Pregled bibliografske jedinice broj: 1041588
Predicting resource allocation and costs for business processes in the cloud
Predicting resource allocation and costs for business processes in the cloud // Proceedings - 2015 IEEE World Congress on Services, SERVICES 2015 / Zhang, Liang-Jie ; Bahsoon, Rami (ur.).
New York (NY): Institute of Electrical and Electronics Engineers (IEEE), 2015. str. 47-54 doi:10.1109/SERVICES.2015.16 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1041588 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Predicting resource allocation and costs for business processes in the cloud
Autori
Toni Mastelić ; Walid Fdhila ; Ivona Brandić ; Stefanie Rinderle-Ma
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings - 2015 IEEE World Congress on Services, SERVICES 2015
/ Zhang, Liang-Jie ; Bahsoon, Rami - New York (NY) : Institute of Electrical and Electronics Engineers (IEEE), 2015, 47-54
ISBN
978-1-4673-7275-6
Skup
2015 IEEE World Congress on Services
Mjesto i datum
New York City (NY), Sjedinjene Američke Države, 27.06.2015. - 02.07.2015
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
cloud ; business processes ; resource allocation
Sažetak
By moving business processes into the cloud, business partners can benefit from lower costs, more flexibility and greater scalability in terms of resources offered by the cloud providers. In order to execute a process or a part of it, a business process owner selects and leases feasible resources while considering different constraints, e.g., Optimizing resource requirements and minimizing their costs. In this context, utilizing information about the process models or the dependencies between tasks can help the owner to better manage leased resources. In this paper, we propose a novel resource allocation technique based on the execution path of the process, used to assist the business process owner in efficiently leasing computing resources. The technique comprises three phases, namely process execution prediction, resource allocation and cost estimation. The first exploits the business process model metrics and attributes in order to predict the process execution and the requires resources, while the second utilizes this prediction for efficient allocation of the cloud resources. The final phase estimates and optimizes costs of leased resources by combining different pricing models offered by the provider.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
