Single system image: A survey

Healy, Philip D.; Lynn, Theo; Barrett, Enda; Morrison, John P.

doi:10.1016/j.jpdc.2016.01.004

Cited by 15 publications

(12 citation statements)

References 92 publications

(102 reference statements)

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“…Infrastructure and Middleware for Big Data Processing. Numerous algorithms, computing infrastructures and middleware for HPC and big data processing have been developed during previous decades [15,16]. In general, current middleware for parallel computating focuses on providing powerful mechanisms for managing communication between processors and environments for parallel machines and computer networks.…”

Section: Enabling Infrastructures and Middleware For Big-data Modellimentioning

confidence: 99%

“…The idea was implemented in many forms, but lately it has grown into three main computing environments: computing clusters, grids and clouds. A survey of software tools for supporting cluster, grid and cloud computing is provided in [15,17,18] [23]) are described in literature [24,25]. Cloud computing infrastructures consisting of services delivered through common centers and built on servers are discussed in [18].…”

Section: Enabling Infrastructures and Middleware For Big-data Modellimentioning

confidence: 99%

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Why High-Performance Modelling and Simulation for Big Data Applications Matters

Grelck

Niewiadomska-Szynkiewicz

Aldinucci

et al. 2019

Lecture Notes in Computer Science

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Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned.

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Section: Enabling Infrastructures and Middleware For Big-data Modellimentioning

confidence: 99%

Section: Enabling Infrastructures and Middleware For Big-data Modellimentioning

confidence: 99%

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Grelck

Niewiadomska-Szynkiewicz

Aldinucci

et al. 2019

Lecture Notes in Computer Science

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“…This allows to break payloads at OS level, which may result in greater execution efficiency in some scenarios. OpenMosix [11] [12] and OSCAR [13] [14] are projects which achieves SSI. There is various downside of this approach, some of them are, a) Although process migration is supported which allows one process to shift to another physical CPU or other CPUcore inside a CPU, in this case other CPU is resides in a different computer.…”

Section: Related Workmentioning

confidence: 99%

“…b) There is no reliable way to know exact CPU load in any computer which is contributing to SSI. c) Only Linux systems are supported by SSI, hence computers running another OS cannot contribute to SSI [14]. d) To properly scale this type of architecture, a lot of low level (kernel level) changes have to be made which can be very time consuming and only highly skilled IT professionals can perform it.…”

Section: Related Workmentioning

confidence: 99%

“…d) To properly scale this type of architecture, a lot of low level (kernel level) changes have to be made which can be very time consuming and only highly skilled IT professionals can perform it. e) Many new hardware components like Nvidia, tesla graphic accelerator and Intel Xeon phi do not support SSI at the time of writing due to driver related issues [23]. Web based solution was chosen to achieve efficient payload distribution as this provides complete compatibility between different OS.…”

Section: Related Workmentioning

confidence: 99%

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Resource provisioning methodology for cloud environment with producer and consumer favorable: an approach of virtual cloud compiler

Vivek¹,

Srinivasan²,

Blessing³

2018

IJET

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Cloud computing is a model where traditional resources such as CPU cycles, storage, security etc. are delivered through web based. It is a technology which has ability to change large part of software development cycle, 3D rendering or any other computationally expensive tasks execution. Much amount of time is wasted on compiling and rendering such computationally expensive tasks due to low power machines, which directly proportional to efficiency of user who is working on that project. Extreme computational tasks such as weather forecast, DNA analyses, encryption breaking takes so much time in consumer grade computing devices that they are realistically not possible to perform. We have proposed a novel approach to perform payload distribution, for the users who wanted to run their computationally expensive tasks efficiently. We have used virtualization technique on data center resources to perform scheduling. Up to 32% cost has been reduced in an environment of 30 users when our technology used instead of traditional standalone desktop environment. This is achieved by replacing 30 standalone computers with a powerful server and thin clients like Raspberry pi as clients. Time wasted in computational task such as rendering and compiling is greatly reduced. We have not only improved the efficiency, but also make sure both cloud producer and consumer are favorable. With simulations and outcomes, we validate that our methodology for payload distribution performs well.

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Distributed Operating System Security and Protection: A Short Survey

Abdelmoumin

Hazzazi

2020

Advances in Intelligent Systems and Computing

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Single system image: A survey

Cited by 15 publications

References 92 publications

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Resource provisioning methodology for cloud environment with producer and consumer favorable: an approach of virtual cloud compiler

Distributed Operating System Security and Protection: A Short Survey

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