Graphs are versatile enough to model many problems, and we see them used for
solving a large spectrum of challenges in various disciplines. As the size of graph
data grew over the years, to satisfy the requirement to process large data sets on
time, distributed graph-processing systems were implemented.
However, these systems were built with a static deployment in mind, only allowing the systems to run on a pre-defined set of machines. Manual optimizations
and tweaks to deployment configurations lead to a waste of resources as well as an
increase in operational costs.
In this thesis, we explore the introduction of elastic deployment in distributed
graph-processing systems. We design and implement a distributed graph-processing
system and a framework for assessing elasticity and performing real-world experimentation.