A toolkit for modelling and simulating data Grids: an extension to GridSim

Simulation techniques have become a powerful tool for deciding the best starting conditions on pay-as-you-go scenarios. This is the case of public cloud infrastructures, where a given number and type of virtual machines (in short VMs) are instantiated during a specified time, being this reflected in the final budget. With this in mind, this paper introduces and validates iCanCloud, a novel simulator of cloud infrastruc-tures with remarkable features such as flexibility, scalability, performance and usability. Further-more, the iCanCloud simulator has been built on the following design principles: (1) it's targeted to conduct large experiments, as opposed to oth-ers simulators from literature; (2) it provides a flexible and fully customizable global hypervisor for integrating any cloud brokering policy; (3) it reproduces the instance types provided by a given cloud infrastructure; and finally, (4) it contains a user-friendly GUI for configuring and launching simulations, that goes from a single VM to large cloud computing systems composed of thousands of machines.Keywords Cloud computing · Cloud computing simulator · Cloud hypervisor · Validation · Scalability 1 to solve a given computational problem. If the same software and configurations are needed, the VMs may be started using the same image. This way, a machine offered by a computing cloud may become whatever the user needs, from a standalone computer to a cluster or Grid node.Nowadays, cloud computing systems are increasing their role due to the fast (r)evolution of computer networks and communication technologies. A very clear proof of this fact is that very important companies like Amazon, Google, Dell, IBM, and Microsoft are investing billions of dollars in order to provide their own cloud solutions [28].As soon as the scientific community had access to cloud production infrastructures, the first applications started to run on the cloud [26,34]. In many Research areas, the leap from traditional cluster and Grid computing to this new paradigm has been mandatory, being the main reason an evolution in the computational needs of the applications [10]. A remarkable fact from this evolution is that in a pre-cloud environment, hardware defines the level of parallelism of an application. In cloud computing, the level of parallelism is defined by the application itself, as there is no restriction in the number of machines, and CPU availability is 100% guaranteed by standard.There

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“…remove(T v temp , min(T v temp )) 22: end for 23: end for 24: {Calculate the temporal CP metric} 25: for v = 0 to x − 1 do 26: setN ( j, v) 27:…”

Section: Cloud Min-minmentioning

confidence: 99%

“…In the case of CloudSim [4], several research articles have been published with results obtained by using it [2,5,6,15]. This tool was initially based on a Grid simulator [4] (this being GridSim [27]). So, a new layer on top of GridSim was implemented to add the ability to simulate clouds.…”

Section: Introductionmentioning

confidence: 99%

iCanCloud: A Flexible and Scalable Cloud Infrastructure Simulator

et al. 2012

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“…The GridSim is a framework which allows modeling and simulation of entities in parallel and distributed computing systems for performance evaluation purposes [34]. Although the processing nodes (i.e., a machine) within a resource in GridSim can be heterogeneous in terms of processing capability and configuration, there is no support to simulate resources in the presence of failures.…”

Section: Fta Simulatormentioning

confidence: 99%

“…We also use the results of trace interpretation explained in Section 5 as the failure traces. The FTA simulator based on GridSim [34] is used to simulate the considered case studies.…”

Section: Performance Evaluationmentioning

confidence: 99%

The Failure Trace Archive: Enabling the comparison of failure measurements and models of distributed systems

Javadi

Kondo

Iosup

et al. 2013

Journal of Parallel and Distributed Computing

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With the increasing presence, scale, and complexity of distributed systems, resource failures are becoming an important and practical topic of computer science research. While numerous failure models and failure-aware algorithms exist, their comparison has been hampered by the lack of public failure data sets and data processing tools. To facilitate the design, validation, and comparison of fault-tolerant models and algorithms, we have created the Failure Trace Archive (FTA)-an online, public repository of failure traces collected from diverse parallel and distributed systems. In this work, we first describe the design of the archive, in particular of the standard FTA data format, and the design of a toolbox that facilitates automated analysis of trace data sets. We also discuss the use of the FTA for various current and future purposes. Second, after applying the toolbox to nine failure traces collected from distributed systems used in various application domains (e.g., HPC, Internet operation, and various online applications), we present a comparative analysis of failures in various distributed systems. underline the need for public availability of trace data from different distributed systems. Last, we show how different interpretations of the meaning of failure data can result in different conclusions for failure modeling and job scheduling in distributed systems. Our results for different interpretations show evidence that there may be a need for further revisiting existing failureaware algorithms, when applied for general rather than for domain-specific distributed systems.

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“…In this architecture, the grid services layer houses the middleware while our pricing model integrates on the top layer of the middleware. In the current study, we integrate the top layer of our pricing model (price and usage optimization level) onto the top layer of GridSim [15]. Figure 1 shows the six layered architecture of the GridSim.…”

Section: Integration Architecture Gridsimmentioning

confidence: 99%

Integrating a financial option based model with GridSim for pricing Grid resources

Allenotor

Thulasiram

2009

2009 10th IEEE/ACM International Conference on Grid Computing

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Abstract-Analysis of grid resources utilization from real grid trace data shows the feasibility of a financial option based model for pricing grid resources to attract more users for profitability for the grid provider while providing high Quality of Service (QoS) to users. However, in the absence of grid resource pricing benchmarks, we simulate grid resources usage in order to justify our pricing model using GridSim toolkit. In this work we integrate a financial option based pricing model with GridSim framework and use it as a grid simulation tool to price grid compute resources.

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A toolkit for modelling and simulating data Grids: an extension to GridSim

Cited by 132 publications

References 11 publications

iCanCloud: A Flexible and Scalable Cloud Infrastructure Simulator

iCanCloud: A Flexible and Scalable Cloud Infrastructure Simulator

The Failure Trace Archive: Enabling the comparison of failure measurements and models of distributed systems

Integrating a financial option based model with GridSim for pricing Grid resources

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