Abhi Joglekar
Coffee Brewer, Runner, Dad, Network Plumber, Bit Mover, Caffeine to Code Converter
In an earlier blog, “Avi Vantage: A Cloud-Scale Distributed Software Load Balancer For Everyone”, we had described the high-level architecture of the Avi Vantage platform. The platform consists of a clustered centralized Controller, a scale-out distributed Layer-7 Reverse Proxy data path called as Service Engine (SE), a Visibility/Analytics engine, a RESTful interface to the Controller that enables integration with external orchestration engines, and a fast and responsive HTML5 UI. It is designed ground up to be a modern cloud-scale Application Delivery Controller (ADC) that enables application deployment across any cloud - private, public, or hybrid.
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Topics:
cloud,
load balancer,
ADC,
application delivery,
Application Delivery Controller,
Load Balancing,
Application Services,
networking,
HTTP,
Routing,
Lua,
Scripts,
Application Routing,
Content-Based Routing,
Content Routing
The Hardware Load Balancer Brick Wall Last month at Networked Systems Design and Implementation (NSDI) conference, Google lifted the covers off Maglev, their distributed network software load balancer (LB) [1]. Since 2008, Maglev has been handling traffic for core Google services like Search and Gmail. Not surprisingly, it's also the load balancer that powers Google Compute Engine and enables it to serve a million requests per sec without any cache pre-warming [2]. Impressive? Absolutely! If you have been following application delivery in the era of cloud, say over last 6 years, you would have noticed another significant announcement at Sigcomm ‘13 by the Microsoft Azure networking team. Azure runs critical services such as blob, table, and relational storage on Ananta [3], its home-grown cloud scale software load balancer on commodity x86, instead of running it on more traditional hardware load balancers. Both Google and Microsoft ran headlong into what can be best described as “the hardware LB brick wall”, albeit at different times and along different paths in their cloud evolution. For Google, it started circa 2008 when the traffic and flexibility needs for their exponentially growing services and applications went beyond the capability of hardware LBs. For Azure, it was circa 2011, when the exponential growth of their public cloud led to the realization that hardware LBs do not scale and forced them to build their own software variant. So, what is this “hardware LB brick wall” that these web-scale companies ran into?
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Topics:
ADC,
SDN,
Closed-Loop Application Delivery,
Architecture,
SSL,
Analytics,
Application Delivery Controller,
Microservices,
metrics,
Software Load Balancer
