Big data processing systems (eg, Hadoop, Spark, Storm) contain a vast number of
configuration parameters controlling parallelism, I/O behavior, memory settings, and …

MapReduce is a framework for processing and managing large-scale datasets in a
distributed cluster, which has been used for applications such as generating search indexes …

MapReduce and Hadoop represent an economically compelling alternative for efficient
large scale data processing and advanced analytics in the enterprise. A key challenge in …

MapReduce systems face enormous challenges due to increasing growth, diversity, and
consolidation of the data and computation involved. Provisioning, configuring, and …

MapReduce has emerged as a viable competitor to database systems in big data analytics.
MapReduce programs are being written for a wide variety of application domains including …

To reduce the impact of network congestion on big data jobs, cluster management
frameworks use various heuristics to schedule compute tasks and/or network flows. Most of …

MapReduce is a programming paradigm for parallel processing that is increasingly being
used for data-intensive applications in cloud computing environments. An understanding of …

Infrastructure-as-a-Service (IaaS) cloud platforms have brought two unprecedented changes
to cluster provisioning practices. First, any (nonexpert) user can provision a cluster of any …

Distributed data processing framework MapReduce is increasingly deployed in Clouds to
leverage the pay-per-usage cloud computing model. Popular Hadoop MapReduce …

We present Purlieus, a MapReduce resource allocation system aimed at enhancing the
performance of MapReduce jobs in the cloud. Purlieus provisions virtual MapReduce …