User:Chenhao178/report3

Introduction
The objective of this project was to improve the efficiency and accessibility of the Canasta Maintenance Script by segregating each Wiki's images and cache in a distributed environment, i.e., a farm. This modification was crucial to allow for independent operation, better resource management, and ease of troubleshooting, thereby drastically improving the utility of the Canasta system.

Methodology
Our main tasks were:


 * 1) Design and Implementation of the Enhanced Maintenance Script: We designed an upgraded maintenance script with the functionality to segregate each wiki's images and cache. This was a crucial enhancement as it allowed the system to independently manage and process each wiki's resources.
 * 2) Distribution of Resources across a Farm: Once the segregation functionality was implemented, we proceeded to distribute the individual resources across a farm. This allowed the system to optimize resource allocation and efficiently process a high volume of requests.

Results and Implications
Post implementation, the enhancement led to significant improvements:


 * 1) Increased Efficiency: The segregation of images and cache per wiki led to a considerable increase in processing efficiency. By dealing with smaller, more manageable units of data, the system can process requests quicker, improving overall performance.
 * 2) Improved Scalability: The distribution of resources across a farm provides the system with high scalability. This means that as demand increases, the system can easily allocate more resources to ensure continued efficient operation.
 * 3) Better Troubleshooting and Maintenance: With each wiki's resources segregated, it's now easier to isolate issues and perform maintenance tasks. This leads to reduced downtime and improved reliability of the Canasta system.
 * 4) Optimized Resource Utilization: Distributing resources across a farm has allowed for optimal resource usage. This, in turn, translates to cost-effectiveness and allows more Wikis to be hosted with the same resources.

Future Work
Our future work is centered on further and comprehensively examining the farm feature to ensure its robustness, performance, and scalability.