Improving distributed traffic generation performance by using IMUNES network emulator
Improving distributed traffic generation performance by using IMUNES network emulator. Valter Vasić, Mirko Su ž njevi ć , Miljenko Mikuc , Maja Matija š evi ć University of Zagreb, Faculty of Electrical Engineering and Computing Unska 3, Zagreb, Croatia mirko.suznjevic@fer.hr. Problem.
Improving distributed traffic generation performance by using IMUNES network emulator
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Improving distributed traffic generation performanceby using IMUNES network emulator Valter Vasić, Mirko Sužnjević, Miljenko Mikuc, MajaMatijašević University of Zagreb, Faculty of Electrical Engineering and Computing Unska 3, Zagreb, Croatia mirko.suznjevic@fer.hr
Problem • How to generateaccuratenetworktrafficin a scalableway? • User Behaviour Based Traffic Generator (UrBBaN-Gen) • Expandability • Virtualization • Virtualization technologies • Linux Containers (LXC, bridges) - current • IMUNES (FreeBSDjails, netgraph) - new • Results: • IMUNES outpreforms LXCs • Higher traffic loads on less hardware • Complex network arhitectures enabled
Outline • Problem • Introduction • UrBBaN-Gen • IMUNES • Methodology • Results • Conclusion
Introduction • Traffic generators • Testing • Hardware – complex, strong, expensive • Software – free, modular • UrBBaN-Gen • Arhitecture for behaviour based traffic generation • Developed for complex services (e.g., Massively Multiplayer Online Role-Playing Games) • Basic idea – user behavior at the application level controls the parameters of the generated traffic
Linux Containers • Lightweightsystemvirtualization mechanism • Not a virtual machine, but a virtual environment that has its own process and network space. • One traffic sender/receiver per LXC • Problems: • Stability • Scalability
IMUNES • Integrated Multiprotocol Network Emulator Simulator • Charcteristics • Virtualization – FreeBSD jails • Network emulation and communication – Netgraph • Transactional file system – ZFS • Ability to create complex emulated networks – no need for network hardware (routers/switches)
Methodology • Comparing two testbeds • Measured • CPU load • Packet loss • Tests • Testing with fixedpps (packets per second) rate andfixed packet size, while changing the number of sendernodes. • Testing with fixedpps rate and fixed number of nodes,while changing the packet size.
Conclusions • How to generate accurate networktrafficin a scalableway? • IMUNES as a virtualizationplatform • Results indicate IMUNES is more stablethan LXCs, andresultswithlowerpacketloss • Future work: full integration of IMUNES with UrBBaN-Gen, distribution of one IMUNES experiment on multiple PCs
