کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
424613 | 685612 | 2013 | 8 صفحه PDF | دانلود رایگان |
کلمات کلیدی
1. مقدمه
2. کارهای مربوطه
3. زمان بندی کار در یک پلت فرم رایانش ابری
4. یک زمان بندی کار چند هدفه در رایانش ابری
4.1 متد مجموع وزن دار
الگوریتم 1: الگوریتم مجموع وزن دار
4.2 رویکرد محدودیتϵ تقریبی
الگوریتم 2: الگوریتمهایی با محدودیت ϵ
4.3 تولید مجموعه Ω
الگوریتم 3 : تولید
5. فاز آزمایشی
جدول 1 نقاط نادیر و ایده آل
6. تولید سناریوهای سازگار
جدول 2: مقایسههای عددی I
جدول 3: مقایسه عددی II
7. کران بالاتر و پایین تر
8. نتایج محاسباتی
جدول 4: نامگذاریها
9. نتیجه گیری و کارهای آینده
• We formulate the multi-objective off-line job scheduling problem in the cloud.
• We optimize the makespan, the total average waiting time and the used hosts.
• We generate a set of test instances suitable for the problem.
• We define an approximate ϵϵ-constraint method.
• We compare the proposed solution approach with the weighted sum method.
Cloud computing is a hybrid model that provides both hardware and software resources through computer networks. Data services (hardware) together with their functionalities (software) are hosted on web servers rather than on single computers connected by networks. Through a device (e.g., either a computer or a smartphone), a browser and an Internet connection, each user accesses a cloud platform and asks for specific services. For example, a user can ask for executing some applications (jobs) on the machines (hosts) of a cloud infrastructure. Therefore, it becomes significant to provide optimized job scheduling approaches suitable to balance the workload distribution among hosts of the platform.In this paper, a multi-objective mathematical formulation of the job scheduling problem in a homogeneous cloud computing platform is proposed in order to optimize the total average waiting time of the jobs, the average waiting time of the jobs in the longest working schedule (such as the makespan) and the required number of hosts. The proposed approach is based on an approximate ϵϵ-constraint method, tested on a set of instances and compared with the weighted sum (WS) method.The computational results highlight that our approach outperforms the WS method in terms of a number of non-dominated solutions.
Journal: Future Generation Computer Systems - Volume 29, Issue 8, October 2013, Pages 1901–1908