In-situ (InS) server systems are typically deployed in special environments to handle InS
workloads which are generated from environmentally sensitive areas or remote places …

Recent advances in composing Cloud applications have been driven by deployments of
inter-networking heterogeneous microservices across multiple Cloud datacenters. System …

Recently, various applications including data analytics and machine learning have been
developed for geo-distributed cloud data centers. For those applications, the ways of …

In this paper, we look into the problem of achieving efficient inter-datacenter AllReduce
operations for geo-distributed machine learning (Geo-DML). Compared with intra-datacenter …

With the wide adoption of large-scale internet services and big data, the cloud has become
the ideal environment to satisfy the ever-growing storage demand. In this context, data …

Graph processing is an emerging computation model for a wide range of applications and
graph partitioning is important for optimizing the cost and performance of graph processing …

In the era of big-data, the jobs submitted to the clouds exhibit complicated structures
represented by graphs, where the nodes denote the sub-tasks each of which can be …

Modern stream processing frameworks, such as Spark Streaming, are designed to support a
wide variety of stream processing applications, such as real-time data analytics in social …

In recent times, a huge amount of data is being created from different sources and the size of
the data generated on the Internet has already surpassed two Exabytes. Big Data …

Traditional batch job schedulers adopt the Compact-n-Exclusive (CE) strategy, packing
processes of a parallel job into as few compute nodes as possible. While CE minimizes inter …