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Core Offloads
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==== Workloads ==== Hyperscale servers are deployed in planetary scale environments, with many sites of tens of thousands or more servers. Each server can run hundreds of tasks across hundreds of cores. Each task can communicate with tens of thousands of peers. Network incast and outcast can reach millions of connections per host, tens of millions at the tail. At this scale, connection establishment rate becomes a significant design consideration besides absolute connection count. From experimental results we derive 100K connections/sec per host as the minimum level that must be supported, and an order of magnitude higher to be future proof for the expected lifetime of new devices. At these levels, systems that scale O(1) with connection count are strongly preferred over those that scale O(N). This is the principal reason to prefer stateless device offloads. Hyperscale servers today should be expected to scale at the 99% to at least * connection count: 10M TCP/IP connections * connection rate:100K connections/sec Hyperscale workloads can be mixes of many applications. They can be generalized into three types. * high priority, latency sensitive, such as user facing traffic * low priority, latency insensitive, such as map-reduce style jobs * high performance computing: dominated by machine learning workloads <br /> Machines may run a mix of workloads to increase hardware utilization. This way they can offer assured service to high priority tasks, while scheduling low priority tasks on surplus resources at best effort. This model requires strong quality of service isolation to meet latency sensitive traffic service level objectives (SLOs). <span id="interface"></span>
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