Publications by Alexandros Daglis

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When a computational task tolerates a relaxation of its specification or when an algorithm tolerates the effects of noise in its execution, hardware, system software, and programming language compilers or their runtime systems can trade deviations from cor ...

The booming popularity of online services is rapidly raising the demands for modern datacenters. In order to cope with data deluge, growing user bases, and tight quality of service constraints, service providers deploy massive datacenters with tens to hund ...

Key-Value Stores (KVS) are foundational infrastructure components for online services. Due to their latency-critical nature, today’s best-performing KVS contain a plethora of full-stack optimizations commonly targeting read-mostly, popularity-skewed worklo ...

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 ...

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 ...

With emerging storage-class memory (SCM) nearing commercialization, there is evidence that it will deliver the much-anticipated high density and access latencies within only a fewfactors of DRAM. Nevertheless, the latency-sensitive nature of memory-residen ...

The increasing demand for extracting value out of ever-growing data poses an ongoing challenge to system designers, a task only made trickier by the end of Dennard scaling. As the performance density of traditional CPU-centric architectures stagnates, adva ...

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 ...

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 ...

A distributed memory system including a plurality of chips, a plurality of nodes that are distributed across the plurality of chips such that each node is comprised within a chip, each node includes a dedicated local memory and a processor core, and each l ...

The emerging paradigm of microservices decomposes online services into fine-grained software modules frequently communicating over the datacenter network, often using Remote Procedure Calls (RPCs). Ongoing advancements in the network stack have exposed the ...

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 ...

Alexandros Daglis

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Exploiting Errors for Efficiency: A Survey from Circuits to Applications

Network-Compute Co-Design for Distributed In-Memory Computing

Cooperative Concurrency Control for Write-Intensive Key-Value Workloads

Manycore Network Interfaces for In-Memory Rack-Scale Computing

Scale-Out NUMA

Design Guidelines for High-Performance SCM Hierarchies

The Mondrian Data Engine

An Analysis of Load Imbalance in Scale-out Data Serving

SABRes: Atomic Object Reads for In-Memory Rack-Scale Computing

Atomic object reads for in-memory rack-scale computing

Cerebros: Evading the RPC Tax in Datacenters

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