Gajaba: Dynamic Rule Based Load Balancing Framework

Load Balancing is crucial for various operations in distributed situations. Now a days, Cloud Computing is getting a lot of consideration. Users are requesting for more administrations as well as effective results. To achieve this, load balancing is necessary hence, it turned into an extremely interesting for research. In distributed environment, adequate amount of resources are required which are used in such a way that resources are not over-utilized or under-utilize in any circumstance. Many researchers suggest different approaches for load balancing. This paper shows the new approach for Dynamic Load Balancing using the concept of Agent. In this new approach, an entity known as mobile agent performs the basic task. Mobile agent is a software program which executes independently. This paper also compares the proposed protocol with the traditional scheme used for load balancing and the results concludes that the proposed approach greatly reduces the communication cost of servers, accelerates the rate of load balancing which indirectly improves the Throughput and Response Time of the cloud.

SpringerBriefs in Applied Sciences and Technology, 2016

Load balancing is an integral part of software systems that require to serve requests with multiple concurrent computing resources such as servers, clusters, network links, central processing units or disk drives. Load balancing aims to optimize resource use, maximize throughput, minimize response time, and avoid overload of any single resource. It can also lead to a higher reliability through redundant resources. Load balancing typically involves two major components: (i) a controller, a piece of software or hardware controlling the routing of requests to the backend resources according to an specific routing policy; (ii) a reasoner that determines the routing policy. The policy can be set at design-time based on the result of the reasoner or at runtime based on periodic observation of response time and throughput. The MODAClouds Load Balancer (Fig. 6.1) is a component for dispatching requests from end users to application servers following certain load balancing policies. It consists of a load balancing controller and a reasoner. The controller extends

The state-of-art of the technology focuses on data processing and sharing to deal with huge amount of data and client's needs. Cloud computing is a promising technology, which enables one to achieve the aforesaid goal, leading towards enhanced business performance. Cloud computing comes into center of attention immediately when you think about what IT constantly needs: a means to increase capacity or add capabilities on the fly without investing in new infrastructure, training new human resources, or licensing new software. The cloud should provide resources on demand to its clients with high availability, scalability and with reduced cost. Cloud Computing System has widely been adopted by the industry, though there are many existing issues which have not been so far wholly addressed. Load balancing is one of the primary challenges, which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. This Paper gives an efficient dynamic load balancing algorithm for cloud workload management by which the load can be distributed not only in a balanced approach, but also it allocates the load systematically and uniformly by checking certain parameters like number of requests the server is handling currently. It balances the load on the overloaded node to under loaded node so that response time from the server will decrease and performance of the system is increased.

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY

Cloud computing is the dynamic delivery of information technology resources and capabilities as a service over the Internet. Cloud computing is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. It generally incorporates infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). [1] Load balancing is one of the biggest challenges in the cloud computing. The concept of load balancing is to equally distribute the workload, resources across all the nodes to guarantee that all the nodes have equal load i.e. no single node is over loaded. As we all know that cloud computing services are mainly product based so in this approach we are using different product based priority queues for different services.

International journal of smart sensors and ad hoc networks, 2022

Virtualization, dispersed registration, systems administration, programming, and web administrations are all examples of "distributed computing." Customers, datacenters, and scattered servers are just a few of the components that make up a cloud. It includes things like internal failure adaption, high accessibility, flexibility, adaptability, lower client overhead, lower ownership costs, on-demand advantages, and so on. The basis of a feasible load adjusting computation is key to resolving these challenges. CPU load, memory limit, deferral, and system load are all examples of heaps. Burden adjustment is a method for distributing the load across the many hubs of a conveyance framework in order to optimize asset utilization and employment response time while avoiding a situation where some hubs are heavily loaded while others are idle or performing little work. Burden adjustment ensures that at any one time, each processor in the framework or each hub in the system does about the same amount of work. This method may be initiated by the sender, the collector, or the symmetric sort (the blend of sender-started and recipient started types). With some example data center loads, the goal is to create several dynamic load balancing techniques such as Round Robin, Throttled, Equally Spread Current Execution Load, and Shortest Job First algorithms.

Cloud computing is an emerging topic in the field of parallel and distributed computing. Many IT giants such as IBM, Sun, Amazon, Google, and Microsoft are promoting and offering various storage and compute clouds. Clouds provide services such as high performance computing, storage, and application hosting. Cloud providers are expected to ensure Quality of Service (QoS) through a Service Level Agreement (SLA) between the provider and the consumer. In this research, we develop a heterogeneous test bed compute cloud and investigate adaptive management of resources for Web applications to satisfy a SLA that enforces specific response time requirements. We develop a system on top of EUCALYTPUS framework that actively monitors the response time of the computed resources assign to a Web application and dynamically allocates the resources required by the application to satisfy the specific response time requirements.

IJMER

Cloud computing is an attracting technology in the field of computer science. In Gartner’s report, it says that the cloud will bring changes to the IT industry. The cloud is changing our life by providing users with new types of services. Users get service from a cloud without paying attention to the details. NIST gave a definition of cloud computing as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. More and more people pay attention to cloud computing. Cloud computing is efficient and scalable but maintaining the stability of processing so many jobs in the cloud computing environment is a very complex problem with load balancing receiving much attention for researchers. Since the job arrival pattern is not predictable and the capacities of each node in the cloud differ, for load balancing problem, workload control is crucial to improve system performance and maintain stability. Load balancing schemes depending on whether the system dynamics are important can be either static or dynamic. Static schemes do not use the system information and are less complex while dynamic schemes will bring additional costs for the system but can change as the system status changes. A dynamic scheme is used here for its flexibility. The model has a main controller and balancers to gather and analyze the information. Thus, the dynamic control has little influence on the other working nodes. The system status then provides a basis for choosing the right load balancing strategy. The load balancing model given in this research article is aimed at the public cloud which has numerous nodes with distributed computing resources in many different geographic locations. Thus, this model divides the public cloud into several cloud partitions. When the environment is very large and complex, these divisions simplify the load balancing. The cloud has a main controller that chooses the suitable partitions for arriving jobs while the balancer for each cloud partition chooses the best load balancing strategy.

2015

Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. Load balancing with cloud computing provides a good efficient strategy to several inquiries residing inside cloud computing environment set. complete balancing must acquire straight into accounts two tasks, one will be the resource provisioning as well as resource allocation along with will be task scheduling throughout distributed System. Round robin algorithm can be via far the Easiest algorithm shown to help distribute populate among nodes.. Because of this reason it is frequently the first preference when implementing a easy scheduler. One of the reasons for it being so simple is that the only information required is a list of nodes. The proposed algorithm eliminates the drawbacks of implementing a simple round robin architecture in cloud computing by introducing a concept of assigning different tim...

International Journal of Engineering & Technology, 2018

Large number of users are shifting to the cloud system for their different kind of needs. Hence the number of applications on public cloud is increasing day by day. Public clouds considered and is the most convenient platform for common cloud users with generic needs and lesser security concerns. Public cloud can cater to the needs of a large group of users and provide a variety of services. Lower cost and timely availability are the other advantages one expects from public clouds. These features make it very much convenient and attractive choice. But on the other hand, handling public cloud become unmanageable in comparison to other counterparts. Monitoring so many users, tasks and resources are difficult task. Sometimes public clouds are divided on geographically. Geographic partitioning of public cloud can resolve these issues by adding manageability and efficiency in this situation. But, partitioned clouds introduce different ends for submission and operations of cloudlets and ...

International Journal Of Engineering And Computer Science, 2017

Cloud computing has boomed its horizon with large pace as commercial infrastructure in the IT industries meeting the vast requirements of computing resources. There are several issues such as load balancing, virtual machine migration, automated service provisioning, algorithm complexity, etc., demanding to be resolved. Each of these issues needs load balancing to be resolved. This aims for distributing the unwanted dynamic workload between the nodes residing in cloud and this desires of every computing resource must be assigned on proficient and reasonable ground. Load balancing has become crucial for efficient performance in distributed environments. Cloud computing is an emerging technology demanding more services and better results. Thus load balancing for the cloud is very interesting and important research area. Many algorithms are proposed to provide efficient techniques for assigning the client's requests to available cloud nodes. This paper studies cloud computing along with research challenges in load balancing. Load balancing has been a major issue for cloud computing environment. Efficient load balancing scheme ensures efficient resource utilization by providing the resources to cloud on-demand of users' basis. By implementing appropriate scheduling criteria load balancing may prioritize users. The aim of this study is to peep in various load balancing algorithms to address its challenges in variety of cloud environment. This study provides a perspective view of the latest approaches in load balancing that will certainly help the future researchers in this field