VM Consolidation Plan for Improving the Energy Efficiency of Cloud

Achieving energy-efficiency with minimal Service Level Agreement (SLA) violation constraint is a major challenge in cloud datacenters owing to financial and environmental concerns. The static consolidation of Virtual Machines (VMs) is not much significant in recent time and has become outdated because of the unpredicted workload of cloud users. In this paper, a dynamic consolidation plan is proposed to optimize the energy consumption of the cloud datacenter. The proposed plan encompasses algorithms for VM selection and VM placement. The VM selection algorithm estimates power consumption of each VM to select the required VMs for migration from the overloaded Physical Machine (PM). The proposed VM allocation algorithm estimates the net increase in Imbalance Utilization Value (IUV) and power consumption of a PM, in advance before allocating the VM. The analysis of simulation results suggests that the proposed dynamic consolidation plan outperforms other state of arts.

INVESTIGATION ON ONLINE VNF SCALING IN A CLOUD DATACENTER USING ILP

NFV is the advanced technology in present situation. Online VNF Scaling in a cloud datacenter under multi-resource constraints were consider for formulating mathematical model. A new novel ILP Scaling algorithm works based on the regularization technique and dependent rounding.

Load balancing in virtualized environments using virtual machine migration: A comprehensive survey

Virtualization is a core and requisite technology in Cloud Computing that provisions scalable virtual resources for execution of varied applications. It enables the cloud datacenter resources to be multiplexed within numerous virtual computing environments recognized as virtual machines. These virtual machines consolidates varied applications with diversified resource requirements. It prompts to increase in load imbalance level leading to reduced performance and SLA violations. In order to achieve load balancing across virtual machines varied approaches are presented in literature and virtual machine migration based load balancing is a popular move in this direction. In this work, recent literature on different migration based load balancing schemes are reviewed. The objective of the work is highlight the features, advantages and shortcomings of the considered literature. Alongside that, the effort is conferred to provide an analytical view over different perspectives which will motivate the research in this area.

Moving Target Defense for SDN-Based Cloud Datacenter Network Protection

The significant advance of software Defined Networking (SDN) technology has enabled several complex system operations to be highly dynamic, flexible and robust; particularly in terms of programmability and controllability with the help of SDN controllers. Accordingly, many security operations have utilized this capability to be optimally deployed in a complex network using the SDN functionalities. Moving target defense (MTD) has emerged as an adaptive and proactive defense mechanism aiming to thwart a potential attacker. The key underlying idea of MTD is to increase uncertainty and confusion for attackers by changing attack surface (i.e., system or network configurations) that can invalidate the intelligence collected by the attackers and interrupt attack execution; ultimately leading to attack failure. In this research, by leveraging the advanced SDN technology, the model of MTD using SDN-based system framework design is proposed. The model uses a runtime model that allows the proposed framework to infer the current state of the system. Based on the obtained information, the MTD mechanism using SDN can provide proactive, adaptive and affordable defense services for the exploitable aspects of the cloud datacenter network to increase uncertainty and complexityto the attackers and reduce the likelihood of an attack and minimize cloud security risk. The research also validates the outperformance of the proposed MTD technique in attack success rate via simulation on SDN-based cloud datacenter network experiments in a virtualized environment.

Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

Efficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to solve the multidimensional virtual machine placement problem. In the algorithm being proposed we use the combination of Modified First Fit Decreasing Algorithm (MFFD) with Particle Swarm Optimization Algorithm, used to solve the best Virtual Machine packing in active Physical Machines to reduce energy consumption; we first screen all Physical Machines for possible accommodation in each Physical Machine and then the Modified Particle Swam Optimization (MPSO) Algorithm is used to get the best fit solution.. In our paper, we discuss how to improve the efficiency of Particle Swarm Intelligence by adapting the efficient mechanism being proposed. The obtained result shows that the proposed algorithm provides an optimized solution compared to the existing algorithms.