Towards Automatic Parameter Tuning of Stream Processing Systems

Abstract

A crucial concern regarding cloud computing is the confidentiality of sensitive data being processed in the cloud. Trusted Execution Environments (TEEs), such as Intel Software Guard eXtensions (SGX), allow applications to run securely on an untrusted platform. However, using TEEs alone for stream processing is not enough to ensure privacy as network communication patterns may leak information about the data.
This paper introduces two techniques – anycast and multicast – for mitigating leakage at inter-stage communications in streaming applications according to a user-selected mitigation level. These techniques aim to achieve network data obliviousness, i.e., communication patterns should not depend on the data. We implement these techniques in an SGX-based stream processing system. We evaluate the latency and throughput overheads, and the data obliviousness using three benchmark applications. The results show that anycast scales better with input load and mitigation level, and provides better data obliviousness than multicast.

Publication
Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems 2018 (DEBS’18)
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M. Bilal
PhD Student

My research interests include distributed systems and cloud computing.