Online services often use replication for improving the performance of user-facing services. However, using replication for performance comes at a price of weakening the consistency levels of the replicated service. To address this tension, recent proposals from academia and industry allow operations to run at different consistency levels. In these systems, the programmer has to decide which level to use for each operation. We present SIEVE, a tool that relieves Java programmers from this error-prone decision process, allowing applications to automatically extract good performance when possible, while resorting to strong consistency whenever required by the target semantics. Taking as input a set of application-specific invariants and a few annotations about merge semantics, SIEVE performs a combination of static and dynamic analysis, offline and at runtime, to determine when it is necessary to use strong consistency to preserve these invariants and when it is safe to use causally consistent commutative replicated data types (CRDTs). We evaluate SIEVE on two web applications and show that the automatic classification overhead is low.
|Title of host publication||Proceedings of the 2014 USENIX Annual Technical Conference, USENIX ATC 2014|
|Number of pages||12|
|Publication status||Published - 1 Jan 2019|
|Event||2014 USENIX Annual Technical Conference, USENIX ATC 2014 - Philadelphia, United States|
Duration: 19 Jun 2014 → 20 Jun 2014
|Conference||2014 USENIX Annual Technical Conference, USENIX ATC 2014|
|Period||19/06/14 → 20/06/14|