Cost-efficient and network-aware dynamic repartitioning-based algorithms for scheduling large-scale graphs in cloud computing environments

Abstract

Large amount of data that is generated by Internet and enterprize applications are stored in the form of graphs. Graph processing systems are broadly used in enterprizes to process such data. With the rapid growth in mobile and social applications and complicated connections of Internet websites, massive concurrent operations need to be handled. On the other hand, the intrinsic structure and the size of real-world graphs make distributed processing of graphs more challenging. Low balanced communication and computation, low preprocessing overhead, low memory footprint, and scalability should be offered by distributed graph analytics frameworks. Moreover, the effects of network factors such as..