Enhancing Reuse of Constraint Solutions to Improve Symbolic Execution

Constraint solution reuse is an effective approach to save the time of constraint solving in symbolic execution. Most of the existing reuse approaches are based on syntactic or semantic equivalence of constraints; e.g. the Green framework is able to reuse constraints which have different representations but are semantically equivalent, through canonizing constraints into syntactically equivalent normal forms. However, syntactic/semantic equivalence is not a necessary condition for reuse--some constraints are not syntactically or semantically equivalent, but their solutions still have potential for reuse. Existing approaches are unable to recognize and reuse such constraints. In this paper, we present GreenTrie, an extension to the Green framework, which supports constraint reuse based on the logical implication relations among constraints. GreenTrie provides a component, called L-Trie, which stores constraints and solutions into tries, indexed by an implication partial order graph of constraints. L-Trie is able to carry out logical reduction and logical subset and superset querying for given constraints, to check for reuse of previously solved constraints. We report the results of an experimental assessment of GreenTrie against the original Green framework, which shows that our extension achieves better reuse of constraint solving result and saves significant symbolic execution time.Comment: this paper has been submitted to conference ISSTA 201

Optimal Reissue Policies for Reducing Tail Latency

Interactive services send redundant requests to multiple different replicas to meet stringent tail latency requirements. These addi- tional (reissue) requests mitigate the impact of non-deterministic delays within the system and thus increase the probability of re- ceiving an on-time response. There are two existing approaches of using reissue requests to reduce tail latency. (1) Reissue requests immediately to one or more replicas, which multiplies the load and runs the risk of overloading the system. (2) Reissue requests if not completed after a fixed delay. The delay helps to bound the number of extra reissue requests, but it also reduces the chance for those requests to respond before a tail latency target. We introduce a new family of reissue policies, Single-Time / Random ( SingleR ), that reissue requests after a delay d with probability q . SingleR employs randomness to bound the reissue rate, while allowing requests to be reissued early enough so they have sufficient time to respond, exploiting the benefits of both immediate and delayed reissue of prior work. We formally prove, within a simplified analytical model, that SingleR is optimal even when compared to more complex policies that reissue multiple times. To use SingleR for interactive services, we provide efficient algorithms for calculating optimal reissue delay and probability from response time logs through data-driven approach. We apply itera- tive adaptation for systems with load-dependent queuing delays. The key advantage of this data-driven approach is its wide applica- bility and effectiveness to systems with various design choices and workload properties. We evaluated SingleR policies thoroughly. We use simulation to illustrate its internals and demonstrate its robustness to a wide range of workloads. We conduct system experiments on the Re- dis key-value store and Lucene search server. The results show that for utilizations ranging from 40 - 60% , SingleR reduces the 99 th-percentile latency of Redis by 30 - 70% by reissuing only 2% of requests, and the 99 th-percentile latency of Lucene by 15 - 25% by reissuing 1% only

High Performance MySQL: Optimization, Backups, Replication, and Load Balancing

Amerika Serikatxvi, 276 p.; 26 cm