MODELING OF DECENTRALIZED AND RECONFIGURABLE … Talks/ericsson-decentr-reconfig-Nov2016.pdf– Cloudsim › Object-oriented › Infrastructure modelling – Google B4 – Google Job

MODELING OFDECENTRALIZED ANDRECONFIGURABLE CLOUDS

Martin Korling, Chris Hogue, [email protected]@ericsson.com

© Ericsson | De-centralized Re-configurable Cloud | 2016-11-01 | Page 2

› Intro› Background› De-centralization› Re-configurability (disaggregation)› Dynamic model (work-in-progress)› Cost-benefit (work-in-progress)› Conclusion

Outline


› Infrastructure for society critical applications, different than today’s

› Use cases:…

› New flexibilities: de-centralization, re-configurability

› Double relevance of Big Data -> Big Control/Decision: – Big control is a workload, – Big control is used as control mechanism within infrastructure

Intro


› how to use decentralization, cost-benefit› how to use hardware re-configurability, cost-benefit› interface between application and infrastructure, service contract parameters› how to design control and management planes› hyperconverged and hyperscale architectures, e.g. data locality› isolation strategies, trade-off with resource fragmentation› policy architecture, space of constraints, beyond labels› serverless/event-based in distributed scenarios.

Intro, problem statement


› Simulations– ROSS, Time-warp

› Object-oriented with message passing– Cloudsim

› Object-oriented

› Infrastructure modelling– Google B4– Google Job packing– Google Borg

› Utilization is not useful– Scaling– Inflating– Shrinking

Background, prior work

Abhishek Verma, Madhukar Korupolu, John Wilkes, Evaluating job packing in warehouse-scale computing

Abhishek Verma, Madhukar Korupolu, John Wilkes, et. al, Large-scale cluster management at Google with Borg

Adrian Cockcroft, “Utilization is Virtually Useless as a Metric!”


› Heterogeneity important› Class/Priority used extensively› Constraints important

› Network traffic information missing!

Background, prior work

J. Wilkes and C. Reiss. Details of the ClusterData-2011-1 trace, 2011. https://code.google.com/p/ googleclusterdata/wiki/ClusterData2011_1

Charles Reiss et.al, Heterogeneity and Dynamicity of Clouds at Scale: Google Trace Analysis”

Sheng Di, Derrick Kondo, Walfredo Cirne. Characterization and Comparison of Google Cloud Load versus Grids. 2012

The Google cluster trace


De-centralization

global/publiclevel

countrylevel

metro levelon-premleveldevice

level

There are important:

• system domain borders

• business domain borders

• legal domain border


De-centralizationDEVICE ON-PREM LOCAL/

METROCOUNTRY GLOBAL/

CENTRALmachine, car,train, …

factory, office,power-station, …

central office,base station,…

datacenter, … datacenter, …

resilience,bandwidth

scale

regulatorycompliance,multicloud

control, security, low latency

control, extreme low latency ”micro

datacenters”

it’s not only about latency


ApplicationsDEVICE ON-PREM LOCAL/






Web,“Cloud”,video upload

“CLIENT-SERVER”

CDN Video streaming

HYBRIDCLOUD

Resourceoffload

COMPLIANTDATA

Personal datastorage


ApplicationsDEVICE ON-PREM LOCAL/






UPSTREAMVIDEO

Upstream video processing

CONTROLSYSTEMS

Extremelow latency

Video stream Metadata, events

Control loop

Intermediatecontrol Metadata, events


› Chassis28094 variants2822 variants

› Rack(28094 + 2822) 20

= 7.658x10275

› Disaggregated RackScaleComponents

– Compute Sleds (2809 variations)

– Storage Sleds (282 variations)

Re-configurability with HDS 8000(Rackscale design (RSD))


› Physical Sleds to Logical Servers

› 4 “bootable” servers shown here– 4 CSU– 6 SSU in SAS Daisy Chain– What is the Storage in each Server?

– Depends on › Discs inside each SSU› Zone Partitioning

From Disaggregated HardwareTo “Composite” Compute Nodes

SSD

HDD

HDD


NOW: FAST, DETAILED HARDWARE MODEL Generation

Pre-computedConfigurations

RequirementsConstraints:BudgetStorage CapacityvCPUs…

FAST ITERATIONS

100’s of Racks in Complex ConfigurationsDAS, SDS, BOM, Price,

Illustrations, CablingInstallation Details


› Go generatedJSON & SQL forms

› Star-Schema– Logical (graph)

› Pods, NodesDevices, Connects

– Physical (hardware)› Racks, Chassis› Switches, Management› CSUs, SSUs› DAS, SDS,› Cables, Discs

Detailed Date Model & ConfigGenerator


GOAL: Simulation including Workloads

RequirementsConstraints:BudgetStorage CapacityvCPUsFloor SpacePowerMass

SimulationWorkloadsUsersTrafficBurstsSLAGrowth

Datacenter ModelsOptimized Configuration

REFINEMENT

MODEL ENTERPRISEWORKLOAD


RUNTIME Data modelAsset

Site Transport

Pools

Workload

Dataset

ComponentSystems

Machine Connection FlowJobCPU Mem

Disk Link


Dynamic modeling

Still rough• machine borders oversimplified• aggregation level, just an exampleObservations• workload components co-varying• workloads competing• stranded resources• datasets impose constraints


The problem

resources

quality

resources

quality

resources

quality

resources

quality

resources

quality

resources

quality

Quality

Capacity

It’s not bin-packing

$

micro behaviors macro behavior


The problemSure, it’s a


Simplified model

Data sources

Access links

Metro/MicroDatacenter

Aggregations/Metro links

CentralDatacenter

~1 customer per linkl_a

~10k customers per linkl_m

~1M customers

~100 metro areas

~10 metro sites in each

~10 central sites

CENTRALIZED DE-CENTRALIZED

computer vision

anomalydetection

computer vision

anomalydetection

Workload descriptionw [1/w] = bitrate/CPU/time

Cost factorsp(l) price of link resource, l_m

p(c) price of compute resource, c_m


Simplified MODELWorkload description

w [w] = instructions/bits

[1/w] = bitrate/MIPS

Cost factors

p(l) production of link resource, l_mp(c) production of compute resource, c_m

relativecost

relativequality

Quality (SLA)

throughput sustained processing bitrate

p(c)/p(l)

1/w > p(c)/p(l)

de-centralizedmore costly

de-centralizedless costly

de-centralizedworse quality

de-centralizedbetter quality

w

p(l)

p(c)

10000

Example numbers

1000

100

(USD/100Mbps/T)

(USD/CPU/T)10 100 1000

p(c)/p(l)0.1M 1M

10M

100M1/w = 5M

1/w = 1Mbps/0.2CPU = 5M

BACK-OF-ENVELOPE, PRELIMINARY, SUBJECT TO SIMPLIFYING ASSUMPTIONS


› de-centralized: improve isolation, regulatory compliance› de-centralized: improve quality / cost (trade-off link and compute bottlenecks) › de-centralized: improve response time / latency› de-centralized: risk of resource fragmentation

› re-configurability: isolation› re-configurability: mitigate resource fragmentation

Tentative cost-benefit


› Intro› Background› De-centralization› Re-configurability (disaggregation)› Dynamic model (work-in-progress)› Cost-benefit (work-in-progress)› Conclusion

Summary

Documents

MODELING OF DECENTRALIZED AND RECONFIGURABLE … Talks/ericsson-decentr-reconfig-Nov2016.pdf– Cloudsim › Object-oriented › Infrastructure modelling – Google B4 – Google Job