Optimizing distributed actor systems for dynamic interactive services

Newell, Andrew J.; Kliot, Gabriel; Menache, Ishai; Gopalan, Aditya; Akiyama, Soramichi; Silberstein, Mark

doi:10.1145/2901318.2901343

Cited by 14 publications

(11 citation statements)

References 16 publications

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“…The actor programming model [22], [27], [28], [29] has a long history of being used to construct massive, distributed systems [22], [28], [30], [31]. Each actor is a lightweight and independent execution unit.…”

Section: Motivations For Using the Actor Modelmentioning

confidence: 99%

<italic>NFVactor</italic>: A Resilient NFV System Using the Distributed Actor Model

Duan

Zhao

et al. 2019

IEEE J. Select. Areas Commun.

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Resilience functionality, including failure resilience and flow migration, is of pivotal importance in practical network function virtualization (NFV) systems. However, existing failure recovery procedures incur high packet processing delay due to heavyweight process-checkpointing, while flow migration has poor performance due to centralized control. This paper proposes NFVactor, a novel NFV system that aims to provide lightweight failure resilience and high-performance flow migration. NFVactor enables these by using actor model to provide a per-flow execution environment, so that each flow can replicate and migrate itself with improved parallelism, while the efficiency of the actor model is guaranteed by a carefully designed runtime system. Moreover, NFVactor achieves transparent resilience: once a new NF is implemented for NFVactor, the NF automatically acquires resilience support. Our evaluation result shows that NFVactor achieves 10Gbps packet processing, flow migration completion time that is 144 times faster than existing system, and packet processing delay stablized at around 20 microseconds during replication.

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Section: Motivations For Using the Actor Modelmentioning

confidence: 99%

<italic>NFVactor</italic>: A Resilient NFV System Using the Distributed Actor Model

Duan

Zhao

et al. 2019

IEEE J. Select. Areas Commun.

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“…In all the following experiments, the vector length is 2 32 and the length of the sub-vectors is 2 10 , which means there are 2 22 messages sent by the client. Figure 1 presents the average response time under varied concurrent granularities.…”

Section: Concurrency Granularity Impactsmentioning

confidence: 99%

ElasticActor: An Actor System with Automatic Granularity Adjustment

Zhao

Liu

Cao

et al. 2018

Int J Parallel Prog

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The actor model has gained significant popularity in recent years, because its high-level abstraction facilitates the construction of massive concurrent distributed systems, e.g. cloud applications. However, building an efficient actor system is a challenging task. In particular, the concurrency granularity has a critical impact on the performance of actor systems. Moreover, the best granularity depends on both the available hardware and the characteristics of the workload. This paper proposes a new actor system, ElasticActor, that automatically adjusts the granularity of parallel execution. In ElasticActor, all actors are assigned to a number of actor stations. Actors in the same station share a mailbox and constitute the minimum scheduling unit. Therefore, the runtime system can adjust the granularity by changing the number of actor stations in the system. More specifically, we model the average latency and derive a closed-form formula to compute the best number of stations. The experimental results show that ElasticActor outperforms two state-of-the-art actor systems, CAF and Orleans. It gains up to 1.3× throughput and decreases the average latency by 0.75× thanks to the granularity adjustment.

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“…The Actor Programming Model [1, 2, 4] has been widely used to construct reliable distributed systems [17,18]. It employs a basic execution unit, called actor, for message handling and passing.…”

Section: Background and Related Workmentioning

confidence: 99%

Gpunfv

Duan

2017

Proceedings of the First Asia-Pacific Workshop on Networking

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This paper presents GPUNFV, a high-performance NFV system providing ow-level micro services for stateful service chains with Graphics Processing Unit (GPU) acceleration. GPUNFV exploits the massively-parallel processing power of GPU to maximize the throughput of the NFV system. Combined with the customized ow handler, GPUNFV achieves a much better throughput than the existing NFV systems. With a carefully designed GPU-based virtualized network function framework, GPUNFV is able to e ciently support both stateful and stateless network functions. We have implemented a number of GPU-based network functions and a preliminary GPUNFV system to demonstrate the exibility and potential of our design.

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Optimizing distributed actor systems for dynamic interactive services

Cited by 14 publications

References 16 publications

<italic>NFVactor</italic>: A Resilient NFV System Using the Distributed Actor Model

<italic>NFVactor</italic>: A Resilient NFV System Using the Distributed Actor Model

ElasticActor: An Actor System with Automatic Granularity Adjustment

Gpunfv

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