A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications

Zhang, Qi; Cherkasova, Ludmila; Smirni, Evgenia

doi:10.1109/icac.2007.1

Cited by 208 publications

(166 citation statements)

References 11 publications

Supporting

Mentioning

159

Contrasting

Order By: Relevance

“…We have introduced a statistical regression-based approach for the CPU demand approximation of different transaction in our earlier paper [25], where we evaluated this approach by using a testbed of a multi-tier e-commerce site that simulates the operation of an on-line bookstore, according to the classic TPC-W benchmark [24]. Using the TPC-W benchmark, we demonstrated that the use of linear regression provides promising results.…”

Section: Related Workmentioning

confidence: 99%

“…The OVSD trace profile has the format shown in Table 1. When we first introduced and applied the regression-based technique for evaluating the transaction cost in [25], there were only 14 different transaction types in TPC-W. The analysis of OVSD workload revealed that the real workloads often have a much higher number of transaction types, e.g., OVSD workload operates over 756 different transaction types.…”

Section: Applying Regression To a Production System With Live Workloadmentioning

confidence: 99%

“…However, TPC-W operates using only 14 transaction types. In this work, we continue applying the linear regression technique for approximating the CPU transaction cost as was introduced in [25] but in a much more challenging environment. Here, we applied and validated this technique with real, live workloads that exhibit much more complex and diverse behavior than the synthetic TPC-W benchmark.…”

Section: Related Workmentioning

confidence: 99%

“…We have introduced this approach in our earlier paper [25], where we evaluated it by using a testbed of a multi-tier ecommerce site that simulates the operation of an on-line bookstore, according to the classic TPC-W benchmark [24]. The challenge is to apply and validate this technique with real, live workloads that exhibit much more complex and diverse behavior than synthetic ones.…”

Section: Cpu Cost Of Transactionsmentioning

confidence: 99%

“…Thus the problem is to approximate the CPU costs of different client transactions at different tiers, and then use these cost functions to evaluate the resource requirement of scaled or modified transaction workload mix in order to accurately size the future system. In this work, we continue developing the approach that is based on linear regression for approximating the CPU transaction cost in a system running the TPC-W benchmark [25]. However, it is much more challenging to apply and validate this modeling approach with real, live workloads that exhibit much more complex and diverse behavior than the synthetic TPC-W benchmark.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Applying Regression To a Production System With Live Workloadmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Cpu Cost Of Transactionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Self Cite

View full text Add to dashboard Cite

show abstract

Configuring large‐scale storage using a middleware with machine learning

Eyers

Routray

Zhang

et al. 2011

Concurrency and Computation

View full text Add to dashboard Cite

SUMMARYThe proliferation of cloud services and other forms of service-oriented computing continues to accelerate. Alongside this development is an ever-increasing need for storage within the data centres that host these services. Management applications used by cloud providers to configure their infrastructure should ideally operate in terms of high-level policy goals, and not burden administrators with the details presented by particular instances of storage systems. One common technology used by cloud providers is the Storage Area Network (SAN). Support for seamless scalability is engineered into SAN devices. However, SAN infrastructure has a very large parameter space: their optimal deployment is a difficult challenge, and subsequent management in cloud storage continues to be difficult.In this article, we discuss our work in SAN configuration middleware, which aims to provide users of large-scale storage infrastructure such as cloud providers with tools to assist them in their management and evolution of heterogeneous SAN environments. We propose a middleware rather than a stand-alone tool so that the middleware can be a proxy for interacting with, and informing, a central repository of SAN configurations. Storage system users can have their SAN configurations validated against a knowledge base of best practices that are contained within the central repository. Desensitized information is exported from local management applications to the repository, and the local middleware can subscribe to updates that proactively notify storage users should particular configurations be updated to be considered as sub-optimal, or unsafe.

show abstract

Exploring nonintrusive measurements of spatio‐temporal portrait of microservices

Zeng

et al. 2023

Softw Pract Exp

View full text Add to dashboard Cite

As cloud native technology advances, the scale and complexity of applications built on microservice architecture continue to expand, leading to increasingly intricate differences between software within the same application. Microservice applications, offering high flexibility, are deployed in data centers as black boxes from the users' perspective, leaving them with no insight into the orchestration of cloud service providers. Consequently, users face challenges in promptly recognizing performance imbalances within their deployed applications. Meanwhile, cloud service providers may cut costs by offering a mix of qualified and unqualified services, potentially deceiving users. To enhance the understanding of microservice application organization, we propose a non‐intrusive measurement framework, termed NMPI. NMPI facilitates rapid identification of microservice application defects, offering insights into cloud services and detecting fraudulent behavior in microservice‐based applications. We model microservice applications using a queue analysis‐based approach and filter the dominant frequency components of average response time signals by employing k‐means on the fast fourier transform (FFT). Our model constructs a library of performance portraits for various software, with these portraits resembling human fingerprints that carry and mark the software's internal information. Utilizing a two‐tier microservices‐based application incorporating a database as a case study allows us to demonstrate the effectiveness of NMPI. Our experimental results show that NMPI can produce differentiable profiles of data service performance portraits across a diverse and extensive range of workloads, enabling the identification of software types and the analysis of performance conditions.

show abstract

A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications

Cited by 208 publications

References 11 publications

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

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

Configuring large‐scale storage using a middleware with machine learning

Exploring nonintrusive measurements of spatio‐temporal portrait of microservices

Contact Info

Product

Resources

About