A Compartive Study of Cloud Computing Middleware

Amrani, Chaker El; Filali, Kaoutar Bahri; Ahmed, Kaoutar Ben; Diallo, Amadou Tidiane; Telolahy, Stephano; El‐Ghazawi, Tarek

doi:10.1109/ccgrid.2012.129

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…Nimbus, which contains the required software to manage cloud, is used as a cloud middleware in SuMegha. Nimbus [6,7] middleware consists of an integrated set of tools that deliver the power and versatility of infrastructure clouds to scientific users. Nimbus is preferred middleware because of the inbuilt support for virtual clusters.…”

Section: Cloud Middlewarementioning

confidence: 99%

SuMegha Cloud Kit: Create Your Own Private Scientific Cloud

Arackal¹,

Arunachalam²,

Kalasagar³

et al. 2017

Proceedings of International Symposium on Grids and Clouds (ISGC) 2016 — PoS(ISGC 2016)

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In this work, the authors discuss the implementation details of SuMegha Scientific Cloud Lab Kit, which enables users to set up their own private scientific cloud. The scientific cloud offers infrastructure, platform, and/or software services for the scientific community working on problems of modeling and simulation. It enables the creation of virtual clusters (High Performance Computing as a Service). The lab kit is very useful to the researchers and the student community, who want to set up a cloud, but lack the expertise to do so. It not only helps to effectively utilize the idle computers in the organization, but also provides MPI or Hadoop based clusters on demand to its users to solve compute-data intensive problems. The fact that the Cloud can be set up in a single desktop can greatly aid in adoption of Cloud Computing, since almost anyone can now establish a cloud in their premises. SuMegha is designed in a modular fashion using a judicious mix of open source and in-house developed components, such as Nimbus cloud middleware for virtual cluster creation (contextualization), Xen hypervisor and SuMegha Portal for enabling users to request the virtual machines/ clusters and other Services. Storage as a service is provided by the in-house developed CloudVault solution integrated into the lab kit. Supplemented with Software as a Service such as Seasonal Forecast Model (SFM), Next Generation Sequencing (NGS), etc., SuMegha Cloud Lab kit offers a comprehensive environment for scientific computing. We believe that this lab kit is very useful to the vast number of academic institutions, to easily set up the required Cloud Lab.

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Section: Cloud Middlewarementioning

confidence: 99%

SuMegha Cloud Kit: Create Your Own Private Scientific Cloud

Arackal¹,

Arunachalam²,

Kalasagar³

et al. 2017

Proceedings of International Symposium on Grids and Clouds (ISGC) 2016 — PoS(ISGC 2016)

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“…For efficient cloud server consolidation, the system may use computer's stand-by option rather than turn off host nodes to reduce waiting time spent on the host nodes booting. Considering computer stand-by option, the energy consumption model of a physical machine is defined in equation (2).…”

Section: Energy Consumption Model For Cloud Environmentmentioning

confidence: 99%

“…Here P all represents the sum of all physical machines' energy consumption and is expressed with the sum of P i in equation (2). N is the number of available physical machines in a cloud infrastructure.…”

Section: Energy Consumption Model For Cloud Environmentmentioning

confidence: 99%

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Reducing Resource Over-Provisioning Using Workload Shaping for Energy Efficient Cloud Computing

Kim¹,

Mvulla²

2013

Appl. Math. Inf. Sci.

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Abstract:Using workload shaping technology, we present an approach to remove hardware over-provisioning implementing task buffers and scheduler, in terms of energy consumption. Task buffers reorder tasks with various priorities and routes them to appropriate virtual machines. Scheduler monitors the task buffering and hardware load status, and decides the optimal number of active physical and virtual machines. In addition, we designed a mechanism wherein tasks with fast executing are routed in fast and high energy consumption machines and slow tasks to slow and low energy consumption machines. As a result, our approach efficiently can shape workloads and manage the optimal number of active virtual machines and physical machines, in terms of energy consumption. To evaluate our approach, we generated synthetic workload data and evaluated it both in simulating and actual cloud environment. Our experimental results demonstrate our approach outperforms in terms of energy consumption to when not using no workload shaping methodology.

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“…More specific studies tend to deeper detail the analyzed solutions, considering a specific point of view. [11] focus on appearance design and novel features, while [32] focus on the scalability comparison of the platforms and [37] on placement policies, platform architecture and networking of the analyzed solutions. [13] details OpenStack and CloudStack solutions, but with deeper details only for OpenStack.…”

mentioning

confidence: 99%

Open Source Solutions for Building IaaS Clouds

Barkat¹,

Santos²,

Ikken³

2015

SCPE

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Cloud Computing is not only a pool of resources and services offered through the internet, but also a technology solution that allows optimization of resources use, costs minimization and energy consumption reduction. Enterprises moving towards cloud technologies have to choose between public cloud services, such as: Amazon Web Services, Microsoft Cloud and Google Cloud services, or private self built clouds. While the firsts are offered with affordable fees, the others provide more privacy and control. In this context, many open source softwares approach the buiding of private, public or hybrid clouds depending on the users need and on the available capabilities. To choose among the different open source solutions, an analysis is necessary in order to select the most suitable according with the enterprise's goals and requirements. In this paper, we present a depth study and comparison of five open source frameworks that are gaining more attention recently and growing fast: CloudStack, OpenStack, Eucalyptus, OpenNebula and Nimbus. We present their architectures and discuss different properties, features, useful information and our own insights on these frameworks.

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A Compartive Study of Cloud Computing Middleware

Cited by 8 publications

References 6 publications

SuMegha Cloud Kit: Create Your Own Private Scientific Cloud

SuMegha Cloud Kit: Create Your Own Private Scientific Cloud

Reducing Resource Over-Provisioning Using Workload Shaping for Energy Efficient Cloud Computing

Open Source Solutions for Building IaaS Clouds

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