TY - GEN
T1 - Need for speed
T2 - 34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015
AU - Huang, Zhe
AU - Balasubramanian, Bharath
AU - Wang, Michael
AU - Lan, Tian
AU - Chiang, Mung
AU - Tsang, Danny H.K.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/21
Y1 - 2015/8/21
N2 - There is an increasing need for cloud service performance that can be tailored to customer requirements. In the context of jobs submitted to cloud computing clusters, a crucial requirement is the specification of job completion-times. A natural way to model this specification, is through client/job utility functions that are dependent on job completion-times. We present a method to allocate and schedule heterogeneous resources to jointly optimize the utilities of jobs in a cloud. Specifically: (i) we formulate a completion-time optimal resource allocation (CORA) problem to apportion cluster resources across the jobs that enforces max-min fairness among job utilities, and (ii) starting with an integer programming problem, we perform a series of steps to transform it into an equivalent linear programming problem, and (iii) we implement the proposed framework as a utility-aware resource scheduler in the widely used Hadoop data processing framework, and finally (iv) through extensive experiments with real-world datasets, we show that our prototype achieves significant performance improvement over existing resource-allocation policies.
AB - There is an increasing need for cloud service performance that can be tailored to customer requirements. In the context of jobs submitted to cloud computing clusters, a crucial requirement is the specification of job completion-times. A natural way to model this specification, is through client/job utility functions that are dependent on job completion-times. We present a method to allocate and schedule heterogeneous resources to jointly optimize the utilities of jobs in a cloud. Specifically: (i) we formulate a completion-time optimal resource allocation (CORA) problem to apportion cluster resources across the jobs that enforces max-min fairness among job utilities, and (ii) starting with an integer programming problem, we perform a series of steps to transform it into an equivalent linear programming problem, and (iii) we implement the proposed framework as a utility-aware resource scheduler in the widely used Hadoop data processing framework, and finally (iv) through extensive experiments with real-world datasets, we show that our prototype achieves significant performance improvement over existing resource-allocation policies.
UR - http://www.scopus.com/inward/record.url?scp=84954234517&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84954234517&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM.2015.7218460
DO - 10.1109/INFOCOM.2015.7218460
M3 - Conference contribution
AN - SCOPUS:84954234517
T3 - Proceedings - IEEE INFOCOM
SP - 891
EP - 899
BT - 2015 IEEE Conference on Computer Communications, IEEE INFOCOM 2015
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 April 2015 through 1 May 2015
ER -