Efficiently scheduling data-parallel computation jobs over cloud-scale computing clusters is
critical for job performance, system throughput, and resource utilization. It is becoming even …

Interactive services often have large-scale parallel implementations. To deliver fast
responses, the median and tail latencies of a service's components must be low. In this …

Datacenter WAN traffic consists of high priority transfers that have to be carried as soon as
they arrive alongside large transfers with pre-assigned deadlines on their completion …

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 …

The success of cloud computing builds largely upon on-demand supply of virtual machines
(VMs) that provide the abstraction of a physical machine on shared resources. Unfortunately …

The MapReduce platform has been widely used for large-scale data processing and
analysis recently. It works well if the hardware of a cluster is well configured. However, our …

One of the key factors in selecting a good scheduling algorithm is using an appropriate
metric for comparing schedulers. But which metric should be used when evaluating …

Today, batch processing frameworks like Hadoop MapReduce are difficult to scale to
multiple clouds due to latencies involved in inter-cloud data transfer and synchronization …

This paper presents a novel algorithm for scheduling big data jobs on large compute
clusters. In our model, each job is represented by a DAG consisting of several stages linked …

MapReduce, a popular programming model for processing data-intensive tasks, has
achieved great success in a wide range of applications such as search indexing, social …