With more and more data generated, it has become a big challenge for traditional
architectures and infrastructures to process large amounts of data within an acceptable time …

Picking the right cloud configuration for recurring big data analytics jobs running in clouds is
hard, because there can be tens of possible VM instance types and even more cluster sizes …

Modern resource management frameworks for largescale analytics leave unresolved the
problematic tension between high cluster utilization and job's performance predictability …

Spark has become one of the main options for large-scale analytics running on top of shared-
nothing clusters. This work aims to make a deep dive into the parallelism configuration and …

Internet services access networked storage many times while processing a request. Just a
few slow storage accesses per request can raise response times a lot, making the whole …

Many companies start using Hadoop for advanced data analytics over large datasets. While
a traditional Hadoop cluster deployment assumes a homogeneous cluster, many enterprise …

Multi-tenant distributed systems composed of small services, such as Service-oriented
Architectures (SOAs) and Micro-services, raise new challenges in attaining high …

Big data is one of the most contemporary issues. It is innovative processing solutions for a
variety of new and existing data to provide real business benefits. Unless it is tied to …

Deep learning (DL) has been widely adopted in various domains of artificial intelligence (AI),
achieving dramatic developments in industry and academia. Besides giant AI companies …

Hadoop MapReduce adopts a two-phase (map and reduce) scheme to schedule tasks
among data-intensive applications. However, under this scheme, Hadoop schedulers do not …