Various types of online learning algorithms have been developed so far to handle concept drift in data streams. We perform more detailed evaluation of these algorithms through new performance metrics - prequential accuracy, kappa statistic, CPU evaluation time, model cost, and memory usage. Experimental evaluation using various artificial and real-world datasets prove that the various concept drifting algorithms provide highly accurate results in classifying new data instances even in a resource constrained environment, irrespective of size of dataset, type of drift or presence of noise in the dataset. We also present empirically the impact of various features- size of ensemble, period value, threshold value, multiplicative factor and the presence of noise on all the key performance metrics.