Geist: a generator for e-commerce &amp; internet server traffic

Kant, Krishna; Tewari, Vijay; Iyer, Ravishankar K.

doi:10.1109/ispass.2001.990676

Cited by 21 publications

(12 citation statements)

References 8 publications

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“…Moreover, some special events (e.g., Christmas) cause peak loads compared to typical weeks [5]. Workloads used in simulations were generated by GEIST [33], while workload predictions were derived from these workloads. GEIST generates a workload assuming a Poisson distribution as the marginal distribution of the arrival process and then adds multifractal and self-similarity properties.…”

Section: Simulation Model Instancementioning

confidence: 99%

“…Regarding external validity, a synthetic e-commerce workload was used. Requests arrivals were generated using a outdated workload generator (GEIST) [33] since more recent workload generators based on recent workload studies were not found. Although our utility model covers many IaaS and SaaS providers business models, our experiments were based on information of one IaaS provider and one SaaS provider and do not account for sensibility of their cost choices.…”

Section: Validity Threatsmentioning

confidence: 99%

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Business-Driven Long-Term Capacity Planning for SaaS Applications

Candeia

Santos

Lopes

2015

IEEE Trans. Cloud Comput.

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Capacity Planning is one of the activities developed by Information Technology departments over the years, it aims at estimating the amount of resources needed to offer a computing service. This activity contributes to achieving high Quality of Service levels and also to pursuing better economic results for companies. In the Cloud Computing context, one plausible scenario is to have Software-as-a-Service (SaaS) providers that build their IT infrastructure acquiring resources from Infrastructure-as-a-Service (IaaS) providers. SaaS providers can reduce operational costs and complexity by buying instances from a reservation market, but then need to predict the number of instances needed in the long-term. This work investigates how important is the capacity planning in this context and how simple business-driven heuristics for long-term capacity planning impact on the profit achieved by SaaS providers. Simulation experiments were performed using synthetic e-commerce workloads. Our analysis show that proposed heuristics increase SaaS provider profit, on average, at 9.6501% per year. Analysing such results we demonstrate that capacity planning is still an important activity, contributing to the increase of SaaS providers profit. Besides, a good capacity planning may also avoid bad reputation due to unacceptable performance, which is a gain very hard to measure.

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Section: Simulation Model Instancementioning

confidence: 99%

Section: Validity Threatsmentioning

confidence: 99%

Business-Driven Long-Term Capacity Planning for SaaS Applications

Candeia

Santos

Lopes

2015

IEEE Trans. Cloud Comput.

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“…Our simulation experiments use a 17-hour workload generated by GEIST [11]. This workload is variable and presents an average request rate of 670 rps.…”

Section: Simulation Parametersmentioning

confidence: 99%

Can Dynamic Provisioning and Rejuvenation Systems Coexist in Peace?

et al. 2005

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Abstract. Dynamic provisioning systems change application capacity in order to use enough resources to accommodate current load. Rejuvenation systems detect/forecast software failures and temporarily remove one or more components of the application in order to bring them to a clean state. Up to now, these systems have been developed unaware of one another. However, many applications need to be controlled by both. In this paper we investigate whether these systems can actuate over the same application when they are not aware of each other, i.e., without coordination. We present and apply a model to study the performance of dynamic provisioning and rejuvenation systems when they actuate over the same application without coordination. Our results show that when both systems coexist application quality of service degrades in comparison with the quality of service provided when each system is acting alone. This suggests that some level of coordination must be added to maximize the benefits gained from the simultaneous use of both systems.

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“…[9]) that generates a load comparable to that expected from a population of real-world clients. Using the π-calculus [13] and postulating the existence of T ∼, a "traffic simulation" relation, this approach is described in Specification 1.…”

Section: Introductionmentioning

confidence: 99%