Showing papers by "Sachin Sharma published in 2020"

Towards explainable artificial intelligence for network function virtualization

Abstract: Network Function Virtualization (NFV) refers to the process of running network functions in virtualized IT infrastructures as softwarized Virtual Network Functions (VNFs). Several telecom service providers are currently benefiting from this concept, as it enables a faster introduction of new network services, thereby meeting changing requirements. Following a trend initially adopted by cloud service providers, telecom service providers are also adopting de-aggregation of the VNFs into microservices (μservices). However, a μservice-based architecture that can manage a large set of diverse and sensitive network functions requires new Artificial Intelligence (AI)-based methodologies to cope with the complexity of the μservice-based NFV paradigm. This paper focuses on the use of explainable AI (XAI) for gradually migrating towards a μservices-based architecture in NFV. The paper first establishes the need for XAI to transform the NFV architecture to a μservice-based architecture and then describes some of our research objectives. Afterwards, our preliminary approach and long-term visions are provided.

Towards AI-enabled Microservice Architecture for Network Function Virtualization

Abstract: Network Function Virtualization (NFV) enables operators to flexibly deploy network services on commodity servers in an on-demand and agile manner. This has recently attracted significant attention from industry and academia. However, there are important challenges for NFV deployment, including performance bottlenecks, degraded fault tolerance, upgrading complexities, and security threats. To overcome these challenges, the microservice approach, which has been applied successfully in cloud computing, has motivated the research communities to apply its principles in NFV domains. In this paper, we first compare the challenges of employing the microservices approach in both the cloud computing and the NFV domains, and then discuss the need for AI-enabled microservices architecture for NFV. Performance evaluation of the microservices approach in NFV is performed on bare-metal machine setups. The results compare the pros and cons of the microservice approach and show the need for AI to handle the complex decisions associated with decomposing network functions into microservices or vice versa. We also propose an AI-enabled microservice architecture and present its potential use cases for personalized live streaming, smart public safety, and enterprise VPN (Virtual Private Network). Open questions and future work are also presented.

Enabling Fast Failure Recovery in OpenFlow networks using RouteFlow

Abstract: OpenFlow provides a protocol to control a network from an external server called controller. Moreover, RouteFlow presents a framework to run Internet routing protocols in OpenFlow networks by running them in virtual machines or containers. The problem is that OpenFlow networks running RouteFlow do not recover fast from a port failure (e.g., port down event). The failure recovery time is dependent on user configurable parameters and is in seconds. To overcome this problem, we implement a solution in which a port failure of a physical OpenFlow node is detected immediately in its corresponding virtual machine and an immediate action is taken by the routing protocol. Therefore, once a routing protocol running on the corresponding virtual machine detects this failure, it broadcasts the failure in the network and a new failure free path is immediately configured over the OpenFlow network. We implement the proposed solution in an OpenFlow controller and test it over single autonomous and multiple autonomous system scenarios (including OpenFlow and non-Openflow scenarios) of the Internet emulated on the virtual wall testbed of the Fed4Fire facility in Europe. The results show that an OpenFlow network can recover from a failure in a short time interval using the proposed solution.