In this paper, we explore the feasibility of enabling the scheduling of mixed hard and soft real-time MapReduce applications. We first present an experimental evaluation of the popular Hadoop MapReduce middleware on the Amazon EC2 cloud. Our evaluation reveals tradeoffs between overall system throughput and execution time predictability, as well as highlights a number of factors affecting real-time scheduling, such as data placement, concurrent users, and master scheduling overhead. Based on our evaluation study, we present a formal model for capturing real-time MapReduce applications and the Hadoop platform. Using this model, we formulate the offline scheduling of real-time MapReduce jobs on a heterogeneous distributed Hadoop architecture as a constraint satisfaction problem (CSP) and introduce various search strategies for the formulation. We propose an enhancement of MapReduce’s execution model and a range of heuristic techniques for the online scheduling. We further outline some of our future directions that apply state-of-the-art techniques in the real-time scheduling literature. 1.

Abstract. The critical task of predicting clicks on search advertisements is typically addressed by learning from historical click data. When enough history is observed for a given query-ad pair, future clicks can be accurately modeled. However, based on the empirical distribution of queries, sufficient historical information is unavailable for many queryad pairs. The sparsity of data for new and rare queries makes it difficult to accurately estimate clicks for a significant portion of typical search engine traffic. In this paper we provide analysis to motivate modeling approaches that can reduce the sparsity of the large space of user search queries. We then propose methods to improve click and relevance models for sponsored search by mining click behavior for partial user queries. We aggregate click history for individual query words, as well as for phrases extracted with a CRF model. The new models show significant improvement in clicks and revenue compared to state-of-the-art baselines trained on several months of query logs. Results are reported on live traffic of a commercial search engine, in addition to results from offline evaluation. Key words: query log mining, clicks, relevance, advertising2 Hillard et al 1