Performance Aware Secure
Code Partitioning
Sri Hari Krishna Narayanan, Mahmut
Kandemir, Richard Brooks
Presenter : Sri Hari Krishna Narayanan
Outline
•
•
•
•
•
•
Introduction to secure code partitioning
Motivation through multi level security
Our Code Partitioning Domain
Workload Balancing Algorithm
Example
Results
2
Introduction
•
Secure code partitioning is a process that
partitions code and data among several mutually
untrusted hosts that need to co-operate to
complete a task in parallel.
Original Application and data
Authenticated
trust
declarations
Compiler
thread
thread
thread
H0
H1
H2
thread
H3
thread
H4
3
Motivation – Multi level security
•
•
Why performance aware? - Secure code partitioning
when performed in a performance agnostic manner
can lead to skewed load across the hosts.
Let us look at Multi-Level-Security (MLS)
• Uses qualifiers on data to classify them according
to their sensitivity level.
• Uses qualifiers on hosts to classify them
according to their capability level.
Classic MLS lattice of 4 levels
unclassified < confidential < secret < top secret
Categories – Used to separate data
COMINT, HUMINT, ELINT
Data A : (CONFIDENTIAL)
(CONFIDENTIAL, {ELINT})
Data B : (SECRET,
(SECRET) {COMINT})
Data C : (UNCLASSIFIED
(UNCLASSIFIED), {HUMINT})
4
Multilevel Security
Data A : (CONFIDENTIAL, {ELINT})
Data B : (SECRET, {COMINT})
Data C : (UNCLASSIFIED , {HUMINT})
COMINT HUMINT ELINT
TOP SECRET
SECRET
CONFIDENTIAL
UNCLASSIFIED
Data A
Data B
Data C
Host A : (CONFIDENTIAL, {COMINT, HUMINT})
Host B : (CONFIDENTIAL, {ELINT})
Host C : (TOP SECRET, {COMINT, HUMINT, ELINT})
5
Multilevel Security
Data A : (CONFIDENTIAL, {ELINT})
Data B : (SECRET, {COMINT})
Data C : (UNCLASSIFIED , {HUMINT})
COMINT HUMINT ELINT
1
Normalized Finish Time
TOP0.9
SECRET
SECRET
Host C
0.8
0.7
Host B
Host C
CONFIDENTIAL
0.6
0.5
0.4
0.3
Host A
Host C
UNCLASSIFIED
0.2
0.1
0
Host A
Data A
Host B
Data B
Host C
Data C
Host A : (CONFIDENTIAL, {COMINT, HUMINT})
Host B : (CONFIDENTIAL, {ELINT})
Host C : (TOP SECRET, {COMINT, HUMINT, ELINT})
6
Our Code Partitioning Domain
•
•
•
The aim is to partition a given task evenly among a
set of hosts.
However, different hosts have access to different
portions of the data.
Further, hosts are hierarchically related. E.g. Host 1
can access all the data that Host 4 and Host 3 can.
Data Decompositions
H0
Host Hierarchy Tree
H2
H3
H2
H0
H1
H1
H4
H4
H3
7
Our Code Partitioning Domain
Original Application and Data
H2
H0
H3
H2
H1
H0
Compiler
H1
H4
H4
thread
thread
thread
H0
H1
H2
thread
H3
H4
thread
H4
8
Workload Balancing Challenges
•
There are three challenges
• Representing the data and computation that
can be performed on them
– In this work, we target codes that are structured as a
series of loops that access data.
– So the unit of workload distribution is a loop iteration.
• Calculating the initial/default workload on each
host
• Reassigning the workload of the hosts
–Three algorithms
9
Determining the data and the iterations
Data accessed by a host in an iteration
Dhi , I k
All iterations that access a particular data object
on a host that may be
accessed by a host
I : r RA j , I k d A j such that
I (hi , I k , A j )
r I d d Dhi , A j I I k
All the iterations that may be executed on a host
I hi , I k
I h , I
i
k
, Aj
j : A j S I k
Default iterations that are executed on a host
I init hi , I k I : hi hmin I , I k
10
Reassigning the workload
ReassignHHT()
1: Navg := Totalnumberofiterations / Totalnumberofhosts
2: BottomToTop(hroot,Navg)
3: while carryout(hroot, Ik) > 0 do
4: Navg := Navg + Navg * 0.1
5: TopToBottom(hroot,Navg, 0)
6: end while
•
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The ideal average, Navg is calculated.
BottomToTop allocates to every host, at most Navg
iterations.
TopToBottom increases the allowed number of
iterations for unbalanced hosts.
11
Example
•
Based on the Gauss Seidel method
for(i = 2 to N-1)
for(j = 2 to N-1)
B[i, j] := (A[i -1,j]+ A[i + 1,j] + A[i, j -1]+ A[i, j + 1]) * 1/ά ;
endfor
endfor
H4
H6
H5
H1
H2
Array A
H4
H7
H6
H5
H1
H3
H0
H2
H2
H3
H0
Array B
H1
H2
H4
H5
H3
H7
H6
HHT
H7
14
Example continued
HHT
H0
H1
H2
H4
H5
H3
H6
H7
15
Example
Assignment of initial iterations.
H0
30
Navg = 400/8 = 50
H1
H2 any node
H3 is 80.
Maximum
load on
80
80
20
While the average is 50.
H4
40
H5
70
H6
40
H7
40
16
Example – Operation of BottomtoTop
Navg = 400/8 = 50
60
H0
30
50
10
30
50
H1
50
80
H2
50
80
0
40 > Navg ?
H4
40
0
20
H5
70
50
H3
20
0
H6
40
0
H7
40
17
Example – Operation of BottomtoTop
Navg = 400/8 = 50
60
H0
50
10
50
H1
50
H2
50
0
H4
40
0
20
H5
50
H3
20
0
H6
40
0
H7
40
18
Example Toptobottom
Increase allowed
Load, Navg to 55
60 55 504540
H0
50
55
5 10
40 45 50
H1
55
50
H2
55
50
0 15 20
Balanced
H4
40
0
H5
50
55
Balanced
H3
20
0
H6
40
0
H7
40
19
Example continued
40
H0
55
5
40
H1
55
H2
55
0
H4
40
0
15
H5
55
H3
20
0
H6
40
0
H7
40
20
Example continued
20
Increase allowed
load to 60
H0
60
0
30
H1
60
H2
60
0
H4
40
0
10
H5
60
H3
20
0
H6
40
0
H7
40
21
Example continued
2
Increase allowed
load to 66
H0
66
0
18
H1
60
H2
66
0
H4
40
0
4
H3
20
0
H5
66
H6
40
0
H7
40
22
Example continued
0
Increase allowed
load to 72
H0
68
0
0
18
The HHT His now ‘balanced’
at
the
root
node.
H2
H3
1
Maximum60load on any
66 node is
20 68 (from 80).
0
H4
40
4
0
H5
66
H6
40
0
H7
40
23
Experimental Results
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Two metrics were studied for multiple HHTs
• Execution Time (EXE)
• Standard deviation of workload (STD)
Two scenarios were studied
• Default data decomposition + multiple HHTs
• Default HHT + multiple decompositions
24
Experimental Results – (1/2)
Default HHT + multiple decompositions
Overall finish time for the
different data decompositions
with a default HHT.
STD for the different data
decompositions
with a default HHT.
25
Experimental Results – (2/2)
Default HHT + multiple decompositions
Overall finish time for the
different HHT with a default
data decomposition
STD for the different HHTs
with a default data
decompostion
26
Conclusion
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•
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Showed that load balancing is required in secure
code partitioning
Proposed performance aware secure code
partitioning to reduces the overall finish time.
Better load balancing compared to the original
method is achieved as well.
27
Thank you
Sri Hari Krishna Narayanan
[email protected]
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