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| 041 |
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|a eng
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| 100 |
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|9 534559
|a علاوي، رافد قحطان
|e مؤلف
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| 245 |
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|a Development Of Optimized Mobile Agent Task Pattern Using Push-All-Data Strategy
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| 260 |
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|a عمان
|c 2019
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| 300 |
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|a 1 - 54
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| 336 |
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|a رسائل جامعية
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| 502 |
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|b رسالة ماجستير
|c جامعة الاسراء الخاصة
|f كلية الدراسات العليا
|g الاردن
|o 0042
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| 520 |
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|a The computer systems have evolved from a monolithic computer device to a much more complex client- server environment in previous years. One of those newly developed technology is the mobile agent. Because of its innovative capabilities and attractive application, mobile agents have long captured the attention of researchers and industry. Mobile agents are computer program that can automatically migrate from host to host, transfer their internal state, enable them to perform task more conveniently, more robustly and more efficiently than traditional client- server application in network and distributed environments. In this work, best path in minimal time is found by migrating the mobile agent from the source node to the destination node using mathematical process and optimization technique. Genetic Algorithm is used to overcome the problem of selecting the best path rather than shortest path that is used in previous work. Shortest path is not mean optimal path in all time. This work focuses on how to minimize the number of nodes that are used to transfer data from source to destination using combining the sequential nodes from time point view using Node Compression Algorithm (NCA). When comparing adaptive algorithms with each other, time is considered as essential measures for selecting best path (minimum time). Using time measurement after 10 optimization iterations as minimum and holding to the result. The proposed approach shows that using hybrid approaches GA and NCA reduce the time of selecting the best path using GA from 336.448 ms to 286.29 ms for 10th iteration and reduce the time in other iterations as well. This results show the importance of using optimization techniques in cloud computing to help of overcome the distributed nature and minimize the time of transferring data within minimum time complexity.
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| 653 |
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|a الخوارزميات الجينية
|a خوارزمية ضغط العقدة
|a استراتيجية ضغط البيانات
|a البرامج الحاسوبية
|a الهواتف النقالة
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| 700 |
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|9 534556
|a Al-Hroob, Aysh
|e Advisor
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| 700 |
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|9 418999
|a Al Shrouf, Faiz
|e Advisor
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| 856 |
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|u 9802-021-002-0042-T.pdf
|y صفحة العنوان
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| 856 |
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|u 9802-021-002-0042-A.pdf
|y المستخلص
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| 856 |
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|u 9802-021-002-0042-C.pdf
|y قائمة المحتويات
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| 856 |
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|u 9802-021-002-0042-F.pdf
|y 24 صفحة الأولى
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| 856 |
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|u 9802-021-002-0042-1.pdf
|y 1 الفصل
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| 856 |
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|u 9802-021-002-0042-2.pdf
|y 2 الفصل
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| 856 |
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|u 9802-021-002-0042-3.pdf
|y 3 الفصل
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| 856 |
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|u 9802-021-002-0042-4.pdf
|y 4 الفصل
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| 856 |
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|u 9802-021-002-0042-O.pdf
|y الخاتمة
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| 856 |
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|u 9802-021-002-0042-R.pdf
|y المصادر والمراجع
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| 930 |
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|d y
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| 995 |
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|a Dissertations
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| 999 |
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|c 991005
|d 991005
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