Data centers are widely used for big data analytics, which often involve data-parallel jobs,
including query and web service. Meanwhile, cluster frameworks are rapidly developed for …

Many data center applications perform rich and complex tasks (eg, executing a search query
or generating a user's news-feed). From a network perspective, these tasks typically …

The rapidly increasing amount of location data available in many applications has made it
desirable to process their large-scale spatial queries in Cloud for performance and …

In this paper, we present generic cloud performance models for evaluating Iaas, PaaS,
SaaS, and mashup or hybrid clouds. We test clouds with real-life benchmark programs and …

Many widely used, latency sensitive, data-parallel distributed systems, such as HDFS, Hive,
and Spark choose to use the Java Virtual Machine (JVM), despite debate on the overhead of …

Datacenter-scale clusters are evolving toward heterogeneous hardware architectures due to
continuous server replacement. Meanwhile, datacenters are commonly shared by many …

We introduce BriskStream, an in-memory data stream processing system (DSPSs)
specifically designed for modern shared-memory multicore architectures. BriskStream's key …

Data centre applications for batch processing (eg map/reduce frameworks) and online
services (eg search engines) scale by distributing data and computation across many …

While Hadoop ecosystems become increasingly important for practitioners of large-scale
data analysis, they also incur tremendous energy cost. This trend is driving up the need for …

Since Jim Gray introduced the concept of rdquodata cuberdquo in 1997, data cube,
associated with online analytical processing (OLAP), has become a driving engine in data …