Debabrata Sarddar

Fuzzy based dynamic load balancing scheme for efficient edge server selection in Cloud-oriented content delivery network using Voronoi diagram

Any given Euclidean space can be partitioned into non-overlapping regions using Voronoi diagram and the Delaunay triangulation connects sites using nearest-neighbor fashion. Realistically in this context, all the edge servers are scattered over the Earth surface and can be clustered using Voronoi diagram. Now nearest edge server selection by Delaunay triangulation over the Voronoi diagram is our prime target. Due to the large demand of Internet content coming from burst crowd, performance of the Cloud-oriented content delivery networks is drastically reduced. To improve the said performance degradation, nearest edge server selection is a primary goal of cloud service provider (like Akamai Technologies, Amazon CloudFront, Mirror Image Internet etc.). Empirically all the time load of the nearest edge server is not eligible for responding the user request. Therefore load balancing is also important criteria for selecting suitable edge server. In this paper, we have presented Fuzzy Based Least Response Time (FLRT) dynamic load balancing algorithm and which is effective for crisps input from different heterogeneous system. Thus, FLRT is a novel paradigm which can select nearest neighbor edge server from users current location where response time and load of the edge server is lowest.

Server Utilization-Based Smart Temperature Monitoring System for Cloud Data Center

The rise in demand for cloud computing services has thrown sharply into focus the subject of energy efficiency and cooling methods. The words “green” and “computing” can often translate into commercial and production successes, vendors and consumers alike are keen to optimize the services offered through cloud data centers as much as possible. While various existing methods help in bringing down rising temperatures of servers operating in cloud data center infrastructure, most authors would agree that pushing in cold air requires energy to be fed to cooling equipment and the associated infrastructure. Based upon existing research conducted, we approached the problem in a new light—concentrating on server utilization to regulate the temperature. We introduce Mean Utilization Factor concept that allows detecting and regulating the amount of cool air that is to be channeled in and around the servers within a cloud data center to bring down the operating temperature.

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.

Secured Mobile Collaborative Application in Cloud Environments

The utilities of mobile collaborative applications have to gain popularity day by day. When web applications are connected with various mobile applications, it reflects a new concept in the field of technology. There are different issues generated in case of mobile collaboration work, such as unstable connection, limited resources, malicious virus attack in the server, etc. In our work, our main objective is to develop a secured layer to ensure maximum security of shared resources with proper maintaining collaborative mobile applications in a cloud environment in a distributive way. In this paper, we design a cryptography-based security model mixed with dynamic early detection technique to manage congestion and provide more secured service. Our main aim is to serve the well-secured collaborative application for achieving high-quality collaborative work in the mobile cloud paradigm. In the cryptographic technique, we use Diffie–Hellman cryptosystem for designing our model, and with the help of dynamic early detection technique, we want to reduce congestion that is created by third party for interrupting mobile collaborative application in case of shared resources.

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.

Fog-Based Hierarchical Search Optimization

In the modern world, information can comprise large amounts of data generated from personal and business use. Cloud computing provides an efficient way of handling those data. The introduction of Fog computing is a useful addition in the scenario of Cloud computing. Apart from data storing and processing, it offers various services to the users connected through the Internet. Fog computing along with cloud technology makes the task of data and information processing easier. It complements cloud computing in such a way that a major portion of data stored in cloud is taken away thus restoring the efficiency of the system. This paper focuses on search optimization in cloud with the help of fog technique. It not only considers the access to recent data but also deals with archival of the data. By separately dealing with recent and archived data, the proposed technique makes data retrieving more time efficient. Moreover it also reduces the complexities of copying data in a central server. The data can be retrieved directly from the cloud connected with Billboard Manager. Thus, this gives users a decentralized system.

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.

On Demand IOPS Calculation in Cloud Environment to Ease Linux-Based Application Delivery

Today’s era of cloud computing and everlasting demands for real-time analysis of the storage data on cloud, it is essential for IT industries to have cognizance about the storage performance. Cloud is elastic computing model where users can hire computing and on demand storage resources from a remote infrastructure and its popularity depends on low cost and on demand availability. Simultaneous execution of huge number of data-intensive applications on the public cloud call for a huge amount of storage in order to access the persistent data leads to degradation of overall system performance. IT personnel have to be assisted with storage performance measurement for prediction of best storage need. Input/Output Operations Per Second (IOPS) calculation helps to determine the amount of I/O’s storage to run. This IOPS calculation is incorporated in cloud environment to alleviate Linux based application delivery.