Change search
ReferencesLink to record
Permanent link

Direct link
Coordinating CPU and Memory Elasticity Controllers to Meet Service Response Time Constraints
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Show others and affiliations
2015 (English)In: 2015 INTERNATIONAL CONFERENCE ON CLOUD AND AUTONOMIC COMPUTING (ICCAC), 2015, 69-80 p.Conference paper (Refereed)
Abstract [en]

Vertical elasticity is recognized as a key enabler for efficient resource utilization of cloud infrastructure through fine-grained resource provisioning, e.g., allowing CPU cycles to be leased for as short as a few seconds. However, little research has been done to support vertical elasticity where the focus is mostly on a single resource, either CPU or memory, while an application may need arbitrary combinations of these resources at different stages of its execution. Nonetheless, the existing techniques cannot be readily used as-is without proper orchestration since they may lead to either under-or over-provisioning of resources and consequently result in undesirable behaviors such as performance disparity. The contribution of this paper is the design of an autonomic resource controller using a fuzzy control approach as a coordination technique. The novel controller dynamically adjusts the right amount of CPU and memory required to meet the performance objective of an application, namely its response time. We perform a thorough experimental evaluation using three different interactive benchmark applications, RUBiS, RUBBoS, and Olio, under workload traces generated based on open and closed system models. The results show that the coordination of memory and CPU elasticity controllers using the proposed fuzzy control provisions the right amount of resources to meet the response time target without over-committing any of the resource types. In contrast, with no coordinating between controllers, the behaviour of the system is unpredictable e.g., the application performance may be met but at the expense of over-provisioning of one of the resources, or application crashing due to severe resource shortage as a result of conflicting decisions.

Place, publisher, year, edition, pages
2015. 69-80 p.
Keyword [en]
cloud-based application, fuzzy control, cloud computing, vertical elasticity, performance, cpu utilization, memory utilization
National Category
Computer Systems
URN: urn:nbn:se:umu:diva-108032DOI: 10.1109/ICCAC.2015.20ISI: 000380476500007ISBN: 978-1-4673-9566-3OAI: diva2:850480
2015 IEEE International Conference on Cloud and Autonomic Computing(ICCAC), Cambridge, MA, USA, September 21-24, 2015

Originally included in thesis in accepted form.

Available from: 2015-09-01 Created: 2015-09-01 Last updated: 2016-09-23Bibliographically approved
In thesis
1. 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.
Report / UMINF, ISSN 0348-0542 ; 15.10
cloud computing, distributed infrastructure, monitoring, accounting, performance modeling, service differentiation
National Category
Computer Systems
Research subject
Computing Science
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)
Available from: 2015-09-07 Created: 2015-08-31 Last updated: 2015-10-07Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Lakew, Ewnetu BayuhElmroth, Erik
By organisation
Department of Computing Science
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 117 hits
ReferencesLink to record
Permanent link

Direct link