This document proposes an approach to optimize the monitorability of applications deployed across multiple cloud providers. It aims to maximize the number of application metrics that can be monitored while minimizing costs, based on each cloud provider's monitoring capabilities. A matchmaking algorithm finds the best deployment by considering users' monitoring requirements, providers' offered metrics and costs, and estimating metrics that are not directly measurable. The approach was validated on a sample deployment and showed response times around 19 seconds for optimizing monitoring of 4 VMs with 7 metrics each across 7 cloud providers.

1. Optimizing Monitorability of
Multi-cloud Applications
E. Fadda, P. Plebani, M. Vitali
Politecnico di Milano, Italy
Politecnico di Torino, Italy

2. Multi-cloud applications
VMVMVMVMVMVM
VMVMVMVM
Multi-cloud
application
developer Cloud
providers

3. Multi-cloud applications
VM
VM
VM
VM
Optimal deployment strategies take usually into account
performances and capabilities of cloud providers

4. MOTIVATION
Developers want to collect information about the
behaviour of their applications deployed in clouds
GOAL
Deployment optimization based on both capabilities,
quality, and cost of application monitoring data
Information on behaviour is obtained gathering
monitoring data
Not all cloud providers offer the same monitoring
capabilities

5. The approach
Monitorability
The possibility to measure and assess behaviour of the
deployed application
Asks for
monitorability
Offers
monitorability

6. The approach
Monitorability
The possibility to measure and assess behaviour of the
deployed application
Ask for
monitorability
Offer
monitorability
VM VM
VM VM

7. Monitorability
● Requested list of dimensions: e.g., availability, cpu load
● Sampling time (not always)
+ capabilities and constraints
+ budget
VM
● Offered list of dimensions: e.g., availability, cpu load
● Sampling time
+ capabilities and constraints
+ cost

8. We want more
VM
Usability
● Application developers can easily define their
requirements
● Technical details should be hidden to the user
Extensibility
● Offering includes monitored dimensions
● … but also estimated (E) dimensions
● … and on-demand (M) dimensions

9. Approach feasibility
Different cloud providers can provide a different set of
metrics.
A cloud provider offers metrics with higher accuracy at a
cost (e.g. Amazon Cloud Watch, Paraleap Cloud Monix)
Some monitoring systems can be extended with custom
metrics (e.g. Nagios, PCMONS, Sensus)

10. Matchmaking
Offerings and Requests are submitted to a Cloud Broker
in charge of finding the best deployment
Ask for
monitorability
Offer
monitorability
VM VM
VM VM

11. Matchmaking
VM VM
VM
VM
Maximizing
● Dimensions coverage
● Quality of monitoring
Minimizing
● Cost

12. Example

13. Example
Number of VMS and
metrics of interest

14. Example
Constraints on VM
deployment

15. Example
Metrics offered by
cloud providers

16. Additional information is required

17. Knowledge Base

18. Knowledge Base
Dimensions abstract information the user want to collect

19. Knowledge Base
Dimensions abstract information the user want to collect
Metrics used to assess the dimension of interest

20. Knowledge Base
Dimensions abstract information the user want to collect
Metrics used to assess the dimension of interest
Metric Measurements used to compose the metric and
provided by probes

21. Metrics estimation
Estimation is used to provide trends of a metric without
need to measure it.
Analysis of stored data to find relations between metrics.
Represented through a Bayesian Network.
Vitali,Pernici, and O’Reilly, “Learning a goal-oriented model for energy efficient adaptive applications in data centers,”
Information Sciences 2015

22. Running optimization
STEP 1 The user specifies for each VM the dimensions
or the metrics he is interested to collect, with their
accuracy
STEP 2 The set of metrics are extracted from the
knowledge base from the dimensions
STEP 3 The optimization algorithm - multi-objective
MILP - is executed to find the set of feasible solutions
Estimating the accuracy for each metric in each configuration

23. The optimization function
Assign VMs to sites to maximize:
monitored(m,s,v) + Δon_demand(m,s,v) + Δestimated(m,s,v)
and minimize
cost
… and constraints

24. Accuracy computation
For monitored and on_demand metric measurements
(mm), accuracy is:
sensor sampling time
desired sampling time
For estimated metric measurements (mm), accuracy is:
min sensor sampling time
desired sampling time
∀ mm parents of the estimated mm

25. Accuracy computation
The accuracy of a metric (m) is:
min(mm1
,..,mmn
)
∀ mm contributing to m

26. Performance evaluation
Performances depend on number of servers, number of
VMs, and number of metrics per VM
Solver: Gurobi
Servers:
Intel Core i7-5500U
8GB RAM

27. Validation
Sites: 7
VMs: 4
Metrics: 7
Response time: 19.2 sec

28. Validation
Sites: 7
VMs: 4
Metrics: 7
Response time: 19.2 sec

29. Future steps
Improving accuracy evaluation
Considering server capability in MILP
New multi-objective goal: integrating performance

30. Optimizing Monitorability of
Multi-cloud Applications
E. Fadda, P. Plebani, M. Vitali
Politecnico di Milano, Italy
Politecnico di Torino, Italy