TY - GEN
T1 - Ambry
T2 - 2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016
AU - Noghabi, Shadi A.
AU - Subramanian, Sriram
AU - Narayanan, Priyesh
AU - Narayanan, Sivabalan
AU - Holla, Gopalakrishna
AU - Zadeh, Mammad
AU - Li, Tianwei
AU - Gupta, Indranil
AU - Campbell, Roy H.
N1 - Publisher Copyright:
© 2016 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2016/6/26
Y1 - 2016/6/26
N2 - The infrastructure beneath a worldwide social network has to continually serve billions of variable-sized media objects such as photos, videos, and audio clips. These objects must be stored and served with low latency and high through- put by a system that is geo-distributed, highly scalable, and load-balanced. Existing file systems and object stores face several challenges when serving such large objects. We present Ambry, a production-quality system for storing large immutable data (called blobs). Ambry is designed in a decentralized way and leverages techniques such as logical blob grouping, asynchronous replication, rebalancing mechanisms, zero-cost failure detection, and OS caching. Ambry has been running in LinkedIn's production environment for the past 2 years, serving up to 10K requests per second across more than 400 million users. Our experimental evaluation reveals that Ambry offers high efficiency (utilizing up to 88% of the network bandwidth), low latency (less than 50 ms latency for a 1 MB object), and load balancing (improving imbalance of request rate among disks by 8x-10x).
AB - The infrastructure beneath a worldwide social network has to continually serve billions of variable-sized media objects such as photos, videos, and audio clips. These objects must be stored and served with low latency and high through- put by a system that is geo-distributed, highly scalable, and load-balanced. Existing file systems and object stores face several challenges when serving such large objects. We present Ambry, a production-quality system for storing large immutable data (called blobs). Ambry is designed in a decentralized way and leverages techniques such as logical blob grouping, asynchronous replication, rebalancing mechanisms, zero-cost failure detection, and OS caching. Ambry has been running in LinkedIn's production environment for the past 2 years, serving up to 10K requests per second across more than 400 million users. Our experimental evaluation reveals that Ambry offers high efficiency (utilizing up to 88% of the network bandwidth), low latency (less than 50 ms latency for a 1 MB object), and load balancing (improving imbalance of request rate among disks by 8x-10x).
KW - Geographically distributed
KW - Load balancing
KW - Object store
KW - Scalable
UR - http://www.scopus.com/inward/record.url?scp=84979681342&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84979681342&partnerID=8YFLogxK
U2 - 10.1145/2882903.2903738
DO - 10.1145/2882903.2903738
M3 - Conference contribution
AN - SCOPUS:84979681342
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
SP - 253
EP - 265
BT - SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data
PB - Association for Computing Machinery
Y2 - 26 June 2016 through 1 July 2016
ER -