Adaptive software in cloud computing
Marin [email protected] UniversityCanada
© Marin Litoiu, EU-Canada Future Internet Workshop
© Marin Litoiu, EU-Canada Future Internet Workshop
Content
Elasticity
Business Driven Elasticity
Architectures
– Model based feedback loops
– Strategy trees based feedback loops
Open Issues
© Marin Litoiu, EU-Canada Future Internet Workshop
Elasticity Traditionally, we sized applications for typical workloads
Wasted capacity
(Cost)
Lost revenue
Lost customers
(lost revenue)
*Berkley report The promise: adapt at runtime
© Marin Litoiu, EU-Canada Future Internet Workshop
Business Driven Elasticity
Elasticity achieves business goals– Minimize cost (and/or increase revenue)
– Includes energy savings– Meet SLO
By monitoring the goals through sensors
Response times, utilization, profit, cost etc..
And by changing control inputs (actuators)
– Hardware resource allocation ( CPUs, memory, storage…)
– Software resources (licences, threads, replicas…)
Subject to policies (constraints)
– Hardware and software capacity limits
5
© Marin Litoiu, EU-Canada Future Internet Workshop
Adaptive Feedback Loops
© Marin Litoiu, EU-Canada Future Internet Workshop
Cloud Landscape
SaaS Management
PaaS Management
IaaS Management
SaaS
(Salesforce, Google, IBM)
PaaS
(Google, IBM)
IaaS
(Amazon, IBM)
CPU
Hardware
Execution EnvironmentProgramming Environment
ApplicationSimple services
(OpenID)
Virtual
machineCPU
AccessServices
Service Models: IaaS, PaaS, SaaS
Deployment Models: private, community, hybrid, public
© Marin Litoiu, EU-Canada Future Internet Workshop
…hence Layered Adaptation
PaaSPaaS
IaaSIaaS
SaaS SaaS
Sensors•Processors , memory, disk utilization•Processors throughput and response
times
Actuators•VM to processor allocation
•VM settings ( memory, CPU ratio)•VM storage
•Network bandwidth
Major Goals:•hardware utilization
Constraints: •capacity
Sensors•VM Utilization, response time,
throughput•Container utilizations, throughput,
response times....
Actuators•Number of VMs, licenses
•Allocation of containers to VMs•Container settings (threads,
caching)...
Major Goals:•Reduce cost
•Increase revenue Constraints:
•capacity
Sensors:- Service (application) QoS
-response time -throughput
Actuators•Deployment topology
•Parameter tuning
Major Goals•QoS (response time,
throughput)
Constraints :•SLA
© Marin Litoiu, EU-Canada Future Internet Workshop
1. Model Based Adaptive Loops
Performance model
Optimization & Control
State estimator
Monitoring
Services
Goals & Policies
Control Change(uc)
Disturbances(pc)
yc,uc,pc,xc
Workload classifier
Cloud Layer (xc,yc)
A
ctuato
rs Sensors
Mo
del id
entificatio
n
© Marin Litoiu, EU-Canada Future Internet Workshop
Adaptation at PaaS Layer
Goal: Profit = Revenue-Cost
– Revenue = proportional with the number of applications
– Cost = Price per VM running + price licences
• thus at a given moment the goal is minimum cost for the given applications
Constraints: SLAs, capacity limits, etc
PaaSPaaSSensors•VM Utilization, response
time, throughput•Container utilizations, throughput, response
times....
Goals:
•platform profit
Actuators•Number of VMs, licenses
•Allocation of containers to VMs
•Container settings (threads, caching)...
© Marin Litoiu, EU-Canada Future Internet Workshop
PaaS Optimization and Control….
sctsht ,,
minCOST =
H
h
T
t hthC1 1
(3)
subject to:
Service level agreement: for each c, fc fc,SLA.
Host capacity: for each h, ht h
t T
. To limit the
maximum processor utilization to h <1 replace Ωh by hΩh .
Flow balance: ht ts
h H s S
(for all t); ts sc
t T c C
(for
all s); sc c scf d (for all s and c)
Nonnegative flows: for all h, t, s, ht ≥ 0, ts ≥ 0, γsc ≥ 0.
Minimize COST in PaaS
-Across N applications
-Subject to
-SLA
-application integrity
-processing capacity
-memory
-licence constraints
By asking IaaS to slice the physical resources into
virtual resources
© Marin Litoiu, EU-Canada Future Internet Workshop
Results (1): SaaS
The cost is low when traffic is low
Response time is kept below a
target
The application uses less physical
machines when traffic is low
multitier interactive applications
© Marin Litoiu, EU-Canada Future Internet Workshop
Results(2): PaaS Results are for multi tier applications, that can scale horizontally and vertically
FO: full optimization
IO: incremental optimization
It is more efficient to redeploy ALL applications periodically (similar to disk defragmentation)
© Marin Litoiu, EU-Canada Future Internet Workshop
2. Policy Based Adaptive Loops
© Marin Litoiu, EU-Canada Future Internet Workshop
Further Challenges
Centralized versus decentralized adaptation
– Geographically distributed clouds
Coordination among different layers
– Sensors and actuators
Global versus local optimization
Accurate models for different layers
© Marin Litoiu, EU-Canada Future Internet Workshop
References
Litoiu M, Woodside M., Wong J., Ng J., Iszlai G., “A Business Driven Cloud Optimization Architecture”, Proceedings of ACM SAC 2010, Sierre, Switzerland, March 24-29, 2010
Simmons B., Litoiu M., “Towards a Cloud Optimization Architecture with Strategy Trees,” Proceedings of IEEE I2TS 2010, Rio de Janeiro, Brazil, Dec 2010.
Ghanbari H., Litoiu M., Simmons B., Barna C., “Feedback-based Optimization of a Private Cloud,” IEEE Conference on Utility and Cloud Computing ( UCC 2010), December, Chennai, India, 2010.
Zheng T., Litoiu M., Woodside M., “Integrated Estimation and Tracking of Performance Model Parameters and their Trends,” 2nd ACM/Spec International Conference on Performance Engineering, Karlsruhe, March 14-16, 2011.
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