Naling Zhang - Academia.edu (original) (raw)
Uploads
Papers by Naling Zhang
Thanks for the introduction. My name is Markus and I'll be presenting this paper. This is a colla... more Thanks for the introduction. My name is Markus and I'll be presenting this paper. This is a collaborative work which obviously would not have been possible without the help from my colleagues Naling and Chao. • This leaves programmers in a bad situation: writing high performance code for a relaxed memory model is difficult. • As a result, it would be nice if we had automated testing and verification tools for multithreaded programs written under relaxed memory models • This leaves programmers in a bad situation: writing high performance code for a relaxed memory model is difficult. • As a result, it would be nice if we had automated testing and verification tools for multithreaded programs written under relaxed memory models • This leaves programmers in a bad situation: writing high performance code for a relaxed memory model is difficult. • As a result, it would be nice if we had automated testing and verification tools for multithreaded programs written under relaxed memory models • Our framework is unified in that it considers all executions, be they sequentially consistent or relaxed, in the same fashion • As a result, we can detect rundundancies and greatly reduce the runtime overhead
ACM SIGPLAN Notices, 2015
Under a relaxed memory model such as TSO or PSO, a concurrent program running on a shared-memory ... more Under a relaxed memory model such as TSO or PSO, a concurrent program running on a shared-memory multiprocessor may observe two types of nondeterminism: the nondeterminism in thread scheduling and the nondeterminism in store buffering. Although there is a large body of work on mitigating the scheduling nondeterminism during runtime verification, methods for soundly mitigating the store buffering nondeterminism are lacking. We propose a new dynamic partial order reduction (POR) algorithm for verifying concurrent programs under TSO and PSO. Our method relies on modeling both types of nondeterminism in a unified framework, which allows us to extend existing POR techniques to TSO and PSO without overhauling the verification algorithm. In addition to sound POR, we also propose a buffer-bounding method for more aggressively reducing the state space. We have implemented our new methods in a stateless model checking tool and demonstrated their effectiveness on a set of multithreaded C bench...
2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Apr 1, 2019
Due to the highly variable execution context in which edge services run, adapting their behavior ... more Due to the highly variable execution context in which edge services run, adapting their behavior to the execution context is crucial to comply with their requirements. However, adapting service behavior is a challenging task because it is hard to anticipate the execution contexts in which it will be deployed, as well as assessing the impact that each behavior change will produce. In order to provide this adaptation efficiently, we propose a Reinforcement Learning (RL) based Orchestration for Elastic Services. We implement and evaluate this approach by adapting an elastic service in different simulated execution contexts and comparing its performance to a Heuristics based approach. We show that elastic services achieve high precision and requirement satisfaction rates while creating an overhead of less than 0.5% to the overall service. In particular, the RL approach proves to be more efficient than its rule-based counterpart; yielding a 10 to 25% higher precision while being 25% less computationally expensive.
Thanks for the introduction. My name is Markus and I'll be presenting this paper. This is a colla... more Thanks for the introduction. My name is Markus and I'll be presenting this paper. This is a collaborative work which obviously would not have been possible without the help from my colleagues Naling and Chao. • This leaves programmers in a bad situation: writing high performance code for a relaxed memory model is difficult. • As a result, it would be nice if we had automated testing and verification tools for multithreaded programs written under relaxed memory models • This leaves programmers in a bad situation: writing high performance code for a relaxed memory model is difficult. • As a result, it would be nice if we had automated testing and verification tools for multithreaded programs written under relaxed memory models • This leaves programmers in a bad situation: writing high performance code for a relaxed memory model is difficult. • As a result, it would be nice if we had automated testing and verification tools for multithreaded programs written under relaxed memory models • Our framework is unified in that it considers all executions, be they sequentially consistent or relaxed, in the same fashion • As a result, we can detect rundundancies and greatly reduce the runtime overhead
ACM SIGPLAN Notices, 2015
Under a relaxed memory model such as TSO or PSO, a concurrent program running on a shared-memory ... more Under a relaxed memory model such as TSO or PSO, a concurrent program running on a shared-memory multiprocessor may observe two types of nondeterminism: the nondeterminism in thread scheduling and the nondeterminism in store buffering. Although there is a large body of work on mitigating the scheduling nondeterminism during runtime verification, methods for soundly mitigating the store buffering nondeterminism are lacking. We propose a new dynamic partial order reduction (POR) algorithm for verifying concurrent programs under TSO and PSO. Our method relies on modeling both types of nondeterminism in a unified framework, which allows us to extend existing POR techniques to TSO and PSO without overhauling the verification algorithm. In addition to sound POR, we also propose a buffer-bounding method for more aggressively reducing the state space. We have implemented our new methods in a stateless model checking tool and demonstrated their effectiveness on a set of multithreaded C bench...
2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Apr 1, 2019
Due to the highly variable execution context in which edge services run, adapting their behavior ... more Due to the highly variable execution context in which edge services run, adapting their behavior to the execution context is crucial to comply with their requirements. However, adapting service behavior is a challenging task because it is hard to anticipate the execution contexts in which it will be deployed, as well as assessing the impact that each behavior change will produce. In order to provide this adaptation efficiently, we propose a Reinforcement Learning (RL) based Orchestration for Elastic Services. We implement and evaluate this approach by adapting an elastic service in different simulated execution contexts and comparing its performance to a Heuristics based approach. We show that elastic services achieve high precision and requirement satisfaction rates while creating an overhead of less than 0.5% to the overall service. In particular, the RL approach proves to be more efficient than its rule-based counterpart; yielding a 10 to 25% higher precision while being 25% less computationally expensive.