  With the rapidly growing demand of graph processing in the real scene, they
have to efficiently handle massive concurrent jobs. Although existing work
enable to efficiently handle single graph processing job, there are plenty of
memory access redundancy caused by ignoring the characteristic of data access
correlations. Motivated such an observation, we proposed two-level scheduling
strategy in this paper, which enables to enhance the efficiency of data access
and to accelerate the convergence speed of concurrent jobs. Firstly,
correlations-aware job scheduling allows concurrent jobs to process the same
graph data in Cache, which fundamentally alleviates the challenge of CPU
repeatedly accessing the same graph data in memory. Secondly, multiple
priority-based data scheduling provides the support of prioritized iteration
for concurrent jobs, which is based on the global priority generated by
individual priority of each job. Simultaneously, we adopt block priority
instead of fine-grained priority to schedule graph data to decrease the
computation cost. In particular, two-level scheduling significantly advance
over the state-of-the-art because it works in the interlayer between data and
systems.
