A performance analysis method for autonomic computing systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Globetp: template-based database replication for scalable web applications
Proceedings of the 16th international conference on World Wide Web
Online recovery in cluster databases
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Agent-based replication for scaling back-end databases of dynamic content web sites
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Policy-based autonomic computing with integral support for self-stabilisation
International Journal of Autonomic Computing
Self-adjustment strategy for models used in autonomic transactional systems
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
Modeling and predicting end-to-end response times in multi-tier internet applications
ITC20'07 Proceedings of the 20th international teletraffic conference on Managing traffic performance in converged networks
Adaptive internet services through performance and availability control
Proceedings of the 2010 ACM Symposium on Applied Computing
Autonomous return on investment analysis of additional processing resources
International Journal of Autonomic Computing
Autonomic mix-aware provisioning for non-stationary data center workloads
Proceedings of the 7th international conference on Autonomic computing
Dynamic database replica provisioning through virtualization
CloudDB '10 Proceedings of the second international workshop on Cloud data management
Journal of Network and Computer Applications
Predicting completion times of batch query workloads using interaction-aware models and simulation
Proceedings of the 14th International Conference on Extending Database Technology
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
Optimal resource allocation in synchronized multi-tier Internet services
Performance Evaluation
Modellus: Automated modeling of complex internet data center applications
ACM Transactions on the Web (TWEB)
An efficient overload control strategy in cloud
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
Provisioning multi-tier cloud applications using statistical bounds on sojourn time
Proceedings of the 9th international conference on Autonomic computing
Transactional auto scaler: elastic scaling of in-memory transactional data grids
Proceedings of the 9th international conference on Autonomic computing
Towards transparent and distributed workload management for large scale web servers
Future Generation Computer Systems
RTP: robust tenant placement for elastic in-memory database clusters
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for Percentile-Based Delay Guarantee
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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In autonomic provisioning, a resource manager allocates resources to an application, on-demand, e.g., during load spikes. Modelling-based approaches have proved very successful for provisioning the web and application server tiers in dynamic content servers. On the other hand, accurately modelling the behavior of the back-end database server tier is a daunting task. Hence, automated provisioning of database replicas has received comparatively less attention. This paper introduces a novel pro-active scheme based on the classic K-nearest-neighbors (KNN) machine learning approach for adding database replicas to application allocations in dynamic content web server clusters. Our KNN algorithm uses lightweight monitoring of essential system and application metrics in order to decide how many databases it should allocate to a given workload. Our pro-active algorithm also incorporates awareness of system stabilization periods after adaptation in order to improve prediction accuracy and avoid system oscillations. We compare this pro-active self-configuring scheme for scaling the database tier with a reactive scheme. Our experiments using the industry-standard TPC-W e-commerce benchmark demonstrate that the pro-active scheme is effective in reducing both the frequency and peak level of SLA violations compared to the reactive scheme. Furthermore, by augmenting the pro-active approach with awareness and tracking of system stabilization periods induced by adaptation in our replicated system, we effectively avoid oscillations in resource allocation.