TY - GEN
T1 - Dynamic resource provisioning for data streaming applications in a cloud environment
AU - Vijayakumar, Smita
AU - Zhu, Qian
AU - Agrawal, Gagan
PY - 2010
Y1 - 2010
N2 - The recent emergence of cloud computing is making the vision of utility computing realizable, i.e., computing resources and services from a cloud can be delivered, utilized, and paid for in the same fashion as utilities like water or electricity. Current cloud service providers have taken some steps towards supporting the true pay-as-you-go or a utility-like pricing model, and current research points towards more fine-grained allocation and pricing of resources in the future. In such environments, resource provisioning becomes a challenging problem, since one needs to avoid both underprovisioning (leading to application slowdown) and overprovisioning (leading to unnecessary resource costs). In this paper, we consider this problem in the context of streaming applications. In these applications, since the data is generated by external sources, the goal is to carefully allocate resources so that the processing rate can match the rate of data arrival. We have developed a solution that can handle unexpected data rates, including the transient rates. We evaluate our approach using two streaming applications in a virtualized environment.
AB - The recent emergence of cloud computing is making the vision of utility computing realizable, i.e., computing resources and services from a cloud can be delivered, utilized, and paid for in the same fashion as utilities like water or electricity. Current cloud service providers have taken some steps towards supporting the true pay-as-you-go or a utility-like pricing model, and current research points towards more fine-grained allocation and pricing of resources in the future. In such environments, resource provisioning becomes a challenging problem, since one needs to avoid both underprovisioning (leading to application slowdown) and overprovisioning (leading to unnecessary resource costs). In this paper, we consider this problem in the context of streaming applications. In these applications, since the data is generated by external sources, the goal is to carefully allocate resources so that the processing rate can match the rate of data arrival. We have developed a solution that can handle unexpected data rates, including the transient rates. We evaluate our approach using two streaming applications in a virtualized environment.
UR - http://www.scopus.com/inward/record.url?scp=79952390204&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79952390204&partnerID=8YFLogxK
U2 - 10.1109/CloudCom.2010.95
DO - 10.1109/CloudCom.2010.95
M3 - Conference contribution
AN - SCOPUS:79952390204
SN - 9780769543024
T3 - Proceedings - 2nd IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2010
SP - 441
EP - 448
BT - Proceedings - 2nd IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2010
T2 - 2nd IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2010
Y2 - 30 November 2010 through 3 December 2010
ER -