  The number of mobile devices (e.g., smartphones, wearable technologies) is
rapidly growing. In line with this trend, a massive amount of spatial data is
being collected since these devices allow users to geo-tag user-generated
content. Clearly, a scalable computing infrastructure is needed to manage such
large datasets. Meanwhile, Cloud Computing service providers (e.g., Amazon,
Google, and Microsoft) allow users to lease computing resources. However, most
of the existing spatial indexing techniques are designed for the centralized
paradigm which is limited to the capabilities of a single sever. To address the
scalability shortcomings of existing approaches, we provide a study that focus
on generating a distributed spatial index structure that not only scales out to
multiple servers but also scales up since it fully exploits the multi-core CPUs
available on each server using Voronoi diagram as the partitioning and indexing
technique which we also use to process spatial queries effectively. More
specifically, since the data objects continuously move and issue position
updates to the index structure, we collect the latest positions of objects and
periodically generate a read-only index to eliminate costly distributed
updates. Our approach scales near-linearly in index construction and query
processing, and can efficiently construct an index for millions of objects
within a few seconds. In addition to scalability and efficiency, we also aim to
maximize the server utilization that can support the same workload with less
number of servers. Server utilization is a crucial point while using Cloud
Computing because users are charged based on the total amount of time they
reserve each server, with no consideration of utilization.
