مقاله انگلیسی رایگان در مورد الگوریتم جدید کرم شب تاب برای برنامه ریزی – IEEE 2019
مشخصات مقاله | |
ترجمه عنوان مقاله | یک الگوریتم جدید کرم شب تاب برای برنامه ریزی برنامه های کاربردی دسته بندی وظایف تحت محدودیت های بودجه روی ابرهای ترکیبی |
عنوان انگلیسی مقاله | A Novel Firefly Algorithm for Scheduling Bag-of-Tasks Applications Under Budget Constraints on Hybrid Clouds |
انتشار | مقاله سال ۲۰۱۹ |
تعداد صفحات مقاله انگلیسی | ۱۴ صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه IEEE |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس نمیباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
۴٫۶۴۱ در سال ۲۰۱۸ |
شاخص H_index | ۵۶ در سال ۲۰۱۹ |
شاخص SJR | ۰٫۶۰۹ در سال ۲۰۱۸ |
شناسه ISSN | ۲۱۶۹-۳۵۳۶ |
شاخص Quartile (چارک) | Q2 در سال ۲۰۱۸ |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی کامپیوتر |
گرایش های مرتبط | مهندسی الگوریتم و محاسبات، رایانش ابری |
نوع ارائه مقاله |
ژورنال |
مجله / کنفرانس | دسترسی – IEEE Access |
دانشگاه | School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China |
کلمات کلیدی | برنامه های کاربردی دسته بندی وظایف، ابرهای ترکیبی، زمان کل، الگوریتم های کرم شب تاب |
کلمات کلیدی انگلیسی | Bag-of-tasks applications, hybrid clouds, makespan, firefly algorithms |
شناسه دیجیتال – doi |
https://doi.org/10.1109/ACCESS.2019.2948468 |
کد محصول | E13889 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract I. Introduction II. Related Work III. Scheduling Model IV. Proposed Novel Firefly Algorithm (NFA) V. Experimental Results Authors Figures References |
بخشی از متن مقاله: |
Abstract
This paper aims at scheduling bag-of-tasks (BoT) applications under budget constraints on hybrid clouds for minimizing the makespan. To solve this NP-hard problem, we propose a novel firefly algorithm (NFA) in which the evaluation of a firefly consists of two steps: (1) mapping a firefly to a scheduling solution (a task sequence); (2) calculating the solution’s objective (its corresponding makespan). In the first step, different from the well-known ranked-order value (ROV) rule, we propose a distancebased mapping operator that relies on the distance between a firefly and the brightest one to determine the mapping relationship between a firefly and a solution. We use a probability model in which solutions corresponding to fireflies closer to the brightest one would have higher probabilities to inherit tasks from the current best solution. In this manner, these solutions can inherit more ‘‘good genes’’ hidden inside the current best solution to evolve into more high-quality solutions. In the second step, we employ an effective heuristic to evaluate solution objectives. We further develop a composite heuristic to generate the initial best solution, providing the proposed NFA with a good start. We also establish a new movement scheme such that fireflies distant from the brightest one can explore a wide range in the search space, whereas fireflies nearby the brightest one can search in a small neighborhood. Experimental results show that, by employing the above-mentioned strategies, NFA outperforms the standard firefly algorithm and the existing best algorithm, in terms of scheduling effectiveness and computational efficiency. Specifically, the distance-based mapping operator is verified to be both more effective and more efficient than the ROV rule. The composite heuristic is capable of generating a good initial solution, leading to the high quality of the final schedule. The movement scheme can further reduce the makespan of BoT applications. Introduction Cloud computing is a popular distributed computing paradigm that can deliver a massive amount of computing resources in a pay-as-you-go manner. In other words, users only need to pay what they have actually used. As a result, users can temporally use computing resources of public clouds while the private cloud cannot provide sufficient resources. In order to achieve this target, cloud vendors offer hybrid cloud solutions that integrates a private cloud with public clouds seamlessly. As illustrated in Figure 1, with the aid of hybrid cloud solutions, administrators or programs of the private cloud can manage the computing resources of the hybrid cloud (i.e., the private cloud and public clouds) via unified interfaces. For example, in case that the available computing resources of the private cloud cannot afford an instance of a ‘‘large’’ virtual machine (VM) type for processing a specific task, an instance of the same VM type can be created on a public cloud to tackle this task. In addition, many cloud providers now can deliver and charge VM instances in seconds (e.g., QingCloud1 and TecentCloud2 ) instead of in hours. This new charging mechanism makes hybrid clouds more popular for cloud customers, since they would not waste a certain fraction of the last hour that has to be paid entirely in traditional charging mechanisms. |