have emerged as a solution to achieve distributed systems. Grid System is a collection of computing resources and users that are scattered around the world. These systems are developing and becoming more widespread with ever-increasing speed. Development of Grids and increase of the number of available resources and also increase in the number of users’ requests to perform their computing tasks with minimum cost and in the least possible time, have made the issue of resource allocation and their scheduling as a challenge in such systems. On the other hand, some of users’ requests may have deadlines and this issue makes scheduling problem more critical. In this paper for the first time, we have proposed a method for tasks scheduling using some reserve resources, which in addition to considering minimization of time and costs, it also considers tasks deadline. The performance evaluation is conducted using MATLAB software and is compared by MinCTT method. We have shown that in addition to performance improvement for tasks which have deadline, less time complexity shall also be obtained.
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Tasks Scheduling on Parallel Heterogeneous Multi processor Systems using Genetic Algorithm

Tasks Scheduling on Parallel Heterogeneous Multi processor Systems using Genetic Algorithm

Tasks scheduling using genetic algorithms in parallel multi-processor systems 4.1 Chromosomes and Encoding The purpose of this study is to find a sequence of tasks so that the number of [r]

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Optimal scheduling of periodic tasks on multiple identical processors

Optimal scheduling of periodic tasks on multiple identical processors

Lemma 3.1 states that if we can nd a feasible ow in which all arc ows are integer, then we can construct a schedule for the original periodic task scheduling problem that satises both the Task and Processor constraints. However, it does not answer the question of whether such a ow exists. The next lemma addresses this issue.

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Scheduling the Tasks in Cloud Computing to Enhance the System Performance

Scheduling the Tasks in Cloud Computing to Enhance the System Performance

The task scheduling algorithm in Cloud Computing is an efficient approach for enhancing the system performance by scheduling and maintaining system resources in an appropriate manner. It supports de-duplication mechanism where files with same contents cannot be uploaded into the cloud thereby avoiding the chances of duplication. The algorithm supports multitasking with single CPU thereby providing a good quality of service. Main advantage of the Task Scheduling Algorithm is user authentication security where only registered users can access the data. This prevents unauthorized users like intruders and attackers from accessing the original files.
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Efficient Task Scheduling Approach Relevant to the Hardware/Software Co-Design of Embedded System

Efficient Task Scheduling Approach Relevant to the Hardware/Software Co-Design of Embedded System

is the partitioning. This is not just partition- ing between hardware and software. It takes also reprogrammable hardware into account, letting designers analyse the additional trade- offs that this kind of design implies. Config- urable hardware has advantages and disadvan- tages: it enables some flexibility and the use of more complex scheduling algorithms, and increases the cost of debugging hardware, re- spectively. Therefore, many developers of ESs choose to run their applications on top of ei- ther an embedded operating system or real-time executive.

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Branch and Bound Method for Scheduling Precedence Constrained Tasks on Parallel Identical Processors

Branch and Bound Method for Scheduling Precedence Constrained Tasks on Parallel Identical Processors

algorithm is presented for the scheduling problem with tasks have to be executed on several parallel identical processors. This problem relates to the scheduling prob- lem [2,3],it has many applications, and it is N P -hard [4]. Branch and bound method [5,6] allows to obtain an exact solution or, when the number of iterations is restricted, a fairly good approximation of the solution. A new method for evaluating partial solutions, selecting the next task and new ways of reducing the exhaustive search was de- signed. To illustrate the effectiveness of this approach we tested it on randomly generated task graphs.
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Unavailable Time Aware Scheduling of Hybrid Task on Heterogeneous Distributed System

Unavailable Time Aware Scheduling of Hybrid Task on Heterogeneous Distributed System

et al.proposed a centralized and decentralized scheduling algorithm in literature[9]. To schedule concurrent bag-of-tasks, the online and off-line scheduling algorithms are presented by Benoit et al.[10]. In literature[11], a decentralized scheduling algorithm, which minimizes the maximum stretch among user-submitted tasks, is designed. Yang Y et al.[12] take the constraints of time, cost, and security into consideration, a scheduling algorithm for data-intensive tasks is designed. Literature [13] investigated both two problems: optimizing the makespan of the tasks under the constraints of energy, or minimizing energy consumption subject to makespan. However, this paper studied the static resource allocation to optimize makespan and energy robust stochastic for bag-of-tasks(BoT) on a heterogeneous computing system. A multi-objective optimization model, which minimizes makespan and resource cost, is established in literature[14]. To solve the optimization model, a scheduling algorithm based on the ordinal optimization method is designed. However, the scheduling algorithm is inefficient when the task number or processing node number is large.
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A Column Generation Approach to Scheduling of Real-Time Networks

A Column Generation Approach to Scheduling of Real-Time Networks

The remainder of Section III is organized as follows. In Section III-A we expand on the LP relaxation. In Sec- tion III-B we explain the branch and bound algorithm. In Section III-C we present the models to scheduling periodic real-time tasks. In Section III-D we show how to handle task chains with end-to-end deadlines. In Section III-E we expand on the FlexRay bus for the global communication.

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Multiobjective GA for Real Time Task Scheduling

Multiobjective GA for Real Time Task Scheduling

Equation (1) and (2) are the objective function in this scheduling problem. In (1) means to minimize the total number of processors used and (2) means to minimize total tardiness of tasks. Constraints conditions are shown from (3) to (5). Equation (3) means that task can be started after its earliest start time, begin its deadline. Equation (4) defines the earliest start time of task based on precedence constraints. Equation (5) is nonnegative condition for the number of processors.

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AFT Scheduling Algorithm for Multicore Architecture: A Novel Approach

AFT Scheduling Algorithm for Multicore Architecture: A Novel Approach

multiple transient faults. In case of multiple faults, the feasibility rate is considerably higher. It is used to dynamically select fault recovery method to overcome the faults occurring in the system. This method makes use of the task utilization for the critical and non- critical tasks. The paper on Tolerance to multiple transient faults [8], it is noted that all the methods proposed in this paper are used to sense only some special types of faults, and therefore there is no appropriate method to detect arbitrary faults that occur in real-time systems.
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Optimization of Tasks using Scheduling Algorithms in Cloud Computin

Optimization of Tasks using Scheduling Algorithms in Cloud Computin

garage. The expenses of numerous property range. As goals be, the charges of responsibilities are additionally specific. Consequently, it is useful to mirror project charges inside the event that we recall the call for distinction of the responsibilities for the belongings in element. On the way to cope with this issue, we propose a aid cost technique. This version can replicate the requests of the duties for the assets in element. Likewise, this paper proposes a multi-goal optimization scheduling version that takes into document the 2 imperatives of execution and price range price. Subsequently, it tackles the multi-target optimization scheduling trouble using the ant colony set of guidelines, which has an superb preferred standpoint in tending to this combinatorial optimization problem.But, it is easy to fall into a nearby most suitable. Even as using the ant colony set of policies, consequently, this paper proposes a licensed ant colony set of rules that might estimate further to adjust the super of the answer with the intention of avoid falling into that local most excellent. The number one goal of this scheduling technique is overall performance and rate variety price; due to the fact it's miles primarily based on the ant colony optimization set of rules, it is named PBQACOMMP. We suggest three models for challenge optimization: there are resource fee model, multi-intention optimization-scheduling model and qualified ant colony optimization model (it's going to solves the trouble that the particular ant colony algorithm that continuously falls into the nearby gold trendy) interest Scheduling is a way in which a selected aid is appointed to an challenge at a predetermined time. The number one motivation at the back of the planning is to increase useful aid talent and its vital format is to lower keeping up instances. The planning of Jobs in allocated computing is a standout many of the maximum difficult jobs. "A green scheduling can extend the framework's proficiency." [2] In 2nd part of this text we keep in mind approximately the associated work. In 1/3 part we make clear The project Optimization set of rules dependent on the MAX-MIN, MIN-MIN and Priorities (PBQACOMMP) and its centers. The correlation amongst PBQACOMMP and distinct algorithms is in fourth detail. Furthermore, stop is spoken to in fifth component.
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Scheduling of Real Time Tasks

Scheduling of Real Time Tasks

Euiseong Seo, et. al. proposed on Energy Efficient Scheduling of Real-Time Tasks on Multicore Processors in November 2008. They tackles the problem of reducing power consumption in a periodic real-time system using DVS on a multicore processor. The processor is assumed to have the limitation that all cores must run at the same performance level. To reduce the dynamic power consumption of such a system, They suggest two algorithms: Dynamic Repartitioning and Dynamic Core Scaling. The former is designed to reduce mainly the dynamic power consumption, and the latter is for the reduction of the leakage power consumption. In the assumed environment, the best case dynamic power consumption is obtained when all the processor cores have the same performance demand. Dynamic Repartitioning tries to maintain balance in the performance demands by migrating tasks between cores during execution accompanying with deadline guarantee. Leakage power is more important in multicore processors than in traditional unicore processors due to their vastly increased number of integrated circuits. Indeed, a major weakness of multicore processors is their high leakage power under low loads. To relieve this problem, Dynamic Core Scaling deactivates excessive cores by exporting their assigned tasks to the other activated cores.
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Scheduling for production teams   Pages 339-350
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Scheduling for production teams Pages 339-350 Download PDF

Results above confirm that the approach to production teams scheduling, which is based on application of order utility functions, produces a satisfactory schedule. The problem solution, which uses the “greedy” algorithm, is able to construct the search tree starting from the initial system state. This algorithm determines the domination bound for tree nodes at each level according to the criterion V , and in addition according to the value of necessary job release date g . The program in VBA language for MS Excel was designed, and the example of its application for scheduling was made. The calculated schedule may be improved in interactive mode, if a certain job is postponed to the next week.
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Task Scheduling Using an Adaptive PSO Algorithm in Cloud Computing Environment

Task Scheduling Using an Adaptive PSO Algorithm in Cloud Computing Environment

When load balancing is considered, tasks are transferred from one VM to another for reduction , as well as response time. The task processing time varies depending on the speed of the virtual machines in different VMs. In the case of a transfer, the execution time of the task may differ optimally due to load balancing. The main purpose of scheduling adaptive tasks in the PSO algorithm is that tasks should be distributed in a virtual machine to minimize the amount of production and maximize the use of resources.

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