Cloud Computing: An Architectural and Technological Overview

Motta, Gianmario; Sfondrini, Nicola; Sacco, Daniele

doi:10.1109/ijcss.2012.37

Cited by 28 publications

(11 citation statements)

References 28 publications

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“…This can enable more dynamic and interactive learning experiences and allow students and teachers in multiple locations to collaborate and communicate more effectively (Alabbadi, 2011). In addition, cloud-based services can offer users and academic institution cost savings and access to scalable computing power (Buyya, Broberg and Goscinski, n.d.;Armbrust et al, 2009;Motta, Sfondrini and Sacco, 2012).…”

Section: Introductionmentioning

confidence: 99%

An Overview of Cloud Services Adoption Challenges in Higher Education Institutions

Alharthi

Yahya

Walters

et al. 2015

Proceedings of the 2nd International Workshop on Emerging Software as a Service and Analytics

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Abstract:Information Technology (IT) plays an important role in enabling education services be delivered to users. Most education online services in universities have been run on the cloud to provide services to support students, lecturers, researchers and administration staff. These are enabled with the emergence of cloud computing in the world of IT. Cloud computing offers on demand Internet-based computing services. This paper presents an overview of cloud computing adoption in higher education, mainly tertiary institutions and universities. The focus of the paper is the challenges of cloud computing in higher education. It introduces the background to cloud computing and reviews research on adoption challenges in higher education institutions. These challenges are important as they provide an overview of the adoption of cloud in higher education. The authors proposed an integrated reference model based on the challenges in the literature integrated with TAM model to investigate the factors influence the users' attitudes and behaviours toward using cloud education services in universities ICT provision.

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Section: Introductionmentioning

confidence: 99%

An Overview of Cloud Services Adoption Challenges in Higher Education Institutions

Alharthi

Yahya

Walters

et al. 2015

Proceedings of the 2nd International Workshop on Emerging Software as a Service and Analytics

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“…In Equation (3), y it presents whether there are any VMs assigned to server i in interval t. A VM is allowed to be assigned to one and only one server, as shown in Equation (4). Each server must satisfy the resource requirements of VMs on it, as with constraints (5) and (6).…”

Section: Dvmp Problemmentioning

confidence: 99%

“…Cloud computing facilitates services at three different levels, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), and four deployment models, including private cloud, community cloud, public cloud, and hybrid cloud [4]. With the increasing size of storage requirements, the inefficient use of resources causes high energy consumption [5,6].…”

Section: Introductionmentioning

confidence: 99%

An Energy Aware Unified Ant Colony System for Dynamic Virtual Machine Placement in Cloud Computing

2017

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Abstract:Energy efficiency is a significant topic in cloud computing. Dynamic consolidation of virtual machines (VMs) with live migration is an important method to reduce energy consumption. However, frequent VM live migration may cause a downtime of service. Therefore, the energy save and VM migration are two conflict objectives. In order to efficiently solve the dynamic VM consolidation, the dynamic VM placement (DVMP) problem is formed as a multiobjective problem in this paper. The goal of DVMP is to find a placement solution that uses the fewest servers to host the VMs, including two typical dynamic conditions of the assignment of new coming VMs and the re-allocation of existing VMs. Therefore, we propose a unified algorithm based on an ant colony system (ACS), termed the unified ACS (UACS), that works on both conditions. The UACS firstly uses sufficient servers to host the VMs and then gradually reduces the number of servers. With each especial number of servers, the UACS tries to find feasible solutions with the fewest VM migrations. Herein, a dynamic pheromone deposition method and a special heuristic information strategy are also designed to reduce the number of VM migrations. Therefore, the feasible solutions under different numbers of servers cover the Pareto front of the multiobjective space. Experiments with large-scale random workloads and real workload traces are conducted to evaluate the performance of the UACS. Compared with traditional heuristic, probabilistic, and other ACS based algorithms, the proposed UACS presents competitive performance in terms of energy consumption, the number of VM migrations, and maintaining quality of services (QoS) requirements.

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“…Moreover, cloud computing has four types of deployment models such as private, public, hybrid, and community clouds [5,6]. Provision of MCC services to users requires largescale cloud computing platform, which drains enormous amount of electric power and increases MCC operational costs, CO 2 emissions.…”

Section: Introductionmentioning

confidence: 99%

MeReg: Managing Energy-SLA Tradeoff for Green Mobile Cloud Computing

Yadav

Zhang

2017

Wireless Communications and Mobile Computing

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Mobile cloud computing (MCC) provides various cloud computing services to mobile users. The rapid growth of MCC users requires large-scale MCC data centers to provide them with data processing and storage services. The growth of these data centers directly impacts electrical energy consumption, which affects businesses as well as the environment through carbon dioxide (CO 2 ) emissions. Moreover, large amount of energy is wasted to maintain the servers running during low workload. To reduce the energy consumption of mobile cloud data centers, energy-aware host overload detection algorithm and virtual machines (VMs) selection algorithms for VM consolidation are required during detected host underload and overload. After allocating resources to all VMs, underloaded hosts are required to assume energy-saving mode in order to minimize power consumption. To address this issue, we proposed an adaptive heuristics energy-aware algorithm, which creates an upper CPU utilization threshold using recent CPU utilization history to detect overloaded hosts and dynamic VM selection algorithms to consolidate the VMs from overloaded or underloaded host. The goal is to minimize total energy consumption and maximize Quality of Service, including the reduction of service level agreement (SLA) violations. CloudSim simulator is used to validate the algorithm and simulations are conducted on real workload traces in 10 different days, as provided by PlanetLab.

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Cloud Computing: An Architectural and Technological Overview

Cited by 28 publications

References 28 publications

An Overview of Cloud Services Adoption Challenges in Higher Education Institutions

An Overview of Cloud Services Adoption Challenges in Higher Education Institutions

An Energy Aware Unified Ant Colony System for Dynamic Virtual Machine Placement in Cloud Computing

MeReg: Managing Energy-SLA Tradeoff for Green Mobile Cloud Computing

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