FastContainer: A Homeostatic System Architecture High-speed Adapting Execution Environment Changes

FastContainer: A Homeostatic System Architecture High-speed Adapting Execution Environment Changes

Paper: https://www.computer.org/csdl/proceedings-article/compsac/2019/260701a270/1bwKZwTiA1i

2019 43rd IEEE International Conference on Computer Software & Applications

2019/07/16
SAKURA internet inc.
SAKURA internet Research Center
Senior Resarcher
Ryosuke Matsumoto / matsumotory

Transcript

1. SAKURA internet Inc,
   (C) Copyright 1996-2019 SAKURA Internet Inc
   Research Center
   FastContainer: A Homeostatic System Architecture
   High-speed Adapting Execution Environment Changes
   2019/07/16 Ryosuke Matsumoto
   2019 43rd IEEE International Conference on Computer Software & Applications
   

   View Slide

2. 1. Introduction
   2. Load distribution and operation technology
   3. FastContainer: Proposed method
   4. Experiment
   5. Conclusion
   2
   ໨࣍
   

   View Slide

3. 1.
   Introduction
   

   View Slide

4. • Opportunities for users to express themselves on the internet are increasing
   • along with the diversification of companies and individuals working on
   the internet.
   • For individuals, by spreading contents created using Twitter or Facebook
   • become possible to increase the number of visits to contents efficiently.
   • become possible to brand-identify individuals
   • web hosting services and cloud services are used for individuals to serve
   web contents
   4
   Background
   

   View Slide

5. • OS virtualization technology is important
   • provide stable and secure execution of multiple execution environments
   of web applications on a single web server
   • using a container-type virtualization technology
   • can manage resources by isolating a user area on a per-process basis
   • can more efficiently accommodate multiple execution environments than
   virtual machines can
   5
   Price reduction and performance improvement
   

   View Slide

6. • For cloud services, users must implement a mechanism of autoscale
   • autoscale can withstand access concentration
   • it is necessary to start up a self-built virtual machine internally or to use an
   external service
   • it takes time to do scale processing against burst access concentration
   • it is difficult for users with insufficient technical knowledge to construct a
   load distribution mechanism quickly
   6
   Autoscale problems
   

   View Slide

7. • on the premise
   • people with knowledge sufficient to use only the standard web hosting
   service distribute web contents
   • without technical expert knowledge
   • propose a system architecture by which service users need not construct a
   load distribution system and operate/manage libraries
   • quickly adapt to changing the execution environment by reactively
   determining startup of a container in an HTTP request
   7
   FastContainer: Proposed method
   

   View Slide

8. 2.
   Load distribution and operation
   technology
   

   View Slide

9. • The server becomes a massive load state and access becomes difficult
   • The opportunity of valuable content diffusion is missed
   • In this section, we organize the autoscale and related operation
   technologies for load balancing
   • in web hosting service and cloud service
   9
   In situations in which access is most concentrated
   

   View Slide

10. • It is difficult to control the resources used on a per-host basis properly or to
    investigate the cause quickly
    • The content of the service user is accommodated in a specific web server
    • Autoscale corresponding to the load is difficult in terms of data
    consistency
    • when updating the library, the effect of restarting the server process
    becomes large
    • many hosts run on a single server process
    10
    Web hosting services
    

    View Slide

11. • although it has a high degree of freedom in terms of being able to design
    the system individually for load balancing
    • it requires expert knowledge
    • cloud providers provide the function of increasing or decreasing instances
    according to the load
    • Even if a virtual machine starts up under a high load situation, the process
    itself for autoscale can not catch up at times of sudden high loads
    • such as the influence of television broadcasting
    • often leading to service stoppage
    11
    Cloud services
    

    View Slide

12. • can define detailed conditions for scaling by external service cooperation
    • using containers exists to solve the difficulty of starting time of virtual
    machines
    • the startup processing of web application server processes such as Ruby
    on Rails on containers is still slow
    • Immediacy is low against a sudden load
    • a method of activating a virtual machine of an expected amount to some
    degree is taken in advance
    • difficult to form an appropriate estimate from the balance of limited costs
    12
    Related works
    

    View Slide

13. • determines the computer resource automatically by installing an
    application by the notation specified by the provider
    • automatically scaling the computer resource on the provider side under
    high load such as AWS Lambda
    • these services are intended for engineers who have technical knowledge
    • challenging to autoscale after publishing web contents without technical
    knowledge for users targeted by the web hosting service
    13
    Related services: Serverless Architecture
    

    View Slide

14. 3.
    FastContainer: Proposed method
    

    View Slide

15. 1. The architecture can scale-up and scale-out instances quickly with a
    granularity of HTTP request units.
    2. The architecture monitors the instance at a granularity of HTTP request
    units and issue scale processing instruction of the instance.
    3. To improve the resource efficiency of servers, the architecture stops
    unnecessary instances. It can activate the instances with an HTTP request
    trigger when necessary.
    15
    The requirements for architecture
    

    View Slide

16. 1. The service provider supports server operation, such as by update of OS
    and the library.
    2. The service provider supports a widely used general web application such
    as WordPress.
    3. The service provider supports autoscale when the load concentrates, even
    if there is no specialized technical knowledge related to the load
    distribution.
    16
    More features details for service
    

    View Slide

17. 4. The service provider supports pay-per-use at the granularity of about the
    web application execution time.
    5. The service reduces hardware costs by increasing the host accommodation
    efficiency.
    6. The service updates OS and libraries to ensure security at high
    frequencies.
    17
    More features details
    

    View Slide

18. FastContainer architecture
    18
    )PTU"
    )PTU"
    )PTU"
    )PTU" )PTU"
    5IF*OUFSOFU
    )PTU"
    )PTU"
    )PTU"
    stop periodically
    start reactively
    scale-up reactively
    Server A
    Server B
    HTTP(S) requests
    scale-out reactively
    • This architecture reactively
    determines changes of states such
    as web application container
    startup, startup duration, the
    number of containers, and scale
    processing judgment for each
    HTTP request
    • can adapt quickly to changes in
    the execution environment.
    • It has homeostasis
    

    View Slide

19. FastContainer Flow
    19
    8FC1SPYZ
    ʢOHY@NSVCZ
    
    $.%#
    ʴ
    "1*
    8FC%JTQBUDIFS
    OHY@NSVCZ
    
    $MJFOU $POUBJOFS
    $POUBJOFS
    $POUBJOFS
    w 5IFDPOUBJOFS
    DPOpHVSBUJPOJOGPSNBUJPO
    JTBDRVJSFEGSPNUIF
    $.%#
    )551 4
    
    3FRVFTUT
    w USBOTGFSTUIFSFRVFTUUPUIF
    TQFDJpFEDPOUBJOFSJGUIFDPOUBJOFS
    JTBDUJWF
    w *GJUJTOPUBDUJWBUFE UIFOUIF
    DPOUBJOFSDPOpHVSBUJPOJOGPSNBUJPO
    JTBDRVJSFEGSPNUIF$.%# UIF
    SFRVFTUJTUSBOTGFSSFEBGUFS
    BDUJWBUJOHUIFDPOUBJOFSpSTU
    $POUBJOFS&OHJOF
    IBDPOJXB
    
    4IBSFE4USBHF
    

    View Slide

20. • the startup processing of web application server processes such as Ruby on
    Rails on containers is still slow
    • Even for containers, immediacy is low against a sudden load
    • uses CRIU to image the process state immediately after finishing the startup
    of the process
    • CRIU restore the process from that image.
    • Even if using software with a long startup time such as Ruby on Rails, it
    can be started at high speed
    20
    High-speed state transition
    

    View Slide

21. 4.
    Experiment
    

    View Slide

22. Prototype for evaluation of FastContainer
    22
    

    View Slide

23. Evaluation of scale processing
    23
    0OUIFDPOUBJOFSJO$PNQVUF
    BDUJWBUF"QBDIF
    JOTUBMM1)1
    POMZFYFDVUFTUIFQIQJOGP
    GVODUJPO
    $16MJNJUFEUPPGDPSF
    5IF#FODINBSLJOH
    TJNVMUBOFPVTDPOOFDUJPO
    UPUBMSFRVFTUT
    DPSSFTQPOEFODFPGUIFTDBMFPVU
    UZQFBOEUIFTDBMFVQUZQF8IFOUIF
    OVNCFSPGQSPDFTTJOHSFRVFTUT
    FYDFFET
    

    View Slide

24. 24
    Scale-out order in 334 s
    starting scale-out in 340 s
    

    View Slide

25. 25
    the response time transition
    when scaling up the CPU
    maximum usage rate to 60% at
    the horizontal axis 301 s.
    

    View Slide

26. Evaluating startup speed from images
    26
    

    View Slide

27. 5.
    Conclusion
    

    View Slide

28. • This study examined our proposed FastContainer of a container
    management architecture
    • allows the user environment composed of containers to autoscale at
    HTTP request timing
    • without requiring specialized knowledge for service users in web hosting
    service
    • the time of the auto scale-out with reactivity is considerably short by CRIU
    • resource efficiency of FastContainer is higher by discarding the container
    during a certain period
    28
    Conclutions
    

    View Slide

29. • it is necessary to clarify the standing position of container related software
    • the comparison target and to make the cooperation of each layer more
    generalized
    • we plan to continue research and development while considering
    collaboration with other tools and technical background firmly
    • in addition to discussion with tool developers like Kubernetes and
    Docker
    29
    Future works
    

    View Slide