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Hybrid Adaptive Checkpointing for Virtual Machine Fault Tolerance
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems Group)
Umeå University, Faculty of Science and Technology, Department of Computing Science. Red Hat Inc..
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2018 (English)In: Proceedings - 2018 IEEE International Conference on Cloud Engineering, IC2E 2018 / [ed] Li J., Chandra A., Guo T., Cai Y., Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 12-22Conference paper, Published paper (Refereed)
Abstract [en]

Active Virtual Machine (VM) replication is an application independent and cost-efficient mechanism for high availability and fault tolerance, with several recently proposed implementations based on checkpointing. However, these methods may suffer from large impacts on application latency, excessive resource usage overheads, and/or unpredictable behavior for varying workloads. To address these problems, we propose a hybrid approach through a Proportional-Integral (PI) controller to dynamically switch between periodic and on-demand check-pointing. Our mechanism automatically selects the method that minimizes application downtime by adapting itself to changes in workload characteristics. The implementation is based on modifications to QEMU, LibVirt, and OpenStack, to seamlessly provide fault tolerant VM provisioning and to enable the controller to dynamically select the best checkpointing mode. Our evaluation is based on experiments with a video streaming application, an e-commerce benchmark, and a software development tool. The experiments demonstrate that our adaptive hybrid approach improves both application availability and resource usage compared to static selection of a checkpointing method, with application performance gains and neglectable overheads.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 12-22
Keywords [en]
Fault Tolerance, Resource Management, Checkpoint, COLO, Control Theory
National Category
Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-152033DOI: 10.1109/IC2E.2018.00023Scopus ID: 2-s2.0-85048315473ISBN: 978-1-5386-5009-7 (print)ISBN: 978-1-5386-5008-0 (electronic)OAI: oai:DiVA.org:umu-152033DiVA, id: diva2:1250625
Conference
2018 IEEE International Conference on Cloud Engineering (IC2E 2018), 17–20 April 2018, Orlando, Florida, USA
Available from: 2018-09-24 Created: 2018-09-24 Last updated: 2019-01-24Bibliographically approved
In thesis
1. Application-aware resource management for datacenters
Open this publication in new window or tab >>Application-aware resource management for datacenters
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Alternative title[sv]
Applikationsmedveten resurshantering för datacenter
Abstract [en]

High Performance Computing (HPC) and Cloud Computing datacenters are extensively used to steer and solve complex problems in science, engineering, and business, such as calculating correlations and making predictions. Already in a single datacenter server, there are thousands of hardware and software metrics – Key Performance Indicators (KPIs) – that individually and aggregated can give insight in the performance, robustness, and efficiency of the datacenter and the provisioned applications. At the datacenter level, the number of KPIs is even higher. The fast growing interest on datacenter management from both public and industry together with the rapid expansion in scale and complexity of datacenter resources and the services being provided on them have made monitoring, profiling, controlling, and provisioning compute resources dynamically at runtime into a challenging and complex task. Commonly, correlations of application KPIs, like response time and throughput, with resource capacities show that runtime systems (e.g., containers or virtual machines) that are used to provision these applications do not utilize available resources efficiently. This reduces datacenter efficiency, which in term results in higher operational costs and longer waiting times for results.

The goal of this thesis is to develop tools and autonomic techniques for improving datacenter operations, management and utilization, while improving and/or minimizing impacts on applications performance. To this end, we make use of application resource descriptors to create a library that dynamically adjusts the amount of resources used, enabling elasticity for scientific workflows in HPC datacenters. For mission critical applications, high availability is of great concern since these services must be kept running even in the event of system failures. By modeling and correlating specific resource counters, like CPU, memory and network utilization, with the number of runtime synchronizations, we present adaptive mechanisms to dynamically select which fault tolerant mechanism to use. Likewise, for scientific applications we propose a hybrid extensible architecture for dual-level scheduling of data intensive jobs in HPC infrastructures, allowing operational simplification, on-boarding of new types of applications and achieving greater job throughput with higher overall datacenter efficiency.

Place, publisher, year, edition, pages
Umeå: Department of computing science, Umeå university, 2018. p. 28
Series
Report / UMINF, ISSN 0348-0542 ; 18.14
Keywords
Resource Management, High Performance Computing, Cloud Computing
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-155620 (URN)978-91-7601-971-9 (ISBN)
Presentation
2018-12-12, MA121, MIT-Huset, Umeå, 20:31 (English)
Opponent
Supervisors
Available from: 2019-01-25 Created: 2019-01-24 Last updated: 2019-02-04Bibliographically approved

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Souza, AbelTomás Bolivar, LuisTordsson, Johan

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