The document describes threshold-based elasticity in cloud computing systems. It shows how resources are scaled out when demand exceeds a threshold and scaled in when demand falls below a lower threshold. It also discusses the need to reproduce bugs in elastic systems and presents an approach using a prototype architecture that can schedule workload and resource variations to systematically test elasticity. Case studies show this approach successfully reproduced bugs in MongoDB and ZooKeeper that could not be triggered without the controlled testing environment.

3. Cloud Computing Elasticity
N. R. Herbst, S. Kounev, and R. Reussner, “Elasticity in Cloud Computing: What It Is, and What It Is Not,” ICAC, pp. 23–27, 2013.
M. M. Bersani, D. Bianculli, S. Dustdar, A. Gambi, C. Ghezzi, and S. Krstić, “Towards the Formalization of Properties of Cloud-based Elastic
Systems,” in Proceedings of the 6th International Workshop on Principles of Engineering Service-Oriented and Cloud Systems, New York,
NY, USA, 2014, pp. 38–47.

4. Elastic Behavior : threshold-based elasticity
Time
Resource
(CPUs)
2
1
0.3
0.8
0.6
1.2
Ready
Resource Allocation
Resource Demand
Scale-out Threshold
Scale-in Threshold

5. Elastic Behavior : threshold-based elasticity
Ready
Time
Resource
(CPUs)
2
1
scale-out reaction time
0.3
0.8
0.6
1.2
Resource Allocation
Resource Demand
Scale-out Threshold
Scale-in Threshold

6. Elastic Behavior : threshold-based elasticity
Time
Resource
(CPUs)
2
1
scale-out reaction time
scale-out time
0.3
0.8
0.6
1.2
Ready
Scaling-Out
Resource Allocation
Resource Demand
Scale-out Threshold
Scale-in Threshold

7. Elastic Behavior : threshold-based elasticity
Time
Resource
(CPUs)
2
1
scale-out reaction time
scale-out time
0.3
0.8
0.6
1.2
Ready
Scaling-Out
Resource Allocation
Resource Demand
Scale-out Threshold
Scale-in Threshold

8. Elastic Behavior : threshold-based elasticity
Time
Resource
(CPUs)
2
1
scale-out reaction time
scale-out time
0.3
0.8
0.6
1.2
scale-in
reaction time
Ready
Scaling-Out
Resource Allocation
Resource Demand
Scale-out Threshold
Scale-in Threshold

9. Elastic Behavior : threshold-based elasticity
Time
Resource
(CPUs)
2
1
scale-out reaction time
scale-out time
scale-in
time
scale-in
reaction time
0.3
0.8
0.6
1.2
Ready
Scaling-Out
Scaling-In
Resource Allocation
Resource Demand
Scale-out Threshold
Scale-in Threshold

10. Elastic Behavior : threshold-based elasticity
Ready
Scaling-Out
Scaling-In
Resource Allocation
Resource Demand
Scale-out Threshold
Scale-in Threshold
Time
Resource
(CPUs)
2
1
scale-out reaction time
scale-out time
scale-in
reaction time
scale-in
time
0.3
0.8
0.6
1.2

12. Motivation : MongoDB’s bug 7974
“ A secondary server shuts down when it
detects a replication error, ... “
●
○
○
○
■

13. Motivation : MongoDB’s bug 7974
Primary
Ready

14. Motivation : MongoDB’s bug 7974
Primary
Ready
Scaling-out

15. Motivation : MongoDB’s bug 7974
Primary
Secondary Secondary
Ready
Scaling-out

16. Motivation : MongoDB’s bug 7974
Primary
Ready
Secondary
Scaling-In
Secondary

17. Motivation : MongoDB’s bug 7974
Primary
Ready
Secondary
Scaling-InCreate Index

18. Motivation : MongoDB’s bug 7974
Primary
Ready
Secondary
Scaling-InCreate Index

19. Motivation : MongoDB’s bug 7974
Primary
Ready
Secondary
Scaling-In
Secondary
Scaling-Out

20. Motivation : MongoDB’s bug 7974
Primary
Ready
Secondary
Scaling-In
Secondary
Scaling-Out
Add doc. with
duplicated key

21. Motivation : MongoDB’s bug 7974
Primary
Ready
Secondary
Scaling-In
Secondary
Scaling-Out
Add doc. with
duplicated key

22. Motivation : MongoDB’s bug 7974
Primary
Ready
Secondary
Scaling-In
Secondary
Scaling-Out
Add doc. with
duplicated key

23. Motivation : MongoDB’s bug 7974
Primary
Ready
Secondary
Scaling-In
Secondary
Scaling-Out
Duplicate Key
Error

24. Requirements for Elasticity Testing Reproduction
●
○
●
○
●
○

25. Elasticity Testing Reproduction
Case Studies
●

27. Prototype’s Architecture
Elasticity
Controller
Mock
Event
Scheduler
Workload
Generator
Cloud-Based
System
Cloud
Monitor

28. Prototype’s Architecture
Elasticity
Controller
Mock
Event
Scheduler
EC = {ec1
=(es1
,W1
), …, ecn
}
SER = {(ec1
, ser1
), …, (ecn
, sern
)}
Workload
Generator
Cloud-Based
System
Cloud
Monitor
S={(ec1
, e1
), …, (ecn
, en
)}

29. Prototype’s Architecture
Elasticity
Controller
Mock
Event
Scheduler
EC = {ec1
=(es1
,W1
), …, ecn
}
SER = {(ec1
, ser1
), …, (ecn
, sern
)}
S={(ec1
, e1
), …, (ecn
, en
)}
Workload
Generator
Wi Cloud-Based
System
Cloud
Monitor

30. Elasticity
Controller
Mock
Event
Scheduler
EC = {ec1
=(es1
,W1
), …, ecn
}
SER = {(ec1
, ser1
), …, (ecn
, sern
)}
S={(ec1
, e1
), …, (ecn
, en
)}
Workload
Generator
Wi Cloud-Based
System
Workload
Cloud
Monitor
Prototype’s Architecture

31. Elasticity
Controller
Mock
Event
Scheduler
EC = {ec1
=(es1
,W1
), …, ecn
}
SER = {(ec1
, ser1
), …, (ecn
, sern
)}
S={(ec1
, e1
), …, (ecn
, en
)}
Workload
Generator
Wi Cloud-Based
System
Workload
Resource Variation
Cloud
Monitor
Prototype’s Architecture

32. Elasticity
Controller
Mock
Event
Scheduler
EC = {ec1
=(es1
,W1
), …, ecn
}
SER = {(ec1
, ser1
), …, (ecn
, sern
)}
S={(ec1
, e1
), …, (ecn
, en
)}
Workload
Generator
Wi Cloud-Based
System
Workload
NewElasticityState
Resource Variation
Cloud
Monitor
Prototype’s Architecture

33. Elasticity
Controller
Mock
Event
Scheduler
EC = {ec1
=(es1
,W1
), …, ecn
}
SER = {(ec1
, ser1
), …, (ecn
, sern
)}
S={(ec1
, e1
), …, (ecn
, en
)}
Workload
Generator
Wi Cloud-Based
System
Workload
Event Execution
Resource Variation
Cloud
Monitor
NewElasticityState
Prototype’s Architecture

34. Elasticity
Controller
Mock
Event
Scheduler
EC = {ec1
=(es1
,W1
), …, ecn
}
SER = {(ec1
, ser1
), …, (ecn
, sern
)}
S={(ec1
, e1
), …, (ecn
, en
)}
Workload
Generator
Wi Cloud-Based
System
Workload
Event Execution
Resource Variation
Cloud
Monitor
NewElasticityState
Prototype’s Architecture

36. Reproduction of Bugs
●
●

37. ●
Reproduction of Bugs
Bug Success of Reproduction
Using Our Approach
Success of Reproduction
Without Our Approach
MongoDB-7974 3 0
ZooKeeper-2164 3 1
ZooKeeper-2172 3 0

38. ●
Reproduction of Bugs
Bug Success of Reproduction
Using Our Approach
Success of Reproduction
Without Our Approach
MongoDB-7974 3 0
ZooKeeper-2164 3 1
ZooKeeper-2172 3 0
● Comercial elastic controllers natively removes either the oldest (first added) or the newest (last
added) resource;
● In our experiment, ZooKeeper node on the oldest resource (VM) is elected the leader;
● At one of the three executions, we configure elastic controller to remove the oldest node;
○ Then, the bug is reproduced.
● If the leader was the node in the middle, the bug would never be reproduced without our
approach.

39. ●
●
○
●
Conclusion

40. ●
●
○
●
Perspectives