Cloud-DLS: Dynamic trusted scheduling for Cloud computing

Expert Systems with Application 39(2012)Wei Wang, Guosun Zeng, Daizhong Tang , Jing Yao

鍾舜璽

Introduction

• Clouds are rapidly becoming an important platform for scientific applications.

• Large scale Cloud computing infrastructure are unified computing platform which tries to connect and share all resources in the Internet– computation resource– storage resource– Information resource– knowledge resource

Introduction (cont.)

Cognitive trust model based on Bayesian method

• Global trust degree

Direct and recommendation trust

times interaction times successful times fail

𝑝

Direct and recommendation trust (cont.)

times interaction times successful times fail

Effect of time

• The more recent the history information is, the more impact the factor has.

Trust relationship analysis

Dynamic level scheduling

• Homogeneous

𝐷𝐿𝑆 (𝑣 𝑖 ,𝑚 𝑗 )=𝑆𝐿 (𝑣 𝑖 )−max❑

{𝑡𝑖𝑗𝐴 , 𝑡 𝑗

𝑀 }

A

B C

D

Task 執行時間 SL

A 2 7+1=8

B 7 1

C 3 1

D 1 0

2

1

5

3

Dynamic level scheduling (cont.)

• Heterogeneous

𝐷𝐿𝑆 (𝑣 𝑖 ,𝑚 𝑗 )=𝑆𝐿 (𝑣 𝑖 )−max❑

{𝑡𝑖𝑗𝐴 , 𝑡 𝑗𝑀 }+∆(𝑣𝑖 ,𝑚 𝑗)

∆ (𝑣 𝑖 ,𝑚 𝑗 )=𝑡 𝑖𝐸−𝑡 𝑖𝑗𝐸

Cloud-DLS algorithm

• Trust dynamic level

𝑇𝐷𝐿 (𝑣𝑖 ,𝑚 𝑗 )=𝑇 (𝑣 𝑖❑ ,𝑛 𝑗

❑)𝛼 𝑖∗(𝑆𝐿 (𝑣 𝑖 )−max❑

{𝑡 𝑖𝑗𝐴 , 𝑡 𝑗𝑀 }+∆ (𝑣 𝑖 ,𝑚 𝑗 ))

𝑚0

𝑚2 𝑚3

𝑚1

A

B C

D

2

1

5

3

A

C

Experiment result

• Experiment benchmark– String Match (SM)– Reverse Index (RI)

– KMeans (KM)– Similarity Score (SS)– Principal Component Analysis (PCA)– Leukocyte Tracking (LT)

Experiment result (cont.)

• Experiment one: the validity of trust model

Experiment result (cont.)

• Experiment two: Cloud-DLS vs. DLS and BSA

Experiment result (cont.)

• Experiment two: Cloud-DLS vs. DLS and BSA

Conclusion

• The main contribution of this study to scheduling systems is that it extends the traditional formulation of the scheduling problem so that both execution time and reliability of applications are simultaneously accounted for.

• Considering other aspects of security in Cloud environment is our future work.