1. A Secure and Efficient Cloud Resource Allocation Scheme with Trust
Evaluation Mechanism Based on
Combinatorial Double Auction
Source：KSII Transactions on Internet and Information Systems
vol. 11, no. 9, Sep. 2017
Authors: Yun-Hao Xia, Han-Shu Hong, Guo-Feng Lin, Zhi-Xin Sun
Speaker: Chit-Jie Chew
Date: 7/12/2018

2. Outline Introduction Related works Proposed scheme Experimental result
Conclusions

3. Introduction - Double Auction
$30
$40
Provider 1
Consumer 1
Profit: $30
$35
$50
Provider 2
Consumer 2
Auctioneer
$30
$20
Consumer 3
Provider 3

4. Introduction - Combinatorial Double Auction
Case 1: Provider 1, 2, 3 Profit: $220
Case 3: Provider 1, 3 Profit: $250
Case 3: Provider 1, 3 Profit: $250
Case 2: Provider 1, 2 Profit: $70
Case 4: Provider 2, 3 Profit: $200
Case 2: Provider 1, 2
Case 4: Provider 2, 3
Case 1: Provider 1, 2, 3
Case 3: Provider 1, 3 1 2
$100 1
$200
Auctioneer
Auctioneer
Auctioneer
Auctioneer
-$300
-$250
-$250
-$400 1 1 2 2 3 5 3 4 5 2 6 5
Consumer 2
Consumer 2
Consumer 2
+$200
+$300
+$300
+$300
Consumer 1 -1 -2 -2 -1 -2 -1 -3 -3 -3
Provider 1
Consumer 1
-$50
-$100 $0
$50 1 1 2 1 3 1 1 3 1 3 2 2 1 2
$150 2 3
$300
Consumer 3
+$120
Consumer 1
Consumer 1
Consumer 1
+$200
+$200
+$200 -1 -1 -1 -1 -1 -2 -1 -1 -1 -1 -1
Profit:
$70 1 1 1 2 1 1 2
Profit:
Profit:
$200
$250
$100
Provider 2
Consumer 2
+$120 -1 -2
Consumer 3
Auctioneer 1 2
$120 1 4
$150
Profit:
$220 2
Consumer 3
Provider 3

5. Related Works – Genetic Algorithm(GA)
∆𝑓=250−200=50
∆𝑓=200−200=0 P1 P2 P3 C1 C2 C3
∆𝑓=−280− −50 =−230
Population number
Chromosome
Fitness
Parents
-$180
-$180 1 5
$200 1
-$50
-$50 2 4
$520 -1 -3 -3
Crossover
-$200 2 4
-$100 2 4
-$130 1 5
$250 1
Mutation
$20 -1 4
$200
$200 1
-$280
-$280 1 3 6
$250
$250 1
New Parents 2 4
$250
$250 1
-$50
$200 1
-$180 1 5

6. Related Works – Simulated Annealing Algorithm(SA)P1 P2 P3 C1 C2 C3
Chromosome
Fitness 4 1
Fitness
$70
Chromosome 4 2
-$50 4 1
$70 1
$50
Variation of temperature
∆𝑓=70 −(−$50)= 120
∆𝑓=50 −$70=− 20
𝑒𝑥𝑝 ∆𝑓 𝑇 =𝑒𝑥𝑝 = 3.32
𝑒𝑥𝑝 ∆𝑓 𝑇 =𝑒𝑥𝑝 − = 0.8
𝐼𝑓 𝑒𝑥𝑝 ∆𝑓 𝑇 >𝑟𝑎𝑛𝑑𝑜𝑚 0~1 :
𝐼𝑓 𝑒𝑥𝑝 ∆𝑓 𝑇 >𝑟𝑎𝑛𝑑𝑜𝑚 0~1 :
accept to new generation
accept to new generation
𝑇=𝑇×0.9=100×0.9=90
𝑇=𝑇×0.9=90×0.9=81

7. Proposed scheme – Model Based on Combinatorial Double Auction

8. Proposed scheme – The Pricing Determination Model
Notations
Sign
Definition
𝑙𝑜𝑎𝑑 𝑡
Current system load
𝑢𝑠𝑒𝑟_𝑎𝑚𝑜𝑢𝑛𝑡
The amount of demand by buyers
𝑝𝑟𝑜𝑣𝑖𝑑𝑒_𝑎𝑚𝑜𝑢𝑛𝑡
The amount of resources supply 𝑖
Resource consumer 𝑗
Resource
𝜔 𝑗
The weight of each kind of resources
𝑎 𝑖𝑗
The amount of consumer 𝑖 requires type 𝑗 resource ℎ
Resource provider
𝑎 ℎ𝑗
The amount of provider ℎ requires type 𝑗 resource 𝑡
Timestamp
𝑝 ℎ 𝑡
The price of provider at the timestamp 𝑡
𝑝 ℎ
Auction price of provider ℎ
𝑝 𝑖 𝑡
The price of consumer at the timestamp 𝑡
𝑝 𝑖
Bidding price of user 𝑖
𝑙𝑜𝑎𝑑 𝑡 = 𝑢𝑠𝑒𝑟_𝑎𝑚𝑜𝑢𝑛𝑡 𝑝𝑟𝑜𝑣𝑖𝑑𝑒_𝑎𝑚𝑜𝑢𝑛𝑡
𝑢𝑠𝑒𝑟_𝑎𝑚𝑜𝑢𝑛𝑡= 𝑖=1 𝑁 𝑗=1 𝐾 | 𝜔 𝑗 𝑎 𝑖𝑗 |
provide_𝑎𝑚𝑜𝑢𝑛𝑡= ℎ=1 𝑁 𝑗=1 𝐾 | 𝜔 𝑗 𝑎 ℎ𝑗 |
If 𝑙𝑜𝑎𝑑 𝑡 >1
𝑝 ℎ 𝑡 = 𝑝 ℎ × 𝑙𝑜𝑎𝑑 𝑡
If 𝑙𝑜𝑎𝑑 𝑡 <1
𝑝 𝑖 𝑡 = 𝑝 𝑖 × 1− 𝑙𝑜𝑎𝑑 𝑡

9. Proposed scheme – The Pricing Determination Model1 2
$100
provide_𝑎𝑚𝑜𝑢𝑛𝑡= ℎ=1 𝑁 𝑗=1 𝐾 | 𝜔 𝑗 𝑎 ℎ𝑗 | = ℎ=1 2 𝑗=1 3 | 𝜔 𝑗 𝑎 ℎ𝑗 |
Provider 1
= 0.3×1+0.3×2+0.4× ×1+0.4×4
=3.2 1 4
$150
user_𝑎𝑚𝑜𝑢𝑛𝑡= 𝑖=1 𝑁 𝑗=1 𝐾 | 𝜔 𝑗 𝑎 ℎ𝑗 | = ℎ=1 2 𝑗=1 3 | 𝜔 𝑗 𝑎 𝑖𝑗 |
= 0.3×1+0.3×1+0.4× ×2+0.4×3
Provider 2
=2.8 1
$240
𝑝 𝑖 𝑡 = 𝑝 𝑖 × 1− 𝑙𝑜𝑎𝑑 𝑡
𝑙𝑜𝑎𝑑 𝑡 = 𝑢𝑠𝑒𝑟_𝑎𝑚𝑜𝑢𝑛𝑡 𝑝𝑟𝑜𝑣𝑖𝑑𝑒_𝑎𝑚𝑜𝑢𝑛𝑡
If 𝑙𝑜𝑎𝑑 𝑡 <1,
Consumer 1
𝑝 1 𝑡 =240× 1−
𝑝 1 𝑡 =300× 1− = 2 3
$300
=0.875
=112
=140
Consumer 2

10. Proposed scheme – The Pricing Determination Model1 2
$100 1 2
$100
Provider 1
Provider 1 1 3 5
-$250
+$240 2 4
-$10
+$300
+$290 1 3 5
-$250
+$112 2 4
-$138
+$140
+$2 1 4
$150 1 4
$150
Provider 2
Provider 2 1
$240 1
$112
Consumer 1
Consumer 1 2 3
$300 2 3
$140
Consumer 2
Consumer 2

11. Proposed scheme – SAGA ∆𝑓=250−200=50 ∆𝑓=200−200=0 P1 P2 P3 C1 C2 C3
∆𝑓=−280− −50 =−230
Chromosome
Fitness
Parents
-$180 1 5
$200 1
-$50
-$50 2 4
$520 -1 -3 -3
Mutation
$20 -1 4
$200
$200 1
-$280
-$280 1 3 6
$250
$250 1
𝑒𝑥𝑝 ∆𝑓 𝑇 =𝑒𝑥𝑝 − = 0.1
𝐼𝑓 𝑒𝑥𝑝 ∆𝑓 𝑇 >𝑟𝑎𝑛𝑑𝑜𝑚 0~1 : SA
0.1>0.05
New Parents
$250
$250 1
-$280 1 3 6
$200 1
-$50 2 4

12. Experimental result – The simulation of the efficiency of SAGA
Parameter
Value
Population number
500
Chromosome number 16
Crossover probability
0.7
Mutation probability
0.08
Number of population genetics 20
Variation of temperature T
𝑇=𝑇×0.95

13. Experimental result – The simulation of the efficiency of SAGA

14. The percentage of malicious nodes
Experimental result – The simulation of the efficiency of trust model
Parameter
Value 𝛿
0.8 𝜗
0.2 𝛼 𝛽
The percentage of malicious nodes
Providers : 𝟏𝟎%
User : 𝟏𝟎%
The number of malicious nodes
𝑵×𝟏𝟎+𝟏
𝒏=𝟎~𝟗
𝑵×𝟏𝟎+𝟖

15. Experimental result – The simulation of the efficiency of trust model
The percentage of malicious nodes
The average number of successful transactions
Malicious SP
Normal SP
Malicious users
Normal users
10%
6.8
619.9
5.3
620.1
20%
5.4
534.6
2.2
535.4
30%
5.6
492.7
2.8
493.9
40%
5.9
507.3
3.7
508.7
50%
2.6
267.5
2.3
268.1

16. Experimental result – The simulation of the efficiency of trust model
Fig. 13. Transaction’s number of normal\malicious users and SP under different percentages

17. Conclusions Secure and efficient combinatorial double auction SAGA
Trust evaluation model