Bigtable: a distributed storage system for structured data
OSDI '06 Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7
The end of an architectural era: (it's time for a complete rewrite)
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Automatic virtual machine configuration for database workloads
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Communications of the ACM
Securing elasticity in the cloud
Communications of the ACM
Benchmarking cloud serving systems with YCSB
Proceedings of the 1st ACM symposium on Cloud computing
Automated control for elastic storage
Proceedings of the 7th international conference on Autonomic computing
Schism: a workload-driven approach to database replication and partitioning
Proceedings of the VLDB Endowment
Dynamically scaling applications in the cloud
ACM SIGCOMM Computer Communication Review
The SCADS director: scaling a distributed storage system under stringent performance requirements
FAST'11 Proceedings of the 9th USENIX conference on File and stroage technologies
On the elasticity of NoSQL databases over cloud management platforms
Proceedings of the 20th ACM international conference on Information and knowledge management
Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Lookup Tables: Fine-Grained Partitioning for Distributed Databases
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Proceedings of the 4th annual Symposium on Cloud Computing
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NoSQL databases manage the bulk of data produced by modern Web applications such as social networks. This stems from their ability to partition and spread data to all available nodes, allowing NoSQL systems to scale. Unfortunately, current solutions' scale out is oblivious to the underlying data access patterns, resulting in both highly skewed load across nodes and suboptimal node configurations. In this paper, we first show that judicious placement of HBase partitions taking into account data access patterns can improve overall throughput by 35%. Next, we go beyond current state of the art elastic systems limited to uninformed replica addition and removal by: i) reconfiguring existing replicas according to access patterns and ii) adding replicas specifically configured to the expected access pattern. MeT is a prototype for a Cloud-enabled framework that can be used alone or in conjunction with OpenStack for the automatic and heterogeneous reconfiguration of a HBase deployment. Our evaluation, conducted using the YCSB workload generator and a TPC-C workload, shows that MeT is able to i) autonomously achieve the performance of a manual configured cluster and ii) quickly reconfigure the cluster according to unpredicted workload changes.