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

Dynamic Congestion Control in Network Layer for Advanced Cloud Computing

Cloud computing becoming attractive tool for delivering web-based services. It can enable rapid development and dynamic scaling and it offers flexible powerful but low cost distribution infrastructure. In paper we proposed new infrastructure capabilities to support dynamic networks. In the network layer Allocation of resource at specific locations and those sites are connects by backbone supporting provisional virtual links. Each location constructs one data center for processing of resource specified by function. Application controller updates the distribution information and multicast to access nodes for load balancing of flow of packets and regulating the traffic flow within application cluster to avoid congestion. The processing elements create the virtual output queues to adjust to prevent output congestion

Efficient Load Balancing for Cloud Computing by Using Content Analysis

Nowadays, computer networks have grown rapidly due to the demand for information technology management and facilitation of greater functionality. The service provided based on a single machine cannot accommodate large databases. Therefore, single servers must be combined for server group services. The problem in grouping server service is that it is very hard to manage many devices which have different hardware. Cloud computing is an extensive scalable computing infrastructure that shares existing resources. It is a popular option for people and businesses for a number of reasons including cost savings and security. This paper aimed to propose an efficient technique of load balance control by using HA Proxy in cloud computing with the objective of receiving and distributing the workload to the computer server to share the processing resources. The proposed technique applied round-robin scheduling for an efficient resource management of the cloud storage systems that focused on an effective workload balancing and a dynamic replication strategy. The evaluation approach was based on the benchmark data from requests per second and failed requests. The results showed that the proposed technique could improve performance of load balancing by 1,000 request /6.31 sec in cloud computing and generate fewer false alarm

Improved Task Graph-based Parallel Data Processing for Dynamic Resource Allocation in Cloud

AbstractIn recent years large-set parallel data processing has gained quantum as one of the predominant applications of Infrastructure-as-a-Service (IaaS) clouds. Data processing frameworks like Google's MapReduce and its open source implementation Hadoop, Microsoft's Dryad and so on are currently in use for parallel data processing in cloud-based companies. But the problem with them is that they are designed for homogeneous environments like clusters and disregard the dynamic and heterogeneous nature of a cloud. As a result, allocation and de-allocation of compute nodes at runtime is ineffective thereby increasing processing time and cost. In this paper we present our approach towards parallel data processing exploiting dynamic resource allocation in IaaS clouds. Our architecture ensures parallel data processing using Directed Acyclic task graph. To reduce the latency and to improve throughput, load balancing is introduced in the architecture. Incoming jobs are divided into tasks that are assigned to different types of virtual machines that are dynamically instantiated and terminated during job execution

Load Balancing and Virtual Machine Allocation in Cloud-based Data Centers

As cloud services see an exponential increase in consumers, the demand for faster processing of data and a reliable delivery of services becomes a pressing concern. This puts a lot of pressure on the cloud-based data centers, where the consumers’ data is stored, processed and serviced. The rising demand for high quality services and the constrained environment, make load balancing within the cloud data centers a vital concern. This project aims to achieve load balancing within the data centers by means of implementing a Virtual Machine allocation policy, based on consensus algorithm technique. The cloud-based data center system, consisting of Virtual Machines has been simulated on CloudSim – a Java based cloud simulator

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres