TY - GEN
T1 - Latency-Sensitive Data Allocation for cloud storage
AU - Yang, Song
AU - Wieder, Philipp
AU - Aziz, Muzzamil
AU - Yahyapour, Ramin
AU - Fu, Xiaoming
N1 - Publisher Copyright:
© 2017 IFIP.
PY - 2017/7/20
Y1 - 2017/7/20
N2 - Customers often suffer from the variability of data access time in cloud storage service, caused by network congestion, load dynamics, etc. One solution to guarantee a reliable latency-sensitive service is to issue requests with multiple download/upload sessions, accessing the required data (replicas) stored in one or more servers. In order to minimize storage costs, how to optimally allocate data in a minimum number of servers without violating latency guarantees remains to be a crucial issue for the cloud provider to tackle. In this paper, we study the latency-sensitive data allocation problem for cloud storage. We model the data access time as a given distribution whose Cumulative Density Function (CDF) is known, and prove that this problem is NP-hard. To solve it, we propose both exact Integer Nonlinear Program (INLP) and Tabu Search-based heuristic. The proposed algorithms are evaluated in terms of the number of used servers, storage utilization and throughput utilization.
AB - Customers often suffer from the variability of data access time in cloud storage service, caused by network congestion, load dynamics, etc. One solution to guarantee a reliable latency-sensitive service is to issue requests with multiple download/upload sessions, accessing the required data (replicas) stored in one or more servers. In order to minimize storage costs, how to optimally allocate data in a minimum number of servers without violating latency guarantees remains to be a crucial issue for the cloud provider to tackle. In this paper, we study the latency-sensitive data allocation problem for cloud storage. We model the data access time as a given distribution whose Cumulative Density Function (CDF) is known, and prove that this problem is NP-hard. To solve it, we propose both exact Integer Nonlinear Program (INLP) and Tabu Search-based heuristic. The proposed algorithms are evaluated in terms of the number of used servers, storage utilization and throughput utilization.
UR - http://www.scopus.com/inward/record.url?scp=85029422152&partnerID=8YFLogxK
U2 - 10.23919/INM.2017.7987258
DO - 10.23919/INM.2017.7987258
M3 - Conference contribution
AN - SCOPUS:85029422152
T3 - Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network and Service Management
SP - 1
EP - 9
BT - Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network and Service Management
A2 - Chemouil, Prosper
A2 - Simoes, Paulo
A2 - Madeira, Edmundo
A2 - Secci, Stefano
A2 - Monteiro, Edmundo
A2 - Gaspary, Luciano Paschoal
A2 - dos Santos, Carlos Raniery P.
A2 - Charalambides, Marinos
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IFIP/IEEE International Symposium on Integrated Network and Service Management, IM 2017
Y2 - 8 May 2017 through 12 May 2017
ER -