  Time-evolving large graph has received attention due to their participation
in real-world applications such as social networks and PageRank calculation. It
is necessary to partition a large-scale dynamic graph in a streaming manner to
overcome the memory bottleneck while partitioning the computational load.
Reducing network communication and balancing the load between the partitions
are the criteria to achieve effective run-time performance in graph
partitioning. Moreover, an optimal resource allocation is needed to utilise the
resources while catering the graph streams into the partitions. A number of
existing partitioning algorithms (ADP, LogGP and LEOPARD) have been proposed to
address the above problem. However, these partitioning methods are incapable of
scaling the resources and handling the stream of data in real-time.
  In this study, we propose a dynamic graph partitioning method called Scalable
Dynamic Graph Partitioner (SDP) using the streaming partitioning technique. The
SDP contributes a novel vertex assigning method, communication-aware balancing
method, and a scaling technique to produce an efficient dynamic graph
partitioner. Experiment results show that the proposed method achieves up to
90% reduction of communication cost and 60%-70% balancing the load dynamically,
compared with previous algorithms. Moreover, the proposed algorithm
significantly reduces the execution time during partitioning.
