Heterogeneity Shifts the Storage-Computation Tradeoff in Secure Multi-Cloud Systems

Autor:	Chi Wan SUNG, Terence Chan, Jiajun CHEN
Přispěvatelé:	Chen, Jiajun, Sung, Chi Wan, Chan, Terence H
Rok vydání:	2023
Předmět:	decoding coded distributed computing secrecy capacity cloud computing codes security Library and Information Sciences heterogeneous systems runtime Computer Science Applications servers multi-cloud computing storage-computation tradeoff resource management Information Systems
Zdroj:	IEEE Transactions on Information Theory. 69:1015-1036
ISSN:	1557-9654 0018-9448
DOI:	10.1109/tit.2022.3206868
Popis:	Refereed/Peer-reviewed This paper considers the design of heterogeneous multi-cloud systems for big data storage and computing in the presence of cloud collusion and failures. A fundamental concept of such a system is the secrecy capacity, which represents the maximum amount of information that can be stored for each unit of storage space under the requirements of secure distributed computing. A capacity-achieving code is designed for matrix multiplication, a computing subroutine widely used in machine learning applications. The code allows fast parallel decoding and unequal data allocation in the clouds. Such a flexibility leads naturally to the idea of optimizing data allocation to minimize the computing time. Given any feasible storage budget, the optimal solution is derived, characterizing explicitly the fundamental tradeoff between storage and computing. Furthermore, it is shown via majorization theory that the whole tradeoff curve improves if the cloud computing rates are more even. Experiments on Amazon EC2 clusters are conducted, corroborating our theoretical observations and the negligibility of decoding overhead
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e1b936601237f3d8e2189dfde2064a3 https://doi.org/10.1109/tit.2022.3206868 Zobrazit plný text záznamu