Christos Kozyrakis
Christos Kozyrakis
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Syrup: User-Defined Scheduling Across the Stack
Suboptimal scheduling decisions in operating systems, networking stacks, and application runtimes are often responsible for poor …
Kostis Kaffes
,
Jack Tigar Humphries
,
David Mazières
,
Christos Kozyrakis
Cite
DOI
Llama: A Heterogeneous & Serverless Framework for Auto-Tuning Video Analytics Pipelines
The proliferation of camera-enabled devices and large video repositories has led to a diverse set of video analytics applications. …
Francisco Romero
,
Mark Zhao
,
Neeraja J. Yadwadkar
,
Christos Kozyrakis
Cite
DOI
Centralized Core-Granular Scheduling for Serverless Functions
In recent years, many applications have started using serverless computing platforms primarily due to the ease of deployment and cost …
Kostis Kaffes
,
Neeraja J. Yadwadkar
,
Christos Kozyrakis
Cite
DOI
Improving Resource Efficiency at Scale with Heracles
User-facing, latency-sensitive services, such as websearch, underutilize their computing resources during daily periods of low traffic. …
David Lo
,
Liqun Cheng
,
Rama Govindaraju
,
Parthasarathy Ranganathan
,
Christos Kozyrakis
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DOI
Tarcil: Reconciling Scheduling Speed and Quality in Large Shared Clusters
Scheduling diverse applications in large, shared clusters is particularly challenging. Recent research on cluster scheduling focuses …
Christina Delimitrou
,
Daniel Sanchez
,
Christos Kozyrakis
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DOI
QoS-Aware Scheduling in Heterogeneous Datacenters with Paragon
Large-scale datacenters (DCs) host tens of thousands of diverse applications each day. However, interference between colocated …
Christina Delimitrou
,
Christos Kozyrakis
Cite
DOI
Paragon: QoS-Aware Scheduling for Heterogeneous Datacenters
Large-scale datacenters (DCs) host tens of thousands of diverse applications each day. However, interference between colocated …
Christina Delimitrou
,
Christos Kozyrakis
Cite
DOI
Flexible Architectural Support for Fine-Grain Scheduling
To make efficient use of CMPs with tens to hundreds of cores, it is often necessary to exploit fine-grain parallelism. However, …
Daniel Sanchez
,
Richard M. Yoo
,
Christos Kozyrakis
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DOI
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