Resource pool management: Reactive versus proactive or let's be friends

Resource pool management: Reactive versus proactive or let's be friends

19 Pages · 2009 · 946 KB · English

The consolidation of multiple workloads and servers enables the efficient use of server and power resources in shared .. Workload Number. 99th Percentile. 97th Percentile. 95th Percentile. Mean Value. Fig. 3. Top percentile of CPU demand for applications under study. 2 Service providers can use the

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This article appeared in a journal published by Elsevier The attached copy is furnished to the author for internal noncommercial research and education use, including for instruction at the authors institution and sharing with colleagues Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited In most cases authors are permitted to post their version of the article (eg in Word or Tex form) to their personal website or institutional repository Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: http://wwwelseviercom/copyright Resource pool management: Reactive versus proactive or let’s be friends Daniel Gmach a,* , Jerry Rolia a, Ludmila Cherkasova a, Alfons Kemper b aHewlettPackard Laboratories, Palo Alto, CA, United StatesbTechnische Universität München, 85748 Garching, München, Germany article info Article history: Available online 20 August 2009 Keywords: Virtualized data centres Resource pool management Enterprise workload analysis Simulation abstract The consolidation of multiple workloads and servers enables the efficient use of server and power resources in shared resource pools We employ a tracebased workload placement controller that uses historical information to periodically and proactively reassign work loads to servers subject to their quality of service objectives A reactive migration controller is introduced that detects server overload and underload conditions It initiates the migra tion of workloads when the demand for resources exceeds supply Furthermore, it dynam ically adds and removes servers to maintain a balance of supply and demand for capacity while minimizing power usage A host load simulation environment is used to evaluate several different management policies for the controllers in a time effective manner A case study involving three months of data for 138 SAP applications compares three integrated controller approaches with the use of each controller separately The study considers tradeoffs between: (i) required capacity and power usage, (ii) resource access quality of service for CPU and memory resources, and (iii) the number of migrations Our study sheds light on the question of whether a reactive controller or proactive workload placement controller alone is adequate for resource pool management The results show that the most tightly integrated controller approach offers the best results in terms of capacity and qual ity but requires more migrations per hour than the other strategies 2009 Elsevier BV All rights reserved 1 Introduction Virtualization is gaining popularity in enterprise envi ronments as a softwarebased solution for building shared hardware infrastructures Forrester Research estimates that businesses generally only end up using between 8% and 20% of the server capacity they have purchased Virtualization technology helps to achieve greater system utilization while lowering total cost of ownership and responding more effectively to changing business condi tions For large enterprises, virtualization offers a solution for server and application consolidation in shared resource pools The consolidation of multiple servers and their workloads has an objective of minimizing the number of resources, eg, computer servers, needed to support the workloads In addition to reducing costs, this can also lead to lower peak and average power requirements Lowering peak power usage may be important in some data centres if peak power cannot easily be increased Applications participating in consolidation scenarios can make complex demands on servers For example, many enterprise applications operate continuously, have unique time varying demands, and have performanceoriented Quality of Service (QoS) objectives To evaluate which workloads can be consolidated to which servers, some pre liminary performance and workload analysis should be done In the simple naive case, a data centre operator may estimate the peak resource requirements of each workload and then evaluate the combined resource requirements of a group of workloads by using the sum of their peak demands However, such an approach can 13891286/$ see front matter 2009 Elsevier BV All rights reserved doi: 101016/jcomnet200908011 *Corresponding author Tel: +1 650 857 4955 Email addresses: [email protected] (D Gmach),[email protected] com (J Rolia), [email protected] (L Cherkasova),alfo[email protected] intumde (A Kemper) Computer Networks 53 (2009) 2905–2922 Contents lists available at ScienceDirect Computer Networks journal homepage: wwwelseviercom/locate/comnet lead to significant resource overprovisioning since it does not take into account the benefits of resource sharing

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