Virtual machine optimization to achieve energy efficient optimum resource utilization in cloud data center

Abstract

Objectives: Cloud offers multiple benefits through its data center-based services. The whole world uses these services that are hosted by physical machines. Millions of virtual machines get an optimized utilization of hardware to utilize these physical machines. The unbalanced distribution of virtual machines to physical machines offers an inefficient utilization of data center hardware that will also lead to more carbon emission and harm the environment as well. Methods/material: A learning function is needed to offer energy efficiency in the cloud data center through VM optimization through its optimal allocation to physical machines. Therefore, an optimal VM placement and migration algorithm is a challenge that is addressed in this paper to reach an efficient energy optimization and resource utilization level. Finding/Novelty: The proposed algorithm is led by a learning function that takes into account the available number of physical machines, number of virtual machines, incoming requests and decides to run an optimal number of physical machines to obtain energy efficiency level for the cloud data center by migrating the virtual machines (VMs).

Analyzing distributed denial of service attacks in cloud computing towards the Pakistan information technology industry

“…What is being attacked is usually not a specific vulnerability, but rather the very fact that the victim is connected to the network. Under normal operating conditions, and assuming that its link(s) and processing capacity have been adequately provisioned, the standard, TCP-like congestion control ensures fair use of the available resources (20)(21)(22) . Under a DDoS attack, the arriving packets do not obey end-to-end congestion control algorithms; instead, they incessantly bombard the victim, causing the well-behaved flows to back off and eventually starve.…”