Title

On minimizing synchronization cost in NFV-based environments

Source of Publication

16th International Conference on Network and Service Management, CNSM 2020, 2nd International Workshop on Analytics for Service and Application Management, AnServApp 2020 and 1st International Workshop on the Future Evolution of Internet Protocols, IPFuture 2020

Abstract

© 2020 IFIP. Network Function Virtualization is known for its ability to reduce deployment costs and improve the flexibility and scalability of network functions. Due to processing capacity limitation, the infrastructure provider needs to instantiate one or more instances of a particular network function when the amount of traffic increases. Most of network functions are stateful, which means that they keep a state that may be frequently read or updated (e.g., statistics like number of packets or bytes per flow). As a result, the instances of the same virtual network function should constantly share the same state to prevent incorrect operation. In this context, a major challenge is how to efficiently ensure the consistency among instances while minimizing communication cost for synchronizing their state and ensuring the synchronization delay does not exceed a certain bound set by the operator.In this paper, we propose a technique to identify the optimal communication pattern between the instances of the same network function in order to minimize their synchronization cost. Moreover, we propose to use a special network function named Synchronization Function to ensure consistency among a set of instances and to minimize the synchronization cost. We first mathematically model the problem of finding the optimal synchronization pattern and the optimal placement and number of synchronization functions as an integer linear program that minimizes the synchronization cost and ensures a bounded synchronization delay. Last, we put forward three algorithms to cope with large-scale scenarios of the problem. Extensive simulations show that the proposed algorithms efficiently find near-optimal solutions with minimal computation time.

Document Type

Conference Proceeding

ISBN

9783903176317

Publisher

IEEE

Publication Date

11-2-2020

DOI

10.23919/cnsm50824.2020.9269121

Scopus ID

85098637923

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