Data storage is very important, particularly “Data Storage Security in Cloud Computing” is more essential. As companies have turned to cloud-based services to store, manage and access big data, it has become clear that the cloud’s promise of virtually, on-demand increases in storage, computing and bandwidth is hindered by series of technical bottlenecks: transfer performance over the WAN, HTTP throughput within remote infrastructures, and size limitation of cloud object stores.
With Distributed File System (DFS), system administrators can make it easy for users to access and manage files that are physically distributed across a network. With DFS, you can make files distributed across multiple servers appear to users as if they reside in one place on the network. Users no longer need to know and specify the actual physical location of files in order to access them.
For example, if you have marketing material scattered across multiple servers in a domain, you can use DFS to make it appear as though all of the material resides on a single server. This eliminates the need for users to go to multiple locations on the network to find the information they need.
Apache supports a variety of features, many implemented as compiled modules which extend the core functionality. These can range from server-side programming language support to authentication schemes. Instead of implementing a single architecture, Apache provides a Multi Processing Modules (MPMs), which allow Apache to run in a process-based, hybrid (process and thread) or event-hybrid mode, to better match the demands of each particular infrastructure. This implies that the choice of correct MPM and the correct configuration is important. Where compromises in performance need to be made, the design of Apache is to reduce latency and increase throughput, relative to simply handling more requests, thus ensuring consistent and reliable processing of requests within reasonable time-frames.
Virtual hosting allows one Apache installation to serve many different Websites. For examples, one machine with one Apache installation cloud simultaneously serves.
SYSTEM SPECIFICATION / CONFIGURATION
Processor : Intel Dual Core 2.6 GHZ
Hard Disk : 500 GB
RAM : 2 GB
Monitor : 19 inches
Mouse : Microsoft optical Mouse.
Keyboard : Microsoft 106 keys
Browser : Internet Explorer
Front End : ASP.NET
Back End : SQL
The existing system proposes a system that provides recommended information based on the captured history of navigation from a list of known users.
In another method using queuing theory and logistic regression modeling methods for profiling computer users based on simple temporal aspects of their behavior.
In another technique to generate readable user profiles that accurately capture interests by observing their behavior on the web.
In another aspect of work present a learner with unlabeled sequential data that discover meaningful patterns of sequential behavior from example streams.
DRAWBACK IN EXISTING SYSTEM
- Focus on only a specific environment and cannot be transferred to other environments.
- Computationally less efficient and slow.
We propose an adaptive approach for creating behavior profiles and recognizing computer users.
We call this approach Evolving Agent behavior Classification based on Distributions of relevant events and it is based on representing the observed behavior of an agent (computer user) as an adaptive distribution of his relevant atomic behaviors (events).
Once the model has been created, EVABCD presents an evolving method for updating and evolving the user profiles and classifying an observed user.
The novel evolving user behavior classifier is based on Evolving Fuzzy Systems and it takes into account the fact that the behavior of any user is not fixed, but is rather changing.
The approach we present is generalizable to all kinds of user behaviors represented by a sequence of events.
ADVANTAGES IN PROPOSED SYSTEM
- Focus on a particular environment.
- Able to create on different file.
- Able to process large amount of data in real time environment.