We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets
programmers perform in-memory computations on large clusters in a fault-tolerant manner …

We present Mesos, a platform for sharing commodity clusters between multiple diverse
cluster computing frameworks, such as Hadoop and MPI. Sharing improves cluster …

Experience from an operational Map-Reduce cluster reveals that outliers significantly
prolong job completion. e causes for outliers include run-time contention for processor …

Many companies are increasingly using MapReduce for efficient large scale data
processing such as personalized advertising, spam detection, and different data mining …

MapReduce is a scalable and fault tolerant framework, patented by Google, for computing
embarrassingly parallel reductions. Hadoop is an open-source implementation of Google …

The past few years have seen a major change in computing systems, as growing data
volumes and stalling processor speeds require more and more applications to scale out to …

Provable data possession is a technique for ensuring the integrity of data in outsourcing
storage service. In this paper, we propose a cooperative provable data possession scheme …

MapReduce offers an ease-of-use programming paradigm for processing large data sets,
making it an attractive model for distributed volunteer computing systems. However, unlike …

Nowadays, large-scale Cloud-based applications have put forward higher demand for
storage ability of data centres. Data in the Cloud need to be stored with high efficiency and …

Parallel dataflow programs generate enormous amounts of distributed data that are short-
lived, yet are critical for completion of the job and for good run-time performance. We call this …