In this paper, we introduce a web farm-inspired framework for dynamic and concurrent computational processing in the cloud. We compare and contrast this with the Hadoopcloud framework, discuss the main problems associated with our approach, and give suggestions on ways to overcome said challenges. To implement the web-inspired framework, we use Node.js - a lightweight, single threaded, server-side framework which uses asynchronous callbacks to allow non-dependent operations (parallel-like sections) to execute while waiting for I/O events such as "fetching a file" or "writing a file to disk." We perform experiments to reveal two preliminary results that showcase the framework's functionality and scalability. One, for non-blocking operations, worker nodes which use Node.js servers are significantly faster than those which use traditional servers. In particular, a single Node.js is (on average) 2.11 times faster than one Ruby Webrick server, and is (on average) 1.88 times faster than two Ruby Webrick servers. Two, we find that increasing the number of worker nodes improves overall performance for blocking computational operations. As the number of worker nodes increase, the total execution time decreases exponentially and the number of requests per second increases linearly.