The Virtual Computing Lab (VCL)

Rindos, Andy; Vouk, Mladen A.; Jararweh, Yaser

doi:10.4018/ijssmet.2014040104

Cited by 31 publications

(9 citation statements)

References 17 publications

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“…The approach explains how to optimally incorporate MCC for data collection into large-scale enterprises. Jararweh et al [3] expanded the work of Al-Ayyoub et al [2], presenting a new adaptable cloudlet structure for the MCC model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The model explains the best way to involve MCC in information assortment in enormous-scale organizations. The goal of the proposed flexible framework is to reduce power consumption and postpone the arrangement of sight and sound applications while using MCC [3]. However, the display issue in portable registration is almost as important as security and battery conservation.…”

Section: Literature Reviewmentioning

confidence: 99%

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Trust delegation-based secure mobile cloud computing framework

Hasan

Hussain

Sarwar

et al. 2023

2023 IEEE International Conference on Emerging Trends in Engineering, Sciences and Technology (ICES&T)

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Nowadays, practically everyone has a smartphone with them. These smart devices store several types of personal information, such as images, files, credit card pins, etc., and are used for communication purposes. Consider the possibility that someone could gain access to it without our knowledge. How could they possibly use it? One of the most critical security concerns is data and privacy in terms of stolen data and malicious file behaviour. However, private and delicate data are required for these practical uses.Similarly, connected phones' other weak points are their limited battery life, slow processor speed, and unpatched vulnerabilities. To overcome such limitations, mobile cloud computing (MCC) is introduced, which analyzes data in the cloud and returns the findings to the smartphone. However, MCC increased the effectiveness of several programs. MCC also considered several types of issues, such as real-time communication data confidentiality. Further, safety, speed, and customer satisfaction are the three fundamental parameters of mobile cloud services that will be highlighted in this article. Finally, a model concept is also elaborate for demonstration purposes to employ the trustbased assignment system to enhance safety ratios, showing the perfection of the five-portable cloud computing architecture.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Trust delegation-based secure mobile cloud computing framework

Hasan

Hussain

Sarwar

et al. 2023

2023 IEEE International Conference on Emerging Trends in Engineering, Sciences and Technology (ICES&T)

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“…For instance, the Virtual Computing Lab 9 is an open source middleware (a cloud orchestration stack) that is used to deliver computational resources for education, optimized for the needs of the academic community at university level (see also other studies 10,11 ) but that has also been applied to K-12 education 12 . For instance, the Virtual Computing Lab 9 is an open source middleware (a cloud orchestration stack) that is used to deliver computational resources for education, optimized for the needs of the academic community at university level (see also other studies 10,11 ) but that has also been applied to K-12 education 12 .…”

Section: Big Data Parkmentioning

confidence: 99%

“…One main area ("cloud for education") is devoted to using the cloud so to empower the process of e-learning. For instance, the Virtual Computing Lab 9 is an open source middleware (a cloud orchestration stack) that is used to deliver computational resources for education, optimized for the needs of the academic community at university level (see also other studies 10,11 ) but that has also been applied to K-12 education 12 . Another example in use is CloudIA, 13 a system allowing the creation of clouds for on-demand computing resources and run e-learning applications and collaboration software.…”

Section: Big Data Parkmentioning

confidence: 99%

Learning the way to the cloud: Big Data Park

Marchiori

2017

Concurrency and Computation

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Big data and cloud systems are two crucial components of modern computing, but they are also a challenge to learn, as they change the basic assumptions of computing, and as such the mental habits that are proper of classical computing teaching. In other words, beyond a technical revolution, we first and foremost are in the presence of a mental revolution. For this reason, we think it is essential to help students to change their mental habits as early as possible and become familiar with at least the basic notions of distributed storage and cloud computing. In this paper, we introduce the Big Data Park system, which has been developed with an ambitious task in mind: gently introduce K-12 kids to the basics of big data and cloud computing, helping them to be part of this revolution. We report on the creation of the system, the challenges, and also the impact on higher K-18 education. The results, extensively tested in the field with a wide variety of kids and children, have been extremely successful, thanks also to advanced educational techniques like gamification.Moreover, the development and testing of the project have also provided valuable insights for the general problem of education of big data computing. INTRODUCTIONBig data and cloud computing are rapidly becoming an essential part of our technology infrastructure: In fact, their wide adoption has been seen as quite an epochal revolution, a new form of mind passing from single-machines computation to fully distributed computation and storage. This paradigm shift is so important also because it is to some extent counter-intuitive to the basic notions of programming that are classically learned around the world. When students are first introduced to big data programming, they often experience an initial resistance, given the educational background that limits their thinking flexibility.It is for these reasons that we have started the development of an online educational tool for big data and cloud computing. Computer-assisted tools can greatly help students and are a field in continuous expansion, given the great benefits they can provide in the educational paths (see for instance other works 1-6 ). However, building a successful educational tool is a rather challenging task, also because its success critically depends on the age of the subjects. The goal of the system that we have built, named Big Data Park (an improvement of the system first described in Marchiori 7 ), is to be able to educate students even from their very early stages: More specifically, covering the full spectrum of K-12 education. We feel this is a crucial step, in order to start shaping the minds of the kids already at their very early stage, therefore freeing their minds and allowing them to understand as early as possible the big changes in thought that are part of the big data and cloud computing revolution. Complementary, we also tried to build up a system that was general enough not to be used by K-12 kids, but possibly also useful in higher education, more specifically even at K-...

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“…Furthermore, handling these clusters requires the use of highly optimised schedulers with a constraint that they should guarantee high level of availability, high cluster utilisation, data locality, and scalability. The integration of big data management with many data generators become a vital issue to maintain an efficient data processing (Quwaider and Jararweh, 2015;Rindos et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

A scalable Map Reduce tasks scheduling: a threading-based approach

Yaseen

AlQudah

Jararweh

et al. 2017

IJCSE

Self Cite

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The Map Reduce paradigm is now considered a standard platform that is used for large-scale data processing and management. A major operation that the Map Reduce platform relies on greatly is tasks scheduling. Although many schedulers have been presented, task scheduling is still one of the major problems that face Map Reduce frameworks. Schedulers need to maintain data locality to achieve an acceptable performance by avoiding several data transmissions. Hence, in this paper, we propose a new scheduling algorithm named 'MTL' that utilises multi-threading principles. The MTL scheduler assigns a dedicated thread for each data block. Indeed, the multi-threading approach shows great results that make our MTL scheduler a scalable one that performs well. At the same time, it maintains the locality property. During the evaluation of the MTL scheduler performance, two main factors were taken into consideration; the simulation time and the energy consumption. The MTL scheduler is then compared with other existing schedulers such as FIFO, matchmaking, and delay schedulers. The MTL scheduler showed favourable results and proved its advantages over other existing schedulers.

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The Virtual Computing Lab (VCL)

Cited by 31 publications

References 17 publications

Trust delegation-based secure mobile cloud computing framework

Trust delegation-based secure mobile cloud computing framework

Learning the way to the cloud: Big Data Park

A scalable Map Reduce tasks scheduling: a threading-based approach

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