• BANSHRI MODHIYA
• POONAM FALDU

Distributed computing is an utility-based model in the conveyed condition which comprises of different quantities of assets with heterogeneous servers. The cloud gives minimal effort and adaptable IT assets (equipment and programming) over the Internet. As more cloud suppliers try to drive more prominent business results and the situations of the cloud become increasingly convoluted, it is clear that the period of the clever cloud has arrived. The astute cloud faces a few difficulties, including streamlining the monetary cloud administration arrangement and adaptively apportioning assets. Specifically, there is a developing pattern toward utilizing AI to improve the insight of cloud the board. The assorted variety and expanding requests of the client applications lead to expanding asset requests, which makes the entire cloud server farm as burden imbalanced. The current calculations manage the heap conveyance in a static and dynamic condition without managing a present heap of the servers which may adjust the heap of the servers at certain time interim however not over the long haul. Along these lines, one of the greatest difficulties in a cloud situation is to expand the asset use of the servers and equalization the heap of the entire cloud server farm for the long haul process. This work will plan a profound learning helped dynamic burden adjusting system dependent on a benchmark neural system procedure which will progressively characterize the servers dependent on the rest of the heap limit of the server and convey the errand to the best-fit virtual machine

Deep learning, Cloud computing, Dynamic Load Balancing

"HYBRID OPTIMUS: AN EFFICIENT (REAL-TIME) DYNAMIC RESOURCE SCHEDULER FOR DEEP LEARNING CLUSTERS IN CLOUD COMPUTING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.403-403, April-2019, Available :http://www.jetir.org/papers/JETIR1904F64.pdf

"HYBRID OPTIMUS: AN EFFICIENT (REAL-TIME) DYNAMIC RESOURCE SCHEDULER FOR DEEP LEARNING CLUSTERS IN CLOUD COMPUTING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp403-403, April-2019, Available at : http://www.jetir.org/papers/JETIR1904F64.pdf