K-means clustering plays a vital role in data mining. As an iterative computation, its performance will suffer when applied to tremendous amounts of data, due to poor temporal locality across its iterations. The state-of-the-art streaming algorithm, which streams the data from disk into memory and operates on the partitioned streams, improves temporal locality but can misplace objects in clusters since different partitions are processed locally. This paper presents a collaborative divide-and-conquer algorithm to significantly improve the state-of-the-art, based on two key insights. First, we introduce a break-and-recluster procedure to identify the clusters with misplaced objects. Second, we introduce collaborative seeding between different partitions to accelerate the convergence inside each partition. Compared with the streaming algorithm using a number of wikipedia webpages as our datasets, our collaborative algorithm improves its clustering quality by up to 35.3% with an average of 8.8% while decreasing its execution times from 0.3% to 80.1% with an average of 48.6%.

Despite the widespread adoption of heterogeneous clusters in modern data centers, modeling heterogeneity is still a big challenge, especially for large-scale MapReduce applications. In a CPU/GPU hybrid heterogeneous cluster, allocating more computing resources to a MapReduce application does not always mean better performance, since simultaneously running CPU and GPU tasks will contend for shared resources.This paper proposes a heterogeneity model to predict the shared resource contention between the simultaneously running tasks of a MapReduce application when heterogeneous computing resources (e.g. CPUs and GPUs) are allocated. To support the approach, we present a heterogeneous MapReduce framework, Hadoop+, which enables CPUs and GPUs to process big data coordinately, and leverages the heterogeneity model to assist users in selecting the computing resources for different purposes.Our experimental results show three benefits. First, Hadoop+ exploits GPU capability, and achieves 1.4x to 16.1x speedups over Hadoop for 5 real applications when running individually. Second, the heterogeneity model can be used to allocate GPUs among multiple simultaneously running MapReduce applications, bringing up to 36.9% (17.6% in average) speedup when multiple applications are running simultaneously. Third, the model is verified to be able to select the optimal or most cost-effective resource consumption.