Leveraging mixed-strategy gaming to realize incentive-driven VNF service chain provisioning in broker-based elastic optical inter-datacenter networks

This paper investigates the problem of how to optimize the provisioning of virtual network function service chains (VNF-SCs) in elastic optical inter-datacenter networks (EO-IDCNs) under elastic optical networking and DC capacity constraints. We take advantage of the broker-based hierarchical control paradigm for the orchestration of cross-stratum resources and propose to realize incentive-driven VNF-SC provisioning with a noncooperative mixed-strategy gaming approach. The proposed gaming model enables tenants to compete for VNF-SC provisioning services due to revenue and quality-of-service incentives and therefore can motivate more reasonable selections of provisioning schemes. We detail the modeling of the game, discuss the existence of the Nash equilibrium states, and design an auxiliary graph-based heuristic algorithm for tenants to compute approximate equilibrium solutions in the games. A dynamic resource pricing strategy, which can set the prices of network resources in real time according to the actual network status, is also introduced for EO-IDCNs as a complementary method to the game-theoretic approach. Results from extensive simulations that consider both static network planning and dynamic service provisioning scenarios indicate that the proposed game-theoretic approach facilitates both higher tenant and network-wide profits and improves the network throughput as well compared with the baseline algorithms, while the dynamic pricing strategy can further reduce the request blocking probability with a factor of ∼2.4×

New Trends in Photonic Switching and Optical Network Architecture for Data Centre and Computing Systems

AI/ML for data centres and data centres for AI/ML are defining new trends in cloud computing. Disaggregated heterogeneous reconfigurable computing systems realized by photonic interconnects and photonic switching expect greatly enhanced throughput and energy-efficiency for AI/ML workloads, especially when aided by an AI/ML control plane

Bowdoin Orient v.85, no.1-25 (1955-1956)

https://digitalcommons.bowdoin.edu/bowdoinorient-1950s/1006/thumbnail.jp