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EECS 582 Midterm Review EECS 582 Midterm Review

EECS 582 Midterm Review - PowerPoint Presentation

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EECS 582 Midterm Review - PPT Presentation

Mosharaf Chowdhury EECS 582 W16 1 Stats on the 18 Reviewers EECS 582 W16 2 Stats on the 21 Papers Weve Reviewed EECS 582 W16 3 Stats on the 21 Papers Weve Reviewed EECS 582 W16 ID: 551109

eecs 582 performance w16 582 eecs w16 performance file programming memory virtual design kernels fault stats balance storage systems

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Slide1

EECS 582 Midterm Review

Mosharaf Chowdhury

EECS 582 – W16

1Slide2

Stats on the 18 Reviewers

EECS 582 – W16

2Slide3

Stats on the 21 Papers

We’ve Reviewed

EECS 582 – W16

3Slide4

Stats on the 21 Papers We’ve Reviewed

EECS 582 – W16

4Slide5

Classics

EECS 582 – W16

5Slide6

Classics

UNIX(Arguably) the first commodity OS

Simplicity is king when you must support diverse applicationsEverything is a file!

System R

The first relational database implementation

Design iteratively and be ready to throw away

Find the

“right” metric and do everything when you must deliver performance (i.e., specialization instead of generalization)

EECS 582 – W16

6Slide7

Storage and File Systems

EECS 582 – W16

7Slide8

Storage and File Systems

RAIDIndustry standard for durable, high-performance storage

Parallelize for performance and fault-toleranceFFSImproved UNIX’s default file systems

Be aware of and exploit hardware characteristics

JFS

Provides crash recovery to file systems

Log what you’ll do before you do it!

EECS 582 – W16

8Slide9

Kernels

EECS 582 – W16

9Slide10

Kernels

ExokernelMinimal kernel instead of a full-fledged one

End-to-end argument: only provide services that everyone needs to balance between specialization (performance) and generalization (applicability)Multikernel

Make communication explicit when you must communicate

Shared-nothing design

EECS 582 – W16

10Slide11

Kernels

IXSeparate control and data planes to provide I/O performance

Kernels aren’t inherently slow; it’s about how we do thingsCommuter

Interfaces dictate scalable design

EECS 582 – W16

11Slide12

Virtual Memory and RPC

EECS 582 – W16

12Slide13

Virtual Memory and RPC

Memory CoherenceShared-everything design requires frequent updates

Keeping things coherent is expensive but provides simple programming modelsRPCMakes distributed nature more explicit while keeping the same programming model as a non-distributed system

No shared memory

EECS 582 – W16

13Slide14

Concurrency and Scheduling

EECS 582 – W16

14Slide15

Concurrency and Scheduling

SEDA and FibersThread- and event-based programming models both have their advantages and drawbacks (ease of programming vs. scalability and performance)

It is possible to find a balance between the twoLottery and Stride Scheduling

Randomized and deterministic proportional scheduling

The key challenge is in determining the proportions (weights)

EECS 582 – W16

15Slide16

Reliability and Fault Tolerance

EECS 582 – W16

16Slide17

Reliability and Fault Tolerance

Eraser

Detecting bugs, specially the non-deterministic ones, is hardProvide a “tight” coverage to allow manual inspection

Nooks

Failure is inevitable

Isolate it and start again

EECS 582 – W16

17Slide18

Virtual Machines

EECS 582 – W16

18Slide19

Virtual Machines

Xen and ESX

Virtualization comes in many shapes and forms (e.g., full virtualization vs paravirtualization)Choose the one that fits your requirements (e.g., performance, consolidation, deployability)

Fit for your workload

Live Migration

Keep things running until you must stop

ReVirt

Quis custodiet ipsos

custodes

? (Roman poet Juvenal 

in

 

Satires

)

It’s turtles all the

way down!

EECS 582 – W16

19