Programmable Network Fabrics for AI and Multi-Cloud Workload Mobility

Abstract

The workload of artificial intelligence is becoming difficult to handle across numerous data centers, cloud regions, and edge environments, which puts significant pressure on network infrastructure that is conceived in a fixed enterprise application context. Conventional network provisioning is based on manual configuration that adds a huge amount of latency between workload migration decisions and deployment, making it impossible to optimize distributed AI deployments in real-time. To support such needs, programmable network fabrics use API based abstractions that separate application connectivity needs and implementation details, allowing the dynamic adjustment of topologies in the face of varying workload requirements. The architectural framework offers service-based models of networking, in which connectivity is on-demand by the provision of standardized interfaces, like compute and storage resources. Declarative requirements of applications are mapped to an optimal configuration of the network via policy-based orchestration, which balances conflicting goals such as minimizing latency, minimizing cost and allocation of bandwidth. Intention APIs receive high-level specifications of desired states of the world instead of being prescriptive commands, and policy engines test their requirements against the current network state to compute the best routing paths and resource assignments. This framework is interoperable with container orchestration systems and AI training systems, allowing synchronized infrastructure management in the compute and connectivity space. Its uses include distributed training models that need synchronization of parameters over geographical boundaries, real-time inference at edge computing scales and multi-cloud GPU bursting to move workloads across providers. Performance evaluation has shown to significantly outperform the performance of the statistical provisioning models due to lower deployment latency, greater efficiency in the use of network resources and cost efficiency due to network-conscious workload placement. The implementation considerations are the security architecture in a multi-tenant environment and operational transformation requirements in policy-driven management paradigms.