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دسترسی آزاد مقاله
1 - TASA: A New Task Scheduling Algorithm in Cloud Computing
Somayeh Taherian Dehkordi Vahid Khatibi BardsiriCloud computing refers to services that run in a distributed network and are accessible through common internet protocols. It merges a lot of physical resources and offers them to users as services according to service level agreement. Therefore, resource management alo چکیده کاملCloud computing refers to services that run in a distributed network and are accessible through common internet protocols. It merges a lot of physical resources and offers them to users as services according to service level agreement. Therefore, resource management alongside with task scheduling has direct influence on cloud networks’ performance and efficiency. Presenting a proper scheduling method can lead to efficiency of resources by decreasing response time and costs. This paper studies the existing approaches of task scheduling and resource allocation in cloud infrastructures and assessment of their advantages and disadvantages. Afterwards, a compound algorithm is presented in order to allocate tasks to resources properly and decrease runtime. The proposed algorithm is built according to conditions of compounding Min-min and Sufferage algorithms. In the proposed algorithm, task allocation between machines takes place alternatively and with continuous change of scheduling algorithms. The main idea of the proposed algorithm is to concentrate on the number of tasks instead of the existing resources. The simulation results reveal that the proposed algorithm can achieve higher performance in decreasing response time. پرونده مقاله -
دسترسی آزاد مقاله
2 - Task Scheduling Algorithm Using Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in Cloud Computing
Ghazaal Emadi Amir Masoud Rahmani Hamed ShahhoseiniThe cloud computing is considered as a computational model which provides the uses requests with resources upon any demand and needs.The need for planning the scheduling of the user's jobs has emerged as an important challenge in the field of cloud computing. It is main چکیده کاملThe cloud computing is considered as a computational model which provides the uses requests with resources upon any demand and needs.The need for planning the scheduling of the user's jobs has emerged as an important challenge in the field of cloud computing. It is mainly due to several reasons, including ever-increasing advancements of information technology and an increase of applications and user needs for these applications with high quality, as well as, the popularity of cloud computing among user and rapidly growth of them during recent years. This research presents the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), an evolutionary algorithm in the field of optimization for tasks scheduling in the cloud computing environment. The findings indicate that presented algorithm, led to a reduction in execution time of all tasks, compared to SPT, LPT, and RLPT algorithms.Keywords: Cloud Computing, Task Scheduling, Virtual Machines (VMs), Covariance Matrix Adaptation Evolution Strategy (CMA-ES) پرونده مقاله -
دسترسی آزاد مقاله
3 - An Effective Task Scheduling Framework for Cloud Computing using NSGA-II
Hanieh Ghorashi Meghdad MirabiCloud computing is a model for convenient on-demand user’s access to changeable and configurable computing resources such as networks, servers, storage, applications, and services with minimal management of resources and service provider interaction. Task scheduli چکیده کاملCloud computing is a model for convenient on-demand user’s access to changeable and configurable computing resources such as networks, servers, storage, applications, and services with minimal management of resources and service provider interaction. Task scheduling is regarded as a fundamental issue in cloud computing which aims at distributing the load on the different resources of a distributed system in order to optimize resource utilization and response time. In this paper, an optimization-based method for task scheduling is presented in order to improve the efficiency of cloud computing. In the proposed approach, three criteria for scheduling, including the task execution time, the task transfer time, and the cost of task execution have been considered. Our method not only reduces the execution time of the overall tasks but also minimizes the maximum time required for task execution. We employ the Multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) for solving the scheduling problem. To evaluate the efficiency of the proposed method, a real cloud environment is simulated, and a similar method based on Multi-Objective Particle Swarm Optimization is applied. Experimental results show the superiority of our approach over the baseline technique. پرونده مقاله -
دسترسی آزاد مقاله
4 - یک مدل زمانبندی وظایف در منابع ناهمگن ابری با ترکیبی از الگوریتم های هوش جمعی
صفدر رستمی علی برومندنیا احمد خادم زادهرایانش ابری محیطی ست ،که کاربران براساس تقاضا و پرداخت به ازای استفاده به منابع محاسباتی اشتراکی دسترسی دارند. با توجه به محدودیت و پویایی منابع محاسباتی برای اجرای درخواست‌های متنوع و متغیر با زمان کاربران، نیاز به یک مکانیزم زمانبندی موثر برای رسیدگی به شرایط پویا چکیده کاملرایانش ابری محیطی ست ،که کاربران براساس تقاضا و پرداخت به ازای استفاده به منابع محاسباتی اشتراکی دسترسی دارند. با توجه به محدودیت و پویایی منابع محاسباتی برای اجرای درخواست‌های متنوع و متغیر با زمان کاربران، نیاز به یک مکانیزم زمانبندی موثر برای رسیدگی به شرایط پویای سیستم و بهره وری منابع و رضایت کاربران امری حیاتی می باشد. از آنجایی که تخصیص وظایف به منابع یک چالش اساسی در محیط های ابری به شمار میرود الگوریتم‌های بسیاری جهت کاهش زمان اجرا و موازی‌سازی زیروظایف ارائه شده است. الگوریتم‌های زمانبندی موجود تلاش می‌کنند با توجه به وضعیت فعلی سیستم، یک زمانبندی بهینه بین منابع و وظایف با توجه به پویایی درخواست های کاربران فراهم آورند، ولی با این وجود اغلب این روش ها نتوانسته اند در بلندمدت نتیجه مطلوبی را ارائه دهند. به دلیل سرعت همگرایی پایین راه‌حل‌ها در الگوریتم‌های فرااکتشافی در این مقاله یک روش زمانبندی متناسب با صف‌های اولویت چندگانه مبتنی بر رتبه‌بندی نامغلوب و به کمک الگوریتم بهینه سازی جستجوی کاپوچین برای سیستم‌های ابری ناهمگن ارائه شده است. نتایج شبیه سازی نشان میدهد که روش پیشنهادی در مقایسه با روش های پیشین از نظر تاخیر، توازن بار و زمان اجرا بهتر عمل می‌کند. پرونده مقاله -
دسترسی آزاد مقاله
5 - GASA: Presentation of an Initiative Method Based on Genetic Algorithm for Task Scheduling in the Cloud Environment
Somayeh Taherian Dehkordi Vahid Khatibi BardsiriThe need for calculating actions has been emerged everywhere and in any time, by advancing of information technology. Cloud computing is the latest response to such needs. Prominent popularity has recently been created for Cloud computing systems. Increasing cloud effic چکیده کاملThe need for calculating actions has been emerged everywhere and in any time, by advancing of information technology. Cloud computing is the latest response to such needs. Prominent popularity has recently been created for Cloud computing systems. Increasing cloud efficiency is an important subject of consideration. Heterogeneity and diversity among different resources and requests of users in the Cloud computing environment creates complexities and problems in task scheduling in the cloud environment. Scheduling consists of selecting the most appropriate resource with the aim to distribute load in resources, and maximum productivity from them, while it should minimize the response time and the time of completion of each task, as well as minimizing the service costs. In addition to analyzing the Cloud computing system and scheduling aspects in it, it has been tried in this article to provide a combined algorithm for appropriate mapping of tasks to the existing virtual machines for reducing the completing times and increasing the productivity of virtual machines. According to the scheduling parameters, the presented method improves the load balancing according to the Sufferage and genetic algorithm as compared to previous algorithms, while it also reduces the total time of requests. The results of simulating the proposed algorithm in CloudSim environment and comparing it with the studied methods show that the proposed algorithm has reached a more optimized response, both for the load balancing and also for the total completion time. پرونده مقاله -
دسترسی آزاد مقاله
6 - A new Shuffled Genetic-based Task Scheduling Algorithm in Heterogeneous Distributed Systems
Mirsaeid HosseiniDistributed systems such as Grid- and Cloud Computing provision web services to their users in all of the world. One of the most important concerns which service providers encounter is to handle total cost of ownership (TCO). The large part of TCO is related to power co چکیده کاملDistributed systems such as Grid- and Cloud Computing provision web services to their users in all of the world. One of the most important concerns which service providers encounter is to handle total cost of ownership (TCO). The large part of TCO is related to power consumption due to inefficient resource management. Task scheduling module as a key component can has drastic impact on both user response time and underlying resource utilization. Such heterogeneous distributed systems have interconnected different processors with different speed and architecture. Also, the user application which is typically presented in the form of directed acyclic graph (DAG) must be executed on this type of parallel processing systems. Since task scheduling in such complicated systems belongs to NP-hard problems, existing heuristic approaches are no longer efficient. Therefore, the trend is to apply hybrid meta-heuristic approaches. In this paper, we extend a meta-heuristic shuffled genetic-based task scheduling algorithm to minimize total execution time, makespan, of user application. In this regard, we take benefit of other heuristics such as Heterogeneous Earliest Finish Time (HEFT) approach to generate smart initial population by applying a new shuffle operator which makes a fortune to explore feasible and promising individuals in the search space. We also conduct other genetic operators in right way to produce final near to optimal solution. To reach concrete results we have conducted several scenarios. Our proposed algorithm outperforms in term of average makespan compared with other existing approaches such as HEFT versions and QGARAR. پرونده مقاله -
دسترسی آزاد مقاله
7 - An Efficient Genetic Algorithm for Task Scheduling on Heterogeneous Computing Systems Based on TRIZ
Mehdi AkbariAn efficient assignment and scheduling of tasks is one of the key elements in effective utilization of heterogeneous multiprocessor systems. The task scheduling problem has been proven to be NP-hard is the reason why we used meta-heuristic methods for finding a suboptim چکیده کاملAn efficient assignment and scheduling of tasks is one of the key elements in effective utilization of heterogeneous multiprocessor systems. The task scheduling problem has been proven to be NP-hard is the reason why we used meta-heuristic methods for finding a suboptimal schedule. In this paper we proposed a new approach using TRIZ (specially 40 inventive principles). The basic idea of this approach is to exploit the advantages of heuristic-based algorithms to reduce space search and the time needed to find good solutions. The proposed algorithm improves the performance of genetic algorithm through significant changes in its genetic functions and introduction of new operators that guarantee sample variety and consistent coverage of the whole space. The achieved results of running this algorithm on the graphs of real-world applications and random graphs in heterogeneous computing systems with a wide range of characteristics, indicated significant improvements of efficiency of the proposed algorithm compared with other task scheduling algorithms. Although the proposed algorithm needs lower repetitions than their genetic counterparts, it needs high frequency of repetition to produce the desired answer. This is a drawback for this algorithm compared to heuristic algorithms such as CPOP and HEFT. پرونده مقاله -
دسترسی آزاد مقاله
8 - Task Scheduling Using Particle Swarm Optimization Algorithm with a Selection Guide and a Measure of Uniformity for Computational Grids
Mona TorabiIn this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the Selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme an چکیده کاملIn this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the Selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme and diversity. Scheduling algorithms play an important role in grid computing, parallel tasks Scheduling and sending them to appropriate resources. The proposed method has less Makespan and price. In addition to implementing a grid computing system, the proposed method which is using three standard test functions in evolutionary multi-objective optimization is evaluated. In this paper, the number of elements in the assessment of the Pareto optimizes set, uniformity and error. The results show that this Search method has more optimization in particle number density and high accuracy with less error than the MOPSO and can be replaced as an effective solution for solving multi-objective optimization. پرونده مقاله -
دسترسی آزاد مقاله
9 - Task Scheduling in Fog Computing: A Survey
Abbas Najafizadeh Afshin Salajegheh Amir Masoud Rahmani Amir SahafiRecently, fog computing has been introducedto solve the challenges of cloud computing regarding Internet objects. One of the challenges in the field of fog computing is the scheduling of tasks requested by Internet objects. In this study, a review of articles related to چکیده کاملRecently, fog computing has been introducedto solve the challenges of cloud computing regarding Internet objects. One of the challenges in the field of fog computing is the scheduling of tasks requested by Internet objects. In this study, a review of articles related to task scheduling in fog computing has been done. At first, the research questions and goals will be introduced, and then we will explain the process of finding and selecting the articles. A comprehensive analysis of the articles will be done. We have identified and listed 10 optimization metrics. Also, according to our study, in 79% of the studied articles, the mathematical model was used to express the problem. In 42% of the articles meta-heuristic algorithms proposed and 84% evaluated their algorithm by simulation. Finally, this paper presents the challenges and open issues of task scheduling in fog computing to the researchers. پرونده مقاله
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