15036

13534

كارشناسي ارشد

هوش مصنوعي

اصفهان : دانشگاه صنعتي اصفهان

1398

دوازده، 70ص. : مصور، جدول، نمودار

محمدحسين منشئي، محمدرضا حيدرپور

فرامرز هندسي، زينب زالي

1398/06/24

كتابنامه

مهندسي كامپيوتر

مهندسي برق و كامپيوتر

1398/06/24

2556986

Workload Offloading in Fog Computing A Cooperative Approach Sina Dehghanian S Dehghanian@ec iut ac ir July 2 2019 Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Farsi Supervisors Mohammad Hossein Manshaei Assoc Prof manshaei@cc iut ac ir Mohammad Reza Heidarpour Assist Prof mrheidar@cc iut ac irAbstract Nowadays the Internet is overgrowing and accessing the Internet is more comfortable than before Ease of access andmore coverage of the Internet result in more devices to be online Besides the massive number of connected devices newapplications have emerged in areas like smart city smart transportation healthcare and emergency response As a result the velocity of generating data is also increasing as same as the volume of generated data This enormous amount of datarequire a comprehensive infrastructure for processing and storing them Also in the Big Data era many useful decisions canbe made using this tremendous data On the other hand terminal devices cannot provide enough resources for processingand make real time decisions To this end Cloud Computing is introduced as a solution for providing the processing andstorage capabilities to this demand Although cloud servers provide high performance resources communication costs areone the drawbacks for sending tasks to the cloud Alternately rather than moving data to the cloud it may be useful to locatethe resources closer to terminal devices Fog Computing as a novel computing paradigm is introduced that aims this issue In Fog Computing there is an extra layer located between the cloud and terminal layer The fog nodes provide sufficientresources for the terminal devices Although fog nodes have better performance than the IoT devices in the terminal layer the capability of fog nodes is still limited due to deployment costs So they cannot serve all of the received tasks and someof them should be offloaded Offloading tasks to the cloud results in high delay and affects the Quality of Service Onthe other hand some applications are delay sensitive and hence cannot tolerate the delay It will be necessary that theremust be an optimized mechanism for offloading tasks In this thesis we inspected possible methods used for offloading infog computing and proposed a novel approach for this task based on reinforcement learning The proposed method usesQ Learning for making the decision on which task should be offloaded and aims to minimize the delay at the same time Afog node when wants to offload a task benefits from Q table and make its decision based on Q value Finally the experimentresults illustrate improvements in the delay which is the aimed parameter to be minimized Key Words Internet of Things Cloud Computing Fog Computing Workload Offloading MachineLearning Reinforcement Learning

محمدحسين منشئي، محمدرضا حيدرپور

فرامرز هندسي، زينب زالي