Modeling variations in load intensity over time

Kistowski, Jóakim von; Herbst, Nikolas; Kounev, Samuel

doi:10.1145/2577036.2577037

Cited by 21 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A research agenda for implementing optimization tools for data-intensive applications has been presented in [18,21]; the main concepts to consider are volume, velocity, and location of data. Kistowski et al [41] proposed to model incoming workload intensity using time-series decomposition to identify seasonal, trend and noise components which could yield in more robust optimization techniques. Andrikopoulos et al [5] proposed a GENTL language for modeling multi-Cloud applications as the foundation for any optimization of its deployment.…”

Section: Other Relevant Researchmentioning

confidence: 99%

QoS-Based Elasticity for Service Chains in Distributed Edge Cloud Environments

Cardellini

Grbac

Nardelli

et al. 2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

With the emerging IoT and Cloud-based networked systems that rely heavily on virtualization technologies, elasticity becomes a dominant system engineering attribute for providing QoS-aware services to their users. Although the concept of elasticity can introduce significant QoS and cost benefits, its implementation in real systems is full of challenges. Indeed, nowadays systems are mainly distributed, built upon several layers of abstraction, and with centralized control mechanisms. In such a complex environment, controlling elasticity in a centralized manner might strongly penalize scalability. To overcome this issue, we can conveniently split the system in autonomous subsystems that implement elasticity mechanisms and run control policies in a decentralized manner. To efficiently and effectively cooperate with each other, the subsystems need to communicate among themselves to determine elasticity decisions that collectively improve the overall system performance. This new architecture calls for the development of new mechanisms and efficient policies. In this chapter, we focus on elasticity in IoT and Cloud-based systems, which can be geo-distributed also at the edge of the networks, and discuss its engineering perspectives along with various coordination mechanisms. We focus on the design choices that may affect the elasticity properties and provide an overview of some decentralized design patterns related to the coordination of elasticity decisions.

show abstract

Section: Other Relevant Researchmentioning

confidence: 99%

QoS-Based Elasticity for Service Chains in Distributed Edge Cloud Environments

Cardellini

Grbac

Nardelli

et al. 2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Suppose users are concerned with a modern software system that is "expected to deliver reliable performance under highly variable load intensities" [23]. Although the performance keywords in this natural-language description are "reliable" and "variable", the real performance concern should be how well the software system scales, according to the specifications of reliability, variability, and scalability [16].…”

Section: Examplementioning

confidence: 99%

DoKnowMe

Zheng

O’Brien

Kihl

2016

SIGMETRICS Perform. Eval. Rev.

View full text Add to dashboard Cite

Software engineering considers performance evaluation to be one of the key portions of software quality assurance. Unfortunately, there seems to be a lack of standard methodologies for performance evaluation even in the scope of experimental computer science. Inspired by the concept of "instantiation" in object-oriented programming, we distinguish the generic performance evaluation logic from the distributed and ad-hoc relevant studies, and develop an abstract evaluation methodology (by analogy of "class") we name Domain Knowledge-driven Methodology (DoKnowMe). By replacing five predefined domain-specific knowledge artefacts, DoKnowMe could be instantiated into specific methodologies (by analogy of "object") to guide evaluators in performance evaluation of different software and even computing systems. We also propose a generic validation framework with four indicators (i.e. usefulness, feasibility, effectiveness and repeatability), and use it to validate DoKnowMe in the Cloud services evaluation domain. Given the positive and promising validation result, we plan to integrate more common evaluation strategies to improve DoKnowMe and further focus on the performance evaluation of Cloud autoscaler systems.

show abstract

“…Our approach focuses on the specification of the behavior of users and offers basic support for modeling workload intensities. An approach focusing on the definition of the workload intensities can be found in [13]. It allows a DSL-based definition of variable and dynamic load profiles and workload scenarios over time.…”

Section: Background and Related Workmentioning

confidence: 99%

Automatic Extraction of Probabilistic Workload Specifications for Load Testing Session-Based Application Systems

Hoorn¹,

Vögele²,

Schulz³

et al. 2015

Proceedings of the 8th International Conference on Performance Evaluation Methodologies and Tools

View full text Add to dashboard Cite

Workload generation is essential to systematically evaluate performance properties of application systems under controlled conditions, e.g., in load tests or benchmarks. The definition of workload specifications that represent the real workload as accurately as possible is one of the biggest challenges in this area. This paper presents our approach for the modeling and automatic extraction of probabilistic workload specifications for load testing session-based application systems. The approach, called Wessbas, comprises (i.) a domain-specific language (DSL) enabling layered modeling of workload specifications as well as support for (ii.) automatically extracting instances of the DSL from recorded sessions logs and (iii.) transforming instances of the DSL to workload specifications of existing load testing tools. During the extraction process, different groups of customers with similar navigational patterns are identified using clustering techniques. We developed corresponding tool support including a transformation to probabilistic test scripts for the Apache JMeter load testing tool. The evaluation of the proposed approach using the industry standard benchmark SPECjEnterprise2010 demonstrates its applicability and the representativeness of the extracted workloads.

show abstract

Modeling variations in load intensity over time

Cited by 21 publications

References 9 publications

QoS-Based Elasticity for Service Chains in Distributed Edge Cloud Environments

QoS-Based Elasticity for Service Chains in Distributed Edge Cloud Environments

DoKnowMe

Automatic Extraction of Probabilistic Workload Specifications for Load Testing Session-Based Application Systems

Contact Info

Product

Resources

About