Monitor-Based Testing of Elastic Cloud Computing Applications

© AtlanMod (atlanmod-contact@mines-nantes.fr)© AtlanMod (atlanmod-contact@mines-nantes.fr)
Monitor-Based Testing of
Elastic Cloud Computing
Applications
Michel Albonico
PhD Student - AtlanMod - EMN (Nantes, France)
(michel.albonico@inria.fr)
Jean-Marie Mottu
Gerson Sunyé
1
5thInt.WorkshoponLargeScaleTesting
Delft,Netherlands-2016

© AtlanMod (atlanmod-contact@mines-nantes.fr)
● Cloud Computing Elasticity
● Motivation
● Test Procedure
● Experiments
● Conclusion and Future Work
Outline
2

Cloud Computing Elasticity
3
● Cloud computing elasticity:
The ability of a cloud infrastructure/system modifying its resource
configuration according to demand.

4
● Thresholds:
○ Scale-out threshold: maximum resource usage, e.g., 80% of CPU
usage;
○ Scale-in threshold: minimum resource usage, e.g., 20% of CPU
usage;
○ Used to decide when varying a resource.
Resource Allocation
Resource Demand
Scale-out Threshold
Scale-in Threshold
Scale-out Threshold Breaching
Scale-in Threshold Breaching
Time (s)
Resource
(Processors)
1
Legend
80% 0.8
20% 0.2

5
● Resource demand varies according to workload variations.
○ Example:
■ number of users increases from 1 to 2, the resource
demand doubles.
Resource Allocation
Resource Demand
Scale-out Threshold
Scale-in Threshold
Time (s)
Resource
(Processors)
1
Legend
80% 0.8
20% 0.2

6
● Resource demand varies over time;
● Scale-out threshold breaching.
Resource Allocation
Resource Demand
Scale-out Threshold
Scale-in Threshold
Time (s)
Resource
(Processors)
1
Legend
scale-out threshold breaching
80% 0.8
20% 0.2

7
● Scale-out threshold breaching;
● Scale-out reaction time;
Resource Allocation
Resource Demand
Scale-out Threshold
Scale-in Threshold
Time (s)
Resource
(Processors)
1
Legend
scale-out reaction time
80% 0.8
20% 0.2

8
● Scale-out threshold breaching;
● Scale-out reaction time;
● Scale-out time, then the thresholds are updated.
Resource Allocation
Resource Demand
Scale-out Threshold
Scale-in Threshold
Time (s)
Resource
(Processors)
2
1
80% 0.8
Legend
scale-out time
80% 1.6
20% 0.2
20% 0.4

9
● Scale-in:
○ Scale-in threshold breaching;
○ Scale-in reaction time (resource is no longer available);
■ Thresholds reconfiguration.
○ Scale-in time.
Resource Allocation
Resource Demand
Scale-out Threshold
Scale-in Threshold
Time (s)
Resource
(Processors)
2
1
Legend
scale-in time
scale-in reaction time
80% 0.8
80% 1.6
20% 0.2
20% 0.4

10
● Elasticity states transition.
scale-out
threshold
breaching
scale-in
threshold
breaching

● Elasticity states transition.
● Related work only test during the ready state;
● Scaling states:
○ Considerable time: in our experiments, scaling-out takes more
than 90 seconds (Amazon EC2);
○ Great part of the adaptation tasks: replication data, leader
election, etc.
Motivation
11

Test Procedure
● Test cloud systems during all the elasticity states;
● Execute tests dynamically:
○ Associate test cases to a set of elasticity states;
○ Execute the test according to the current elasticity state.
● Test execution:
○ Periodically monitor the resource during the test execution;
■ Current elasticity state.
○ (Re)-execute the associated test cases during the current
elasticity state.
12

Experiments
● Research questions (answered by the experiments):
1. Is it necessary to run the test during different elasticity states?
a. Does a cloud system react distinctly depending on the
elasticity state?
2. Is it possible to execute the test during different elasticity states
and to assign the test verdicts accordingly?
13

Experiments
Question 1: system behavior during different elasticity state.
● First experiment:
Measure the performance of a cloud system during different
elasticity states.
○ Manually executed;
○ Workload (50% read / 50% write);
○ 2500 operations per second (ops).
14

Experiments
● First experiment results:
○ 2000 ops: covers all the performance drops;
○ Elasticity states extracted from the log files;
RQ1:
It is necessary to run the test during different elasticity states.
15
Performance-OperationsperSecond(ops)
Minimal Performance
Measured Performance
R R R R R RSISISO SO SOSI
200
400
1000
1200
800
600
Time (s)

Question 2: test execution during different elasticity states + test
verdicts assignment.
● Second experiment: (same workload)
○ We use our test procedure;
○ We monitor the elasticity states throughout the test
execution;
○ Test Case:
■ answered operation >= 2000 ops -> pass
■ otherwise -> fail
○ Same test case associated to every elasticity state.
■ Test case re-executed throughout the cloud system
execution.
Experiments
16

Experiments
● Result of the second experiment:
○ Test through different elasticity states;
○ Assign test verdicts to different elasticity states;
■ Proportional to the previous experiment (correct elasticity
states).
RQ2:
It is possible to execute the test according to the elasticity state, and
we are able to assign the test verdicts correctly.
17

Conclusion and Future Work
● Identify all the performance problems;
● Assign the test verdicts to the correct elasticity states (at
runtime);
● Address the scaling states, which are not addressed by related
work;
● Future work:
○ Write functional test cases;
○ Apply to other study cases;
○ Generate test cases based on elasticity states.
18

Monitor-Based Testing of
Elastic Cloud Computing
Applications
Michel Albonico
PhD Student - AtlanMod - EMN (Nantes, France)
(michel.albonico@inria.fr)
Jean-Marie Mottu
Gerson Sunyé
19
5thInt.WorkshoponLargeScaleTesting
Delft,Netherlands-2016

Monitor-Based Testing of Elastic Cloud Computing Applications

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