Adaptive Load Balancing Using RR and ALB: Resource Provisioning in Cloud

Cloud Computing context, load balancing is an issue. With a rise in the number of cloud-based technology users and their need for a broad range of services utilizing resources successfully or effectively in a cloud environment is referred to as load balancing, has become a significant obstacle. Load balancing is crucial in storage systems to increase network capacity and speed up response times. The main goal is to present a new load-balancing mechanism that can balance incoming requests from users all over globally who are in different regions requesting data from remote data sources. This method will combine effective scheduling and cloud-based techniques. A dynamic load balancing method was developed to ensure that cloud environments have the ability to respond rapidly, in addition to running cloud resources efficiently and speeding up job processing times. Applications' incoming traffic is automatically split up across a number of targets, including Amazon EC2 instances, network addresses, and other entities by elastic load balancing. Elastic load balancing offers three distinct classifications of load balancer, and each one provides high availability, intelligent scalability, and robust security to guarantee the error-free functioning of your applications. Application load balancing and round robin are the two load balancing mechanisms in database cloud that are focus of this research study

Managing NFV using SDN and control theory

Control theory and SDN (Software Defined Networking) are key components for NFV (Network Function Virtualization) deployment. However little has been done to use a control-theoretic approach for SDN and NFV management. In this paper, we describe a use case for NFV management using control theory and SDN. We use the management architecture of RINA (a clean-slate Recursive InterNetwork Architecture) to manage Virtual Network Function (VNF) instances over the GENI testbed. We deploy Snort, an Intrusion Detection System (IDS) as the VNF. Our network topology has source and destination hosts, multiple IDSes, an Open vSwitch (OVS) and an OpenFlow controller. A distributed management application running on RINA measures the state of the VNF instances and communicates this information to a Proportional Integral (PI) controller, which then provides load balancing information to the OpenFlow controller. The latter controller in turn updates traffic flow forwarding rules on the OVS switch, thus balancing load across the VNF instances. This paper demonstrates the benefits of using such a control-theoretic load balancing approach and the RINA management architecture in virtualized environments for NFV management. It also illustrates that GENI can easily support a wide range of SDN and NFV related experiments

Extending sensor networks into the cloud using Amazon web services

Sensor networks provide a method of collecting environmental data for use in a variety of distributed applications. However, to date, limited support has been provided for the development of integrated environmental monitoring and modeling applications. Specifically, environmental dynamism makes it difficult to provide computational resources that are sufficient to deal with changing environmental conditions. This paper argues that the Cloud Computing model is a good fit with the dynamic computational requirements of environmental monitoring and modeling. We demonstrate that Amazon EC2 can meet the dynamic computational needs of environmental applications. We also demonstrate that EC2 can be integrated with existing sensor network technologies to offer an end-to-end environmental monitoring and modeling solution