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Optimizing Monitorability of Multi-cloud Applications
E. Fadda, P. Plebani, M. Vitali
Politecnico di Milano, ItalyPolitecnico di Torino, Italy
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Multi-cloud applications
VMVMVMVMVMVMVMVMVMVM
Multi-cloud application developer Cloud
providers
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Multi-cloud applications
VM
VM
VM
VM
Optimal deployment strategies take usually into account performances and capabilities of cloud providers
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MOTIVATION
Developers want to collect information about the behaviour of their applications deployed in clouds
GOALDeployment optimization based on both capabilities, quality, and cost of application monitoring data
Information on behaviour is obtained gathering monitoring dataNot all cloud providers offer the same monitoring capabilities
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The approach
Monitorability The possibility to measure and assess behaviour of the deployed application
Asks for monitorability
Offersmonitorability
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The approach
Monitorability The possibility to measure and assess behaviour of the deployed application
Ask for monitorability
Offermonitorability
VM VM
VM VM
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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
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We want more
VMUsability● 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
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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)
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MatchmakingOfferings and Requests are submitted to a Cloud Broker in charge of finding the best deployment
Ask for monitorability
Offermonitorability
VM VM
VM VM
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Matchmaking
VM VM
VM
VM
Maximizing
● Dimensions coverage
● Quality of monitoring
Minimizing
● Cost
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Example
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ExampleNumber of VMS and metrics of interest
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Example
Constraints on VM deployment
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Example
Metrics offered by cloud providers
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Additional information is required
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Knowledge Base
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Knowledge Base
Dimensions abstract information the user want to collect
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Knowledge Base
Dimensions abstract information the user want to collect
Metrics used to assess the dimension of interest
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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
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Metrics estimationEstimation 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
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Running optimizationSTEP 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
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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
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Accuracy computation
For monitored and on_demand metric measurements (mm), accuracy is:
sensor sampling timedesired sampling time
For estimated metric measurements (mm), accuracy is:
min sensor sampling time desired sampling time
∀ mm parents of the estimated mm
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Accuracy computation
The accuracy of a metric (m) is:
min(mm1,..,mmn)
∀ mm contributing to m
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Performance evaluationPerformances depend on number of servers, number of VMs, and number of metrics per VM
Solver: Gurobi
Servers:Intel Core i7-5500U 8GB RAM
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Validation
Sites: 7VMs: 4
Metrics: 7Response time: 19.2 sec
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Validation
Sites: 7VMs: 4
Metrics: 7Response time: 19.2 sec
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Future stepsImproving accuracy evaluation
Considering server capability in MILP
New multi-objective goal: integrating performance
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Optimizing Monitorability of Multi-cloud Applications
E. Fadda, P. Plebani, M. Vitali
Politecnico di Milano, ItalyPolitecnico di Torino, Italy