Stochastic performance analysis and capacity planning of publish/subscribe systems

Schröter, Arnd; Mühl, Gero; Kounev, Samuel; Parzyjegla, Helge; Richling, Jan

doi:10.1145/1827418.1827468

Cited by 17 publications

(12 citation statements)

References 24 publications

(23 reference statements)

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“…Existing benchmarking studies [1,2,6,7,12,14,15] of publishsubscribe systems are typically strictly limited to eventtriggered notification mechanism, and are inapplicable to determine (ξ, ψ). Prior-art on analytical performance models [7,8,12,13] are typically not useful for runtime performance prediction [12] and also cannot be used to predict performance of periodic time-triggered and event-triggered notification mechanisms in a unified manner. [12] has further proposed a periodic event pull design pattern for event-based systems.…”

Section: Related Workmentioning

confidence: 99%

Benchmark based performance characterization and scalable design of sensing-as-a-service platform

Mukherjee¹,

Kumar²,

Chander³

et al. 2014

Proceedings of the Posters &Amp; Demos Session on - Middleware Posters and Demos '14

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Advancements and proliferation of sensing and actuation technologies in diverse computing and physical devices call for a flexible Sensing-as-a-Service Platform (SeaaS-P) that can enable massive-scale next generation applications. A key aspect of such a platform, which has been largely unexplored in terms of applicability at a massive scale, is the notification system (e.g. city incident notification) to send event alerts to subscribers. Conventional event-based systems (e.g. publish-subscribe) typically trigger notifications with every event arrival. Observations show resources (e.g. CPU) can become bottleneck and get overused when event rates and number of subscriptions go beyond certain thresholds. Thus, it may be necessary to opportunistically delay the notification, i.e. not send the notifications with every event update (e.g., time-triggered periodic notifications), depending on the delay-tolerance of the application. This paper proposes a: (i) benchmark based performance profiling of SeaaS-P in terms of resource utilizations and % notification drop; and (ii) design framework to determine the design boundaries of SeaaS-P notification system for both event-triggered and time-triggered notifications.

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Section: Related Workmentioning

confidence: 99%

Benchmark based performance characterization and scalable design of sensing-as-a-service platform

Mukherjee¹,

Kumar²,

Chander³

et al. 2014

Proceedings of the Posters &Amp; Demos Session on - Middleware Posters and Demos '14

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“…Some studies have been presented for analyzing and modeling the service latency of event-based systems. They have been mainly focusing on different classes of implementations such as the Publish/Subscribe systems [23,27] and message-oriented middlewares [24,25].…”

Section: Related Workmentioning

confidence: 99%

Modeling performance of a parallel streaming engine

Bedini¹,

Sakr²,

Theeten³

et al. 2013

Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering

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While data are growing at a speed never seen before, parallel computing is becoming more and more essential to process this massive volume of data in a timely manner. Therefore, recently, concurrent computations have been receiving increasing attention due to the widespread adoption of multi-core processors and the emerging advancements of cloud computing technology. The ubiquity of mobile devices, location services, and sensor pervasiveness are examples of new scenarios that have created the crucial need for building scalable computing platforms and parallel architectures to process vast amounts of generated streaming data. In practice, efficiently operating these systems is hard due to the intrinsic complexity of these architectures and the lack of a formal and in-depth knowledge of the performance models and the consequent system costs. The Actor Model theory has been presented as a mathematical model of concurrent computation that had enormous success in practice and inspired a number of contemporary work in this area. Recently, the Storm system has been presented as a realization of the principles of the Actor Model theory in the context of the large scale processing of streaming data. In this paper, we present, to the best of our knowledge, the first set of models that formalize the performance characteristics of a practical distributed, parallel and fault-tolerant stream processing system that follows the Actor Model theory. In particular, we model the characteristics of the data flow, the data processing and the system management costs at a fine granularity within the different steps of executing a distributed stream processing job. Finally, we present an experimental validation of the described performance models using the Storm system.

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“…Previous work [18,19] presented analytical models that allow to derive basic performance metrics (such as message forwarding probabilities and message rates) as well as advanced measures derived from the basic ones (such as the utilization of brokers and overlay links as well as the delays introduced by them). However, these models made several simplifying assumptions.…”

Section: Introductionmentioning

confidence: 99%

“…However, these models made several simplifying assumptions. In particular, the models presented in [19] were based on a rather elementary data and filter model for notifications and subscriptions, respectively. Furthermore, only uniform probability distributions were considered, while in [18] several extensions and generalizations were only briefly sketched.…”

Section: Introductionmentioning

confidence: 99%

Analyzing content-based publish/subscribe systems

Mühl

Parzyjegla

Prellwitz

2015

Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems

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Publish/subscribe communication is increasingly often the basis for distributed, event-based, and loosely-coupled applications. With this communication paradigm, application components communicate indirectly by publishing notifications and by subscribing to those notifications of interest. To precisely specify the subscribers' interests, content-based publish/subscribe systems provide expressive filter models that, on the one hand, ease implementing versatile and entangled interaction patterns. On the other hand, however, resulting notification flows evolve dynamically and implicitly making an analysis the more challenging and complex.In this paper, we focus on modeling the interrelationship between the notifications being advertised, produced, and requested by publishers and subscribers, respectively. The presented analytical models, in their general form, support various data and filter models to compute matching and forwarding probabilities, which provide the basis to derive message rates as well as more advanced performance metrics. To demonstrate their applicability, we instantiate the general models with a specific data and filter model based on a non-uniform, multi-dimensional distribution of published and subscribed notifications. Our results show the impact of the distributions' skewness and, thereby, prove the necessity to precisely model the content interrelationships in order to make reliable performance predictions.

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Stochastic performance analysis and capacity planning of publish/subscribe systems

Cited by 17 publications

References 24 publications

Benchmark based performance characterization and scalable design of sensing-as-a-service platform

Benchmark based performance characterization and scalable design of sensing-as-a-service platform

Modeling performance of a parallel streaming engine

Analyzing content-based publish/subscribe systems

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