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A Novel Algorithm for Reducing Energy Consumption in Cloud Computing Environment: Web Service Computing Approach

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Moganarangan, N. (Author)
مؤلفين آخرين: Babukarthik, R.G. (Co-Author) , Bhuvaneswari, S. (Co-Author) , Basha, M.S. Saleem (Co-Author) , Dhavachelvan, P. (Co-Author)
المجلد/العدد: مج28, ع1
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2016
الصفحات: 56 - 67
DOI: 10.33948/0584-028-001-005
ISSN: 1319-1578
رقم MD: 973788
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
كلمات المؤلف المفتاحية:
ACO Ant Colony Optimization | CS Cuckoo Search | VSF Voltage Scaling Factor | ECPSO Extended Compact Particle Swarm Optimization
رابط المحتوى:
صورة الغلاف QR قانون
حفظ في:
LEADER 02739nam a22002777a 4500
001 1716640
024 |3 10.33948/0584-028-001-005 
041 |a eng 
044 |b السعودية 
100 |9 525095  |a Moganarangan, N.  |e Author 
245 |a A Novel Algorithm for Reducing Energy Consumption in Cloud Computing Environment:  |b Web Service Computing Approach 
260 |b جامعة الملك سعود  |c 2016 
300 |a 56 - 67 
336 |a بحوث ومقالات  |b Article 
520 |b Cloud computing slowly gained an important role in scientific application, on-demand facility of virtualized resources is provided as a service with the help of virtualization without any additional waiting time. Energy consumption is reduced for job-scheduling problems based on makes pan constraint, which in turn leads to significant decrease in the energy cost. Additionally, there is an increase in complexity for scheduling problems mainly because the application is not based on makes pan constraint. In this paper we propose a new Hybrid algorithm combining the benefits of ACO and cuckoo search algorithm. It is focused on the voltage scaling factor for reduction of energy consumption. Performance of the Hybrid algorithm is considerably increased from 45 tasks onward when compared to ACO. Energy consumed by Hybrid algorithm is measured and energy improvement is evaluated up to 35 tasks. Energy consumption is the same as ACO algorithm because as the number of tasks increases (45 to 70) there is a considerable decrease in the energy consumption rate. Make span of Hybrid algorithm based on number of tasks is compared with ACO algorithm. Further we have analyzed the energy consumption for a number of processors and its improvement rate – up to 6 processors, energy consumption is considerably reduced and the energy consumption tends to be in steady state with further increase in the number of processors. 
653 |a الخوارزميات  |a الحوسبة السحابية  |a خدمات الويب 
692 |b ACO Ant Colony Optimization  |b CS Cuckoo Search  |b VSF Voltage Scaling Factor  |b ECPSO Extended Compact Particle Swarm Optimization 
700 |9 525096  |a Babukarthik, R.G.  |e Co-Author 
700 |9 525097  |a Bhuvaneswari, S.  |e Co-Author 
700 |9 524434  |a Basha, M.S. Saleem  |e Co-Author 
700 |9 524438  |a Dhavachelvan, P.  |e Co-Author 
773 |c 005  |e Journal of King Saud University (Computer and Information Sciences)  |f Maǧalaẗ ǧamʼaẗ al-malīk Saud : ùlm al-ḥasib wa al-maʼlumat  |l 001  |m مج28, ع1  |o 0584  |s مجلة جامعة الملك سعود - علوم الحاسب والمعلومات  |v 028  |x 1319-1578 
856 |u 0584-028-001-005.pdf 
930 |d y  |p y  |q n 
995 |a science 
999 |c 973788  |d 973788 

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