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Journal ArticleDOI

CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

TL;DR: The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.
Abstract: Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end-users under a usage-based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd.
Citations
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Proceedings ArticleDOI
22 Mar 2017
TL;DR: This paper proposes a heuristic algorithm for VM allocation for providing MapReduce as a cloud service that allocates VMs in same or nearby PMs and hence reduces data transfer delay between VMs.
Abstract: MapReduce-as-a-Service cloud is of great importance because of the data growth and increase in opportunities in big data analytics. MapReduce platforms provided through cloud help the end user by providing ready to use MapReduce clusters. Since the cloud environment is virtualized, allocating Virtual Machines (VMs) efficiently has high relevance. If the VMs allocated for a MapReduce cluster are hosted in distant Physical Machines (PMs), the interaction between VMs causes delays depending upon the distance between the PMs hosting them. In this paper, we propose a heuristic algorithm for VM allocation for providing MapReduce as a cloud service. This algorithm allocates VMs in same or nearby PMs and hence reduces data transfer delay between VMs. Simulation results demonstrate the improvement on execution time of the VM allocation algorithm without compromising the performance of applications running on the allocated VMs.

Cites methods from "CloudSim: a toolkit for modeling an..."

  • ...For simulating our cloud setup, we have added the concept of racks in CloudSim....

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  • ...We have tested our algorithm in a simulated environment, that is, the popular cloud simulation platform CloudSim [22]....

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01 Jan 2011
TL;DR: A scheduling model of access request in open storage architectures is introduced and it is expected that this proposed scheduler will be used in nowadays cloud computing environment and provide the ability of multiobjective optimization scheduling in the future.
Abstract: In cloud computing era, datacenter can be orga- nized by resources distributed geographically on demand into a dynamic logical entity. All these virtualized datacenters (VDCs) migrate transparently to another resources set for the best cost-effectiveness. Open storage architecture is one of the basic infrastructures in Cloud. This paper introduces a scheduling model of access request in open storage architectures. This scheduler runs during the migration of VDCs. We discuss the environment model and scheduling criteria in detail. And then we propose the objective functions and the scheduling function Ω .I n this model, all components including users, VDCs, and scheduler cooperates with each other via associative broadcast. We expect that this proposed scheduler will be used in nowadays cloud computing environment and provide the ability of multiobjective optimization scheduling in the future. Index Terms—Schedule, Virtualized Datacenter, Migration, Cloud Computing, Open Storage Architecture.
Proceedings ArticleDOI
Sang-Chul Kim1
05 Jul 2016
TL;DR: This study develops an image-processing algorithm in which different markers are detected and processed in a roll-to-roll (R2R) system and it can be said that the productivity and accuracy of an R2R system depends entirely on the quality of the CCD cameras and their image- processors.
Abstract: This paper proposes an image-processing algorithm for register marker detection in a roll-to-roll (R2R) system. Recently, R2R systems have been receiving considerable international attention from researchers because of their ability to print electronic devices on flexible substrates. During such printing, an R2R system must adjust its printing position by continuously checking the positions of the markers in order to ensure correct positioning of the printing roll. By acquiring the differences in the position information between referenced and printed marker positions, an R2R system can control the printing position by adjusting the printing roll speed. To capture and analyze referenced and printed marker images, an R2R system uses charge-coupled device (CCD) cameras and their image-processing algorithms. Therefore, it can be said that the productivity and accuracy of an R2R system depends entirely on the quality of the CCD cameras and their image-processing algorithms. This study develops an image-processing algorithm in which different markers are detected and processed.

Additional excerpts

  • ...MFC provides the latest features of Windows and is the standard for Windows programming class libraries [5]....

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01 Dec 2020
TL;DR: In this article, an autonomous model is presented which detects overloaded servers in the analysis phase by a prediction algorithm, and at the planning phase, a multi heuristic algorithm based on learning automata is proposed to find proper servers for virtual machine placement.
Abstract: Today, with the rise of cloud data centers, power consumption has increased and cloud infrastructure management has become more complex. On the other hand, meeting the needs of cloud users is an important goal in the cloud infrastructure. To solve such problems, an autonomous model with predictive capability is needed to do virtual machine consolidation at runtime effectively. In fact, using the feedback system of autonomous systems can make this process simpler and more optimized. The goal of this research is to propose a cloud resource management model that makes the virtual machine consolidation process autonomous, and by using a prediction method compromises between service level agreement violations and energy consumption reduction. In this research, an autonomous model is presented which detects overloaded servers in the analysis phase by a prediction algorithm. Also, at the planning phase, a multi heuristic algorithm based on learning automata is proposed to find proper servers for virtual machine placement. Cloudsim version 3.0.3 was used to evaluate the proposed model. The results show that the proposed model has reduced averagely the service level agreement violations, energy and migration counts by 67.08%, 11.61% and 70.64% respectively, compared to other methods.
DOI
01 May 2023
TL;DR: In this paper , the authors proposed an air pollution control measure using green metrics (GMs) to minimize the carbon emission from traditional data centers (DCs) by designing the "Green Data Centers" (GDCs).
Abstract: Growing air pollution has become a global threat to the environment. Controlling this global threat is very challenging and costly. Therefore, this paper proposes an air pollution control measure using green metrics (GMs). For doing this, we minimize the carbon emission from traditional data centers (DCs) by designing the ‘Green Data Centers’ (GDCs). GDCs are the control mechanism, which includes a set of different green protocols. GDCs are designed in such a way that they can minimize the carbon emission (i.e., CO, and $CO_{2})$ from the traditional DCs. The design of GDCs is also responsible for optimizing energy consumption, cost-effectiveness, efficient network infrastructures, load scheduling algorithms, and the number of used devices like switches, ports, and linecards. GDCs are constructed by taking care of the idle server because it consumes massive energy than the computing server. This paper also presents a taxonomy of the existing research work, which contains the research of GDCs related to DCs, like cloud computing and cooling techniques. Apart from this, we discuss various green metrics (GMs), green computing, and networking proposals of GDCs.
References
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Journal ArticleDOI
TL;DR: The clouds are clearing the clouds away from the true potential and obstacles posed by this computing capability.
Abstract: Clearing the clouds away from the true potential and obstacles posed by this computing capability.

9,282 citations


"CloudSim: a toolkit for modeling an..." refers background in this paper

  • ...As Cloud computing R&D is still in the infancy stage [1], a number of important issues need detailed investigation along the layered Cloud computing architecture (see Figure 1)....

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  • ...the potential to transform a large part of the IT industry, making software even more attractive as a service’ [1]....

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  • ...Thus, they can focus more on innovation and creation of business values for their application services [1]....

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Book
01 Oct 1998
TL;DR: The Globus Toolkit as discussed by the authors is a toolkit for high-throughput resource management for distributed supercomputing applications, focusing on real-time wide-distributed instrumentation systems.
Abstract: Preface Foreword 1. Grids in Context 2. Computational Grids I Applications 3 Distributed Supercomputing Applications 4 Real-Time Widely Distributed Instrumentation Systems 5 Data-Intensive Computing 6 Teleimmersion II Programming Tools 7 Application-Specific Tools 8 Compilers, Languages, and Libraries 9 Object-Based Approaches 10 High-Performance Commodity Computing III Services 11 The Globus Toolkit 12 High-Performance Schedulers 13 High-Throughput Resource Management 14 Instrumentation and Measurement 15 Performance Analysis and Visualization 16 Security, Accounting, and Assurance IV Infrastructure 17 Computing Platforms 18 Network Protocols 19 Network Quality of Service 20 Operating Systems and Network Interfaces 21 Network Infrastructure 22 Testbed Bridges from Research to Infrastructure Glossary Bibliography Contributor Biographies

7,569 citations

Journal ArticleDOI
TL;DR: This paper defines Cloud computing and provides the architecture for creating Clouds with market-oriented resource allocation by leveraging technologies such as Virtual Machines (VMs), and provides insights on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain Service Level Agreement (SLA) oriented resource allocation.

5,850 citations


"CloudSim: a toolkit for modeling an..." refers background or methods in this paper

  • ...The well-known examples of services operating at this layer are Amazon EC2, Google App Engine, and Aneka....

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  • ...The CloudSim framework aims to ease-up and speed the process of conducting experimental studies that use Cloud computing as the application provisioning environments....

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  • ...It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time....

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  • ...Some of the examples for emerging Cloud computing infrastructures/platforms are Microsoft Azure [5], Amazon EC2, Google App Engine, and Aneka [11]....

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Journal ArticleDOI
TL;DR: The main purpose is to update the designers and users of parallel numerical algorithms with the latest research in the field and present the novel ideas, results and work in progress and advancing state-of-the-art techniques in the area of parallel and distributed computing for numerical and computational optimization problems in scientific and engineering application.
Abstract: Edited by Tianruo Yang Kluwer Academic Publisher, Dordrech, Netherlands, 1999, 248 pp. ISBN 0-7923-8588-8, $135.00 This book contains a selection of contributed and invited papers presented and the workshop Frontiers of Parallel Numerical Computations and Applications, in the IEEE 7th Symposium on the Frontiers on Massively Parallel Computers (Frontiers '99) at Annapolis, Maryland, February 20-25, 1999. Its main purpose is to update the designers and users of parallel numerical algorithms with the latest research in the field. A broad spectrum of topics on parallel numerical computations, with applications to some of the more challenging engineering problems, is covered. Parallel algorithm designers and engineers who use extensively parallel numerical computations, as well as graduate students in Computer Science, Scientific Computing, various engineering fields and applied mathematics should benefit from reading it. The first part is addressed to a larger audience and presents papers on parallel numerical algorithms. Two new libraries are presented: PSPASSES and PoLAPACK. PSPASSES is a collection of parallel direct solvers, for sparse symmetric positive definite linear systems, which are characterized by high performance and good scalability. PoLAPACK library contains LU and QR codes based on a new blocking strategy that guarantees good performance regardless of the physical block size. Next, an efficient approach to solving stiff ordinary differential equations by diagonal implicitly iterated Runge-Kutta (DIIRK) method is described. DIIRK renders a fast parallel implementation due to a reduced number of function evaluation and an automatic stepsize control mechanism. Finally, minimization of sufficiently smooth non-linear functionals is sought via parallel space decomposition. Here, a theoretical background of the problem and two equivalent algorithms are presented. New research directions for classical solvers are treated in the next three papers: first, reduction of the global synchronization in the biconjugate gradient method, second, a new more efficient Jacobi ordering for the multiple-port hypercubes, and finally, an analysis of the theoretical performance of an improved version of the Quasi-minimal residual method. Parallel numerical applications constitute the second part of the book, with results from fluid mechanics, material sciences, applications to signal and image processing, dynamic systems, semiconductor technology and electronic circuits and systems design. With one exception, the authors expose in detail parallel implementations of the algorithms and numerical results. First, a 3D-elasticity problem is solved using an additive overlapping domain decomposition algorithm. Second, an overlapping mesh technique is used in a parallel solver for the compressible flow problem. Then, a parallel version of a complex numerical algorithm to solve a lubrication problem studied in tribology is introduced. Next, a timid approach to parallel computing of the cavity flow by the finite element method is presented. The problem solved is rather small for today's needs and only up to 6 processors are used. This is also the only paper that does not present results from numerical experiments. The remaining applications discussed in the subsequent chapters are: large scale multidisciplinary design optimization problem with application to the design of a supersonic commercial aircraft, a report on progress in parallel solution of an electromagnetic scattering problem using boundary integral methods and an optimal solution to the convection-diffusion equation modeling the concentration of a pollutant in the air. The book is of definite interest to readers who keep up-to-date with the parallel numerical computation research. The main purpose, to present the novel ideas, results and work in progress and advancing state-of-the-art techniques in the area of parallel and distributed computing for numerical and computational optimization problems in scientific and engineering application is clearly achieved. However, due to its content it cannot serve as a textbook for a computer science or engineering class. Overall, is a reference type book to be kept by specialists and in a library rather than a book to be purchased for self-introduction to the field. Most of the papers presented are results of ongoing research and so they rely heavily on previous results. On the other hand, with only one exception, the results presented in the papers are a great source of information for the researchers currently involved in the field. Michelle Pal, Los Alamos National Laboratory

4,696 citations


"CloudSim: a toolkit for modeling an..." refers background in this paper

  • ...Hence, as against Grids, Clouds contain an extra layer (the virtualization layer) that acts as an execution, management, and hosting environment for application services....

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  • ...In the past decade, Grids [14] have evolved as the infrastructure for delivering high-performance services for compute- and data-intensive scientific applications....

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Journal ArticleDOI
TL;DR: This work states that clusters, Grids, and peer‐to‐peer (P2P) networks have emerged as popular paradigms for next generation parallel and distributed computing and introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, continuously changing resource conditions, and politics.
Abstract: SUMMARY Clusters, Grids, and peer-to-peer (P2P) networks have emerged as popular paradigms for next generation parallel and distributed computing. They enable aggregation of distributed resources for solving largescale problems in science, engineering, and commerce. In Grid and P2P computing environments, the resources are usually geographically distributed in multiple administrative domains, managed and owned by different organizations with different policies, and interconnected by wide-area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, continuously changing resource conditions, and politics. The resource management and scheduling systems for Grid computing need to manage resources and application execution depending on either resource consumers’ or owners’ requirements, and continuously adapt to changes in resource availability. The management of resources and scheduling of applications in such large-scale distributed systems is a complex undertaking. In order to prove the effectiveness of resource brokers and associated scheduling algorithms, their performance needs to be evaluated under different scenarios such as varying number of resources and users with different requirements. In a Grid environment, it is hard and even impossible to perform scheduler performance evaluation in a repeatable and controllable manner as resources and users are distributed across multiple organizations with their own policies. To overcome this limitation, we have developed a Java-based discrete-event Grid simulation toolkit called GridSim. The toolkit supports modeling and simulation of heterogeneous Grid resources (both time- and space-shared), users and application models. It provides primitives for creation of application tasks, mapping of tasks to resources, and their management. To demonstrate suitability of the GridSim toolkit, we have simulated a Nimrod-G

1,604 citations


"CloudSim: a toolkit for modeling an..." refers background or methods in this paper

  • ...On the other hand, GridSim is an event-driven simulation toolkit for heterogeneous Grid resources....

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  • ...As discussed previously, GridSim is one of the building blocks of CloudSim....

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  • ...However, GridSim uses the SimJava library as a framework for event handling and inter-entity message passing....

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  • ...To support research, development, and testing of new Grid components, policies, and middleware; several Grid simulators such as GridSim [8], SimGrid [6], OptorSim [10], and GangSim [3] have been proposed....

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  • ...Considering that none of the current distributed (including Grid and Network) system simulators [3][6][8] offer the environment that can be directly used for modeling Cloud computing environments; we present CloudSim: a new, generalized, and extensible simulation framework that allows seamless modeling, simulation, and experimentation of emerging Cloud computing infrastructures and application services....

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