Design of a New Cloud Computing Simulation Platform

Núñez, Alberto; Vázquez-Poletti, José Luis; Caminero, Agustín C.; Carretero, Jesús; Llorente, Ignacio

doi:10.1007/978-3-642-21931-3_45

Cited by 35 publications

(35 citation statements)

References 19 publications

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“…Cloud Computing simulators include CloudSim [12], GreenCloud [13], iCanCloud [14] and MDCSim [15]. GreenCloud [13] is an extension of the NS2 network simulator for evaluation of energy-aware Cloud DCs.…”

Section: B Cloud Simulationmentioning

confidence: 99%

“…MDCSim [15] is a commercial discrete event simulator that models specific hardware characteristics of different DC components such as servers, communication links, and switches. On the contrary, iCanCloud [14] is a hypervisor simulator specifically aimed at simulating instance types provided by Amazon. Some tools that can simulate an entire Cloud stack include CloudSim [12] and DISSECT-CF [2].…”

Section: B Cloud Simulationmentioning

confidence: 99%

See 1 more Smart Citation

Modelling Low Power Compute Clusters for Cloud Simulation

Kecskeméti

Hajji

Tso

2017

2017 25th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)

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Abstract-In order to minimise their energy use, data centre operators are constantly exploring new ways to construct computing infrastructures. As low power CPUs, exemplified by ARM-based devices, are becoming increasingly popular, there is a growing trend for the large scale deployment of low power servers in data centres. For example, recent research has shown promising results on constructing small scale data centres using Raspberry Pi (RPi) single-board computers as their building blocks. To enable larger scale experimentation and feasibility studies, cloud simulators could be utilised. Unfortunately, stateof-the-art simulators often need significant modification to include such low power devices as core data centre components. In this paper, we introduce models and extensions to estimate the behaviour of these new components in the DISSECT-CF cloud computing simulator. We show that how a RPi based cloud could be simulated with the use of the new models. We evaluate the precision and behaviour of the implemented models using a Hadoop-based application scenario executed both in real life and simulated clouds.

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Section: B Cloud Simulationmentioning

confidence: 99%

Section: B Cloud Simulationmentioning

confidence: 99%

Modelling Low Power Compute Clusters for Cloud Simulation

Kecskeméti

Hajji

Tso

2017

2017 25th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)

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“…This model ignores sharing and contention effects, making it not representative of actual storage resource usage. iCanCloud [4] provides the most sophisticated model: it considers individual disk blocks and simulates seek times based on the locality of block accesses, but instantiating such a model in a realistic way is non-trivial. The same authors also proposed SIMCAN [13], a framework for the simulation of distributed architectures that encompasses a complex storage modeling module.…”

Section: Related Workmentioning

confidence: 99%

“…For each of the aforementioned types of distributed computing infrastructures, there exists simulation toolkits in the literature that offer some storage simulation capacities [2], [3], [4], [5], [6], [7]. However, their use is often limited to the specific study for which they have been designed.…”

Section: Introductionmentioning

confidence: 99%

Adding Storage Simulation Capacities to the SimGrid Toolkit: Concepts, Models, and API

Lèbre¹,

Legrand

Suter

et al. 2015

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Abstract-For each kind of distributed computing infrastructures, i.e., clusters, grids, clouds, data centers, or supercomputers, storage is a essential component to cope with the tremendous increase in scientific data production and the ever-growing need for data analysis and preservation. Understanding the performance of a storage subsystem or dimensioning it properly is an important concern for which simulation can help by allowing for fast, fully repeatable, and configurable experiments for arbitrary hypothetical scenarios. However, most simulation frameworks tailored for the study of distributed systems offer no or little abstractions or models of storage resources.In this paper, we detail the extension of SimGrid, a versatile toolkit for the simulation of large-scale distributed computing systems, with storage simulation capacities. We first define the required abstractions and propose a new API to handle storage components and their contents in SimGrid-based simulators. Then we characterize the performance of the fundamental storage component that are disks and derive models of these resources. Finally we list several concrete use cases of storage simulations in clusters, grids, clouds, and data centers for which the proposed extension would be beneficial.

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“…Nunez et al proposed iCanCloud simulator [7][8]. It is built on SimCAN, a simulation framework for modeling HPC architectures.…”

Section: Related Workmentioning

confidence: 99%