Abstract
Datacenters demand big memory servers for big data. For blade servers, which disaggregate memory across multiple blades, we derive technology and architectural models to estimate communication delay and energy. These models permit new case studies in refusal scheduling to mitigate NUMA and improve the energy efficiency of data movement. Preliminary results show that our model helps researchers coordinate NUMA mitigation and queueing dynamics. We find that judiciously permitting NUMA reduces queueing time, benefiting throughput, latency and energy efficiency for datacenter workloads like Spark. These findings highlight blade servers' strengths and opportunities when building distributed shared memory machines for data analytics.
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