With the ever-increasing need to analyze large amounts of data to get useful insights, it is
essential to develop complex parallel machine learning algorithms that can scale with data …

This paper proposes SmartBench, a benchmark focusing on queries resulting from (near)
real-time applications and longer-term analysis of IoT data. SmartBench, derived from a …

A long-standing challenge in cluster scheduling is to achieve a high degree of utilization of
heterogeneous resources in a cluster. In practice there exists a substantial disparity between …

Graph analytics applications often target large-scale web and social networks, which are
typically power-law graphs. Graph algorithms can often be recast as generalized Sparse …

This paper argues for better concurrency control to support mammoth transactions, which
read and write to many items. While these requests are prevalent on graph data, few …

Low-latency I/O services are essential for latency-sensitive workloads when they co-run with
throughput-oriented workloads in cloud data centers. Although advanced SSDs such as Intel …

Interactive query interfaces have become a popular tool for ad hoc data analysis and
exploration. Compared with traditional systems that are optimized for throughput or batched …

Nowadays, scientific and commercial applications are often deployed to cloud environments
requiring multiple resource types. This scenario increases the necessity for efficient resource …

Data variety is one of the most important features of Big Data. Data variety is the result of
aggregating data from multiple sources and uneven distribution of data. This feature of Big …

The increasing demands of big data applications have led researchers and practitioners to
turn to in-memory computing to speed processing. For instance, the Apache Spark …