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Alexandros Daglis

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Publications associées (16)

The Case for RackOut: Scalable Data Serving Using Rack-Scale Systems

Babak Falsafi, Edouard Bugnion, Boris Robert Grot, Alexandros Daglis, Stanko Novakovic

To provide low latency and high throughput guarantees, most large key-value stores keep the data in the memory of many servers. Despite the natural parallelism across lookups, the load imbalance, introduced by heavy skew in the popularity distribution of k ...

2016

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Despite the natural parallelism across lookups, performance of distributed key-value stores is often limited due to load imbalance induced by heavy skew in the popularity distribution of the dataset. To avoid violating service level objectives expressed in ...

2016

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Modern in-memory services rely on large distributed object stores to achieve the high scalability essential to service thousands of requests concurrently. The independent and unpredictable nature of incoming requests results in random accesses to the objec ...

Ieee2016

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Datacenter operators rely on low-cost, high-density technologies to maximize throughput for data-intensive services with tight tail latencies. In-memory rack-scale computing is emerging as a promising paradigm in scale-out datacenters capitalizing on commo ...

2015

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A computing system that uses a Scale-Out NUMA (“soNUMA”) architecture, programming model, and/or communication protocol provides for low-latency, distributed in-memory processing. Using soNUMA, a programming model is layered directly on top of a NUMA memor ...

2015

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Emerging datacenter applications operate on vast datasets that are kept in DRAM to minimize latency. The large number of servers needed to accommodate this massive memory footprint requires frequent server-to-server communication in applications such as ke ...

2014

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