Distributed Graph Processing in Cloud Computing: A Review of Large-Scale Graph Analytics

Abstract

The rapid growth of graph data in various domains has propelled the need for efficient distributed graph processing techniques in cloud computing environments. This paper presents a comprehensive review of distributed graph processing for graph analytics of massive size in the context of cloud computing. The paper begins by highlighting the challenges associated with distributed graph processing, including load balancing, communication overhead, scalability, and partitioning strategies. It provides an overview of existing frameworks and tools specifically designed for distributed graph processing in cloud environments. Furthermore, the review encompasses various techniques and algorithms employed in distributed graph processing. The paper also reviews recent research advancements in optimizing distributed graph processing in cloud computing. To provide practical insights, the paper presents a comparative analysis of representative large-scale graph analytics applications implemented on different cloud computing platforms. Performance, scalability, and efficiency metrics are evaluated under varying workload sizes and graph characteristics. Overall, this comprehensive review paper serves as a highly prized asset for researchers and large-scale graph analytics professionals who are practitioners in the field. It provides a holistic understanding of the state-of-the-art distributed graph processing techniques in cloud computing and guides future research efforts towards more efficient and scalable graph processing in cloud environments.