Dynamic Provisioning of Multi-tier Internet Applications

Urgaonkar, Bhuvan; Shenoy, Prashant; Chandra, Abhishek; Goyal, Pawan

doi:10.1109/icac.2005.27

Cited by 237 publications

(168 citation statements)

References 13 publications

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“…There is a number of capacity planning techniques proposed for different popular applications. Among these techniques, queuing theory is a widely used methodology for modeling a system behavior and answering capacity questions [16][17][18]. Modeling of a singletier system, such as a simple HTTP server, has been studied extensively.…”

Section: Related Workmentioning

confidence: 99%

R-Capriccio: A Capacity Planning and Anomaly Detection Tool for Enterprise Services with Live Workloads

Zhang¹,

Cherkasova²,

Mathews³

et al. 2007

Lecture Notes in Computer Science

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Abstract. As the complexity of IT systems increases, performance management and capacity planning become the largest and most difficult expenses to control. New methodologies and modeling techniques that explain large-system behavior and help predict their future performance are now needed to effectively tackle the emerging performance issues. With the multi-tier architecture paradigm becoming an industry standard for developing scalable client-server applications, it is important to design effective and accurate performance prediction models of multi-tier applications under an enterprise production environment and a real workload mix. To accurately answer performance questions for an existing production system with a real workload mix, we design and implement a new capacity planning and anomaly detection tool, called R-Capriccio, that is based on the following three components: i) a Workload Profiler that exploits locality in existing enterprise web workloads and extracts a small set of most popular, core client transactions responsible for the majority of client requests in the system; ii) a Regression-based Solver that is used for deriving the CPU demand of each core transaction on a given hardware; and iii) an Analytical Model that is based on a network of queues that models a multi-tier system. To validate R-Capriccio, we conduct a detailed case study using the access logs from two heterogeneous production servers that represent customized client accesses to a popular and actively used HP Open View Service Desk application.

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Section: Related Workmentioning

confidence: 99%

R-Capriccio: A Capacity Planning and Anomaly Detection Tool for Enterprise Services with Live Workloads

Zhang¹,

Cherkasova²,

Mathews³

et al. 2007

Lecture Notes in Computer Science

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“…Thus CAFe uses components as the functional granularity which has the advantage of utilizing the available resources in the nodes in a much better way, as shown in Section 2. The work in [3] presents techniques to predict workload that could be used along with CAFe.…”

Section: Related Workmentioning

confidence: 99%

“…Closed queuing models of multi-tier internet application appear in [2,3] and [12]. Both model a single tier as a queue and do not have any concept of a component in their model.…”

Section: Related Workmentioning

confidence: 99%

“…Their model is based on linear fitting and the effect of colocating components is estimated approximately. Other work [2,3] checks response time while provisioning resources using queuing theory.…”

Section: Introductionmentioning

confidence: 99%

“…Application placement in clustered servers has been studied extensively [1][2][3][4][5][6]. For example, [4][5][6] estimate resource usage of components and place components onto nodes by limiting their sum of resource usage within available node resource.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Component Assignment Framework for Improved Capacity and Assured Performance in Web Portals

Roy

Gokhale

Dowdy

et al. 2009

On the Move to Meaningful Internet Systems: OTM 2009

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Abstract. Web portals hosting large-scale internet applications have become popular due to the variety of services they provide to their users. These portals are developed using component technologies. Important design challenges for developers of web portals involve (1) determining the component placement that maximizes the number of users/requests (capacity) without increasing hardware resources and (2) maintaining the performance within certain bounds given by service level agreements (SLAs). The multitude of behavioral patterns presented by users makes it hard to identify the incoming workloads. This paper makes three contributions to the design and evaluation of web portals that address these design challenges. First it introduces an algorithmic framework that combines bin-packing and modeling-based queuing theory to place components onto hardware nodes. This capability is realized by the Component Assignment Framework for multi-tiered internet applications (CAFe). Second, it develops a component-aware queuing model to predict web portal performance. Third, it provides extensive experimental evaluation using the Rice University Bidding System (RUBiS). The results indicate that CAFe can identify opportunities to increase web portal capacity by 25% for a constant amount of hardware resources and typical web application and user workloads.

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