Hybrid optimization enabled VM scaling based load distribution and optimal switching strategy in cloud data center.

Cloud data centers provide incredible services to their customers ubiquitously based on demand and pay-per-use strategy. In virtualized data centers (DC), CPU, RAM, and bandwidth are assigned to a virtual machine (VM) from a group of pooled resources. One of the key issues for virtualized DC is VMs consolidation as it achieves better performance and also reduces the cost. In recent years, researchers have paid more attention to developing the global best solution in DCs. Meanwhile, the global best solution results in a number of redundant migrations and is not desirable for large-scale cloud computing environments. While designing modern software, care must be taken for scaling processes based on consumer demands to slash down the costs of the system. This research proposes an effective strategy based on optimization-enabled VM scaling-based load distribution and optimal switching strategy in the cloud data center. Here, horizontal scaling or vertical scaling is employed to address the overloading complications. The network structure of the cloud is defined depending upon the fat tree model and load distribution of cloud DCs is carried out using pelican Taylor manta ray foraging optimization (P-Taylor MRFO) algorithm by considering multiple objectives, such as power, load, latency, and bandwidth. Based on the load distribution, the switching of the cloud DC to the desired mode is carried out utilizing actor critic neural network. If the system is overloaded, either vertical scaling or horizontal scaling is done based on a predefined threshold elastic scaling using the proposed pelican Adam optimization algorithm (PAOA) based on the horizontal cost that is based on CPU, memory, and hard disk. The PAOA is devised by integrating Pelican optimization algorithm and Adam optimization. However, the proposed model has attained superior results with a minimum load of 0.329, power of 0.532, energy consumption of 0.357, and latency of 0.315. [ABSTRACT FROM AUTHOR]