Task Scheduling Research Papers - Academia.edu (original) (raw)

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- by
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- Computer Science, Integer Programming, Data Replication, Task Scheduling
- by Bikash sharma and +1
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- Modeling, Metrics, Benchmarking, Development Methodology

Petroleum industry production systems are highly automatized. In this industry, all functions (e.g., planning, scheduling and maintenance) are automated and in order to remain competitive researchers attempt to design an adaptive control system which optimizes the process, but also able to adapt to rapidly evolving demands at a fixed cost. In this paper, we present a multi-agent approach for the dynamic task scheduling in petroleum industry production system. Agents simultaneously insure effective production scheduling and the continuous improvement of the solution quality by means of reinforcement learning, using the SARSA algorithm. Reinforcement learning allows the agents to adapt, learning the best behaviors for their various roles without reducing the performance or reactivity. To demonstrate the innovation of our approach, we include a computer simulation of our model and the results of experimentation applying our model to an Algerian petroleum refinery.

- by Hesham Ali
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- Computer Science, Heuristics, Scheduling, Parallel Processing

With respect to on-line scheduling algorithms that must direct the service of sporadic task requests we quantify the benefit of clairvoyancy, i.e., the power of possessing knowledge of various task parameters of future events. Specifically, we consider the problem of preemptively sheduling sporadic task requests in both uni- and multi-processor environments. If a task request is successfuly scheduled to completion, a value equal to the task's execution time is obtained; otherwise no value is obtained. We prove that no on-line scheduling algorithm can guarantee a cumulative value greater than 1/4th the value obtainable by a clairvoyant scheduler; i.e., we prove a 1/4th upper bound on the competitive factor of on-line real-time schedulers. We present an online uniprocessor scheduling algorithm TD 1 that actually has a competitive factor of 1/4; this bound is thus shown to be tight. We further consider the effect of restricting the amount of overloading permitted (the loading facto...

- by Dennis Shasha
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- Computer Science, Distributed Computing, Information Science, Scheduling
- by Hesham Ali
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- Heuristics, Computer Aided Manufacturing, Resource Allocation, Resource Management
- by Orhan Engin
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- Information Systems, Applied Mathematics, Flow Shop Scheduling, Experimental Design
- by omar al jadaan and +1
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- Genetic Algorithm, Optimization Problem, Grid Scheduling, Task Scheduling
- by Marek Muszynski
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- History, Scheduling, Constraint Programming, Multidisciplinary
- by Cesar Marcon and +4
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- Embedded Software, OPERATING SYSTEM, Real Time Systems, Time Management
- by Salim Hariri
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- Distributed Computing, Computer Software, Critical path of history, High performance
- by Peter Buckle
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- Social Support, Industrial Ergonomics, Focus Group, Active Control

With the advent of the number of smart devices across the globe, increasing the number of users using the Internet. The main aim of the fog computing (FC) paradigm is to connect huge number of smart objects (billions of objects) that can make a bright future for smart cities. Due to the large deployments of smart devices, devices are expected to generate huge amounts of data and forward the data through the Internet. FC also refers to an edge computing framework that mitigates the issue by applying the process of knowledge discovery using a data analysis approach to the edges. Thus, the FC approaches can work together with the internet of things (IoT) world, which can build a sustainable infrastructure for smart cities. In this paper, we propose a scheduling algorithm namely the weighted round-robin (WRR) scheduling algorithm to execute the task from one fog node (FN) to another fog node to the cloud. Firstly, a fog simulator is used with the emergent concept of FC to design IoT inf...

- by Ahmad AL Smadi
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- Computer Networks, Smart City, Task Scheduling, Fog Computing
- by Harendra Kumar
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- Computer Systems, Task Scheduling, Scheduling Algorithm
- by nedhal alsaiyd
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- Genetic Algorithms, Genetic Algorithm, Data Structure, Multiprocessor Scheduling
- by David Meyer
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- Psychology, Psychological Assessment, Cognitive Science, Computability Theory

Cloud computing is the requirement based on clients and provides many resources that aim to share it as a service through the internet. For optimal use, Cloud computing resources such as storage, application, and other services need managing and scheduling these services. The principal idea behind the scheduling is to minimize loss time, workload, and maximize throughput. So, the scheduling task is essential to achieve accuracy and correctness on task completion. This paper gives an idea about various task scheduling algorithms in the cloud computing environment used by researchers. Finally, many authors applied different parameters like completion time, throughput, and cost to evaluate the system.

- by Subhi R M Zeebaree
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- Cloud Computing, Cloud Services, Task Scheduling

The rise of multi-cloud systems has been spurred. For safety-critical missions, it is important to guarantee their security and reliability. To address trust constraints in a heterogeneous multi-cloud environment, this work proposes a novel scheduling method called matching and multi-round allocation (MMA) to optimize the makespan and total cost for all submitted tasks subject to security and reliability constraints. The method is divided into two phases for task scheduling. The first phase is to find the best matching candidate resources for the tasks to meet their preferential demands including performance, security, and reliability in a multi-cloud environment; the second one iteratively performs multiple rounds of re-allocating to optimize tasks execution time and cost by minimizing the variance of the estimated completion time. The proposed algorithm, the modified cuckoo search (MCS), hybrid chaotic particle search (HCPS), modified artificial bee colony (MABC), max-min, and min-min algorithms are implemented in CloudSim to create simulations. The simulations and experimental results show that our proposed method achieves shorter makespan, lower cost, higher resource utilization, and better trade-off between time and economic cost. It is more stable and efficient.

- by IEEE/CAA J. Autom. Sinica
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- Security, Reliability, Task Scheduling, Multi-cloud environment

This paper presents a manned-vehicle/unmanned-aerial-vehicle (UAV) mission system that enables an operator in a manned aircraft to issue mission level commands to an autonomous aircraft in real time. A natural language interface allows the manned and unmanned vehicle to communicate in languages understood by both agents. A task scheduler transforms the commands into a dynamic mission plan consisting of task waypoints. These are then given to a mixed-integer linear programming (MILP)-based ...

- by José Manuel Urriza
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- Mobile Device, Real Time, Task Scheduling, Performance Optimization

The scheduled task is quite a common task in Ruby on Rails application. Cron can be scheduled to send a reminder email, or scheduling to crawl something from another website on daily basis. Ruby on Rails does not have this type of “out-of-the-box” feature, but we can use the Cron job to achieve the purpose. In this blog, let’s quickly go through how to use Cron job.

- by Michelle Leo
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- Web Applications, Mobile application development, Mobile apps, Ruby on Rails
- by Mohand Kechadi
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- Computer Science, Grid Computing, Computational Modeling, Computer Networks

Scheduling of tasks is one of the main concerns in the Cloud Computing environment. The whole system performance depends on the used scheduling algorithm. The scheduling objective is to distribute tasks between the Virtual Machines and balance the load to prevent any virtual machine from being overloaded while other is underloaded. The problem of scheduling is considered an NP-hard optimization problem. Therefore, many heuristics have been proposed to solve this problem up to now. In this paper, we propose a new Spider Monkeys algorithm for load balancing called Spider Monkey Optimization Inspired Load Balancing (SMO-LB) based on mimicking the foraging behavior of Spider Monkeys. It aims to balance the load among virtual machines to increase the performance by reducing makespan and response time. Experimental results show that our proposed method reduces tasks' average response time to 10.7 seconds compared to 24.6 and 30.8 seconds for Round Robin and Throttled methods respectively. Also, the makespan was reduced to 21.5 seconds compared to 35.5 and 53.0 seconds for Round Robin and Throttled methods respectively.

- by Mohammed Al-Marie and +1
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- Optimization (Mathematical Programming), Artificial Intelligence, Python, metaheuristics in Optimization

The provider of the service today is expected to serve many users. The increasing number of requests for services from the users to the providers has caused the providers of the service to have to offer scalable solutions. In the cloud computing environment, various scheduling algorithms have been proposed, such as the (SJF) and (FCFS) algorithms. Using cloud computing, the paper seeks to improve the shortest job scheduling algorithm. In tasks scheduling (TS), makepan and response time are the most important parameters. In order to reduce the length of time to complete the last task (Makespan), decrease the average response time, and maximize resource utilization, we present a Shortest Job First algorithm. This method consists of two functions, one is the calculation of the average task length, and the other is load balancing between virtual machines. Sending the longest tasks to the fastest machine is one of the benefits of SJF.

- by Abhishek Kumar Singh
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- Computer Science, Operating Systems, Cloud Computing, CloudSim

In the present scenario of Information and Technology, Cloud Computing has become buzzword. Here, dynamically scalable services and distributed virtualized resources are provided over the internet on pay-as-per use basis. Instantaneously, there are huge numbers of users accessing services of cloud and various tasks need to be handled in the cloud computing environment, the high effective task scheduling algorithm is one of the crucial problems that the cloud computing is required to solve. Cloud task scheduling is an NP-hard optimization problem and many different meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy dynamically according to the changing environment and the types of tasks. Aiming to the model structure of cloud computing, in this article we have introduced modified Ant Colony Optimization algorithm (ACO) to combine with optimized task scheduling algorithm which is dynamic and adapt according to the availability of resources. This paper relates advanced heuristic and combinatorial optimization problem solving technique i.e. Ant Colony Optimization (ACO) which outperforms over other evolutionary algorithm and optimization technique. In proposed algorithm, group of tasks are represented as workflow are scheduled by ants based on heuristic function to the virtual machine. This means all the available tasks are efficiently scheduled to the very best of its optimization. We recompile the cloudsim and simulate the proposed algorithm and results of this algorithm are compared with sequential task scheduling. The experimental results indicates that proposed algorithm has high performance in terms of least execution time that considers heterogeneous resources and elasticity of clouds that can be dynamically acquired on pay-per-use basis. This algorithm is not only beneficial to user and service provider, but also provides better efficiency by applying load-balancing feature i.e. benefit at system level.

- by GJESR Journal
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- Ant Colony Optimization, Pheromones, Cloud Computing, Task Scheduling

Cloud computing is a development of parallel, distributed and grid computing which provides computing potential as a service to clients rather than a product. Clients can access software resources, valuable information and hardware devices as a subscribed and monitored service over a network through cloud computing.Due to large number of requests for access to resources and service level agreements between cloud service providers and clients, few burning issues in cloud environment like QoS, Power, Privacy and Security, VM Migration, Resource Allocation and Scheduling need attention of research community.Resource allocation among multiple clients has to be ensured as per service level agreements. Several techniques have been invented and tested by research community for generation of optimal schedules in cloud computing. A few promising approaches like Metaheuristics, Greedy, Heuristic technique and Genetic are applied for task scheduling in several parallel and distributed systems. This paper presents a review on scheduling proposals in cloud environment.

Cloud Computing is the latest networking technology and also popular archetype for hosting the application and delivering of services over the network. The foremost technology of the cloud computing is virtualization which enables of building the applications, dynamically sharing of resources and providing diverse services to the cloud users. With virtualization, a service provider can guarantee Quality of Service to the user at the same time as achieving higher server consumption and energy competence. One of the most important challenges in the cloud computing environment is the VM placemnt and task scheduling problem. This paper focus on Metaheuristic Swarm Optimisation Algorithms(MSOA) deals with the problem of VM placement and Task scheduling in cloud environment. The MSOA is a simple parallel algorithm that can be applied in different ways to resolve the task scheduling problems. The proposed algorithm is considered an amalgamation of the SO algorithm and the Cuckoo search (CS) algorithm; called MSOACS. The proposed algorithm is evaluated using Cloudsim Simulator. The results proves the reduction of the makespan and increase the utilization ratio of the proposed MSOACS algorithm compared with SOA algorithms and Randomised Allocation Allocation (RA).

- by J4R - Journal for Research
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- Virtualization, Metaheuristics, Quality of Service, Task Scheduling
- by min you
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- Distributed Computing, Computer Software, Critical path of history, High performance
- by Toni Juan and +2
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- Information Systems, Computer Architecture, Computer Hardware, Parallel Processing
- by saeed parsa
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- Task Scheduling

Over the years, we have worked on hierarchical scheduling frameworks from a theoretical point of view. In this paper we present our initial results of the implementation of our hierarchical scheduling framework in a commercial operating system VxWorks. The purpose of the implementation is twofold:(1) we would like to demonstrate feasibility of its implementation in a commercial operating system, without having to modify the kernel source code, and (2) we would like to present detailed figures of various key properties ...

- by Reinder Bril
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- OPERATING SYSTEM, Source Code, Point of View, Task Scheduling

Task scheduling is one of the most important research topics in Cloud Computing environment. Dynamic Multi-objective task scheduling in Cloud Computing are proposed by using modified particle swarm optimization. This paper presents efficient allocation of tasks to available virtual machine in user level base on different parameters such as reliability, time, cost and load balancing of virtual machine. Agent used to create dynamic system. We propose mathematical model multi-objective Load Balancing Mutation particle swarm optimization (MLBMPSO) to schedule and allocate tasks to resource. MLBMPSO considers two objective functions to minimize round trip time and total cost. Reliability can be achieved in system by getting task failure to allocate and reschedule with available resource based on load of virtual machine. Experimental results demonstrated that MLBMPSO outperformed the other algorithms in time and cost.

- by ACSIJ Journal
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- Particle Swarm Optimization, Cloud Computing, Load Balancing, Task Scheduling

Cloud computing is a fast-emerging area with so many benefits for the end users. Many leading firms make use of cloud mainly to store their huge volume of data and other such purposes. When speaking about cloud, we need to focus on the data centers. This is because the virtual machines (VM) present in the cloud are mapped to the physical devices that are present in the data centers. This seems to be a simple process, but the tricky part is when the number of users increases. Number of users send the request for a particular or more than one service to the cloud. The service providers have to handle the entire request and also concentrate on the satisfaction of the end user along with the quality of service (QoS). Most important thing is that the service provider must give a quick response to the cloud consumers, i.e., the delay must be minimized. Only then the QoS provided could be maintained. In this paper, we are going to see about how the tasks are being scheduled and allocated optimally to the VMs present in the cloud. We are going to delineate the various algorithms that are implemented so far for this process. In this paper, we have described an overview of cloud, task scheduling and VM allocation.

- by Publishing India Group
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- Cloud Computing, Data Center, Service Provider, Task Scheduling
- by Kleanthis Thramboulidis
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- Distributed Computing, Computer Networks, Scheduling, Distributed Control

Today the number of students in every educational institution increased a lot. As a result more courses are to be offered by the institutions and employ more teachers as well. Day by day due the increase of the courses and course teachers the assigning of teachers to respective courses has become time worthy and difficult.This raises a problem in the educational sector. Till now a lot of researches have been carried out to find reasonable algorithms for efficient automated processes. In this paper we tried to solve the problem of assigning teachers to respective courses. The problem arises when a timetable is to be prepared without overlapping the class timings. We have developed an algorithm for automated system based on searching and sorting. Moreover the process will advance with two separate lists of teachers and course. As an additional constraint we have considered the preferred course for each teacher. The final output will be a complete time table. The algorithm was designed on the basis of a simulation with a set of teachers and class schedules of a University. The simulation produced solutions that can be favorably compared with the solutions proposed by the experts.

- by Mir Riyanul Islam and +2
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- Class Room Scheduling System, Course Scheduling Problems, Task Scheduling, Automated Course Scheduling Timetable

- by Karwan Jacksi
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- Cloud Computing, Cloud Services, Task Scheduling

The paper presents a performance model that can be used to optimally schedule arbitrary tasks on a network of heterogeneous computers when there is an upper bound on the size of the task that can be solved by each computer. We formulate a problem of partitioning of an n-element set over p heterogeneous processors using this advanced performance model and give its efficient solution of the complexity O(p3 × log2 n).

- by Ravi Reddy
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- Computer Science, Distributed Computing, Grid Computing, Parallel Programming
- by Damiano Zanardini
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- Static Analysis, IT Value, Upper Bound, Task Scheduling

- by Hanan Shukur
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- Cloud Computing, Cloud Services, Task Scheduling

Navigation assistance systems are aiming to improve safety and provide traffic optimization, and have become more and more popular in modern vehicular technology. The reason is given by the significant traffic increase and numerous congestion events in large cities, large complexity of road infrastructure and unexpected or hazardous conditions that can be found on roads. This paper proposes an innovative navigation system solution which intelligently gathers traffic data provided by integrated car sensors and/or onboard security systems and uses it to warn other participants. System concepts, architecture, design, implementation and performance evaluation aspects are presented, and also tasks organization for an embedded cost-effective implementation using a real-time kernel is illustrated.

- by Qing Xie
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- Information Systems, Software Maintenance, Software Development, Level Of Detail (LOD)
- by Hesham Alfares
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- Engineering, Mathematical Sciences, Personnel Scheduling, Task Scheduling

The coordinated use of geographically distributed computers, or metacomputing, can in principle provide more accessible and cost- effective supercomputing than conventional high-performance systems. However, we lack evidence that metacomputing systems can be made easily usable, or that there exist large numbers of applications able to exploit metacomputing resources. In this paper, we present work that addresses both these concerns. The

- by Ian Foster
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- Distributed Computing, Parallel Processing, Case Study, Cost effectiveness

Because of the dynamic and heterogeneous nature of a grid infrastructure, the client/server paradigm is a common programming model for these environments, where the client submits requests to several geographically remote servers for executing already deployed applications on its own data. According to this model, the applications are usually decomposed into independent tasks that are solved concurrently by the servers (the so called Data Grid applications). On the other hand, as many scientific applications are characterized by very large set of input data and dependencies among subproblems, avoiding unnecessary synchronizations and data transfer is a difficult task. This work addresses the problem of implementing a strategy for an efficient task scheduling and data management in case of data dependencies among subproblems in the same Linear Algebra application. For the purpose of the experiments, the NetSolve distributed computing environment has been used and some minor changes h...

- by Giuliano Laccetti
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- Grid Computing, Data Management, Synchronization, Numerical Linear Algebra
- by Sanem Sariel
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- Mechanical Engineering, Naval Engineering, Scheduling, Underwater Communication