Data allocation in distributed database systems
ACM Transactions on Database Systems (TODS)
An analytic performance model of disk arrays
SIGMETRICS '93 Proceedings of the 1993 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Multi-Dimensional Database Allocation for Parallel Data Warehouses
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IEEE Transactions on Parallel and Distributed Systems
Memory resource management in VMware ESX server
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Dynamic resource allocation for database servers running on virtual storage
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ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Wikipedia workload analysis for decentralized hosting
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Automatic virtual machine configuration for database workloads
ACM Transactions on Database Systems (TODS)
Modeling workloads and devices for IO load balancing in virtualized environments
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Dynamic resource allocation for shared data centers using online measurements
IWQoS'03 Proceedings of the 11th international conference on Quality of service
Declarative automated cloud resource orchestration
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The evolving landscape of data management in the cloud
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"Cut me some slack": latency-aware live migration for databases
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Robust estimation of resource consumption for SQL queries using statistical techniques
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Consolidation and replication of VMs matching performance objectives
ASMTA'12 Proceedings of the 19th international conference on Analytical and Stochastic Modeling Techniques and Applications
Towards non-intrusive elastic query processing in the cloud
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SWAT: a lightweight load balancing method for multitenant databases
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CloudOptimizer: multi-tenancy for I/O-bound OLAP workloads
Proceedings of the 16th International Conference on Extending Database Technology
PMAX: tenant placement in multitenant databases for profit maximization
Proceedings of the 16th International Conference on Extending Database Technology
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Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Parallel analytics as a service
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
RTP: robust tenant placement for elastic in-memory database clusters
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Application level ballooning for efficient server consolidation
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Performance and resource modeling in highly-concurrent OLTP workloads
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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In most enterprises, databases are deployed on dedicated database servers. Often, these servers are underutilized much of the time. For example, in traces from almost 200 production servers from different organizations, we see an average CPU utilization of less than 4%. This unused capacity can be potentially harnessed to consolidate multiple databases on fewer machines, reducing hardware and operational costs. Virtual machine (VM) technology is one popular way to approach this problem. However, as we demonstrate in this paper, VMs fail to adequately support database consolidation, because databases place a unique and challenging set of demands on hardware resources, which are not well-suited to the assumptions made by VM-based consolidation. Instead, our system for database consolidation, named Kairos, uses novel techniques to measure the hardware requirements of database workloads, as well as models to predict the combined resource utilization of those workloads. We formalize the consolidation problem as a non-linear optimization program, aiming to minimize the number of servers and balance load, while achieving near-zero performance degradation. We compare Kairos against virtual machines, showing up to a factor of 12× higher throughput on a TPC-C-like benchmark. We also tested the effectiveness of our approach on real-world data collected from production servers at Wikia.com, Wikipedia, Second Life, and MIT CSAIL, showing absolute consolidation ratios ranging between 5.5:1 and 17:1.