Ananta: cloud scale load balancing

By Parveen Patel, Deepak Bansal, Lihua Yuan, Ashwin Murthy, Albert G. Greenberg, David A. Maltz, Randy Kern, Hemanth Kumar, M. Zikos, Hongyu Wu and 2 more. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2013

331 citations recorded by the source corpus.

Layer-4 load balancing is fundamental to creating scale-out web services. We designed and implemented Ananta, a scale-out layer-4 load balancer that runs on commodity hardware and meets the performance, reliability and operational requirements of multi-tenant cloud computing environments. Ananta combines existing techniques in routing and distributed systems in a unique way and splits the components of a load balancer into a consensus-based reliable control plane and a decentralized scale-out data plane. A key component of Ananta is an agent in every host that can take over the packet modification function from the load balancer, thereby enabling the load balancer to naturally scale with the size of the data center. Due to its distributed architecture, Ananta provides direct server return (DSR) and network address translation (NAT) capabilities across layer-2 boundaries. Multiple instances of Ananta have been deployed in the Windows Azure public cloud with combined bandwidth capacity exceeding 1Tbps. It is serving traffic needs of a diverse set of tenants, including the blob, table and relational storage services. With its scale-out data plane we can easily achieve more than 100Gbps throughput for a single public IP address. In this paper, we describe the requirements of a cloud-scale load balancer, the design of Ananta and lessons learnt from its implementation and operation in the Windows Azure public cloud.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/5c295df1a7f302c97f6f379eab6abba592811d42

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Citations in the corpus

Bohatei: Flexible and Elastic DDoS Defense (2015, background): “Threat model: We focus on a general DDoS threat against the victim, who is a customer of the ISP.”
Packet-Level Telemetry in Large Datacenter Networks (2015, methodology): “) Packet encapsulation is often used in DCNs for load balancing [28] and network virtualization [24].”
A Survey of Security in Software Defined Networks (2016, background): “, Google’s backbone network [6], Microsoft’s public cloud [7], NTT’s edge gateway [8] etc.”
SilkRoad: Making Stateful Layer-4 Load Balancing Fast and Cheap Using Switching ASICs (2017, methodology): “A previous study [36] reports that an average of 44% of cloud tra c is VIP tra c and thus needs load balancing function.”
End-to-end Performance Isolation Through Virtual Datacenters (2014, background): “For example, Amazon EC2 offers over twenty services including networked storage, monitoring, load balancing, and elastic caching [1].”
Pingmesh: A Large-Scale System for Data Center Network Latency Measurement and Analysis (2015, methodology): “See [9, 14] for the details of how SLB works.”
Maglev: A Fast and Reliable Software Network Load Balancer (2016, methodology): “Ananta [34] is a distributed software load balancer.”