TY - GEN
T1 - Right-Sizing Geo-distributed Data Centers for Availability and Latency
AU - Narayanan, Iyswarya
AU - Kansal, Aman
AU - Sivasubramaniam, Anand
N1 - Funding Information:
This work has been supported in part by NSF grants 1302225, 1302557, 1213052, 1439021, 1629129, and 1526750.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/13
Y1 - 2017/7/13
N2 - We show cloud developers how to right size data center (DC) capacity for geo-distributed applications deployed on several multi-megawatt DCs, possibly also using many smaller edge DCs. Note that capacity considerations for a geo-distributed infrastructure do not decompose into individual DC capacity planning. When edge DCs are used, heterogeneous availability and costs affect the capacity split between the edge and core DCs. Non-uniform spatial distribution of clients and interdependence between latency and availability constraints make it non-trivial to provision the right capacity at each DC. We develop a geo-distributed capacity planning framework to capture the key factors that influence capacity, ranging from application demand patterns, latency and availability requirements, DC cost-availability trade-offs, and data replication overheads. We apply our framework to a realistic application and DC infrastructure setting to gather insights into how capacity should be provisioned and allocated across DCs for a representative set of requirements and costs.
AB - We show cloud developers how to right size data center (DC) capacity for geo-distributed applications deployed on several multi-megawatt DCs, possibly also using many smaller edge DCs. Note that capacity considerations for a geo-distributed infrastructure do not decompose into individual DC capacity planning. When edge DCs are used, heterogeneous availability and costs affect the capacity split between the edge and core DCs. Non-uniform spatial distribution of clients and interdependence between latency and availability constraints make it non-trivial to provision the right capacity at each DC. We develop a geo-distributed capacity planning framework to capture the key factors that influence capacity, ranging from application demand patterns, latency and availability requirements, DC cost-availability trade-offs, and data replication overheads. We apply our framework to a realistic application and DC infrastructure setting to gather insights into how capacity should be provisioned and allocated across DCs for a representative set of requirements and costs.
UR - http://www.scopus.com/inward/record.url?scp=85027278173&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027278173&partnerID=8YFLogxK
U2 - 10.1109/ICDCS.2017.118
DO - 10.1109/ICDCS.2017.118
M3 - Conference contribution
AN - SCOPUS:85027278173
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 230
EP - 240
BT - Proceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017
A2 - Lee, Kisung
A2 - Liu, Ling
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017
Y2 - 5 June 2017 through 8 June 2017
ER -