دانلود رایگان ترجمه مقاله الگوریتم Memetic مبتنی بر جستجو محلی متغیر برای مشکل متعادل بار – اسپرینگر 2016
دانلود رایگان مقاله انگلیسی + خرید ترجمه فارسی | |
عنوان فارسی مقاله: |
جست و جوی متغیر محلی بر اساس الگوریتم ممتیک برای مسئله توازن بار در رایانش ابری |
عنوان انگلیسی مقاله: |
A Variable Local Search Based Memetic Algorithm for the Load Balancing Problem in Cloud Computing |
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مشخصات مقاله انگلیسی (PDF) | |
سال انتشار | 2016 |
تعداد صفحات مقاله انگلیسی | 16 صفحه با فرمت pdf |
رشته های مرتبط با این مقاله | مهندسی کامپیوتر |
گرایش های مرتبط با این مقاله | مهندسی الگوریتم ها و محاسبات، رایانش ابری |
چاپ شده در مجله (ژورنال) | کنفرانس اروپایی در مورد کاربرد محاسبات تکاملی |
کلمات کلیدی | جستجوی محلی، الگوریتم های ممتیک، توازن بار، ابر رایانه، الگوریتم های فرا ابتکاری |
ارائه شده از دانشگاه | دانشکده علوم کامپیوتر و IT، دانشگاه RMIT، ملبورن، استرالیا |
رفرنس | دارد ✓ |
کد محصول | F987 |
نشریه | اسپرینگر – Springer |
مشخصات و وضعیت ترجمه فارسی این مقاله (Word) | |
وضعیت ترجمه | انجام شده و آماده دانلود |
تعداد صفحات ترجمه تایپ شده با فرمت ورد با قابلیت ویرایش | 18 صفحه با فونت 14 B Nazanin |
ترجمه عناوین تصاویر و جداول | ترجمه شده است ✓ |
ترجمه متون داخل تصاویر | ترجمه شده است ✓ |
ترجمه متون داخل جداول | ترجمه شده است ✓ |
درج تصاویر در فایل ترجمه | درج شده است ✓ |
درج جداول در فایل ترجمه | درج شده است ✓ |
درج فرمولها و محاسبات در فایل ترجمه | به صورت عکس درج شده است ✓ |
منابع داخل متن | به صورت عدد درج شده است ✓ |
کیفیت ترجمه | کیفیت ترجمه این مقاله متوسط میباشد |
فهرست مطالب |
چکیده
1 . مقدمه
2 . بیان مسئله
3 . روش شناسی
1.3 . الگوریتم ژنتیک
2.3 . شاخص تنوع
3.3 . جستجوی محلی متغیر
4 . شرایط آزمایشی
1.4 . نمونه مسئله
2.4 . پارامترهای تنظیمات
5 . نتایج و مقایسه
1.5 . مقایسه الگوریتم ممتیک پیشنهادی با الگوریتم پیشرفته و سایر الگوریتم ممتیک ها
2.5 . مقایسه با حالت روش پیشرفته
6 . نتیجه گیری
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بخشی از ترجمه |
چکیده : 1 . مقدمه |
بخشی از مقاله انگلیسی |
Abstract. Load balancing (LB) is an important and challenging optimisation problem in cloud computing. LB involves assigning a set of services into a set of machines for which the goal is to optimise machine usages. This study presents a memetic algorithm (MA) for the LB problem. MA is a hybrid method that combines the strength of population based evolutionary algorithms with local search. However the effectiveness of MA mainly depends on the local search method chosen for MA. This is because local search methods perform differently for different instances and under different stages of search. In addition, invoking local search at every generation can be computationally expensive and compromise the exploration capacity of search. To address these issues, this study proposes a variable local search based MA in the context of LB problem. The proposed MA uses multiple local search mechanisms. Each one navigates a different area in search space using a different search mechanism which can leads to a different search path with distinct local optima. This will not only help the search to avoid being trap in a local optima point, but can also effectively deal with various landscape search characteristics and dynamic changes of the problem. In addition, a diversity indicator is adopted to control the local search processes to encourage solution diversity. Our MA method is evaluated on instances of the Google machine reassignment problem proposed for the ROADEF/EURO 2012 challenge. Compared with the state of the art methods, our method achieved the best performance on most of instances, showing the effectiveness of variable local search based MA for the Load Balancing problem. 1 Introduction Cloud computing is a fast growing technology that provides on-demand computing services over the Internet [3,6]. It offers network access to a various shared pool of configurable computing resources including storage, processing, bandwidth and memory. A cloud provider, such as Google and Amazon, manages a data centre of which the computing resources are to be shared by end users. With the rapid growth of the demand in cloud services, optimal resources allocation becomes one the most important targets in cloud computing [6]. Load balancing (LB) is one of the cloud resource allocation tasks seeking for the best arrangement of services into a set of machines so the usage of these machines can be improved. In this paper, we consider the LB problem introduced by Google for the ROADEF/EURO 2012 challenge [1]. The task is named as Machine Reassignment Problem (MRP). The goal of MRP is to improve the usage of resources by reassigning a set of processes into a set of machine, while all problem constraints must be satisfied. A range of methods have been proposed to solve MRP. These include variable neighbourhood search [8], constraint programming-based large neighbourhood search [15], large neighbourhood search [4], multi-start iterated local search [14], simulated annealing [20] and restricted iterated local search [13]. In this study, we propose a memetic algorithm (MA) based method for this load balancing problem. MA is a stochastic optimisation search method which combines population based algorithm with local search. The rationale of MA is to synergise the exploration power of population based algorithms with the exploitation capability of local search [16]. MA has been proven very successful in solving various difficult optimisation problems [17]. However the success of MA is not automatic [21,22,25]. There are two important aspects that have to be considered when designing MA for a particular problem [18]. Firstly, the choice of local search is important. The performance of MA heavily depends on the selected local search algorithm. It is difficult for one local search to fit with diverse features of different instances of different problems. Even for the same instance, the characteristic of search space under different stages may vary significantly [24]. That makes the choice of local search method difficult and critical. Secondly, MA often faces the challenge of how to preserve the diversity of a search process [23]. Excessive use of local search may consume more computation on exploitation compromising the effort on exploration. To address these two issues, we propose a variable local search based memetic algorithm. It combines genetic algorithm (GA) with multiple local search algorithms in which each one can navigate a different area in the search space. Different search mechanisms can lead to a different search path with distinct local optima. Furthermore a diversity indicator is adopted to control the invocation of local search to prevent lost of diversity in the population of solutions. With the proposed method, there is no need to examine the nature of a load balancing problem and to choose an appropriate local search for the problem. The need of tuning the local search is also unnecessary in the proposed MA approach. The proposed algorithm are evaluated on small and large scale instances of the machine reassignment problem from ROADED/EURO 2012 challenge. For comparison purposes, the state of the art algorithms for this challenge are included as well. In Sect. 2, this challenge is described in details. The proposed variable local search based MA is presented in Sect. 3. Section 4 shows the experiment settings while the results are listed in Sect. 5. Section 6 concludes this study. |