On optimization of simulation execution on Amazon EC2 spot market

Kamiński, Bogumił; Szufel, Przemysław

doi:10.1016/j.simpat.2015.05.008

Cited by 29 publications

(5 citation statements)

References 25 publications

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“…There are a limited number of works on modeling the behavior of SIs in a simulated environment [5], [6], [7], [8], [9], [10], whereas our evaluation is based on real EC2 cloud experiments. Previous work simplifies the behavior of SIs at the price of an inaccurate modeling for the spot market.…”

Section: Introductionmentioning

confidence: 99%

Performance and Behavior Characterization of Amazon EC2 Spot Instances

Pham

Ristov

Fahringer

2018

2018 IEEE 11th International Conference on Cloud Computing (CLOUD)

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Amazon EC2's spot instances (SIs) represent a competitive Cloud resource in terms of price compared to reliable and fixed price options. The drawback, however, is that SIs may not always be available and they can be revoked at any given time. In this paper, we describe a comprehensive experimental evaluation for EC2 SIs to characterize their performance and behavior in three different regions each of which in a different continent. We describe the life cycle of SIs with the most important phases of an SI, introduce the most relevant events that can prevent a user from obtaining SIs, and draw important conclusions that can be exploited by the research community to effectively use the spot market. Our results reveal the fulfillment rate of requests for SIs, waiting time until requested SIs become fulfilled, details about the interruption rate of SIs, and how long SIs run before being interrupted. Our study also indicates that the SI interruption rate influences the fulfillment rate, SIs are highly reliable in the first 30 minutes after deployment, existing biding strategies are likely to fail for SIs with medium interruption rate, and SIs can be reclaimed by EC2 anytime regardless of an SI's bid price or workload.

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Section: Introductionmentioning

confidence: 99%

Performance and Behavior Characterization of Amazon EC2 Spot Instances

Pham

Ristov

Fahringer

2018

2018 IEEE 11th International Conference on Cloud Computing (CLOUD)

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“…AWS operates EC2 spot instances in 14 locations with about 40 products (Amazon Web Services 2017), which can substitute one another. As AWS' excess capacity, EC2 spot instances are normally cheaper than regular on-demand instances based on a fixed price (Kamiński and Szufel 2015). Similar to spot markets for stocks, electricity, and commodities, a market mechanism brings together demand (bids) and supply (offers) in a Vickrey auction to form EC2 spot prices (Cheng et al 2016).…”

Section: Cloud Computing Spot Pricesmentioning

confidence: 99%

Scheduling Flexible Demand in Cloud Computing Spot Markets

et al. 2019

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The rapid standardization and specialization of cloud computing services have led to the development of cloud spot markets on which cloud service providers and customers can trade in near real-time. Frequent changes in demand and supply give rise to spot prices that vary throughout the day. Cloud customers often have temporal flexibility to execute their jobs before a specific deadline. In this paper, the authors apply real options analysis (ROA), which is an established valuation method designed to capture the flexibility of action under uncertainty. They adapt and compare multiple discrete-time approaches that enable cloud customers to quantify and exploit the monetary value of their short-term temporal flexibility. The paper contributes to the field by guaranteeing cloud job execution of variable-time requests in a single cloud spot market, whereas existing multi-market strategies may not fulfill requests when outbid. In a broad simulation of scenarios for the use of Amazon EC2 spot instances, the developed approaches exploit the existing savings potential up to 40 percent-a considerable extent. Moreover, the results demonstrate that ROA, which explicitly considers time-of-day-specific spot price patterns, outperforms traditional option pricing models and expectation optimization.

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“…A possible approach to improve the performance of the Cloudbased simulators [200][201][202] is developing the Cloud service model as Simulation-as-a-Service(SiaS), which means that SaaS have to be adapted and customized precisely to run simulators. So by using such a simulation service, the simulator runs on the Cloud servers while leaving the user's local resources free.…”

Section: Cloud-based Simulationmentioning

confidence: 99%

Manycore simulation for peta-scale system design: Motivation, tools, challenges and prospects

Zarrin

Aguiar

Barraca

2017

Simulation Modelling Practice and Theory

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThe architecture design of peta-scale computing systems is complex and presents lots of difficulties to designs, as current tools lack support for relevant features of future scenarios. Novel systems must be designed with great care and tools, such as manycore architecture simulators, must be adapted accordingly. However, current simulation tools are very slow, often specific-purposeoriented, suffer from various issues and are rarely able to simulate thousands of cores. The emergence of peta-scale systems and the upcoming manycore era brings nevertheless new challenges to computing systems and architectures, adding further difficulties and requirements on the development of the corresponding simulators. Furthermore, the design of architecture simulators for manycore systems involve methods and techniques from various interdisciplinary research areas, which in turn brings more challenges in different aspects. As system complexity grows, the growth of the simulation capacity is being outpaced (reaching the so called simulation wall). In this paper, we present the challenges for simulating future large scale manycore environments, and we investigate the adequacy of current modeling and simulation tools, methodologies and techniques. The aim of this work is to highlight how current approaches can best deal with the identified problems, smoothing the challenges of research in future peta-scale systems.

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On optimization of simulation execution on Amazon EC2 spot market

Cited by 29 publications

References 25 publications

Performance and Behavior Characterization of Amazon EC2 Spot Instances

Performance and Behavior Characterization of Amazon EC2 Spot Instances

Scheduling Flexible Demand in Cloud Computing Spot Markets

Manycore simulation for peta-scale system design: Motivation, tools, challenges and prospects

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