Latency and Availability Driven VNF Placement in a MEC-NFV Environment

Multi-access Edge Computing (MEC) is gaining momentum as it is considered as one of the enablers of 5G ultra-Reliable Low-Latency Communications (uRLLC) services. MEC deploys computation resources close to the end user, enabling to reduce drastically the end-to-end latency. ETSI has recently leveraged the MEC architecture to run all MEC entities, including MEC applications, as Virtual Network Functions (VNF) in a Network Functions Virtualization (NFV) environment. This evolution allows taking advantage of the mature architecture and the enabling tools of NFV, including the potential to apply a variety of service-tailored function placement algorithms. However, the latter need to be carefully designed in case of MEC applications such as uRLLC, where service access latency is critical. In this paper, we propose a novel placement scheme applicable to a MEC in NFV environment. In particular, we propose a formulation of the problem of VNF placement tailored to uRLLC as an optimization problem of two conflicting objectives, namely minimizing access latency and maximizing service availability. To deal with the complexity of the problem, we propose a Genetic Algorithm to solve it, which we compare with a CPLEX implementation of our model. Our numerical results show that our heuristic algorithm runs efficiently and produces solutions that approximate well the optimal, reducing latency and providing a highly-available service.

[1]  Tarik Taleb,et al.  Optimal VNFs Placement in CDN Slicing Over Multi-Cloud Environment , 2018, IEEE Journal on Selected Areas in Communications.

[2]  Kin K. Leung,et al.  Dynamic service migration in mobile edge-clouds , 2015, 2015 IFIP Networking Conference (IFIP Networking).

[3]  Leandros Tassiulas,et al.  SLA-Driven VM Scheduling in Mobile Edge Computing , 2016, 2016 IEEE 9th International Conference on Cloud Computing (CLOUD).

[4]  Guy Pujolle,et al.  QoS-Aware VNF Placement Optimization in Edge-Central Carrier Cloud Architecture , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[5]  Otto Carlos Muniz Bandeira Duarte,et al.  Orchestrating Virtualized Network Functions , 2015, IEEE Transactions on Network and Service Management.

[6]  Jing Xu,et al.  Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[7]  Zoltán Ádám Mann,et al.  Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms , 2015, ACM Comput. Surv..

[8]  Tarik Taleb,et al.  On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration , 2017, IEEE Communications Surveys & Tutorials.

[9]  Zhenyu Wen,et al.  Cost Effective, Reliable, and Secure Workflow Deployment over Federated Clouds , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[10]  Nam Thoai,et al.  Energy-Saving Virtual Machine Scheduling in Cloud Computing with Fixed Interval Constraints , 2016, Trans. Large Scale Data Knowl. Centered Syst..

[11]  Raouf Boutaba,et al.  Delay-aware VNF placement and chaining based on a flexible resource allocation approach , 2017, 2017 13th International Conference on Network and Service Management (CNSM).

[12]  Peng Zhang,et al.  Energy-Saving Virtual Machine Placement in Cloud Data Centers , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[13]  Pantelis A. Frangoudis,et al.  Balancing between Cost and Availability for CDNaaS Resource Placement , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[14]  Navid Nikaein,et al.  Providing Low Latency Guarantees for Slicing-Ready 5G Systems via Two-Level MAC Scheduling , 2018, IEEE Network.

[15]  Jan Broeckhove,et al.  Cost-Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.