Structure- and cost-aware partitioning for large graphs over geo-distributed datacenters

Delong MA; Ye YUAN; Hangxu JI; Yishu WANG; Yuliang MA

doi:10.1007/s11704-025-50141-5

Front. Comput. Sci. ›› 2026, Vol. 20 ›› Issue (2) : 2002610 DOI: 10.1007/s11704-025-50141-5

Information Systems

LETTER

Structure- and cost-aware partitioning for large graphs over geo-distributed datacenters

Author information +

History +

PDF (476KB)

Graphical abstract

Cite this article

Download citation ▾

Delong MA, Ye YUAN, Hangxu JI, Yishu WANG, Yuliang MA. Structure- and cost-aware partitioning for large graphs over geo-distributed datacenters. Front. Comput. Sci., 2026, 20(2): 2002610 DOI:10.1007/s11704-025-50141-5

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Fan W, Liu M, Lu P, Yin Q . Graph algorithms with partition transparency. IEEE Transactions on Knowledge and Data Engineering, 2023, 35( 2): 1554–1566

[2]	Dolev S, Florissi P, Gudes E, Sharma S, Singer I . A survey on geographically distributed big-data processing using mapreduce. IEEE Transactions on Big Data, 2019, 5( 1): 60–80

[3]	Gonzalez J E, Low Y, Gu H, Bickson D, Guestrin C. PowerGraph: distributed graph-parallel computation on natural graphs. In: Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation. 2012, 17–30

[4]	Zhou A C, Shen B, Xiao Y, Ibrahim S, He B . Cost-aware partitioning for efficient large graph processing in geo-distributed datacenters. IEEE Transactions on Parallel and Distributed Systems, 2020, 31( 7): 1707–1723

[5]	Kong D, Xie X, Zhang Z. Clustering-based partitioning for large web graphs. In: Proceedings of the 38th International Conference on Data Engineering. 2022, 593–606

[6]	Ding Z, Kong D, Zhang Z, Xie X, Xu J . ClusPar: a game-theoretic approach for efficient and scalable streaming edge partitioning. IEEE Transactions on Computers, 2025, 74( 1): 116–130