RDNA Balance: Balanceamento de Carga por Isolamento de Fluxos Elefante em Data Centers com Roteamento na Origem
Resumo
As redes de Data Center, que precisam atender de forma dinâmica uma grande quantidade de fluxos com diferentes requisitos de serviço, necessitam de mecanismos de balanceamento de carga. Entretanto, as abordagens tradicionais de balanceamento de carga não permitem a completa utilização dos recursos de rede de forma simples, programável e escalável. Nesse contexto, este artigo propõe RDNA Balance que explora a balanceamento por isolamento de fluxos elefante e roteamento na origem, com suporte no núcleo da rede, e operações de classificação realizadas na borda usando recursos existentes no protocolo OpenFlow. Os resultados mostram que essa abordagem é capaz de prover um balanceamento de carga simples, escalável, e programável.
Referências
Alizadeh, M. et al. (2014). CONGA: Distributed Congestion-Aware Load Balancing for Datacenters. ACM SIGCOMM Computer Communication Review, 44(4):503–514.
Benson, T. et al. (2010). Understanding data center traffic characteristics. SIGCOMM Comput. Commun. Rev., 40(1):92–99.
Bolla, R. and Bruschi, R. (2006). Rfc 2544 performance evaluation and internal measurements for a linux based open router. In High Performance Switching and Routing, 2006 Workshop on, pages 6–pp. IEEE.
Cai, Y. et al. (2012). RFC 6754 - protocol independent multicast equal-cost multipath (ECMP) redirect. Technical report.
He, K. et al. (2015). Presto: Edge-based Load Balancing for Fast DCN. Sigcomm 2015, pages 465–478.
Jin, X. et al. (2016). Your data center switch is trying too hard. In Proceedings of the Symposium on SDN Research, SOSR ’16, pages 12:1–12:6, NY, USA. ACM.
Jyothi, S. A. et al. (2015). Towards a flexible data center fabric with source routing. In Proceedings of the 1st ACM SIGCOMM Symposium on Software Defined Networking Research, page 10. ACM.
Kandula, S. et al. (2009). The nature of data center traffic. In Proceedings of the 9th ACM SIGCOMM conference on IMC ’09, page 202, New York, New York, USA. ACM.
Katta, N. et al. (2015). Ravana: Controller fault-tolerance in software-defined networking. In Proceedings of the 1st ACM SIGCOMM symposium on software defined networking research, page 4. ACM.
Katta, N. et al. (2016). Hula: Scalable load balancing using programmable data planes. In Proceedings of the Symposium on SDN Research, SOSR ’16, pages 10:1–10:12, NY, USA. ACM.
Liberato, A. et al. (2018). RDNA: Residue-Defined Networking Architecture Enabling Ultra-Reliable Low-Latency Datacenters. IEEE TNSM, 4537(c):1–1.
Martinello, M. et al. (2014). Keyflow: A prototype for evolving SDN toward core network fabrics. Network, IEEE, 28:12–19.
Popoviciu, C., Hamza, A., Van de Velde, G., and Dugatkin, D. (2008). RFC 5180 - IPv6 benchmarking methodology for network interconnect devices. Technical report.
Rasley, J. et al. (2014). Planck: Millisecond-scale Monitoring and Control for Commodity Networks Jeff. In Proceedings of the 2014 ACM conference on SIGCOMM - SIGCOMM ’14, pages 407–418, New York, New York, USA. ACM Press.