http://localhost:8080/xmlui/handle/123456789/972 Research Article/Paper Tue, 23 Jun 2026 06:27:05 GMT 2026-06-23T06:27:05Z http://localhost:8080/xmlui/handle/123456789/977 Title: Enhancing Performance of Applications in Cloud using Hybrid Scaling Technique Authors: Shelar, Madhukar; Sane, Shirish; Kharat, Vilas Abstract: In Infrastructure as a Service (IaaS) model of cloud computing paradigm, users acquire computing resources such as CPU, memory, storage and network bandwidth from an IaaS provider and these resources are used to deploy and run their applications. Cloud service providers share computing resources of a physical machine by running isolated Virtual Machines (VM) for web applications. As the load on web application increases, the associated VM must be able to scale up resources to support the increasing load. At the same time, VM should also be able to scale-down resources at light load. In this paper the novel architecture is proposed that provides the hybrid solution of vertical followed by horizontal scaling techniques of resource management in cloud data center. As per the dynamic load on web applications, the proposed algorithm takes the appropriate scaling decision to allocate resources from available pool of resources. Generally dynamic scaling is achieved by the conventional live VM migration technique to create additional VM instances, but VM migration spends CPU time and consumes large amount of IO and network traffic. The proposed technique postpones live VM migration as long as possible with the help of vertical scaling technique to improve the performance of applications. Wed, 01 Jun 2016 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/977 2016-06-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/976 Title: DATABASE MANAGEMENT CHALLENGES IN CLOUD ENVIRONMENT Authors: Shelar, Madhukar; Sane, Shirish; Kharat, Vilas Abstract: Recently cloud computing is widely used technology in delivering computing resources as a service. There are variety of web-based applications hosted on cloud computing platforms, majority of them are data driven. Therefore database management is now become critical component and challenging task for the DBMS designers and researchers. Scalability, elastic load balancing, pay-per-use pricing and self-managing are the major reasons for the successful cloud database management. This is the review of research published/presented on databases in cloud platform. Various challenges of managing databases in the cloud and various techniques proposed by researchers to face these challenges are presented in this review. It is based on various parameters like database systems in the cloud, scalability and elasticity, autonomy or self-managing database systems and preserving consistency of database in cloud. Thu, 01 Sep 2016 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/976 2016-09-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/974 Title: Multi Label Learning with MEKA Authors: Tidke, Vaishli; Sane, Shirish Mon, 01 Aug 2016 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/974 2016-08-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/973 Title: A Novel Approach for Discrimination Prevention and Privacy Preservation in Data Mining Authors: Wakchaure, Manoj; Sane, Shirish Abstract: Recently increasing importance of Data Mining technology as it will help for extract important knowledge data from large amount of data. Hence there negative sociality able to see about data mining. Peoples belonging some categories on that based peoples are treating unfairly. Data mining and data collection techniques classified the mining rules which is covered automated decisions, e.g. grant or denied loan request, insurance premium computation. Discriminatory attributes are gender, cast, region, race etc. if data set biased on above attributes decision may emanate. Discrimination having two types one is direct and indirect. When on the base of sensitive attributes made decision it’s called direct discrimination. When no any sensitive attribute include for made decision it’s called indirect discrimination and which are relate with biased sensitive one. In these studies we focus on discrimination prevention in data mining and our proposed system used for direct or indirect discrimination prevention individually or both at the same time. We focus on how to data discrimination decision convert in anti - discriminatory as cleaning training data set and outsourced dataset. Also we define the new metrics for evaluate with our approaches and we compare these approaches. This experiment explain that the proposed system how effectively removing direct or indirect discrimination while store data quality Wed, 01 Jun 2016 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/973 2016-06-01T00:00:00Z