Security-Aware Scheduling for Real-Time Parallel Applications on Clusters

04/10/23 Department of Computer Science and Software EngineeringAuburn University

1

Security-Aware Scheduling for Real-Time Parallel Applications on Clusters

Xiao Qin


2

Clusters


3

The PrairieFire Cluster at the University of Nebraska-Lincoln


4

Parallel Applications on Clusters


5

Security-Sensitive Real-Time Applications

Online Transaction Stock Trading

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Common Threats and Security Services

Snooping

Alteration

Spoofing

Confidentiality

Authentication

Integrity

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Scheduling Plays a Key Role

Conventional scheduling algorithms are inadequate for security-sensitive real-time applications on clusters

A process of assigning tasks to a set of resources

Head

Nodes

Tasks Users


8

Motivation

Improve Utilizatio

n

KeepLoad-Balancing

SupportScalability

Promote Throughput

EnableSecurity

Awareness

ReduceResponse

Time


9

Security-Aware System Architecture

OSHardware

Platform interfacePlatform interface

OSHardware

Middleware Services (including security services)

Low-Level Security Service APIs

User interface

Framework

Mapping to Middleware Services

Framework Private Service

Application Tool

High-Level Security Service APIs

Application Application

Quality of Security Control Manager (QSCM)


10

Quality of Security Control Manager - QSCM Module

Application Task

Application Task

Application Task

Low Level Security Service APIs

Global Security

Optimization

Local Security

Optimization

Security Optimization

Resource MonitoringSecurity Service 1

Security Service n

Local Schedulability

Analyzer

Quality of Security Control Manager


11

Task Submission StructureDEFINE Task : flight_control{

Input = (altitude: 1230, heading: 35, …); Output = (takeoff_distance, climb_rate);

Type = “Real Time”;Deadline = 80;Completion_Time = 0;Owner = “Gary Xie”;Cmd = “flight_con”;Processor_num= 5;Data_secured=250;Constraint Arch == “INTEL”; OS == “UNIX”; Disk >= 480;

Memory >=128; Deadline = 80;

0.3 <= Authentication <=0.6; 0.4 <= Integrity <= 0.8; 0.5 <= Confidentiality <= 0.9;}


12

Security Overhead Model

Security is achieved at the cost of performance degradation

P

S

SecurityOverheads

SP


13

Cryptographic Algorithms for Confidentiality Service

Cryptographic Algorithms

Security Level

Performance (KB/ms)

RC4 0.22 96.43

Blowfish 0.56 37.5

Knufu/Khafre 0.63 33.75

RC5 0.72 29.35

Rijndael 1.00 21.09


14

Hash Functions for Integrity Service

Hash Functions Security Level Performance (KB/ms)

MD4 0.18 23.90

MD5 0.26 17.09

RIPEMD 0.36 12.00

RIPEMD-128 0.45 9.73

SHA-1 0.63 6.88

RIPEMD-160 0.77 5.69

Tiger 1.00 4.36


15

Authentication Methods

Authentication Methods

Security Level Computation Time (ms)

HMAC-MD5 0.3 90

HMAC-SHA-1 0.6 148

CBC-MAC-AES 0.9 163


16

System Model

Rejected Queue

Dispatch Queue

Local Queue

N1

N2

NmUser

p

User 2

User 1

Schedule Queue

Admission Controller

Security Level

Optimizer

TAPADS


17

Parallel Application

A single application (job) that has multiple processes that run concurrently

t1

t11

e2

t4

t9

t8

t3

t2

t5 t6

t10

t7

e1

e3 e4 e5

e7e6 e10

e8 e9


18

Task Model

Deadline Constraints

Security Constraints

Precedence Constraints


19

Directed Acyclic Graphs (DAG)

a parallel application is defined as a vector (T, E, d) T: {t1, t2,...,tn}

E : a set of weighted and directed edges used to represent communication among tasks, e.g., (ti, tj) E is a message transmitted from task ti to tj

d : Deadline


20

A Task

A task ti = (ei, li, Si)

ei :execution time

li : amount of data to be protected

Si: a vector of security requirements


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A DAG

e2

t1

t4

t9

t8

t3

t2

t11

t5 t6

t10

t7

e1

e3 e4 e5

e7e6 e10

e8 e9

10Sec.,500KB,

{ [0.3,0.6], [0.4,0.8],

[0.5,0.9] }

10Sec.,500KB,

{ [0.3,0.6], [0.4,0.8],

[0.5,0.9] }

10KB, { [0.4,0.8],

[0.5,0.9] }

10KB, { [0.4,0.8],

[0.5,0.9] }


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PE3

Link

PE1

Link

PE2

t6 t8 t9

e5 e7 e9

t1 t10t7t4t3t2

e4 e10

t5 t11

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60deadline

Befpre Security Optimization

Slack Time


23

After Security Optimization

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60

t10

e9

t4t3t2t1

e4 e10

t11t5

e5

t6

e7

t8 t9

t7

PE3

Link

PE1

Link

PE2

deadline


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Security Requirements for A Task Ti

1iS q

iSjiSSi = ( ,…, ,…, )

Security level range of the j th security service for task Ti

1iS [0.3,0.6] 2

iS [0.4,0.8] 3iS [0.5,0.9]


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Security Benefits Gained by Task Ti

qiiii ssss ,...,, 21 and10 j

iw

q

j

jiw

1

1

q

j

ji

jii swsSL

1

)(

Weight of the j th security service for task Ti

Security level of the j th security service for task Ti


26

Weights of Security Services

>

>





27

Security Benefits Gained by A Task Set

n

iiSL

1

SL s )()(T

qiiii ssss ,...,, 21The task set


28

Optimize Security Benefit of An Application

maximizesubject to:

i k

n q

ki

ki swTSL

1 1

ks kk ),max()min( iii SS

SL s )( iThe task set


29

Security Requirements of Message (ti, tj)

)ˆ,...,ˆ,ˆ(ˆ 21 pijijijij SSSS

The required security level range

of the p th security service

i j(ti, tj)


30

Security Benefits Gained by One Message (ti, tj)

p

k

kij

kijij swsSL

1

ˆˆ)ˆ(

1ˆ0 kijw

p

j

kijw

1

1ˆand )ˆ,...,ˆ,ˆ(ˆ 21 pijijijij ssss

Security level of the

k th security service


31

Security Benefits Gained by A Message Set

Ett

ij

ji

sSLESL),(

)ˆ()(

.

)ˆ,...,ˆ,ˆ(ˆ 21 pijijijij ssss


32

Optimize Security Benefit of Message Set

,ˆˆ)(),( 1

Ett

p

k

kij

kij

ji

swESL

),ˆmax(ˆ)ˆmin( kij

kij

kij SsS

maximize

subject to

The message set )ˆ( ijsSL


33

Security Benefit of A Parallel Application

)()( ESLTSLSV

The message setThe task set

Security Value


34

The TAPADS Task Allocation Algorithm

Compute the critical path

path critical

min )(it

ii cef

Slack time= d – f

Allocate all ti subject to minimal security requirements

Identify the best candidate in V and E that has the highest benefit-cost ratio

Increase security levels of more important services at the minimal cost

Update the schedule in accordance with the increased security level

yes

Slack time > 0 ?no

Update slack time

End


35

Time Complexity of TAPADS

The time complexity of TAPADS is O(k(q|V|+p|E|))where k : the number of times Step 7 is repeatedq : the number of security services for computationp : the number of security services for communication


36

Performance Evaluation

LISTMIN: Selects the lowest security level of each security service required by each task and message of a parallel job

LISTMAX: Chooses the highest security level for each security requirement posed by each task and message within a parallel job

LISTRND: Randomly picks a value within the security level range of each service required by a task and a message


37

Experimental Parameters Parameter Value (Fixed) - (Varied)

CPU Speed 1000 million instructions/second or MIPS

Network bandwidth 1Gbps

Task execution time (min, top, max)=(1, 5, 10), (10,20,40), (40,80,160), (160,320,640) second

Number of nodes (32, 64,128, 256), (8, 12, 16, 20)

Deadlines (100, 200, 300, 400, 500, 600) second

Deadline ranges ([100, 200], [200, 300], [300, 400], [400, 500]) second

Out degrees (25, 50, 75, 100)

Size of data to be secured

(min, top, max)=(0.02, 0.1, 0.5), (0.2, 1, 5), (1, 5, 10), (10, 20, 30) MB

Weight of security services

0.2 (authentication), 0.5 (encryption), 0.3 (integrity)


38

Performance Metrics

Security Value Schedulability: a fraction of total submitted jobs that are

schedulable Quality of security (QSA): quality of security for applications

Guarantee factor: it is zero if a job’s deadline cannot be met. Otherwise, it is one.

Job completion time: earliest time that a job can finish its execution

n

i

q

k

ki

ki swSV

1 1

,ˆˆ),( 1

Ett

p

k

kij

kij

ji

sw

)()()( XPXPXP LCSC


39

Experiment One: Overall Performance

One job with 433 tasks

32 nodes in a cluster

Deadline varies from 0 to 600 seconds


40

Overall Performance Comparisons(1)


41


Improvement97.7%

Improvement25%


42


Improvement54.5%

Improvement25.7%


43

Experiment Two: Adaptability

1000 diverse task graphs (54 tasks ~ 543 tasks)

4 deadline ranges [100, 200], [200, 300], [300, 400] and [400, 500]

32 nodes clusters


44

Adaptability(1)

TAPADS ties with LISTMIN

LISTMAX isthe worst


45

Adaptability(2)

TAPADS is always the best

TAPADS outperforms LISTMAX significantly

TAPADS outperforms LISTMAX significantly


46

Adaptability(3)

TAPADS noticeably outperforms all others


47

Experiment Three: Scalability

32 ~ 256 nodes in a cluster

A task graph with 520 tasks (nodes)

Deadline is set to 400 Seconds


48

Scalability


49

Experiment Four: Degree of Task Parallelism

A parallel application with 1074 tasks

Deadline is set to 400 Seconds

Number of nodes is 128

Maximal number of out degree varies from 25 to 100


50

Sensitivity to Degree of Task Parallelism


51

Experiment Five: Security Sensitive Data Size

Size of security sensitive data is in a triangle distribution

(min, top, max)=(0.02, 0.1, 0.5), (0.2, 1, 5), (1, 5, 10), (10, 20, 30) MB


52

Impact of Size of Security Sensitive Data

dx

dC

C

D

dt

dxv

B

dx

dC

C

D

dt

dxv

B

dx

dC

C

D

dt

dxv

B


53

Evaluation in Digital Signal Processing (1)

(a) Guarantee factor (b) Security value (c) QSA

Performance impact of deadline for DSP


54

Evaluation in Digital Signal Processing (2)

(a) Security value (b) QSA (c) Job completion time

Performance impact of number of nodes for DSP


55

Conclusions TAPADS can generate optimal allocations that

maximize quality of security for parallel applications running on clusters.

A security overhead model is proposed.

Experimental results show that TAPADS significantly improves the performance in terms of quality of security and schedulability over three existing allocation schemes.


56

Ph.D. Dissertation ProjectsMais Nijim [Summer 2007]

Adaptive quality of security control in storage systems.

Ziliang Zong [Ph.D. Candidate, Spring 2008 Expected] Conserving energy in clusters through resource allocation

Mohammed Alghamdi [Ph.D. Student, Spring 2008 Expected] Energy-efficient packet transmissions in real-time wireless

networks

Kiranmai Bellam [Ph.D. Student, Spring 2009 Expected] Power, fault tolerance, and security issues in real-time systems


57

Questions?


58

Real-Time Stock Quote System


59

Some Typical Security Levels

Routing + message security

Routing + SSL

Routing + SSL + message security

Routing + SSL + client authentication

Routing + SSL + message security + client authentication


60

Related Work

[Hou&Shin] A task allocation scheme to schedule periodic tasks with precedence constraints in distributed real-time systems.

[He et al.] Dynamic scheduling of parallel real-time jobs executing on heterogeneous clusters.

[Yurcik et al.] Tools for managing cluster security via process monitoring.

[Azzedin&Maheswaran] The notion of “trust” into resource

management of a large-scale wide-area system.


61

Future Work

Extend our security overhead models to multi-dimensional computing resources

Accommodate more security services into our security overhead model

Apply TAPADS scheme to heterogeneous clusters


62

Selected Journal Publications X. Qin and T. Xie, “Allocation of Tasks with Availability Constraints in Heterogeneous Systems,”

IEEE Transactions on Computers. Accepted April 2007.

M. Nijim, X. Qin, and T. Xie, “Modeling and Improving Security of a Local Disk System for Write-Intensive Workloads,” ACM Transactions on Storage, vol. 2, no. 4, pp. 400-423, Nov. 2006.

T. Xie and X. Qin, “Improving Security for Periodic Tasks in Embedded Systems through Scheduling,” ACM Transactions on Embedded Computing Systems, vol. 6, no. 1, 2007.

T. Xie and X. Qin, “Scheduling Security-Critical Real-Time Applications on Clusters,” IEEE Transactions on Computers, vol. 55, no. 7, pp. 864-879, July 2006.

X. Qin, “Performance Comparisons of Load Balancing Algorithms for I/O-Intensive Workloads on Clusters,” Journal of Network and Computer Applications, 2007. Accepted

X. Qin, “Design and Analysis of a Load Balancing Strategy in Data Grids,” Future Generation Computer Systems: The Int'l Journal of Grid Computing, vol. 23, no. 1, pp. 132-137, Jan. 2007.

Z.-L. Zong, M. Nijim, and X. Qin, “Energy-Efficient Scheduling for Parallel Applications on Mobile Clusters,” Cluster Computing: The Journal of Networks, Software Tools and Applications, 2007. [In press]

M. Nijim, X. Qin, and Z.-L. Zong, “StReD: A Quality of Security Framework for Storage Resources in Data Grids,” Future Generation Computer Systems: The Int'l Journal of Grid Computing, 2007. [In press]

X. Qin and H. Jiang, “A Dynamic and Reliability-driven Scheduling Algorithm for Parallel Real-time Jobs on Heterogeneous Clusters,” Journal of Parallel and Distributed Computing, vol. 65, no. 8, pp.885-900, Aug. 2005.


63

Selected Conferences Publications X. Qin, M. Alghamdi, M. Nijim, and Z.-L. Zong, “Scheduling of Periodic Packets in Energy-

Aware Wireless Networks,” Proc. the 26th IEEE Int'l Performance Computing and Communications Conf. (IPCCC'07), New Orleans, Louisiana, April 2007.

T. Xie and X. Qin, “A Security-Oriented Task Scheduler for Heterogeneous Distributed Systems,” Proc. 13th Annual IEEE Inter’l Conf. on High Performance Computing (HiPC), Bangalore, India, Dec. 18-21, 2006. (Acceptance Rate: 15.5%, 52/335)

M. Nijim, X. Qin, and T. Xie, “Adaptive Quality of Security Control in Networked Parallel Disk Systems,” Proc. 15th Int’l Conf. Computer Communications and Networks (ICCCN'06), Arlington, Virginia, Oct. 2006. (Acceptance Rate: 32%, 71/221)

Z.-L. Zong, A. Manzanares, B. Stinar, and X. Qin, “Energy-Efficient Duplication Strategies for Scheduling Precedence Constrained Parallel Tasks on Clusters,” Proc. IEEE 8th Int’l Conf. Cluster Computing (Cluster'06), Sept. 2006. (Acceptance Rate: 33%, 42/127)

T. Xie and X. Qin, “Stochastic Scheduling with Availability Constraints in Heterogeneous Systems,” Proc. IEEE 8th Int’l Conf. Cluster Computing (Cluster'06), 2006. (Acceptance Rate: 33%, 42/127)

T. Xie, X. Qin, and M. Nijim, “Solving Energy-Latency Dilemma: Task Allocation for Parallel Applications in Heterogeneous Embedded Systems,” Proc. 35th Int’l Conf. Parallel Processing (ICPP), Columbus, Ohio, Aug. 2006. (Acceptance Rate: 32%, 64/200)

T. Xie and X. Qin, “SAHA: A Scheduling Algorithm for security-Sensitive Jobs on Data Grids,” Proc. IEEE/ACM 6th Int'l Symp. Cluster Computing and the Grid (CCGrid), 2nd Int'l Workshop on Cluster Security, May 2006. (Acceptance Rate: 25%)

T. Xie and X. Qin, “SHARP: A New Real-Time Scheduling Algorithm to Improve Security of Parallel Applications on Heterogeneous Clusters,” Proc. the 25th IEEE Int’l Performance Computing and Communications Conf. (IPCCC'06), Phoenix, AZ, April 2006. (Acceptance Rate: 35%)


64

Selected Conferences Publications (cont.) M. Nijim, X. Qin, T. Xie, and M. Alghamdi, “Awards: An Adaptive Write Scheme for Secure

Local Disk Systems,” Proc. the 25th IEEE Int’l Performance Computing and Communications Conf. (IPCCC'06), April 2006. (Acceptance Rate: 35%)

T. Xie and X. Qin, “A New Allocation Scheme for Parallel Applications with Deadline and Security Constraints on Clusters,” Proc. the 7th IEEE Int’l Conf. Cluster Computing (Cluster 2005), 2005. (Acceptance Rate: 32%, 48/150)

T. Xie, X. Qin, and A. Sung, "SAREC: A Security-Aware Scheduling Strategy for Real-Time Applications on Clusters," Proc. the 34th Int’l Conf. Parallel Processing (ICPP 2005), pp.5-12, Norway, June 14-17, 2005. (Acceptance Rate: 28%, 69/241)

X. Qin and Hong Jiang, “Improving Effective Bandwidth of Networks on Clusters using Load Balancing for Communication-Intensive Applications,” Proceedings of the 24th IEEE International Performance, Computing, and Communications Conference (IPCCC 2005), pp.27-34, Phoenix, Arizona, April 7-9, 2005. (Acceptance Rate: 35%, 36/103)

X. Qin, “Improving Network Performance through Task Duplication for Parallel Applications on Clusters,” Proc. the 24th IEEE Int’l Performance, Computing, and Communications Conference (IPCCC 2005), 2005. (Acceptance Rate: 35%, 36/103)

X. Qin, H. Jiang, Y. Zhu, and D. Swanson, "Dynamic Load Balancing for I/O-Intensive Tasks on Heterogeneous Clusters," Proceedings of the 10th International Conference on High Performance Computing (HiPC 2003), pp.300-309, 2003 (Acceptance Rate: 29%)

X. Qin, H. Jiang, Y. Zhu, and D. Swanson, "Towards Load Balancing Support for I/O-Intensive Parallel Jobs in a Cluster of Workstations," Proc. of the 5th IEEE International Conference on Cluster Computing(Cluster 2003), 2003. (Acceptance Rate: 29%)


65

Adaptive Quality of Security Control in Storage Systems

Xiao Qin


66

Outline

Introduction to Storage Systems Local Disk Systems Parallel Disk Systems Security-Aware Cache Partitioning Conclusion Publications


67

Data-Intensive Applications

Video Surveillance Digital Libraries

Radio Astronomy Observatory


68

Data-Intensive Applications (Cont.)

long running simulations

remote-sensing database systems

biological sequence analysis


69

Motivation

Existing storage systems fail to meet the security requirements of modern data- intensive applications

There is no way to dynamically choose security services to meet disk requests flexible security requirements

Existing storage systems are not suitable to guarantee desired response times of disk requests


70

Common Threats and Security Services

Snooping

Alteration

Spoofing

Confidentiality

Authentication

Integrity


71

Cache Partitioning Scheme

Topics Security-Aware Local Disk Systems

Adaptive Quality of Security Control in Parallel Disk Systems


72

System model of a Data Grid


73

Quality of Security Framework for Disk Systems


74

Security-Aware Local Disk Systems


75

Contributions

A Security-Aware Adaptive Write Strategy (AWARDS) for Local Disk Systems

AWARDS can achieve high security for local disk systems while making the best effort to guarantee desired response times

AWARDS

Security

Performance


76

The Architecture of AWARDS

Security Service 1Security Service 1 Security Service mSecurity Service m

Adaptive Security Service ControllerAdaptive Security Service Controller

Disk Request SchedulerDisk Request Scheduler

Disk Request

Security Mechanism

Disk DriverUntrusted Local Disk


77

Modeling Disk Requests

Each disk request specifies quality of service requirement A security requirement can be defined as a lower bound security level The range is between 0.1 and 1.0 A performance requirement is specified as a desired response time

Disk Requests


78

Quality of security for each security service is measured by a security level

For example: An encryption service with high security level means the

high quality of security provided by the service A disk request specifies a lower bound security level as 0.4 Encryption services with security levels higher than or equal

to 0.4 can successfully meet the disk request’s security requirements

Modeling Disk Requests (Cont.)


79

r = (o, a, d, s, t) o: type of the request

a: disk address

d: data size (KB)

s: lower security level bound

t: desired response time



80

Rr

i

i

Security Level

. and ,1: iiiii tsRr

Disk Request

Desired response time

Real response time

Subject to

Maximize



81

Security Overhead Model

Eight encryption algorithms In accordance with the cryptographic algorithms’

performance Each cryptographic algorithm is assigned a

security level from 0 to 1 e.g., Assign security level 1 to the strongest yet

slowest encryption algorithm (IDEA)


82

The AWARDS Strategy

To aim at improving the quality of security for local disks (i.e., to increase the security levels)

To guarantee timing constraints. (i.e., response time desired response time)


83

Example

Requests

Data Size (di) Minimal Security Level (si)

Desired Response Time (ti)

Response Time (T) under AWARDS

Security Level (i)

under AWARDS

r190 KB 0.2 18 ms 17.7 ms 0.8

r2150 KB 0.1 41 ms 40.7 ms 0.7

r3

30 KB 0.3 55 ms 54.5 ms 0.9

r1 r2 r3

r1 r2 r3

Time

Time

Sl = 0.1 Sl = 0.3Sl = 0.2

SO= 0.93ms SO= 0.89ms SO= 0.8ms

Security level of r1 = 0.8Response time =17.7 ms




84

The AWARDS Algorithm


85

StartStart

Insert ri into Q

For each ri in Q

Initialize Security Level

Sl < 1.0

For each ri in the Q

Sl = Sl + 0.1

For each rk

rk can’t finsihed Sl = Sl - 0.1

Yes

Yes

NoENDEND

No

ENDEND


86

Property of AWARDS

If the security level ri is increased by 0.1, the following conditions must hold.

1. The current security level of ri is less than 1.0, i.e., i < 0.1

2.

.),()es(:, kkkkikk trTrttQr

Start time processing time


87

Estimated Start Time (es)

lll ttQr

llk rTr,

),,()es(

),()()(),( iisecuritydisk

iirotiseekii dT

B

daTaTrT


88

Experimental Result

Disk Parameters

IBM Ultrastar 36Z15

Size 18.4 GB

RPM 15000

Seek Time, Tseek 7.18 ms

Rotational Time, Trot4.02 ms

Disk Bandwidth, Bdisk 30 MB/Sec.


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Experimental Result

Workload Configurations

Parameter Value (Fixed) - (Varied)

Disk Bandwidth 30MB/Sec.

Request Arrival Rate (0.1, 0.2, 0.3, 0.4, 0.5) No./Sec.

Desired Response Time 10 Sec.

Security Level (0.5) - (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9)

Write Ratio (100%) - (0%, 10%, 20%, 30%, … 100%)

Data Size (500 KB) – (300, 400, 500, 600, 700) KB


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Performance Metrics

Satisfied ratio: a fraction of total arrived disk requests that are found to be finished before their desired response times

Average security level: measured by the average value of security levels of all disk requests issued

Average security overhead : measured in sec. Overall performance: product of satisfied ratio and

the average security level


91

Impact of Arrival Rate

Improvement138.2%

Improvement125.6%


92

Impact of Data Size


93

Impact of Disk Bandwidth


94

Sparse Cholesky



95

Lu Decomposition



96Sparse Cholesky

Bandwidth


97

Lu Decomposition

Bandwidth


98

Adaptive Quality of Security Control

in Parallel Disk Systems


99

Parallel Disk Systems


100

Motivation

Existing parallel disk systems lack the means to adaptively control quality of security for dynamically changing workloads

To develop an adaptive quality of security control scheme for parallel disk systems (ASPAD)


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Contributions

ASPAD aims to adapt to changing security requirements and workload conditions

ASPAD endeavors to determine security services for disk requests while guaranteeing the desired response time for the requests

ASPAD

Security

Performance


102

Disk 1 Disk 2 Disk m

Adaptive Security Quality Controller

Data Partitioning mechanism

Security Service Middleware

Security Service q Security Service 1

Clients

Disk Requests

Parallel Disk System

Network

Response Time Estimator

Security Service 2

The ASPAD Framework


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Quality of Security

The quality of security for each security service is measured by security level.

0.1 to 1.0 The quality of security can be quantitatively

measured using seven levels Extremely high, very high, high, medium, low, very

low, and no security protection Translation mechanism is implemented to make the

conversions


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Modeling Quality of Security

ip

jijirS

1

)( Security level of the jth stripe

unit of ri

mps iiij and

Parallelism degree

No. of disks


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Modeling Quality of Security (Cont.)

nrrrR ,,, 21

n

iirSRS

1

)()(


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Optimize Quality of Security

To maximize security benefit of the parallel disk system

Maximize

n

i

p

jij

i

RS1 1

Subject to

a) ,max:11

ipj

ij tnii

b) mps iiij and

Where θij : the response time of jth strip unit of request ri


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Optimize Quality of Security (Cont.)

The response time of all stripe unit in request ri must be smaller than the desired response time

The parallelism degree of ri ≤ number of disks in the system


108

The ASPAD Framework

Data Partitioning

Response time estimator

Adaptive Quality of Security Controller

Adaptive control


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Data Partitioning

Determine the optimal parallelism degree for disk request Reduces the response time of the disk request to increase

the security level Dynamically calculate the optimal parallelism degree of the

request


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Data Partitioning (cont.)

Expected disk service time

,),()()(),( iitransirotiseekiidisk pdTEpTEpTEpdTE

Where

),( and ,)(,)( iitransirotiseek pdTEpTEpTE

Expected values of seek time, rotational time, and transfer time


111

Data Partitioning (cont.) Scheuermann et al., VLDB98

fpbaeCpTE iiseek )ln(1)(

Where C: number of cylinders on disk

a, b : two disk type independent constants

e, f : disk type dependent constants


112

Data Partitioning (cont.)

The expected value of rotation time

The expected transfer time

ROTi

iirot T

p

ppTE

1)(

diski

iiitrans Bp

dpdTE

1),(


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Data Partitioning (cont.) Scheuermann et al., VLDB98

Expected disk service time

Parallelism degree

.1

1)ln(1),(

diski

iROT

i

iiiidisk Bp

dT

p

pfpbaeCpdTE

.0

1

)1(1

),(22

diski

i

ii

ROTi

i

ROT

i

iidisk

Bp

d

p

eCb

p

Tp

p

T

pdE

pdTdE

The optimal parallelism degree is given by min(pi,m)


114

Estimate Response Time

Estimate the maximum response time of a disk request

Response time is the interval between the time a request sent by a client and the time the parallel disk system complete disk I/O operation


115

Estimate Response Time (cont.)

The response time of a disk request is:

),,(max),,(1

iiproc

p

ipartitionqueue prTTTprT

p : is the parallelism degree

: request vector of security level for p stripes unit

Tqueue : queuing delay at the client side

Tpartition : time spent in data partition

: system processing delay

),,,( 21 p

iprocT


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The ASPAD Algorithm


117

Start

Insert r into Q

For each r in Q

Calculate pi of ri

Partition ri into pi stripe unit

For each stripe unit

Initialize SL

Ph

a se1

. Da t

a P

a rt i

tio n

i ng

Estimate response time

Ph

ase2

res

pon

se t

ime

SL < 1.0

While est. < desired

YSL = SL + 0.1

Estimate response timeEND

N

EST >des. dec. SLYN

Apply the security service with level ij to the jth stripe unit


118

Property of ASPAD

With respect to the ith request, the following two conditions must hold if the jth stripe unit’s security level is increased by 0.1:

1. The current security level ij is less than 1.0;

2. , where Tj is the response time of the jth

stipe unit, ti is the desired response time of the request,

and . iiiij tprT ),,(


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Experimental Results

a) data size is 100KB and P = 3


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Impact of Arrival Rate

a) data size is 100KB and P = 3

ASPAD is always the best


121

Impact of Parallelism Degree

The impact of the parallelism degree when arrival rate = 0.5 No./sec.

ASPAD noticeably outperforms the other

Add more slides for results!!!


122

A Caching Strategy to Improve Security of Cluster Storage Systems


123

Security Service 1Security Service 1 Security Service mSecurity Service m

Cache (Volatile/Non-volatile memory)Cache (Volatile/Non-volatile memory)

Adaptive Security Service ControllerAdaptive Security Service Controller

Security-aware cache management mechanismSecurity-aware cache management mechanism

A Cluster Storage SystemA Cluster Storage System

Network

Clients

Disk Request

Disk1 Disk 2 Disk n


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Cache Partitioning

The entire cache of the cluster storage system is divided into separate partitions, one for each disk, by a security-aware cache partitioning mechanism.

Each cache partition for a disk is managed separately using the conventional LRU replacement algorithm.


125

ip

jdijdi PrSLPrS

1

),(),(

,, PPmp di Total cache size

is the partition size of the dth disk


126


127

Conclusion

AWARDS and ASPAD maximize the quality of security for local and parallel disk system

Experimental result shows that AWARDS and ASPAD significantly increase the security level as well as the overall performance over an existing algorithm

A security-aware cache management mechanism (CaPaS) for cluster storage systems. CaPaS can achieve high security and

desired performance for clusters.


128

Future Work

Security-Aware Load Balancing Energy-Efficient Mobile Storage Systems


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StReD : A Quality of Security Framework for Storage Resources in Data Grids. M. Nijim, Z.-L. Zong, and X. Qin, Future Generation Computer Systems: The Int'l Journal of Grid Computing, 2007. (Forthcoming)

Modeling and Improving Security of a Local Disk System for Write-Intensive Workloads. M. Nijim, X. Qin, and T. Xie, ACM Transactions on Storage, vol. 2, no. 4, pp. 400-423, Nov. 2006

Performance Analysis of an Admission Controller for CPU- and I/O-Intensive Applications in Self-Managing Computer Systems. M. Nijim, T. Xie, and X. Qin, ACM Operating Systems Review, vol. 39, no. 4, pp.37-45, October, 2005

Energy-Efficient Scheduling for Parallel Applications on Mobile Clusters. Z.-L. Zong, M. Nijim, and X. Qin, Cluster Computing: The Journal of Networks, Software Tools and Applications, 2007. (In press)

Journal Publications


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Awards: An Adaptive Write Scheme for Secure Local Disk Systems. M. Nijim, X. Qin, T. Xie, and M. Alghamdi, Proc. 25th IEEE Int'l Performance Computing and Communications Conference (IPCCC), April 2006 (Acceptance rate 30%)

Integrating a Performance Model in Self-Managing Computer Systems under Mixed Workload Conditions. M. Nijim, T. Xie, and X. Qin, Proc. IEEE Int’l Conf. Information Reuse and Integration, Aug. 2005

An Adaptive Strategy for Secure Distributed Disk Systems. M. Nijim, T. Xie, Z.-L. Zong, and X. Qin, NASA/IEEE Conference on Mass Storage Systems and Technologies, WIP Session, May 2006

Sharp: A New Real-Time Scheduling Algorithm to Improve Security of Parallel Applications on Heterogeneous Clusters. T. Xie, X. Qin, and M. Nijim, Proc. 25th IEEE Int'l Performance Computing and Communications Conference (IPCCC), April 2006. (Acceptance rate 30%)

Solving Energy-Latency Dilemma: Task Allocation for Parallel Applications in Heterogeneous Embedded Systems. T. Xie, X. Qin, and M. Nijim, Proc. 35th International Conference on Parallel Processing (ICPP), Columbus, Ohio, Aug. 2006. (Acceptance rate 28%)

Adaptive Quality of Security Control in Networked Parallel Disk Systems. M. Nijim, X. Qin, and T. Xie, Proc. 15th Int'l Conference on Computer Communications and Networks (ICCCN), Oct. 2006 (Acceptance rate 29%)

Selected Conference Publications


131

Questions?


132

AWARDS Complexity

The complexity of AWARDS is O(n2)

Proof : To increase the security level of the request, it takes O(n).

There is O(n) number of write requests


133

Download the presentation slideshttp://www.slideshare.net/xqin74

Google: slideshare Xiao Qin


134

Complexity of ASPAD

The time complexity is O(n2p)

P: the maximum parallelism degree

n: is the number of disk requests

Security-Aware Scheduling for Real-Time Parallel Applications on Clusters

Technology

Transcript of Security-Aware Scheduling for Real-Time Parallel Applications on Clusters