Biswajit Biswas

Load Balancing of Unbalanced Matrix with Hungarian Method

We know that cloud computing is an online-based servicing. So there are more than a million number of web servers, who are connected to online cloud computing to offer various types of online web services to cloud customers. Limited numbers of web servers connected to the cloud networks have to execute more than a million number of tasks at the same time. So, it is not simple to execute all tasks at a particular moment. Some machines execute all tasks, so there is a need to balance all loads at a time. Load balance minimizes the completion time as well as executes all tasks in a particular way. It is not possible to have an equal number of servers to execute equal tasks. Tasks to be completed in cloud environment system or environment will be greater than the connected components. Hence, a less number of servers have to execute a greater numbers of jobs. We propose a new algorithm in which some machines complete the jobs, where a number of jobs are greater than the number of machines and balance every machine to maximize the excellence of services in the cloud system.

Load Balancing with Job Switching in Cloud Computing Network

Cloud computing, described as distributed online computing, is a kind of Internet-based computing that provides pooled web resources and applications to connected servers and other machines on user’s demand. It is a web system for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources which can be rapidly provisioned and released with minimal management effort. Load balancing is an important issue in the cloud computing. Cloud computing comprises of many web resources and managing. This plays a vital role in executing a user’s request. In this present condition the load balancing algorithms should be very efficient in allocating the user request and also ensuring the usage of the resources in an intelligent way so that underutilization of the resources will not occur and preemptive based resource management be there in the cloud environment. Cloud computing services different types of nodes connected to cloud to assist the execution of great number of tasks. As a result, to select suitable node or machine for executing a task is able to develop the performance of cloud computing system. A job switching is the switching of the job from one machine to another machine to minimize the overall completion time. In this paper, we propose a load balancing algorithm combining minimum completion time as well as load balancing strategies with job switching.

Cloud Adoption: A Future Road Map for Indian SMEs

The Cloud Environment has taken an important role in present Indian organizations mainly for small and medium-scaled enterprises (SME). The Software as Service (SAAS) model of cloud implementation has enabled them pay-per-use strategy to leverage their business needs with minimum investments. The various initiatives, like moving toward cashless transactions, taken by Government of India in recent past, forced them to adapt software system for their daily business requirements. It has created a huge business opportunity for the cloud solution providers. In this paper, authors have surveyed and outlined the current scenario of cloud adoption by Indian enterprises. The authors also tried to focus on various facets of cloud adoption along with the factors that are blockers for cloud adoption for Indian enterprises.

Load Balancing of Unbalanced Matrix Problem of the Sufficient Machines with Min-Min Algorithm

Nowadays, cloud computing as a developing Internet accommodation concept has been propagating to provide different Internet resources to users. Cloud computing occupies a variety of computing Internet applications for facilitating the execution of sizable voluminous-scale tasks. Cloud computing is a web predicated distributed computing. There is more than a million number of servers connected to the Internet to provide several types of accommodations to provide cloud users. Constrained numbers of servers execute fewer numbers tasks at a time. So it is not too easy to execute all functions at a time. Some systems run all functions, so there are needed to balance all loads. Load balance reduces the completion time as well as performs all tasks in a particular way. There are not possible to remain an equal number of servers to execute equal tasks. Tasks to be executed in cloud computing would be less than the connected servers sometime. Excess servers have to execute a fewer number of tasks. Here, we are going to present an algorithm for load balancing and performance with minimization completion time and throughput. We apply here a very famous Hungarian method to balance all loads in distributing computing. Hungarian Technique helps us to minimize the cost matrix problem.

Improved Cost-Effective Technique for Resource Allocation in Mobile Cloud Computing

Mobile cloud computing (MCC) is a big research topic in this modern technology-based era. This technology combines cloud computing with mobile computing in an innovative way to give better performance and cost-effective service to mobile users. MCC gives opportunities to execute different applications on the mobile devices by transferring the compute-intensive job to the cloud, but there are some problem arising in case of connectivity with mobile devices and cloud servers. To satisfy the user’s demand and accessing cloud server to offload, the task from mobile device to cloud in mobile cloud computing is a difficult job. According to our knowledge we know that the cloud computing has been built upon the growth of distributing computing and virtualization concept. Thus, efficient mapping of tasks to available resource in cost-effective way in the mobile cloud environment is a challenging issue. Our main aim is to allocate nodes to their respective resource at cloud server by maintaining optimal response time and increase the quality of service by maintaining both resource cost and computation performances in mobile cloud environment.

Load Balancing of Unbalanced Matrix Problem with More Machines

In nowadays cloud computing as a developing web accommodation model has been propagating to offer different Internet resources to users. Cloud computing occupies a range of computing Internet applications for facilitating the finishing of sizable voluminous-scale tasks. Cloud computing is a web predicated distributed computing. There is more than a million number of servers connected to the Internet to provide several types of accommodations to provide cloud users. Constrained numbers of servers execute fewer numbers tasks at a time. So it is not too easy to execute all tasks at a time. Some systems execute all tasks, so there are needed to balance all loads. Load balance minimizes the completion time as well as executes all tasks a particular way.

A Proposed Model to Integrate Business Intelligence System in Cloud Environment to Improve Business Function

Business intelligence system (BIS) has gained a high momentum and played a significant role in enhancing the business environment especially with the help of cloud. The BIS with cloud computing is the solution to overcome complex problems where large amounts of data (market analysis, customer’s view, feedback and organization response time) are required to be processed. The objective of this paper is to show the realistic perspective of the possibilities for maximizing benefits and minimizing risks in Indian business. The authors have highlighted the importance of cloud computing in business and tried to develop a framework for integrating BIS with cloud computing, and an attempt has also been made to analyse the efficiency of the proposed model which improves performance over the cloud. Some of the challenging issues as well as the future research directions have also been discussed.

An Innovative Method for Load Balanced Clustering Problem for Wireless Sensor Network in Mobile Cloud Computing

Mobile Cloud Computing is a revolutionary way where global world is progressing in massive way. Connecting wireless sensor network with Mobile Cloud computing is a novel idea in this era. In this year several research has demonstrated to integrate wireless sensor networks (WSNs) with mobile cloud computing, so that cloud computing can be exploited to process the sensory data collected by WSNs and allow these date to the mobile clients in fast, reliable and secured way. For rising lifetime of wireless sensor network, minimizing energy consumption is an important factor. In this case clustering sensor nodes is one of the effective solutions. It is required to gain some excessive load for cluster heads of cluster based WSN in case of collection of huge data, aggregation and communication of this respective data to base station. Particle Swarm Optimization or PSO is an efficient solution of for this problem.