Dynamic resource allocation and offloading optimization for network slicing in B5G multi-tier multi-tenant systems

Abstract

For differentiating and customizing different types of flows guaranteeing individual 5QI QoS requirements, 5 G and Beyond 5 G (B5G) first specify several key technologies, e.g., 1) virtualizing radio resources, network functions and network servers, 2) network slicing, 3) Service Function Chaining (SFC) and flow steering, etc. Furthermore, for reducing E2E delay and path/link traffic congestion for diverse flowing while accessing cloud computing, Multi-access Edge Computing (MEC) is specified in 5 G/B5G ETSI. Although several extensively related studies discussed network slicing, SFC and MEC, they seldom consider both resource allocation and traffic offloading in a tenant efficiently, simultaneously. Thus, for efficiently addressing above critical issues, three motivations are proposed, including 1) to dynamically allocate resource for B5G with multi-tier multi-tenant networking, 2) to propose adaptively vertical and horizontal offloading to the computing node for diverse types of flows, and 3) to minimize the blocking rate while guaranteeing the delay constraint. Two novel efficient algorithms are proposed: Fast Latency Decrease Resource Allocation (FLDRA) and Minimum Cost Resource Allocation (MCRA). These two proposed algorithms achieve dynamic E2E resource allocation and optimal vertical and horizontal offloading to the computing node while guaranteeing the E2E latency and 5QI QoS requirements for different types of flows. Numerical results demonstrate that FLDRA minimizes resource allocation while MCRA balances the loading of resource availability. The proposed algorithms of FLDRA and MCRA significantly outperform the compared approaches in blocking rates. Moreover, the proposed MCRA algorithm yields the highest resource utilization, the least network delay, etc.