Fig 2. shows a framework of under which load balancing algorithms work in a cloud computing environment. Cloudlet submits tasks to Job Manager and then Job Manager gives all jobs to Load Balancer. Load Balancer applies Load Balancing Algorithm for Submitted Tasks and schedule all task such that each Virtual Machine will get equal number of task for execution.
In cloud computing, if users are increasing load will also be increased, the increase in the number of users will lead to poor performance in terms of resource usage, if the cloud provider is not configured with any good mechanism for load balancing and also the capacity of cloud servers would not be utilized properly. This will confiscate or seize the performance of heavy loaded node. If some good load balancing technique is implemented, it will equally divide the load (here term equally defines low load on heavy loaded node and more load on node with less load now) and thereby we can maximize resource utilization. One of the crucial issue of cloud computing is to divide the workload dynamically.
Abstract — Cloud computing is organize in the data centre where physical machine are virtualized can be seen in this paper. Over the Internet in general, Cloud computing is a term used for anything that involves delivering hosted services. As cloud computing is a new technology which has both merits and demerits, load balancing is one of the major issue faced by cloud computing. In Virtualization many virtual machines can be run. Many researches & studies need to be carried out for Load balancing as it is an important topic in cloud computing. The data centre is built with many systems where balancing is becomes a very difficult task especially for cloud computing. Most of the research of cloud computing is done in distributed environments. Distributed load balancing on cloud computing is already in the list; despite of the fact that the use of semi-distributed load balancing in cloud computing is not discussed in any literature. A new algorithm for the cloud computing can be designed by using the method of semi-distributed load balancing.
Abstract: Internet, from its beginning so far, has undergone a lot of changes which some of them has changed human’s lifestyle in recent decades. One of the latest changes in the functionality of the Internet has been the introduction of Cloud Computing. Cloud Computing is a new internet service, which involves virtualization, distributed computing, networking, software etc. This technology is becoming popular to provide various services to users. Naturally, any changes and new concepts in the world of technology have its own problems and complexities. Using Cloud Computing is no exception and has many challenges facing the authorities in this area such as load balancing, security, reliability, ownership, data backup and data portability. Load balancing is one of the essential factors to enhance the working performance of the Cloud service provider by shifting of workload among the processors. Proper load balancing aids in minimizing resource consumption, implementing fail-over, enabling scalability, avoiding bottlenecks and over- provisioning etc. Given the importance of the process of load balancing in Cloud Computing, the aim of this paper is to review the process and to compare techniques in this field.
Abstract: Cloud computing can be define as a structured model that which defines computing services, in which resources as well as data are retrieve from cloud service supplier via internet through some well shaped web-based device and application. It provides the on demand services for various applications and infrastructure to the user. Cloud service providers are required to provide the service efficiently and effectively. For that, a cloud provider utilizes all the resource from the node. Thus, the node that are meant for creating a task in the cloud computing must be considered for efficient usage of the available resources. Resources have to be properly selected according to the properties of the task. By analyzing the present research on cloud computing, we have come to the most common and important issue of load balancing. Load balancing has been always a study topic whose purpose is to make sure that all computing resources are circulated proficiently and fairly. As numbers of users are increasing on the cloud, the load balancing has become the challenge for the cloud provider. Load balancing being subject of research, proposed algorithm for load balancing which will work dynamically for optimal usage of resource utilization.
ABSTRACT: Cloud computing in a nutshell provides on-demand access to visualized IT resources that can be shared by others on “pay-as-use” policy. It is an awesome platform in next stage of evolution of internet that leverages various opportunities to improve the way in which we think about and implement the practices and technology needed to secure the things that matters us the most. With the recent advent of technology, it has revolutionized the information technology industry by enabling elastic on-demand provisioning of computing resources. Central to these issues lies the establishment of an effective load balancing algorithm. The load can be CPU load, memory, capacity, delay or network load. Load balancing is the process of distributing the load among various nodes of a distributed system to improve both resource utilization and job response time by avoiding a situation where some of the nodes are heavily loaded while other under -loaded or nodes are idle. Load balancing ensures that all the processor in the system or every node in the network distributes equal amount of work at any instant of time. Technique can be sender initiated, receiver initiated or symmetric type (combination of sender initiated and receiver initiated types) for balancing load and can also be categorized static or dynamically. This paper is a brief discussion on different load balancing techniques and comparison between them.
Load balancing is one of the central issues in cloud computing. It is a mechanism that distributes the dynamic local workload evenly across all the nodes in the whole cloud to avoid a situation where some nodes are heavily loaded while others are idle or doing little work. It helps to attain a high customer satisfaction and resource utilization ratio , consequently improving the overall performance and resource utility of the system.
Today is the era of smart computing. Everybody wants to use resources instantly. So researchers started to think about a technology, which can serve anywhere anytime. Cloud computing is the latest technology that provides computational resources, storage and many more computing services on a pay per basics. Cloud computing provides all services on the bases of virtualization in which cloud provider provides virtual machine to the user on his demand. B. Workflow Scheduling and Load Balancing
Cloud computing provides online resources and online storage to the user’s .It provides access to the resources and all the data at a lower cost to them. In Cloud computing the cloud provider outsources all the resources to their client. There are many existing issues in cloud computing. The main problem is load balancing in cloud computing. Load balancing helps to distribute all loads between all the nodes. It also ensures that every computing resource is distributed efficiently and fairly. It provides high satisfaction to the users. Load balancing is a relatively new technique that provides high resource utilization and better response time. Sometimes our system gets hanged up or it seems to take few decades for pages to come out of printer. All this happens because there is a queue of requests waiting for their turn to access resources which are shared among them. But these requests cannot be serviced as the resources required by each of these requests are held by another process or request by virtual machines. One cause for all these problems is called deadlock. Load balancing is a new approach that assists networks and resources by providing a high throughput and least response time.
The cloud computing is one of the most trending technologies in IT domain. It is a technique of handling and pooling services like servers, data base, storage, software and more over the internet based on the user's need or demand. Users can get the resources from the data centers as per their requirements from anywhere through an internet connected computer or hand held devices. One of the challenging task in cloud computing is load balancing used to allocate work load among the data centers. Datacenters are physical machines that has the responsibility to complete the request and demand of cloud users. So load balancing is required to manage the load across data centers, reduce the overload, improve performance, minimize average execution time and provide better resource utilization. Load balancing can minimize the response time and maximize the user's satisfaction. It also increase the source utilization and limit the energy consumption. The classification of load balancing algorithms are of two types: static and dynamic. Load balancing is static when it needs previous data of system. It is dynamic when it requires current data of system.
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 time slices to individual processes depending on their are priorities.
Following are the benefits of cloud computing: Lower IT infrastructure and computer costs for users Abstract: Cloud computing is latest emerging technology for large scale distributed computing and parallel computing. Cloud computing gives large pool of shared resources, software packages, information, storage and many different applications as per user demands at any instance of time. Cloud computing is emerging quickly; a large number of users are attracted towards cloud services for more satisfaction. Balancing the load has become more interesting research area in this field. Better load balancing algorithm in cloud system increases the performance and resources utilization by dynamically distributing work load among various nodes in the system. This paper presents cloud computing, cloud computing architecture, virtualization, load balancing, challenges and various currently available load balancing algorithms.
Cloud computing term is used for the delivery of hosted services over the internet. The main advantage to utilize this technology is to pay as you go. On the basis of rental charges user will use cloud service by demanding from the cloud service provider. The dependency of cloud computing is based on the main three parameters viz. abstraction, encapsulation and isolation of the resources on the demand basis. Several machines are in general connected with cloud data centers and the distributed nature approach is used by implementing any cloud strategy on cloud servers. Majorly, grid computing is work behind it. The main purpose to utilize this is it consumes less power of energy when especially operates on a large scale and can be easily managed by the utilization of centralized server approach. That ultimately results show the overall energy saving . There are some important factors studied by the authors who are very important and more helpful to create an ambiance for cloud. For implementing ambiance MATLAB tools are used by the authors in this research paper. In this research paper, authors considered main two things first one is effective job scheduling when come in multi-queue at a same time on cloud server and the second one is proper utilization of resources. This paper proposes an enhanced multi-queue job scheduling strategy that helps to provide a facility of effective job scheduling by load balancing  without any delay. The
Hierarchical Load Balancing involves heterogeneous levels in load balancing decisions. Every node is maintained or balanced by its parent node. Parent node takes the responsibility for load balancing. Hierarchical load balancing can be used in both homogeneous and heterogeneous environment. Cluster can also play a vital role in hierarchical load balancing. Clustering is the process of organizing same type of objects into groups. Virtual Machine’s having similar characteristics are grouped logically. The last level is the Virtual Machine.
job so that all customers can benefit from it. A trivial example would be using a proportional cost sharing scheme, Minimization with a concave cost function usually falls into the class of NP-hard problems This partially suggests the hardness of our scheduling problem. Though we have not formally proved its NP-harness, we have discovered the properties of optimal scheduling with a general concave cost function. Furthermore, these properties have inspired us to find an optimal offline scheduling algorithm for a special concave cost function. In this section, we present the properties that an optimal schedule should have and point out why it is hard to come up with an optimal scheduling algorithm with polynomial complexity. They used Offline algorithm , That is based on the priority-based scheduling, it has been considered by history and time. First who approaches may get first preference. In existing, The customer is not receiving the appropriate discount prize because of the cloud-broker, the Cloud-broker is not issuing the allocated discount to the customer. In existing system, Load balancing is not very efficient that’s why mostly real time websites hangs or throws some error. Example: Anna University / Irctc.
a) Distribution of Cloud Nodes: There are many algorithms being proposed for loadbalancing in cloud computing. Among them some algorithms might produce efficient results withsmall networks or a network with closely located nodes. Such algorithms are not suitable forlarge networks because those algorithms cannot produce the same efficient results whenapplied to larger networks. There are many reasons that affect the efficiency in larger networkslike speed of the network, distance between the clients and server nodes and also the distancebetween all the nodes in the network . So while developing a load balancing algorithm oneshould try for better results in spatially distributed nodes balancing the load effectively reducing network delays.
Our algorithm estimates the resources assignment depending on the VM requirements. Here we describe the server a hotspot and if the usage exceeds the above the hot threshold then it shows that the server is overloaded and VM' s are moved away. The temperature is zero when the server is not a hot spot. We describe a cold spot when the resource usage are below the clod threshold which shows that the server is idle and it has to be turned off for saving energy. This is performed when mostly all servers are actively utilized below the green computing threshold else it is made inactive. A. Hot Spot Mitigation: In sorted lists of hot spots are arranged in chronological order so that we can remove else to manage low temperature. Our objective is to leave a VM that can decrease the temperature servers. Among all, we select the one that can decrease skewness.
unique identifier is associate with each of data items at each node. Peer 2 peer system used DTH (dynamic hash table) abstraction In this method two functions are associated : put (id, item), put function used to store an data item to associate identifier over a network connection, and Get(id) that requests for retrieve the data item. Load of any node can be dynamic because data items are storing and removing continuously and nodes get connected and disconnected continuously. Directory based management of load information is the most important part of this technique, a directory is collection of information of peer nodes where numbers of directories are periodically maintain and reassignment of VM is capture in the directory. Here Transfer of a virtual server to node when a node get overloaded.
Abstract - Cloud computing there are many tasks that needs to be executed by the available resources to acquire high performance, reduce task completion time, minimize response time, utilization of resource usage and etc. Scheduling theory for cloud computing is gaining a lot of attention with increasing popularity in this cloud era. This technology aims to offer distributed, virtualized, and elastic resources as utilities to end users. It has the potential to support full realization of ‘computing as a utility’ in the near future. With the support of virtualization technology, cloud platforms enable enterprises to lease computing power in the form of virtual machines to users. Because these users may use hundreds of thousands of virtual machines (VMs), it is difficult to manually assign tasks to computing resources in clouds.
Abstract— Cloud computing is an online primarily based computing. This computing paradigm has increased the employment of network wherever the potential of 1 node may be used by alternative node. Cloud provides services on demand to distributive resources like info, servers, software, infrastructure etc. in pay as you go basis. Load reconciliation is one amongst the vexing problems in distributed atmosphere. Resources of service supplier have to be compelled to balance the load of shopper request. Totally different load reconciliation algorithms are planned so as to manage the resources of service supplier with efficiency and effectively. This paper presents a comparison of assorted policies used for load reconciliation.