sim towards an optimisation-aware data centre prediction toolkit · cactos prediction toolkit...
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Towards an Optimisation-Aware Data Centre Prediction Toolkit
Towards an Optimisation-Aware Data Centre Prediction Toolkit
Management of cloud applications and data centre resources has become increasingly complex, due in large part to a substantial increase in the degree of heterogeneity and scale. Topology optimisation tools and algorithms currently in use for optimal placement of applications across such heterogeneous resources are typically trial-and-error based. Predicions enable the evaluation of such optimisation algorithms and their complex interactions towards better reasoning at decision time, e.g. optimising for a specific trade-off.
This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstra?on under
grant agreement no. 610711
Sim
CactoScale acquires and analyses application behaviour and infrastructure performance data. It consolidates multiple sources of performance and error monitoring data and updates Infrastructure Models accordingly.
CactoOpt applies mathematical models to optimise application-resource mappings and create Optimisation Plans. It includes a library of configurable multi-objective optimisation algorithms to maximise data centre efficiency.
CACTOS Runtime Toolkit
Logical Load Model
Physical Load Model
Logical Data Centre Model
Physical Data Centre
Model
identifiesPhysical
Configuration
measures Load
identifies Logical
Configuration
creates
updates
extractsPhysical State
feeds
CactoOpt
adapts
reconfigures & deploys
CactoScale
CACTOS Prediction Toolkit
VMI OpenStack
VMI FCO
Virt
ualis
atio
n M
iddl
ewar
e In
tegr
atio
n
reconfigures & deploys
OptimisationPlan Model
creates
feeds
updatesCactoSim
Infrastructure Models
optimises
measures Load
identifies Physical and Logical Configuration
simulates
captures
provides Optimisation
Actions
VMI CactoSim
simulatesSystem Load
Real-WorldSimulation
provides Measurements
Data Centre
www.cactosfp7.eu
2014 Symposium on Software Performance, November 26-28, Stuttgart, Germany
Sergej Svorobej, James Byrne, PJ Byrne Dublin City University, Dublin 9, Ireland Henning Groenda, Christian Stier FZI Forschungszentrum Informatik, Karlsruhe, Germany
IVMIService
OpenStackAPI
CDO Model Generator
Runtime Model Storage
Prediction Model Storage
Cyclic Optimiser
CactoSim EngineCactoSim
IDE (Eclipse-based)
HBase
SQL DB
SQL DB
Chukwa
CDO
OptimizationEngine
CACTOS Chukwa Agent
CSE API
EMF Store
Virtualisation Middleware Integration
Control
FCOAPI
Conforms Provides Component Type (CPCT)
CPCT
CSEConnector
CACTOS Runtime Toolkit Version 1
COSControl
controlChukwaCollector
HBaseZooKeeperHadoop
Pig
CactoScale
CactoOpt Infrastructure
Optimiser
CactoOptIntegration
CACTOS Prediction Toolkit Version 1
CactoSim
OfflineAnalysis
VMIFCO
VMIOpenStack
CactoOptKnowledge DB
VMICactoSim
CyclicOptimiserSimulation
CactoSim’s Architecture is driven by the integration of the CACTOS Prediction Toolkit and the CACTOS Runtime Toolkit. CactoSim Engine and IDE build on Palladio and the SimuLizar Plugin for analyzing self-adaptive systems. They use a more complex Infrastructure Model and employ QVTo model transformations internally to map between the PCM and this model. The Prediction Model Storage uses EMF Store for versioning different data centre design and configuration alternatives. The Cyclic Optimiser Simulation uses this mapping to delegate optimisation plan creation to the CactoOpt Infrastructure Optimiser. This plan is enacted by VMI CactoSim within a simulation run.
Colors: CactoScale: 175 40 30 CactoOpt 60 150 240 CactoSim 190 190 40 Cactos 141 198 63 Green Lines 190 190 40
CactoSim is a discrete event simulation (DES) framework that enables the evaluation of the effect of provisioned CactoOpt runtime optimization strategies in a simulated environment at design time. This offers significant benefit over the utilization of testbeds which are typically costly to provide on a large scale and can have a high level of complexity. CactoSim allows for reproducible and controlled experimentation with different workload mix and resource scenarios enabling both performance and risk analysis to be performed as well as the tuning of potential bottlenecks in a data centre.
The current prototype integrates with the Runtime Toolkit and enables detailed data centre modelling. The simulation provides results for decision support through seamless model transformation to PCM. Further iterations will improve the mapping and prediction factors.