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Title:Characterization of containers in emerging applications: Microservices, FAAS and GPUS
Author(s):Darbaz, Haldun Umur
Advisor(s):Kim, Nam Sung
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Docker
Containers
Microservices
FaaS
GPUs
TLB
Computer
Architecture
Operating
Systems
Page
Tables
Virtualization
Linux
Abstract:Containers have enabled new computing paradigms such as Functions-as-a- Service in data centers today. Containers are inherently more lightweight than virtual machines. This is caused by the fact that containers share the kernel with the host system, removing the need for a two-dimensional page walk. Containers also do not require a hypervisor. They rely on thin management layers in container frameworks and existing Linux functionality. Linux process and resource management features such as cgroups and namespaces are tightly integrated to containers. This allows for simple management and isolation of containerized applications. Docker is currently the most prominent container framework. This thesis utilizes Docker containers to create data center use cases with databases, web servers, graph analytics, Functions-as-a-Service, and GPU-accelerated stencil, lower-upper decomposition, object tracking and neural network applications. Furthermore, this thesis analyzes Docker Engine performance by bringing up containers and breaks down bring-up overheads at function granularity. The virtual memory management aspects of Docker containers are also characterized with a focus on container infrastructure, page tables and page faults. This thesis reports on average 59.86% duplicated page table entries and 35.7% duplicated page faults across four containerized processes sharing a core. Additionally, this thesis identifies the source of 40% of container bring-up overhead and attributes it to memory allocation, garbage collection and process creation in Go and Linux. This thesis also identifies a 7% slowdown in containerized GPU applications with NVIDIA-Docker compared to native execution. Finally, this thesis provides guidance to architects for enabling container support in high-performance architectures, and identifies future work to be done in the area.
Issue Date:2018-11-29
Type:Text
URI:http://hdl.handle.net/2142/102816
Rights Information:Copyright 2018 Haldun Umur Darbaz
Date Available in IDEALS:2019-02-07
Date Deposited:2018-12


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