SGXTuner: Performance Enhancement of Intel SGX Applications Via Stochastic Optimization

Intel
SGX
has started to be widely adopted. Cloud providers (Microsoft Azure, IBM Cloud, Alibaba Cloud) are offering new solutions, implementing
data-in-use
protection via SGX. A major challenge faced by both academia and industry is providing transparent SGX support to legacy applications. The approach with the highest consensus is linking the target software with SGX-extended
libc
libraries. Unfortunately, the increased security entails a dramatic performance penalty, which is mainly due to the intrinsic overhead of context switches, and the limited size of protected memory. Performance optimization is non-trivial since it depends on key parameters whose manual tuning is a very long process. We present the architecture of an automated tool, called
SGXTuner
, which is able to find the best setting of SGX-extended
libc
library parameters, by iteratively adjusting such parameters based on continuous monitoring of performance data. The tool is — to a large extent — algorithm agnostic. We decided to base the current implementation on a particular type of stochastic optimization algorithm, specifically
Simulated Annealing
. A massive experimental campaign was conducted on a relevant case study. Three client-server applications —
Memcached
,
Redis
, and
Apache
— were compiled with SCONE's
sgx-musl
and tuned for best performance. Results demonstrate the effectiveness of
SGXTuner
.