AidOps

Lugones, Diego; Aroca, Jordi Arjona; Jin, Yue; Sala, Alessandra; Hilt, Volker

doi:10.1145/3127479.3129250

Cited by 8 publications

(1 citation statement)

References 27 publications

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“…We choose Redis [19] and Cassandra [20] because they belong to different data store families, i.e.,Redis is a key-value store with a flat key space while Cassandra organizes data in tables. Redis has been used in NF systems due to its consistency guarantees [12], while the fault tolerance capabilities of Cassandra can be leveraged with use-cases with very stringent availability requirements [25]. Moreover, Redis and Cassandra are both carrier-grade data stores, i.e.,they are used and maintained by major IT companies: using carrier-grade data stores for NF state management provides further benefits, as discussed in Section VI-C. We also implemented the driver for an in-memory hashmap.…”

Section: A Key Enablersmentioning

confidence: 99%

FlexState: Flexible State Management of Network Functions

et al. 2021

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Network function (NF) developers have traditionally prioritized performance when creating new packet processing capabilities. This was usually driven by a market demand for highly available solutions with differentiating features running at line rate, even at the expense of flexibility and tightly-coupled monolithic designs. Today, however, the market advantage is achieved by providing more features in shorter development cycles and quickly deploying them in different operating environments. In fact, network operators are increasingly adopting continuous software delivery practices as well as new architectural styles (e.g., microservices) to decouple functionality and accelerate development. A key challenge in revisiting NF design is state management, which is usually highly optimized for a deployment by carefully selecting the underlying data store. Therefore, migrating to a data store that suits a different use case is time-consuming as it requires code refactoring and adaptation to new application programming interfaces, APIs. As a result, refactoring NF software for different environments can take up to months, reducing the pace at which new features and upgrades can be deployed in production networks. In this paper, we demonstrate experimentally that it is feasible to introduce an abstraction layer to decouple NF state management from the data store adopted while still approaching line-rate performance. We present FlexState, a state management system that exposes data store functionality as configuration options, which reduces code refactoring efforts. Experiments show that FlexState achieves significant flexibility in optimizing the state management, and accelerates deployment on new scenarios while preserving performance and scalability.

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Section: A Key Enablersmentioning

confidence: 99%