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Management of distributed resource allocations in multi-cluster environments
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science. (UMIT)
2012 (English)In: Performance Computing and Communications Conference (IPCCC) 2012, 31st International, IEEE, New York, USA: IEEE , 2012, 275-284 p.Conference paper, Published paper (Other academic)
Abstract [en]

We present a fully distributed solution for managing resource allocation for services running across multiple clusters in a large-scale cloud computing environment. Our solution allows individual services running across clusters to compete dynamically for allocations based on their rate of consumption while maintaining the global cloud level allocation limits. The solution monitors resource consumption by services that are spread over a number of clusters. Global polls are triggered only when the allocated balance in a cluster decreases below a threshold and allocations are reassigned in a manner that avoids further immediate global polls. Our solution achieves scalability by minimizing global message exchanges, increases performance by distributing requests, and improves availability by avoiding a single point of failure. We perform a range of simulations to verify the accuracy of our approach, to validate our theoretical results, and to evaluate against competing approaches.

Place, publisher, year, edition, pages
New York, USA: IEEE , 2012. 275-284 p.
Series
2012 IEEE 31ST International performance computing and communications conference (IPCCC), ISSN 1097-2641
Keyword [en]
Distributed monitoring, distributed resource allocations management, distributed quota management
National Category
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-66430DOI: 10.1109/PCCC.2012.6407768ISI: 000313524400040ISBN: 978-1-4673-4881-2 (print)OAI: oai:DiVA.org:umu-66430DiVA: diva2:607250
Conference
IEEE 31st International Performance Computing and Communications Conference (IPCCC)2012-12-01--03, Austin, Texas, USA
Available from: 2013-02-22 Created: 2013-02-19 Last updated: 2015-09-01Bibliographically approved
In thesis
1. Managing Resource Usage and Allocations in Multi-Cluster Clouds
Open this publication in new window or tab >>Managing Resource Usage and Allocations in Multi-Cluster Clouds
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

 The emergence of large-scale Internet services has fueled a trend toward large-scale systems composed of geographically distributed clusters. Managing resource allocations and resource usage is an important task for such services.

Resource allocations and resource usage management mechanisms for services running across clusters play vital roles in the performance of the entire system, economical sustainability of the provider, and level of customers satisfaction provided by the system. However, when providing the utmost customer satisfaction the service provider ought to make sure not to over-commit resources beyond the agreed limit between the customer and the provider. Moreover, statistics of resources consumed by different services should be monitored and collected using an efficient mechanism with minimal overhead and interference on the system and the services. Thus, resource usage collection and allocations mechanisms should impose economical constraints to both sides, the customer and the cloud provider.

This thesis focuses on decentralized resource allocation and resource usage management for services running in multi cluster environments. Theoretical as well as experimental results indicate that our proposed approaches provide efficient management of resources for services running in a large-scale geographically distributed systems.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2013. 26 p.
Series
Report / UMINF, ISSN 0348-0542 ; 13.16
National Category
Engineering and Technology Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-87580 (URN)978-91-7459-687-8 (ISBN)
Presentation
2013-06-13, MA121, MIT-building, Umeå University, Umeå, 10:00 (English)
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework ProgrammeeSSENCE - An eScience Collaboration
Available from: 2014-04-08 Created: 2014-04-04 Last updated: 2014-04-08Bibliographically approved
2. Autonomous cloud resource provisioning: accounting, allocation, and performance control
Open this publication in new window or tab >>Autonomous cloud resource provisioning: accounting, allocation, and performance control
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The emergence of large-scale Internet services coupled with the evolution of computing technologies such as distributed systems, parallel computing, utility computing, grid, and virtualization has fueled a movement toward a new resource provisioning paradigm called cloud computing. The main appeal of cloud computing lies in its ability to provide a shared pool of infinitely scalable computing resources for cloud services, which can be quickly provisioned and released on-demand with minimal effort. The rapidly growing interest in cloud computing from both the public and industry together with the rapid expansion in scale and complexity of cloud computing resources and the services hosted on them have made monitoring, controlling, and provisioning cloud computing resources at runtime into a very challenging and complex task. This thesis investigates algorithms, models and techniques for autonomously monitoring, controlling, and provisioning the various resources required to meet services’ performance requirements and account for their resource usage.

Quota management mechanisms are essential for controlling distributed shared resources so that services do not exceed their allocated or paid-for budget. Appropriate cloud-wide monitoring and controlling of quotas must be exercised to avoid over- or under-provisioning of resources. To this end, this thesis presents new distributed algorithms that efficiently manage quotas for services running across distributed nodes.

Determining the optimal amount of resources to meet services’ performance requirements is a key task in cloud computing. However, this task is extremely challenging due to multi-faceted issues such as the dynamic nature of cloud environments, the need for supporting heterogeneous services with different performance requirements, the unpredictable nature of services’ workloads, the non-triviality of mapping performance measurements into resources, and resource shortages. Models and techniques that can predict the optimal amount of resources needed to meet service performance requirements at runtime irrespective of variations in workloads are proposed. Moreover, different service differentiation schemes are proposed for managing temporary resource shortages due to, e.g., flash crowds or hardware failures.

In addition, the resources used by services must be accounted for in order to properly bill customers. Thus, monitoring data for running services should be collected and aggregated to maintain a single global state of the system that can be used to generate a single bill for each customer. However, collecting and aggregating such data across geographical distributed locations is challenging because the management task itself may consume significant computing and network resources unless done with care. A consistency and synchronization mechanism that can alleviate this task is proposed.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2015. 39 p.
Series
Report / UMINF, ISSN 0348-0542 ; 15.10
Keyword
cloud computing, distributed infrastructure, monitoring, accounting, performance modeling, service differentiation
National Category
Computer Systems
Research subject
Computing Science
Identifiers
urn:nbn:se:umu:diva-107955 (URN)978-91-7601-334-2 (ISBN)
Public defence
2015-09-28, MA121 (MIT building), Umeå University, Umeå, 10:15 (English)
Opponent
Supervisors
Available from: 2015-09-07 Created: 2015-08-31 Last updated: 2017-01-17Bibliographically approved

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