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BTH’s Research in NV, NFV and Cloud Networking

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1 BTH’s Research in NV, NFV and Cloud Networking
GENI Nordic Meeting, Stockholm, Kurt Tutschku With Patrik Arlos, Anders Carlsson, Dragos Ilie and Markus Fiedler Blekinge Institute of Technology (BTH), Faculty of Computing Department of Communication Systems (DIKO)

2 Capacities at BTH Blekinge Institute of Technology
7200 students; 500 staff emphasis on applied information technology and inno- vation for sustainable growth in industry and society strong industrial environment in communication industry, both legacy (Ericsson, Telenor Sverige) and startup (CompuVerde, HyperIsland, CityNetwork, ….) network: 1GBit; upgrade to full 10GBit (in 2015)

3 Capacities at BTH’s DIKO Department
Department of Communication Systems (DIKO) focus on future network/FI architectures and technologies, Quality of Experience, Cloud computing, performance evaluation, wireless communications, Internet of Things and security currently four professors, four senior lecturers, two university adjuncts and 10 Ph.D. students Current and past involvements in Future Internet projects Current FI projects: XIFI (eXperimental Infrastructures for the Fu-ture Internet, EU), Queen (EU, Celtic plus), ETSI’s Industry Specifi-cation Group (ISG) for Network Function Virtualization (NFV), FI-PPP FI-STAR (EU), ENGENSEC (EU), BTH’s CloudLab Selected past contributions to FI projects: Akari (J), G-Lab (Ger), Mevico (Celtic, EU), PlanetLabEurope, Future Internet Assembly, FI-PPP setup (AT representative)

4 BTH CloudLab Started in early 2014
Integration effort for BTH’s FI, NV, NFV, Cloud and SDN research Integrated projects and labs: XIFI, DIKO’s Network Performance Lab, ENGENSEC Hardware: XIFI: 4x Dell PowerEdge 715 (AMD, 128 cores, 512GB Ram, 5TByte disk) ENGENSEC: 48 cores (8boxes; Intel I7); future AMD Opteron128 cores NPL: e.g. Endace DAG 4.3GE x4, DAG 4.2GE x2, DAG 3.5 x4, DAG 3.6 x4 Software: OpenStack (XIFI; ENGENSEC: Havana)

5 XIFI (eXperimental Infrastructures for the Future Internet, EU)

6 Possible use of GEs between IF-PPP Cloud environment and UE
BTH’s XIFI Testbed BTH’s XIFI Testbed XIFI adapter DPMI NTAS Front-end monitoring in NPL: Back-end moni- toring in Cloud Lab BTH’s XIFI-enhanced CloudLab running Generic Enablers (GEs) UE executing FI-PPP applications MP Monitoring on network layer Monitoring on user layer and client control Possible use of GEs between IF-PPP Cloud environment and UE Link to SUNET/GÉANT network

7 Educating the Next gener-ation experts in Cyber Security (ENGENSEC)
Objective: create new Master’s program in IT Security as response on current and emerging cybersecurity threats by educating next generation experts Funding organization: EU Tempus program Number or participants: 21 Participating countries: Sweden (coordinator), Poland, Latvia, Greece, Germany, Ukraine, Russia Project activities: Defining framework of joint Master’s program, Cloud-based security lab development, Development of the joint course curriculum, Develop new and further develop existing courses, Teacher training, Effective quality control ensured and project management, Dissemination of new Master’s programs benefits, Give pilot courses in a summer school, Prepare for participating Universities to launching new Master’s program

8 Direct Involvement of BTH in FI-PPP
FI-STAR = one out of five Call-2 FI-PPP use cases BTH’s role Major Swedish participant (with significant labs) Requirement engineering (co-chair of FI-STAR WP 1) Validation (Co-chair FI-STAR WP6) Functional testing Quality of Service (QoS) measurements Quality of Experience (QoE) assessment Health Technology Assessment BTH’s work is strongly focused on Generic Enablers (GE) and their performance Synergy with XIFI: Hosting would provide full control and unique QoS measurement facilities

9 A Very Brief View on Network Function Virtualization (NFV)
Kurt Tutschku Blekinge Institute of Technology (BTH), Faculty of Computing Department of Communication Systems (DIKO)

10 What is Network Function Virtualization (NFV)?
Aims at network operators! Transform network architecture and operation by applying standard IT virtualization technology Members: >250 companies; only few academics (5); member since Jan. 2013 Amongst other: work on future curricular

11 Move this box into the cloud!
Example: BRAS – Broadband Residential Access Server Move this box into the cloud!

12 Suggested PoC by SK Telecom
Example: Service Chaining in NFV for Video Acceleration Suggested PoC by SK Telecom

13 (More) Detailed Architectural Framework

14 Rules of Thumb, Educated Guesses or Scientific Results?
Initial Evaluation: Virtualization Concepts and Their Rough Performance Rules of Thumb, Educated Guesses or Scientific Results?

15 A Metric for Isolation and Trans-parency of Virtual Elements
Kurt Tutschku Blekinge Institute of Technology (BTH), Faculty of Computing Department of Communication Systems (DIKO) With acknowledgements to the definitions and descriptions of M. Fiedler (BTH) and D. Stezenbach (University of Vienna)

16 Scope and Causes of Reduced Virtualization Features?
Sharing Resources Server (Host Machine) CPU Memory I/O Virtual Machine Monitor Virtual Machine Guest OS Virtual appliance Virtual appliance Virtual I/O Virtual Memory Virtual CPU Main cause for reduced quality of virtualization is resource sharing! (Typically) “atomic” resources: only a single request can be served at a time. However, requests might arrive in parallel (from other VEs due to sharing) Concurrency is resolved by serialization. But, this might introduce additional delay (jitter) for the deferred request. Thought experiment: two virtual appliances, arbitrary scheduling But, severity depends on “tolerable” delay and in particular on the delay variance. Does this happen in real life?

17 Experiment: Sharing among Virtual Routers
Set-up: Server: consumer hardware (Intel Core 2 Duo E8500, 4GB RAM, Ubuntu 12.10); network interfaces: 2x1Gbit/s 100Mbit/s Virtual router appliances: Ubuntu 12.10, XEN or VirtualBox ; packet forwarding using vSwitch; four appliances used Measurement traffic: 4 parallel UDP streams; 120B Frame size (Ethernet); CBR traffic: inter-packet time 61µs (per stream),15,65Mbit/s per Flow, 62,62Mbit/s total Be aware: these are data packets but in general this can be extended to signaling/control request

18 Experiment: Comparison of ingress and egress
Packet sequence: Average throughput Ingress (all flows) Egress (all flows)  Observations: Ingress: strict round-robin Egress: arbitrary packet order Ingress (all flows) Egress (all flows) Throughput variation is indicator for reduced isolation and transparency! Methodology: comparison of ingress with egress (independent of traffic type) Implementation: compare coefficient of variation at ingress and egress

19 Power of the Metric: Comparison Virtualization Technologies – Use of VirtualBox instead of Xen
VirtualBox introduces less variation than Xen (our current assumption: this is due to VirtualBox not using the complex vSwitch) Attention: metric does not analyze why a specific virtualization technology has a better isolation/transparency! Focus of metric is on enabling a comparison! VirtualBox Xen

20 Tack så mycket! Frågor? 

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