The shielded computation of hardware-based trusted execution environments such as Intel Software Guard Extensions (SGX) can provide secure cloud computing on remote systems under untrusted privileged system software. However, hardware overheads for securing protected memory restrict its capacity to a modest size of several tens of megabytes, and more demands for protected memory beyond the limit cause costly demand paging. Although one of the widely used applications benefiting from the enhanced security of SGX, is the in-memory key-value store, its memory requirements are far larger than the protected memory limit. Furthermore, the main data structures commonly use fine-grained data items such as pointers and keys, which do not match well with the coarse-grained paging of the SGX memory extension technique. To overcome the memory restriction, this paper proposes a new in-memory key-value store designed for SGX with application-specific data security management. The proposed key-value store, called ShieldStore, maintains the main data structures in unprotected memory with each key-value pair individually encrypted and integrity-protected by its secure component running inside an enclave. Based on the enclave protection by SGX, ShieldStore provides secure data operations much more efficiently than the baseline SGX key-value store, achieving 8--11 times higher throughput with 1 thread, and 24--30 times higher throughput with 4 threads.

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Consolidating multiple applications on a system can improve the overall resource utilization of data center systems. However, such consolidation can adversely affect the performance of some applications due to interference caused by resource contention. Despite many prior studies on the interference effects in single-node systems, the interference behaviors of distributed parallel applications have not been investigated thoroughly. With distributed applications, a local interference in a node can affect the whole execution of an application spanning many nodes. This paper studies an interference modeling methodology for distributed applications to predict their performance under interference effects in consolidated clusters. This study first characterizes the effects of interference for various distributed applications over different interference settings, and analyzes how diverse interference intensities on multiple nodes affect the overall performance. Based on the characterization, this study proposes a static profiling-based model for interference propagation and heterogeneity behaviors. In addition, this paper presents use case studies of the modeling method, two interference-aware placement techniques for consolidated virtual clusters, which attempt to maximize the overall throughput or to guarantee the quality-of-service.