Abstract Context: Hadoop, Spark, Storm, and Mesos are very well known frameworks in both
research and industrial communities that allow expressing and processing distributed …

The continuous shift towards data-driven approaches to business, and a growing attention to
improving return on investments (ROI) for cluster infrastructures is generating new …

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

Data centers are evolving to host heterogeneous workloads on shared clusters to reduce the
operational cost and achieve higher resource utilization. However, it is challenging to …

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 …

As Hadoop is becoming increasingly popular in large-scale data analysis, there is a growing
need for providing predictable services to users who have strict requirements on job …

The deployment of MapReduce in datacenters and clouds present several challenges in
achieving good job performance. Compared to in-house dedicated clusters, datacenters and …

MapReduce is a software framework for processing data-intensive applications with a
parallel manner in cloud computing systems. Some MapReduce jobs have the deadline …

In large-scale data enters, software and hardware failures are frequent, resulting in failures
of job executions that may cause significant resource waste and performance deterioration …

In this paper, we present WOHA, an efficient scheduling framework for deadline-aware Map-
Reduce workflows. In data centers, complex backend data analysis often utilizes a workflow …