Scibox: Online Sharing of Scientific Data via the Cloud Jian Huang - - PowerPoint PPT Presentation

Scibox: Online Sharing of Scientific Data via the Cloud

Jia Jian Huang†, Xuechen Zhang†, Greg Eisenhauer†, Karsten Schwan†

Matthew Wolf†,‡, Stephane Ethierǂ, Scott Klasky‡

†CERCS Research Center, Georgia Tech ǂPrinceton Plasma Physics Laboratory ‡Oak Ridge National Laboratory

Supported in part by funding from the US Department of Energy for DOE SDAV SciDac

1

Outline

• Background and Motivation
• Problems and Challenges
• Design and Implementation
• Evaluation
• Conclusion and Future Work

2

Cloud Storage is Popular

Easy-of-use Pay-as-you-go model Universal accessibility Good scalability and durability

3

Cloud Storage is Popular

Easy-of-use Pay-as-you-go model Universal accessibility Good scalability and durability

3

Works based on cloud storage

• Dropbox, GoogleDrive, iCloud, SkyDrive, and etc.

Cloud Storage is Popular

Easy-of-use Pay-as-you-go model Universal accessibility Good scalability and durability

3

Works based on cloud storage

• Dropbox, GoogleDrive, iCloud, SkyDrive, and etc.

Sc Scibo ibox: fo focus on

• n sc

scien ientif ific ic data sh sharin ing

Use Cases for Cloud Storage

4

Combustion Experimental Data

Private Cloud

Aero Cluster

Use Cases for Cloud Storage

4

Combustion Experimental Data

Private Cloud

Aero Cluster

Use Cases for Cloud Storage

4

Combustion Experimental Data

Private Cloud

Image Processing

Aero Cluster Student PC

Use Cases for Cloud Storage

4

Combustion Experimental Data GTS/ LAMMPS

Private Cloud

Image Processing

Aero Cluster Vogue Student PC

Use Cases for Cloud Storage

4

Combustion Experimental Data GTS/ LAMMPS

Private Cloud

Image Processing

Aero Cluster Vogue Student PC

Public Cloud

Use Cases for Cloud Storage

4

Combustion Experimental Data GTS/ LAMMPS

Private Cloud

Image Processing Visualization

Aero Cluster Vogue Student PC GeorgiaTech (Atlanta) WSU (Detroit) OSU (Columbus)

Public Cloud

Use Cases for Cloud Storage

4

Combustion Experimental Data GTS/ LAMMPS

Private Cloud

Image Processing Visualization

Aero Cluster Vogue Student PC GeorgiaTech (Atlanta) WSU (Detroit) OSU (Columbus)

Public Cloud

1. Easy of use 2. Universal accessibility 3. Good scalability

Outline

• Background and Motivation
• Problems and Challenges
• Design and Implementation
• Evaluation
• Conclusion and Future Work

5

Cloud Storage is Not Ready for HEC

Scientific applications are data-intensive

• Generate large amounts of data

6

Cloud Storage is Not Ready for HEC

Scientific applications are data-intensive

• Generate large amounts of data

6

Networking bandwidth is limited

• Inadequate levels of ingress and egress bandwidths available to/from

remote cloud stores

Cloud Storage is Not Ready for HEC

Scientific applications are data-intensive

• Generate large amounts of data

6

Networking bandwidth is limited

• Inadequate levels of ingress and egress bandwidths available to/from

remote cloud stores

High costs imposed by cloud providers

• Expensive for large amounts of data when using the pay-as-you-go model

Cloud Storage is Not Ready for HEC

Scientific applications are data-intensive

• Generate large amounts of data

6

Networking bandwidth is limited

• Inadequate levels of ingress and egress bandwidths available to/from

remote cloud stores

High costs imposed by cloud providers

• Expensive for large amounts of data when using the pay-as-you-go model

An example: A GTS runs on 29K cores on the Jaguar machine at OLCF generates over 54 Terabytes of data in a 24 hour period. Amazon S3: ~$0.03/GB for storage and $0.09/GB for data transfer out. Cost: $6635.52/day, increases with increasing number of collaborators

Problem: Too Much Data Movement

Issue: naïve approach transfers lots of data, even if only some of it is needed

7

Cloud

Data producer Time step 0 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn Time step 1 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn … Time step 0 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn Time step 1 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn … Data consumer

Example Output of GTS fusion modeling simulation:

Checkpoint data, diagnosis data, visualization data and etc. Each data subset includes many elements

Problem: Too Much Data Movement

Issue: naïve approach transfers lots of data, even if only some of it is needed

7

Cloud

Data producer Time step 0 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn Time step 1 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn … Time step 0 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn Time step 1 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn … Data consumer

Problem: Too Much Data Movement

Issue: naïve approach transfers lots of data, even if only some of it is needed

7

Cloud

Data producer Time step 0 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn Time step 1 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn …

• Scientific data formats: BP/HDF5

Structured and meta-data rich

• Standard I/O interface: ADIOS

Almost transparent to users

Time step 0 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn Time step 1 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn … Data consumer

Problem: Too Much Data Movement

Issue: naïve approach transfers lots of data, even if only some of it is needed

7

Cloud

Data producer

• Scientific data formats: BP/HDF5

Structured and meta-data rich

• Standard I/O interface: ADIOS

Almost transparent to users

Time step 0 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn Time step 1 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn … Data consumer

Problem: Too Much Data Movement

Issue: naïve approach transfers lots of data, even if only some of it is needed

7

Cloud

Data producer

• Scientific data formats: BP/HDF5

Structured and meta-data rich

• Standard I/O interface: ADIOS

Almost transparent to users

Time step 0 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn Time step 1 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn … Data consumer

Problem: Too Much Data Movement

Issue: naïve approach transfers lots of data, even if only some of it is needed

7

Cloud

Data producer

• Scientific data formats: BP/HDF5

Structured and meta-data rich

• Standard I/O interface: ADIOS

Almost transparent to users

Time step 0 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn Time step 1 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn …

Goal: Reduce data transfer from producers to consumers

Data consumer

Solutions for Minimizing Data Transfers for Data Sharing

Compression

• Helps, but compression ratio can be low for floating-point scientific data

8

Solutions for Minimizing Data Transfers for Data Sharing

Compression

• Helps, but compression ratio can be low for floating-point scientific data

Data selection

• Files: users can ask for subsets of data files, by specifying file offsets
• Requires knowledge about data layout in files

8

Solutions for Minimizing Data Transfers for Data Sharing

Compression

• Helps, but compression ratio can be low for floating-point scientific data

Data selection

• Files: users can ask for subsets of data files, by specifying file offsets
• Requires knowledge about data layout in files

Content-based data indexing

• Useful, but may require large amounts of meta-data

8

Solutions for Minimizing Data Transfers for Data Sharing

Compression

• Helps, but compression ratio can be low for floating-point scientific data

Data selection

• Files: users can ask for subsets of data files, by specifying file offsets
• Requires knowledge about data layout in files

Content-based data indexing

• Useful, but may require large amounts of meta-data

8

Scibox Approaches:

• Filter unnecessary data at producer-side via metadata (uploads)

Solutions for Minimizing Data Transfers for Data Sharing

Compression

• Helps, but compression ratio can be low for floating-point scientific data

Data selection

• Files: users can ask for subsets of data files, by specifying file offsets
• Requires knowledge about data layout in files

Content-based data indexing

• Useful, but may require large amounts of meta-data

8

Scibox Approaches:

• Filter unnecessary data at producer-side via metadata (uploads)
• Merge overlapping subsets when multiple users share the same data

(uploads)

Solutions for Minimizing Data Transfers for Data Sharing

Compression

• Helps, but compression ratio can be low for floating-point scientific data

Data selection

• Files: users can ask for subsets of data files, by specifying file offsets
• Requires knowledge about data layout in files

Content-based data indexing

• Useful, but may require large amounts of meta-data

8

Scibox Approaches:

• Filter unnecessary data at producer-side via metadata (uploads)
• Merge overlapping subsets when multiple users share the same data

(uploads)

• Minimize data sharing cost in cloud storage via new software protocol

(downloads)

Challenge: How to Filter Data

Recall: scientific data is structured and meta-data rich

9

Time step 0 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn Time step 1 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn … Time step 0 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn … Time step 1 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn …

Time steps Variable { dimensions, attributes }

Challenge: How to Filter Data

Recall: scientific data is structured and meta-data rich

9

Time step 0 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn Time step 1 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn … Time step 0 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn … Time step 1 A0 A1 A2 … An B0 B1 B2 … Bn C0 C1 C2 … Cn …

Time steps Variable { dimensions, attributes }

Analytics users know what can/needs to be filtered

Outline

• Background and Motivation
• Problems and Challenges
• Design and Implementation
• Evaluation
• Conclusion and Future Work

10

Scibox Design

D(ata)R(eduction)-Function for data filtering

• Reduce cloud upload/download volumes
• Permit end users to identify the exact data needed for each specific

analytics activity, using filters

11

Scibox Design

D(ata)R(eduction)-Function for data filtering

• Reduce cloud upload/download volumes
• Permit end users to identify the exact data needed for each specific

analytics activity, using filters

Merge shared data objects before uploading

• Promote data sharing in the cloud to reduce data redundancy on the

storage server and data volumes transferred across the network

11

Scibox Design

D(ata)R(eduction)-Function for data filtering

• Reduce cloud upload/download volumes
• Permit end users to identify the exact data needed for each specific

analytics activity, using filters

Merge shared data objects before uploading

• Promote data sharing in the cloud to reduce data redundancy on the

storage server and data volumes transferred across the network

Utilize ADIOS metadata-rich I/O methods New ADIOS I/O transport

• Write output to cloud that can be directly read by subsequent,

potentially remote data analytics or visualization codes

• Transparent on both the producer and consumer sides

11

Scibox Design

D(ata)R(eduction)-Function for data filtering

• Reduce cloud upload/download volumes
• Permit end users to identify the exact data needed for each specific

analytics activity, using filters

Merge shared data objects before uploading

• Promote data sharing in the cloud to reduce data redundancy on the

storage server and data volumes transferred across the network

Utilize ADIOS metadata-rich I/O methods New ADIOS I/O transport

• Write output to cloud that can be directly read by subsequent,

potentially remote data analytics or visualization codes

• Transparent on both the producer and consumer sides

Partial object access for private cloud storage

• Patch the OpenStack Swift object store

11

Cloud-IO Transport

12

HPC Applications ADIOS API MPI-IO POSIX-IO LIVE/DataTap DART HDF-5 pnetCDF Viz Engines CLOUD-IO Others (Plug-in)

Proxy Node Auth Node Store Node Store Node Store Node Store Node

HPC Platform Swift Object Storage DR-Function Run-time Execution

Authentication Account Server Container Server Object Server Account Server Container Server Object Server Account Server Container Server Object Server Account Server Container Server Object Server

Scibox Architecture

Amazon S3/ OpenStack Swift Scibox Client Cloud-IO (Write)

Data Producer

Scibox Client Cloud-IO (Read)

Data Consumer Cloud Storage Control Flow Data Flow User Group

38

Scibox Architecture

Amazon S3/ OpenStack Swift Scibox Client Cloud-IO (Write)

Data Producer

Scibox Client Cloud-IO (Read)

Data Consumer Cloud Storage Control Flow Data Flow User Group

39

Scibox Architecture

Amazon S3/ OpenStack Swift Scibox Client Cloud-IO (Write)

Data Producer

Scibox Client Cloud-IO (Read)

Data Consumer Cloud Storage Control Flow Data Flow User Group

40

Scibox Architecture

Amazon S3/ OpenStack Swift Scibox Client Cloud-IO (Write)

Data Producer

Scibox Client Cloud-IO (Read)

Data Consumer Cloud Storage Control Flow Data Flow User Group

41

Scibox Architecture

Amazon S3/ OpenStack Swift Scibox Client Cloud-IO (Write)

Data Producer

Scibox Client Cloud-IO (Read)

Data Consumer Cloud Storage Control Flow Data Flow User Group

42

Scibox Architecture

Amazon S3/ OpenStack Swift Scibox Client Cloud-IO (Write)

Data Producer

Scibox Client Cloud-IO (Read)

Data Consumer Cloud Storage Control Flow Data Flow User Group

43

Scibox Architecture

Amazon S3/ OpenStack Swift Scibox Client Cloud-IO (Write)

Data Producer

Scibox Client Cloud-IO (Read)

Data Consumer Cloud Storage Control Flow Data Flow User Group

44

Scibox Architecture

Amazon S3/ OpenStack Swift Scibox Client Cloud-IO (Write)

Data Producer

Scibox Client Cloud-IO (Read)

Data Consumer Cloud Storage Control Flow Data Flow User Group

45

Sample XML File

14

<?xml version="1.0"?> <adios-config host-language="Fortran"> <adios-group name="restart" coordination-communicator="comm"> <var name="mype" type="integer"/> <var name="numberpe" type="integer"/> <var name="istep" type="integer"/> <var name="MIMAX_VAR" type="integer"/> <var name="NX" type="integer"/> <var name="NY" type="integer"/> <var name="zion0_1Darray" gwrite="zion0" type="double" dimensions="MIMAX_VAR”/> <var name="phi_2Darray" gwrite="phi" type="double" dimensions="NX, NY"/> <!– for reader.xml --> </adios-group> <method group="restart" method=”CLOUD-IO”>;</method> <buffer size-MB="20" allocate-time="now"/> </adios-config>

Sample XML File

14

<?xml version="1.0"?> <adios-config host-language="Fortran"> <adios-group name="restart" coordination-communicator="comm"> <var name="mype" type="integer"/> <var name="numberpe" type="integer"/> <var name="istep" type="integer"/> <var name="MIMAX_VAR" type="integer"/> <var name="NX" type="integer"/> <var name="NY" type="integer"/> <var name="zion0_1Darray" gwrite="zion0" type="double" dimensions="MIMAX_VAR”/> <var name="phi_2Darray" gwrite="phi" type="double" dimensions="NX, NY"/> <!– for reader.xml --> <rd type=8 name="phi_2Darray” cod=“int i; double sum = 0.0; for(i = 0; i<input.count; i= i+1) { sum = sum + input.vals[i]; } return sum;” /> </adios-group> <method group="restart" method=”CLOUD-IO”>;</method> <buffer size-MB="20" allocate-time="now"/> </adios-config>

Data Reduction (DR) Functions

15

Definition

• Function defined by end-users that can transform/reduce data to prepare

it for cloud storage

Data Reduction (DR) Functions

15

Definition

• Function defined by end-users that can transform/reduce data to prepare

it for cloud storage

Creation

• Programmed by end users
• Generated from higher level descriptions
• Derived from users’ I/O access patterns

Data Reduction (DR) Functions

15

Definition

• Function defined by end-users that can transform/reduce data to prepare

it for cloud storage

Creation

• Programmed by end users
• Generated from higher level descriptions
• Derived from users’ I/O access patterns

Current implementation

• Customized CoD (C on Demand)

require producer-side computational resources

• DR-function library

same DR-function specified by multiple clients, will be executed only

• nce, and its output data will be reused for multiple consumers

DR-functions Provided by SciBox

51

Type Description Example DR1 Max(variable) Max(var_double_2Darray) DR2 Min(variable) Min(var_double_2Darray) DR3 Mean(variable) Mean(var_double_2Darray) DR4 Range(variable, dimension, start_pos, end_pos) Range(var_int_1Darray, 1, 100, 1000) DR5 Select(variable, threshold1, threshold2) Select var.value where var.value in (threshold1, threshold2) DR6 Select(variable, DR_Function1, DR_Function2) Select var.value where var.value ≥ Mean(var) DR7 Select(variable1, variable2, threshold1, threshold2) Select var2.value where var1.value in (threshold1, threshold2) DR8 Self defined function Double proc(cod_exec_context ec, input_type * input, int k, int m) {int I; intj; double sum = 0.0; double average=0.0; for(i=0;i<m;i++)sum+=input.tmpbuf[i+k*m];aver age=sum/m; resturn average;}

DR-functions Provided by SciBox

52

Type Description Example DR1 Max(variable) Max(var_double_2Darray) DR2 Min(variable) Min(var_double_2Darray) DR3 Mean(variable) Mean(var_double_2Darray) DR4 Range(variable, dimension, start_pos, end_pos) Range(var_int_1Darray, 1, 100, 1000) DR5 Select(variable, threshold1, threshold2) Select var.value where var.value in (threshold1, threshold2) DR6 Select(variable, DR_Function1, DR_Function2) Select var.value where var.value ≥ Mean(var) DR7 Select(variable1, variable2, threshold1, threshold2) Select var2.value where var1.value in (threshold1, threshold2) DR8 Self defined function Double proc(cod_exec_context ec, input_type * input, int k, int m) {int I; intj; double sum = 0.0; double average=0.0; for(i=0;i<m;i++)sum+=input.tmpbuf[i+k*m];aver age=sum/m; resturn average;}

C on Demand (CoD): Consumer: a string Producer:

• 1. registration
• 2. compile and execute on demand.

Data Object Management

17

Group Metadata User 0 Metadata User 1 Metadata User N Metadata

Scibox Super File

Writer.xml Reader0.xml Reader1.xml …… …… ReaderN.xml Group Metadata Container User0 Metadata Object User1 Metadata Object UserN Metadata Object …… Object 0 Object 1 Object N …… User Metadata Container Data Container

Data Object Management

17

Group Metadata User 0 Metadata User 1 Metadata User N Metadata

Scibox Super File

Writer.xml Reader0.xml Reader1.xml …… …… ReaderN.xml Group Metadata Container User0 Metadata Object User1 Metadata Object UserN Metadata Object …… Object 0 Object 1 Object N …… User Metadata Container Data Container

Enable object sharing

Determination of Object Size

18

Merge overlapping data sets

• reduce upload data size

Partial object access

• Current Amazon S3 and OpenStack Swift stores do not support this
• Users have to download the whole object, even if only a small portion of its

data is needed

Determination of Object Size

18

Merge overlapping data sets

• reduce upload data size

Partial object access

• Current Amazon S3 and OpenStack Swift stores do not support this
• Users have to download the whole object, even if only a small portion of its

data is needed

Two approaches used in Scibox

• Private cloud

modify the software to enable partial object access Object size is determined by predicting upload throughput

Determination of Object Size

18

Merge overlapping data sets

• reduce upload data size

Partial object access

• Current Amazon S3 and OpenStack Swift stores do not support this
• Users have to download the whole object, even if only a small portion of its

data is needed

Two approaches used in Scibox

• Private cloud

modify the software to enable partial object access Object size is determined by predicting upload throughput

• Public cloud

limit object size considering storage pricing Object size is determined by comparing the cost w/ sharing and w/o sharing

Cost Model

19

Definition

α: $/GB of standard cloud storage β: $/GB of data transfer into cloud γ: $/GB of data transfer out from cloud

Assumption

n clients request Data1, Data2, … Datan respectively

Cost Model

19

Definition

α: $/GB of standard cloud storage β: $/GB of data transfer into cloud γ: $/GB of data transfer out from cloud

Assumption

n clients request Data1, Data2, … Datan respectively

Without sharing:

Cost Model

19

Definition

α: $/GB of standard cloud storage β: $/GB of data transfer into cloud γ: $/GB of data transfer out from cloud

Assumption

n clients request Data1, Data2, … Datan respectively

Without sharing: With sharing ( n objects can be merged into one):

Cost Model

19

Definition

α: $/GB of standard cloud storage β: $/GB of data transfer into cloud γ: $/GB of data transfer out from cloud

Assumption

n clients request Data1, Data2, … Datan respectively

Without sharing: With sharing ( n objects can be merged into one):

Outline

• Background and Motivation
• Problems and Challenges
• Design and Implementation
• Evaluation
• Conclusion and Future Work

20

Experimental Setup

21 63

Aerospace App GTS

Aero Cluster Vogue

OpenStack Swift

Experimental Setup

21 64

Aerospace App GTS Images Processing

Aero Cluster Vogue Jedi Cluster

OpenStack Swift

Experimental Setup

21 65

Aerospace App GTS Images Processing Visualization

Aero Cluster Vogue Jedi Cluster GeorgiaTech (Atlanta) WSU (Detroit) OSU(Columbus)

OpenStack Swift

Workloads

Synthetic Workloads

• 10 variables shared by multiple consumers
• 1,000 requests generated by each consumer
• 8 types of DR functions, uniformly distributed
• 1 data producer serving 1,000 requests x #client servers
• 3 self-defined DR-functions:

FFT, histogram diagnosis, and average of row values of a matrix

22

Workloads

Synthetic Workloads

• 10 variables shared by multiple consumers
• 1,000 requests generated by each consumer
• 8 types of DR functions, uniformly distributed
• 1 data producer serving 1,000 requests x #client servers
• 3 self-defined DR-functions:

FFT, histogram diagnosis, and average of row values of a matrix

22

Real Workloads

• GTS workload

128 parallel processes, consumers are from 3 different states in USA

• Combustion workload

10, 000 512X512 12-bit double framed images (~1.5 MB per image)

Latency Breakdown of Swift Object Store

23

34.63% 63.82% 73.58% 84.93% 88.69% 91.30% 93.28% 0.48 s 0.88 s 1.28 s 2.17 s 3.65 s 6.99 s 14.64 s

0% 20% 40% 60% 80% 100% 1 4 8 16 32 64 128 Percentage Object Size (MB)

Headers Transfer Data Transfer & Server Processing Disk Write & CheckSum Authorization & Container Checking Software Overhead & Others 0.43 s 0.61 s 0.92 s 1.49 s 2.62 s 5.05 s 9.36 s 17.04% 41.77% 54.38% 67.33% 75.62% 78.78% 83.20%

0% 20% 40% 60% 80% 100% 1 4 8 16 32 64 128 Percentage Object Size (MB)

Data Transfer & Server Processing Disk Read Authorization & Container Checking Software Overhead & Others

Put Get

Latency Breakdown of Swift Object Store

23

34.63% 63.82% 73.58% 84.93% 88.69% 91.30% 93.28% 0.48 s 0.88 s 1.28 s 2.17 s 3.65 s 6.99 s 14.64 s

0% 20% 40% 60% 80% 100% 1 4 8 16 32 64 128 Percentage Object Size (MB)

Headers Transfer Data Transfer & Server Processing Disk Write & CheckSum Authorization & Container Checking Software Overhead & Others 0.43 s 0.61 s 0.92 s 1.49 s 2.62 s 5.05 s 9.36 s 17.04% 41.77% 54.38% 67.33% 75.62% 78.78% 83.20%

0% 20% 40% 60% 80% 100% 1 4 8 16 32 64 128 Percentage Object Size (MB)

Data Transfer & Server Processing Disk Read Authorization & Container Checking Software Overhead & Others

Put Get Data transfer dominates the request latency.

Execution Time of DR-functions

24

0.5 1 1.5 2 2.5 3 3.5 4 64 MB 128 MB 256 MB 512 MB 1 GB 1.5 GB

Execution Time (seconds) Variable Size

DR1 DR2 DR3 5 10 15 20 25 30 35 40 64 MB 128 MB 256 MB 512 MB 1 GB 1.5 GB

Execution Time (seconds) Variable Size

DR5 DR6 DR7

1 2 3 4 5 6 7 64 MB 128 MB 256 MB 512 MB 1 GB 1.5 GB

Execution Time (seconds) Variable Size

DR4-1K DR4-1M DR4-16M DR4-64M DR4-128M

Recall: DR7:Select var2.value where var1.value in (r1, r2). var1 is small. Recall: DR6:var.value where var.value>Mean(var). Double scan of input data.

SciBox System Scalability

25

842.81 968.86 1329.83 2643.06 4962.91 92.39 109.65 151.44 271.46 412.12

500 1000 1500 2000 2500 3000 3500 4000 4500 5000 1 2 4 8 16 Execution Time (seconds) Number of Producers Stock System Scibox

4.34 5.1 9.54 19.66 42.25 81.91 1.98 2 3.09 6.48 12.68 21.94

10 20 30 40 50 60 70 80 90 2 4 8 16 32 64 Latency (seconds) Number of Consumers in the Same Sharing Group Stock System Scibox

• With Scibox, data is merged before upload
• With Scibox, partial object access is supported

GTS Workload

26

200 400 600 800 1000 1200 1400 1600 Gatech-Scibox Gatech-FTP OSU-Scibox OSU-FTP WSU-Scibox WSU-FTP Latency (seconds) Data Sharing Schemes Post-Computation Download Filter-Computation GTS-Computation

WSU OSU GT GT 900 KB/s 4.4 MB/s 44 MB/s

Combustion Workload

27

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 1 2 4 8 16 Speedup of Performance Number of Clients in the Same Sharing Group

1 Process 2 Processes 4 Processes 8 Processes 16 Processes 32 Processes

• DR-function: (ImageName, DR8, FFT)
• 10, 000 images (~1.5 MB/image) shared via Scibox

Outline

• Background and Motivation
• Problems and Challenges
• Design and Implementation
• Evaluation
• Conclusion and Future Work

28

Conclusions and Future Work

Scibox: Cloud-based support for scientific data sharing

• Can operate across both public and private cloud stores

29

Conclusions and Future Work

Scibox: Cloud-based support for scientific data sharing

• Can operate across both public and private cloud stores

Data Reduction functions

• Exploit the structured nature of scientific data
• Reduce the amount of transferred data

29

Conclusions and Future Work

Scibox: Cloud-based support for scientific data sharing

• Can operate across both public and private cloud stores

Data Reduction functions

• Exploit the structured nature of scientific data
• Reduce the amount of transferred data

Transparent to Applications/Data Producers

• Implemented in ADIOS
• Can be also applied to other I/O libraries

29

Conclusions and Future Work

Scibox: Cloud-based support for scientific data sharing

• Can operate across both public and private cloud stores

Data Reduction functions

• Exploit the structured nature of scientific data
• Reduce the amount of transferred data

Transparent to Applications/Data Producers

• Implemented in ADIOS
• Can be also applied to other I/O libraries

Future Work

• Deploy Scibox on national labs’ facilities to better understand potential use

cases

• Additional optimizations of cloud storage for scientific data management

29

Thanks!

30