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Autonomic ElasticWorkflow Advanced Reservation Planning Algorithm for Grids and Clouds – EWARP

خوارزمية مستقلة لتخطيط الحجز المسبق المرن للتطبيقات التدفقية في بيئات "الشبكيات" و "السحابات"*

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 Publication date 2012
and research's language is العربية
 Created by Shamra Editor




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Advanced Reservation (AR) is used to guarantee resource provisioning for many different types of applications including workflows. This technique is still under a huge controversy in both Business and Research communities because of its potentiality of reducing resource utilization. Most of the works proposed in this domain suggest reservation for the whole workflow schedule, and on all available resources at the same time, which worsen the problem of resource utilization. Many solutions are introduced to improve resource utilization under advanced reservation through generating relaxed and elastic reservation plans that local scheduling systems could modify to improve utilization and decrease internal fragmentation. These solutions depend mainly on changing rigid AR, which considered to be the most difficult kind of reservation, into relaxed and elastic ones through adding extra time on the resulted schedule and then distributing it on all tasks of the workflow. This paper presents a new autonomic algorithm (EWARP) for producing elastic reservation plans for workflow applications which doesn’t add extra times. Instead, it depends on exploiting the timing gaps produced by the different scheduling algorithms. The new algorithm use the technique of discovering timing gaps, but modifies it, and adds to it to be used for producing an elastic reservation plan for workflows. The results presented in this paper demonstrate how the proposed algorithm outperforms existing works in the fields by a lower bound approximating 25%.This shows that (EWARP) algorithm offer efficient and practical solutions for the problem of scheduling workflow applications under QoS constrains.


Artificial intelligence review:
Research summary
تتناول هذه الورقة البحثية خوارزمية جديدة لتوليد مخطط حجز مسبق مرن لمهمات دفق الأعمال دون إضافة أي زمن إضافي. تعتمد الخوارزمية على الاستغلال الأمثلي للفجوات الزمنية الموجودة في المجدول الناتج عن خوارزميات الجدولة المختلفة. تظهر نتائج الاختبارات أن الخوارزمية الجديدة تتفوق على الخوارزميات الأخرى بنسبة تصل إلى 25% في تحسين استغلالية الموارد وتقليل التجزئة الداخلية. تعتمد الخوارزمية على تقنيات استطلاع الفجوات الزمنية وإعادة ترتيب المهمات لتحسين استغلالية الموارد وتقديم حلول كفوءة لتطبيقات دفق الأعمال التي تتطلب قيود جودة الخدمة. تم اختبار الخوارزمية في بيئات موزعة واسعة النطاق مثل الشبكات والسحابات، وأظهرت النتائج تفوقها في تحقيق أهداف الجدولة مثل تصغير زمن التنفيذ النهائي وتحسين استغلالية الموارد.
Critical review
دراسة نقدية: تقدم الورقة البحثية خوارزمية مبتكرة وفعالة لتوليد مخطط حجز مسبق مرن، ولكن هناك بعض النقاط التي يمكن تحسينها. أولاً، تعتمد الخوارزمية بشكل كبير على الفجوات الزمنية الموجودة في المجدول، مما قد يجعلها أقل فعالية في البيئات التي تكون فيها هذه الفجوات قليلة أو غير موجودة. ثانياً، لم تتناول الورقة بشكل كافٍ تأثير التغيرات الديناميكية في بيئات الشبكات والسحابات على أداء الخوارزمية. ثالثاً، يمكن تحسين الورقة بإضافة دراسات مقارنة مع خوارزميات أخرى في بيئات تنفيذ مختلفة لتقديم صورة أكثر شمولية عن أداء الخوارزمية. على الرغم من هذه النقاط، فإن الورقة تقدم مساهمة قيمة في مجال جدولة دفق الأعمال وتحسين استغلالية الموارد.
Questions related to the research
  1. ما هي الخوارزمية الجديدة التي تقدمها الورقة؟

    الخوارزمية الجديدة هي لتوليد مخطط حجز مسبق مرن لمهمات دفق الأعمال دون إضافة أي زمن إضافي، وتعتمد على الاستغلال الأمثلي للفجوات الزمنية الموجودة في المجدول.

  2. ما هي الفائدة الرئيسية للخوارزمية الجديدة مقارنة بالخوارزميات الأخرى؟

    الفائدة الرئيسية هي تحسين استغلالية الموارد بنسبة تصل إلى 25% وتقليل التجزئة الداخلية دون الحاجة إلى إضافة زمن إضافي.

  3. ما هي التقنيات التي تعتمد عليها الخوارزمية الجديدة؟

    تعتمد الخوارزمية على تقنيات استطلاع الفجوات الزمنية وإعادة ترتيب المهمات لتحسين استغلالية الموارد.

  4. في أي بيئات تم اختبار الخوارزمية الجديدة؟

    تم اختبار الخوارزمية في بيئات موزعة واسعة النطاق مثل الشبكات والسحابات.


References used
(I. Foster, C. Kesselman; The Grid 2: Blueprint for a New Computing Infrastructure, 2nd edn. Morgan Kaufmann, San Francisco (2004
(J. Blythe, S. Jain, E. Deelman, Y. Gil, K. Vahi, A. Mandal and K. Kennedy. Resourec Allocation Strategies for Workflows in Grids. In IEEE International Symposium on Cluster Computing and the Grid ( CCGrid 2005
A. Mandal, K. Kennedy, C. Koelbel, G. Marin, J. Mellor-Gremmey, B. Liu and L. Johnsson. Scheduling Strategies for Mapping Application Workflows onto Grids. In IEEE international Symposium on High Performance Distributed Computing (HPDC 2005), 2005
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Many researches showed the ability of advance reservation to improve the predictability of the system; that allows it to deliver the applications required time constrains. Applications with many tasks require the system to ensure a number of reserv ations on many different distributed resources, which usually carried out through multi-level negotiation adding by that additional overhead on the application total response time. The extra overhead depends on many parameters including system workload and contention. Workflows add more complexity due to their tasks’ dependencies; thus, any rejection of or delay for a task reservation would increase application complete time. This paper suggests the use of elastic advanced reservation plans that depend on time gaps presented in the sub-optimal schedules, in order to improve the reservation acceptance rate. It presents an elastic co-reservation agent which provides the needed reservations using First Fit allocation strategy. The results show the ability of the proposed agent to always improve the acceptance rate with an average of (22.25%). The more important came out result is the agent ability to increase the reservation acceptance rate with the increasing of system competence, reaching (48.4%) for simultaneous 90 users at the system.
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