Cloud Scheduling of Virtual Machine Using Machine Learning and Decision Tree

Abstract

This paper tries to provide a possible solution to a very common issue in
Virtualization technology, in cloud computing, i.e., the placement of virtual machines (vm’s)
in the physical machine. The physical machine here is considered as the server, where each
of the vm’s are placed. The challenge is to reduce the time and may be the space complexity
of the scheduling. This paper here talks about probably a new kind of approach, as a
solution to this problem. The algorithm here used takes the inspiration in the way the
human brain behave while making decisions, where it first rely upon the past experience
and then either modifies the experience in accordance to the situations, or else go for new
techniques, if inferred. The algorithm here however is not capable of inferring, or deducing
new techniques, however it is capable enough to store the past experience or the pattern in
which the vm’s were allocated and if that repeats, it instead of again calculating or in other
words, traversing through the whole algorithm, it directly allocate the vm’s in the server.