Preliminary results from optimisation models validation and experimentation: project deliverable D6.2
Castañé, Gabriel González
Byrne, Peter J.
FakultätFakultät für Ingenieurwissenschaften, Informatik und Psychologie
InstitutionInstitut für Organisation und Management von Informationssystemen
Externe KooperationenDublin City University
FZI Forschungszentrum Informatik am Karlsruher Institut für Technologie
Ressourcen- / MedientypBericht, Text
Datum der Erstveröffentlichung2017-04-21
Since the arrival of cloud computing, a significant amount of research has been and continues to be carried out towards the creation of efficient optimisation strategies for meeting certain optimisation goals such as energy efficiency, resource consolidation or performance improvement within virtualised data centres. However, investigating whether specific optimisation algorithms can achieve the desired function in a production environment, and investigating how well they operate are quite complex tasks. Untested optimisation rules typically cannot be directly deployed in the production system, instead requiring manual test-bed experiments. This technique can be prohibitively costly, time consuming and cannot always account for scale and other constraints. This work presents a design-time optimisation evaluation solution based on discrete event simulation for cloud computing. By using a simulation toolkit (CactoSim) coupled with a runtime optimisation toolkit (CactoOpt), a cloud architect is able to create a direct replica model of the data centre production environment and then run simulations which take into account optimisation strategies. Results produced by such simulations can be used to estimate the optimisation algorithm performance under various conditions. In order to test the CactoSim and CactoOpt integration concept, a validation process has been performed on two different scenarios. The first scenario investigates the VM placement algorithm performance within a simulated testbed when admitting new VMs into the system. The second scenario analyses consolidation optimisation strategy impact on resource utilisation, with the objective being to free up nodes towards the goal of energy saving. This deliverable represents the initial part of two iterative pieces of work.
LizenzCC BY-ND 4.0 International
Electric network topology
Context-aware cloud topology
DDC-SachgruppeDDC 004 / Data processing & computer science
Das könnte Sie auch interessieren:
Bowater, James (Universität Ulm, 2017-04-19)This deliverable will provide an update on the CACTOS Dissemination and Collaboration Report laid out in D2.3.1. Firstly, the objectives, intentions and targeted impact of CACTOS dissemination activities are presented, ...
Papazachos, Zafeirios; Wesner, Stefan; Domaschka, Jörg; Sheridan, Craig; Stewart, Claire; Innes, Alasdair; Stier, Christian; Henß, Jörg; Ali-Eldin, Ahmed; Tsitsipas, Athanasios; Byrne, James (Universität Ulm, 2017-04-19)This deliverable will provide an update on the CACTOS Dissemination and Collaboration Report laid out in D2.3.2. It marks the final report of the CACTOS project concerning dissemination. Firstly, the objectives, intentions ...
Innes, Alasdair; Sheridan, Craig; Wesner, Stefan; Mann, Sandra; Eisinger, Uwe; Byrne, James; Groenda, Henning (Universität Ulm, 2017-04-19)This deliverable will detail the plan for the project dissemination strategy to be adopted during the project lifetime. This will include procedures for partners to follow when publishing articles and will show how ...