Abhinay R. Nagpal

Optimization of disk replacement as a large domain objective function

There are several motivations for trying to find hardware infrastructure which can be replaced. Most administrators believe in owning a diversified portfolio of disks, tapes to replace older ones, a strategy that has been widely accepted and practiced. When attempting to create an efficient portfolio of disks to be replaced there are numerous factors to consider set of fitness heuristics over a population of disks, the goal here is to find a portfolio that has a high probability of failure and at the same time will ensure savings in power consumption and carbon emission. Given the fundamental financial and price information of power consumed, cooling required, we attempt to use GA (Genetic Algorithm) and a hybrid PSO-EO (Particle Swarm Optimization) algorithm to identify disks that are likely to outperform the existing ones by preventing failure and having excess returns and compare these two approaches.

Agile Disk Replacement Strategy for Data Cloud Environment Using Evolutionary Algorithms

Information Technology has become a key differentiator for companies and plays an increasingly critical role within corporations at the same time it commands significant resources and growing respect. Across different types and size of business, the overall dependency on technology is rising. Moreover, due to recent economic turbulence IT spending has come under scrutiny and enterprises are more prudent than before to cut costs. Consequently the cut downs are not only related to macroeconomic parameters, but they influence routine activities like maintenance and hardware replacement in a more direct way. This feature is undesirable for the system administrator/investor but it is also unavoidable. The best that one can do is to try to reduce this uncertainty. Disk failure Prediction (or Forecasting) is one of the instruments in this process. However they are not pragmatic to satiate the needs of today’s enterprise. Given the fundamental financial and price information of power consumption by a particular disk along with its shelf life, we attempt to use Genetic Algorithm to identify disks which if replaced are likely to outperform and at the same time ensuring savings

FLEXI: A toolkit for malleable web sites

The subject of Web mining and personalization has generated a lot of interest across the research community as well as industry. The popularity of a Web site depends on the recommendations it gets from other Web sites. The way the user navigates through Web site content is dynamic. Each Web users traversal on a Web site can be seen as a Markov chains. We can build a Markov model of the Web site using past traversals on the hyperlinks as their accumulated navigation behavior. We analyze heatmap of clicks on a HTML page, showing hot and cold click zones and generate the page such that users view the hot zones area on the requested page. Our approach can enhance the users browsing by using searching and relevance ranking methods to retrieve relevant information and render personalized Web pages. This approach can also be useful to focus on relevant components of the Web page where the sizes of user displays/screens are small, like PDAs.