Cloud computing is new hot topic that explain everything in Internet would work as in Cloud. Machine learning says learning a machine to extends its performance. So, Is there any area where these two can be link and research can be performed?
Dear Shivam Vatshayan,
Cloud Computing, Fog Computing and Artificial Intelligence (especially part Machine Learning) are hot topics in the Internet of Things. Maybe my answers can help you - see:
https://www.researchgate.net/post/Can_any_body_suggest_a_dissertation_question_related_to_Internet_of_things_and_Artificial_Intelligence
https://www.researchgate.net/post/What_is_difference_between_Fog_computing_and_Cloud_computing
https://www.researchgate.net/post/What_are_the_hot_research_topics_in_Fog_computing_technology
Much success and best regards
Anato Badach
machine learning can be used in cloud computing such as "user access pattern learning for delivery of QoS" "intelligent cloud monitoring"
Where Cloud computing can be link with Machine learning?
One of the use cases is Machine Learning (ML) can be run / deployed on cloud computing i.e. using cloud computing (public, private, hybrid clouds) as platform to serve ML by scaling up & out resource workload etc.
Another use cases is to use ML to provide deeper analytics on how to manage the cloud computing more effectively including: preventive maintenance, compute / storage / network resource self-healing, which DC to host a customer VM / container in view of data sovereignty regulation, internal & external audit compliance recommendation, how best to move workload across DCs to balance out the inbound / outbound access & power cooling, monitor cloud computing usage / metering & propose appropriate targeted marketing campaigns to enhance customer stickiness / loyalty etc.
Fog computing utlizes computing resources closer to the users, i.e edge devices.
The terminology used for Fog and Cloud is analogous to actual cloud and fog in atmosphere. Cloud is much higher in the sky and fog is much more close to the earth surface and therefore much dense and covers bigger area.
Fog computing allows cloud resources to shed some of their load to edge devices and therefore fog complements the cloud.
For example, the sensors can collect the raw data, and instead of sending all the collected data to the cloud for further processing, fog computing can be utilized to perform some processing such as filtering, prediction, classification, etc etc. Then the final concise information can then be sent to the cloud for storage and further data analysis. Obviously any algorithms can be applied at the edges including ML.
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