FlexState: Flexible State Management of Network Functions

Abstract

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.

Application research of computer database technology in information management

“…Moreover, these frameworks generally do not fully leverage the benefits of parallel processing architectures in dynamic network environments. For instance, FlexState [13] assumes that shared states can be partitioned without synchronization, an approach that is not always feasible for VNFs with inherent state-sharing requirements. OpenNF [6] introduces a two-phase statesharing protocol, opting for state transfer across instances for eventual consistency or broadcasting updates for strong consistency as needed.…”

“…Additionally, harnessing the capabilities of parallel architectures through strategic optimization techniques is fundamental to boost the overall performance and scalability of SFCs. Techniques such as state caching and workload balancing have been proposed to optimize VNF performance by reducing redundant state accesses and balancing loads [7], [8], [13]. Nonetheless, advancing the execution of stateful SFCs requires addressing challenges that include adaptively responding to variable traffic workloads and maximizing multicore architecture utilization, all while considering the integrated structure of the SFC.…”

“…However, they do not adequately address the needs of VNFs dealing with shared cross-flow states. Frameworks such as OpenNF [6], S6 [7], and FlexState [13]…”

Placement Approach for the Data Storage Management Function in a RAN-CN Converged Network

“…However, it is well known that some NFs are stateful, and the resulting NF placement assuming all NFs are stateless is not a real optimal solution. Based on this, a state management system which decouples the state management of NFs from the data storage is proposed in [15], in a way that the data storage becomes a configuration parameter, thus achieving the system flexibility. However, the specific NF placement approach of the decoupled network is not given in this paper.…”