20109 A unified framework for multi-access edge computing (MEC) network slicing in 5G networks


It is now clear that advanced softwarization and virtualization paradigms such as network slicing will be the cornerstone of 5G networks and the Internet of Things. Indeed, by sharing a common underlying physical infrastructure, network operators (NOs) can dynamically deploy multiple “slices” tailored for specific services (e.g., video streaming, augmented reality), as well as requirements (e.g., low latency, high throughput, low jitter), avoiding the static—thus, inefficient network deployments that have plagued traditional hardware-based cellular networks. To further decrease latency, increase throughput, and provide improved services to their subscribers, NOs have recently started integrating multi-access edge computing (MEC) technologies, which are expected to become essential to reach the sub-1 ms latency requirements of 5G.

Organizations across the globe are under increasing pressure to minimize the capital expenditure and operational costs and become more agile and efficient. This rising pressure has resulted in the widespread adoption of server virtualization and SDN (software defined network) technologies.   

The key challenge that sets MEC slicing apart from traditional resource allocation problems is that edge nodes depend on tightly-intertwined and strictly-constrained networking, computation, and storage resources. Therefore, instantiating MEC slices without incurring in resource over-provisioning is challenging with existing slicing algorithms. 


This invention delivers a unified edge computing slicing framework that allows network operators to instantiate heterogeneous slice services (ex. video streaming, caching, 5G network access) on edge devices. The framework merges network slicing and MEC to efficiently allocate physical resources to different network services and operators.

The invention includes computational, networking and storage resources to compute reliable and accurate slicing strategies without overprovisioning. Algorithms are designed for different network topologies and size configurations.

Current state-of-the-art does not consider coupling among heterogenous resources, typically resulting in >300% resource overprovisioning. This solution quickly computes network slicing strategies for MEC-enabled 5G systems for both centralized and distributed solutions


- Determine network slices to be deployed to maximize the profit of network operators (e.g., admit  the most profitable slices)

- Maximize the number of slices to be admitted (e.g., admit as many slices as possible)


  • Network slicing 
  • Overall Mobile Edge Computing (MEC) market 
  • Private cellular networking
  • Localized services in applications such as augmented reality, multimedia content delivery and Lachine learning on the edge

Provisional filed

Patent Information:
For Information, Contact:
Mark Saulich
Associate Director of Commercialization
Northeastern University
Salvatore D'Oro
Tommaso Melodia
Francesco Restuccia
Leonardo Bonati
5G Networks
Cellular Networks
Edge Computing
Network Slicing