ClouDiA: a deployment advisor for public clouds

TY - GEN

T1 - ClouDiA

T2 - 39th International Conference on Very Large Data Bases

AU - Zou, Tao

AU - Bras, Ronan Le

AU - Vaz Salles, Marcos António

AU - Demers, Alan

AU - Gehrke, Johannes

N1 - Conference code: 39

PY - 2012/12

Y1 - 2012/12

N2 - An increasing number of distributed data-driven applications are moving into shared public clouds. By sharing resources and operating at scale, public clouds promise higher utilization and lower costs than private clusters. To achieve high utilization, however, cloud providers inevitably allocate virtual machine instances noncontiguously, i.e., instances of a given application may end up in physically distant machines in the cloud. This allocation strategy can lead to large differences in average latency between instances. For a large class of applications, this difference can result in significant performance degradation, unless care is taken in how application components are mapped to instances. In this paper, we propose ClouDiA, a general deployment advisor that selects application node deployments minimizing either (i) the largest latency between application nodes, or (ii) the longest critical path among all application nodes. ClouDiA employs mixedinteger programming and constraint programming techniques to efficiently search the space of possible mappings of application nodes to instances. Through experiments with synthetic and real applications in Amazon EC2, we show that our techniques yield a 15% to 55% reduction in time-to-solution or service response time, without any need for modifying application code.

AB - An increasing number of distributed data-driven applications are moving into shared public clouds. By sharing resources and operating at scale, public clouds promise higher utilization and lower costs than private clusters. To achieve high utilization, however, cloud providers inevitably allocate virtual machine instances noncontiguously, i.e., instances of a given application may end up in physically distant machines in the cloud. This allocation strategy can lead to large differences in average latency between instances. For a large class of applications, this difference can result in significant performance degradation, unless care is taken in how application components are mapped to instances. In this paper, we propose ClouDiA, a general deployment advisor that selects application node deployments minimizing either (i) the largest latency between application nodes, or (ii) the longest critical path among all application nodes. ClouDiA employs mixedinteger programming and constraint programming techniques to efficiently search the space of possible mappings of application nodes to instances. Through experiments with synthetic and real applications in Amazon EC2, we show that our techniques yield a 15% to 55% reduction in time-to-solution or service response time, without any need for modifying application code.

U2 - 10.14778/2535568.2448945

DO - 10.14778/2535568.2448945

M3 - Conference article

SN - 2150-8097

VL - 6

SP - 121

EP - 132

JO - Proceedings of the VLDB Endowment

JF - Proceedings of the VLDB Endowment

IS - 2

Y2 - 26 August 2013 through 30 August 2013

ER -