proactive caching in 5g networks - king's college london · as-a-service in cloud-based 5g...

Pro active Caching in Virtualized 5G Networks

5G V2X Communications - Summer School

12th of June 2018

Vasilis FriderikosFaculty of Natural and Mathematical Sciences

Department of Informatics King’s College London

Bush House, Strand, 30 Aldwych London, WC2B 4BG, United Kingdom email:

vasilis.friderikos@kcl.ac.uk

Blog: friderikos.blogspot.com

Prologue • Primary focus is to outline the area of pro active caching and the role that has to play in emerging 5G networks

• However, caching need to be considered part of a Virtual Network Function (VNF) chain in 5G and beyond virtualized networks with emphasis on mobility effects, routing and latency

• We will provide a birds-eye view on a number of open ended research with a potential broad application in the design of emerging and future 5G networks

• We have been focusing on lower bounds on the performance via linear integer mathematical programming formulations where VNF chains and caching are jointly optimized

Rationale • Viral and popular videos streams, as well as cachable content dominate aggregate mobile Internet traffic with a superlinear growth

• By 2020, 82% of all IP traffic will be video and two-thirds of all Internet traffic will be generated from wireless and mobile devices

• 12B mobile ready devices and connections by 2021

• Hence, caching of popular content deserves paying a special attention in terms of VNF hosting location, routing (mobility) and chaining

Cisco VNI forecast

Benefits of Caching: Reduced Latency

Benefits of Caching: Reduced aggregated traffic in the network

Benefits of Caching: Reduced utilization levels of busy servers

100% utilization50% utilization

Setting the Scene• Significant amount of work over the last few

years on advanced caching techniques

• At the same time, and as a more recent thread, emphasis has been placed on the SDN/NFV framework, which is deemed as the main architectural ingredient of emerging 5G networks

• However, little attention has been placed on issues related to caching when considered within the ‘lenses’ of an SDN/NFV enabled 5G network architecture

VNF Chaining with Preserved Ordering

• Cached content must be visited before other VNFs can be applied; service flow might originate from different possible network locations depending on the caching strategy.

• So, the location of caches in a VNF service chain, greatly affect the overall chain orchestration as well as the aggregate traffic dynamics in the network (ease of congestion episodes)

• Since different VNFs can be placed in different nodes in the network we end up in a hard optimization problem: where to host the different VNFs per service request and routing between them to create an end-to-end service.

• Predicting future user locations

(points of attachments)

• Provide suitable locations for

proactively caching content

• Reducing latency and increasingoverall network efficiency

Example of Proactive Caching

Research Objectives

• Minimize overall routing and VNF hosting cost in pro active caching

• Cache sharing for multiple tenants

• Load balancing via VNF chaining

• Delay minimization of time critical VNF chains such as haptic/tactile applications

Cache sharing over multiple tenants in virtual networks

Load balancing via VNF chaining

• A number of objectives can be used to provide load balancing across the nodes that run different virtual network functions.

• Assume that we denote by the load at node i

Load balancing via VNF chaining

• A number of objectives can be used to provide load balancing across the nodes that run different virtual network functions.

• Assume that we denote by the load at node i

• Minimize the maximum load of a node

Load balancing via VNF chaining

• A number of objectives can be used to provide load balancing across the nodes that run different virtual network functions.

• Assume that we denote by the load at node i

• Minimize the maximum load of a node

• Minimize the summation of a quadratic load function

Load balancing via VNF chaining

• A number of objectives can be used to provide load balancing across the nodes that run different virtual network functions.

• Assume that we denote by the load at node i

• Minimize the maximum load of a node

• Minimize the summation of a quadratic load function

• Minimize the relative percentage of imbalances between nodes

Load balancing via VNF chaining

• These problems lead to non-linear integer programming formulations which are intractable and require linearizationtechniques

• Here is a small example of a min-max problem,

Load balancing via VNF chaining

• We can linearize by including an additional decision variable z, which represents the maximum cost (load for example)

• This constraint must be imposed,

• So we have,

Load balancing via VNF chaining

• The second problem, i.e., quadratic based load balancing lead also to non-linear integer programming formulations, (intractable and requires also linearization techniques)

• Here is a small example for a possible non-linear quadratic load problem,

Load balancing via VNF chaining

• Introduce a new binary variable

• These variables take the value of the quadratic terms, i.e.,

• To do so the following constraints need to be added:

linearized version

Delay minimization for VNF chaining

• This can be done using queueing theory results and modelling VNF hosting nodes and links (in the simplest case) as an M/M/1 queueing system

• This leads (again) to non-linear programming formulation that require linearization techniques in order to be efficiently solved

utilization

Delay minimization for VNF chaining • Piecewise linearization techniques can be

used to tackle this problem

utilization

ICT 2018 | 25th International Conference on TelecommunicationJune 26-28 2018, Saint-Malo, France

Illustrative Use Cases

Effect of mobility on optimality of VNF hosting

• Case I entails a sub-optimal allocation when user mobility is also taken into account.

• However, Case II represent a more suitable VNF chaining location

VNF placement in different nodes

• Optimal location of caching and the other VNFs in the service chain is in different nodes

• Figure shows the optimal location of caching is in node (b) whereas the VNF for video acceleration is located at node (d).

• Hence, we consider the issue of caching and service chaining in a holistic integrated manner

Illustrative Use Cases

Illustrative Use Cases

Limited availability of resources

• A case where VNF chaining and pro-active caching take place independently.

• The case here is that not in all pre-selected locations from the caching algorithm it is possible to host the other VNFs due to numerous reasons such as for example reservation policies, placement based on affinity and/or anti-affinity rules, etc.

Preliminaries

Preliminaries (cont.)

Mathematical Programming Formulation

With the binary decision variables

defined as follows:

Mathematical Programming Formulation

Placement cost of hosting VNFs at a node.

Mathematical Programming Formulation

Placement cost of hosting VNFs at a node

Routing for the cache [1st VNF] to the 2nd VNF from the VNF graph

…

Mathematical Programming Formulation

Placement cost of hosting VNFs at a node

Routing cost for intermediate VNF of the VNF graph for each request

Routing for the cache [1st VNF] to the 2nd VNF from the VNF graph

…

… …

Mathematical Programming Formulation

Placement cost of hosting VNFs at a node

Routing cost for intermediate VNF of the VNF graph for each request

Routing for the last VNF of the VNF graph for each request

Routing for the cache [1st VNF] to the 2nd VNF from the VNF graph

…

… …

…

Mathematical Programming Re-Formulation

To linearize the formulation a new decision

variable need to be introduced

Mathematical Programming Re-Formulation

2nd term of the objective function:

Linearization

Mathematical Programming Re-Formulation

and the following new binding constraints need to be added:

Scale Free Heuristic Algorithms

• We have also defined heuristic approaches to find competitive sub-optimal solutions

• This is required since optimal solutions can be found for only small network instances whereas we require an almost real-time operation

• The main philosophy of the proposed algorithms is to create a set of candidate pro-active caching points for each possible visited node which are then weighted by the probability of visiting each access router and explore node combinations for creating the service chain.

Simulation Parameters

Key Results • Via a wide set of Monte Carlo simulations we

experience performance gains of over 12%

compared to techniques that consider

chaining and pro-active caching

independently

• More importantly, our observations confirm

that achievable gains are robust against

different network sizes/topologies (considered

various randomized network topologies

resembling ring, star and hybrid layouts)

• Also, we have seen that as the mobility

increases the gains can reach up to 15%.

• We have also considered blocking effects to

further understand improvement gains

Emerging areas

• Amalgamation of machine learning with

integer programming related to “what” to cache

in addition in addition to “where”

• Energy consumption of caching and VNF

chains running on VMs

• Joint network slicing (virtual network

embedding) and VNF chaining

• Caching and VNF chains when considering of

UAVs and drones with terrestrial 5G networks

as aerial flying base stations (BSs)

Epilogue & Future Avenues ofResearch

• We provided an overview of some recent challenges in the area of caching and VNF chaining

• Characteristic use cases and a number of frameworks to tackle those issues have been presented

• Open areas of research have been discussed

• The area require significant further work and there are a number of significant challenges to further explore in the research frontier

ReferencesZ. Zaidi, V. Friderikos, et al., Will SDN Be Part of 5G?, IEEE Communications Surveys and Tutorials, to appear

2018

G. Chochlidakis, V. Friderikos, Mobility Aware Virtual Network Embedding, IEEE Transactions on Mobile

Computing, vol. 16, no. 5, pp. 1343-1356, May 2017

J. Carmona-Murillo, V. Friderikos, JL. Sánchez, A Hybrid DMM Solution and Trade-off Analysis for Future Wireless

Networks, Computer Networks, vol. 133, pp. 17-32, 2018

E. Ghoreishi, D. Karamshuk, V. Friderikos, N. Sastry, M. Dohler, H. Aghvami, A Cost-Driven Approach to Caching-

as-a-Service in Cloud-Based 5G Mobile Networks, IEEE Transactions on Mobile Computing, to appear 2018

R. Gouareb, V. Friderikos, H. Aghvami, Delay Sensitive Virtual Network Function Placement and Routing,

submitted 2018

Y. Wang, V. Friderikos, Delay Constrained Caching as a Chain, work in progress, 2018

G. Zheng, C. Wang, V. Friderikos, M. Dohler, High Mobility Multi Modal E-Health Services, IEEE ICC 2018

G. Zheng, V. Friderikos, Optimal Proactive Cache Management in Mobile Networks, IEEE ICC 2016

Z. Yousaf, M. Gramaglia, V. Friderikos, B. Gajic, D. Hugo, B. Sayadi, V. Sciancalepore, M. Crippa, Network Slicing

with Flexible Mobility and QoS/QoE Support for 5G Networks, IEEE ICC 2017

M. Crippa, P. Arnoldy, V. Friderikos, B. Gajic, C. Guerrero, O. Holland, I. Labrador, V. Sciancalepore, D. Hugo, S.

Wong, F. Yousaf, B. Sayad, Resource Sharing for a 5G Multi-tenant and Multi-service Architecture, IEEE EuCNC

2017

M. Gramaglia, I. Digon, V. Friderikos, D.Hugo, C. Mannweiler, M.A. Puente, K. Samdanis, and B. Sayadi, Flexible

connectivity and QoE/QoS management for 5G Networks: The 5G NORMA view, IEEE ICC 2016

J. Gang, V. Friderikos, Optimal Resource Sharing in Multi-Tenant 5G Networks, IEEE WCNC 2018