•Containerized regional ocean-modeling system is implemented in various clouds.•Container-based architecture is useful for reproducibility and portability of ROMS.•Container-based HPC architecture ...increases flexibility in private and public clouds.•Proposed container-based HPC architecture reduces preparation time for model setup.•Kubernetes-managed container cluster architecture is used for ocean modeling.
Various numerical models have been used to understand and predict ocean dynamics. For this reason, many information technology (IT) resources are required for high-resolution global ocean modeling. The development of cloud-computing technologies has enabled earth scientists to easily use numerical ocean models that require high-performance computing (HPC) and message-passing interface (MPI) software in private and public clouds. Although it is easier today to use computing resources than it was in the past, computational reproducibility and portability in diverse IT environments remain crucial issues. This study proposes a model execution architecture for computational reproducibility, portability, and agility based on container-based virtualization and orchestration technologies. We implement a containerized regional ocean-modeling system (ROMS), an MPI-based numerical ocean model that exists in various public or private cloud environments (e.g., personal computers and multiple-node servers). The preparation time for model setup is greatly reduced using our container-based HPC architecture. Containerization of ROMS is tested for its support of the portability of numerical modeling in a wide range of public-cloud environments. When leveraging an abstraction layer of complex and diverse infrastructure environments, we can run the ocean model more easily while obtaining computational reproducibility using a shareable deployment code. This advancement can be used to guide the containerization of various numerical models and to run them in parallel in public and private cloud-computing environments.
Cloud computing is becoming an essential tool in lowering Information Technology (IT) costs amongst Small, Medium and Micro-sized Enterprises (SMMEs). As such amongst a myriad of challenges, SMMEs ...are faced with a general lack of resource capability including the lack of Information and Communications Technology (ICT) infrastructure and skills. This further disables the SMMEs ability to compete with big business and industry peers. As such cloud computing offers SMMEs the ability to access high level ICT services either through SaaS (Software-as-a-Service), PaaS (Platform-as-a-Service) or IaaS (Infrastructure-as-a-Service) service delivery models. Cloud computing adoption amongst SMMEs is relevant in the sense that SMMEs can realise the full benefits of reduced capital expenditure, improved access to ICT systems, heightened security of data and low costs for agile development amongst a myriad of cloud computing benefits. The overall intention is to ensure that SMMEs always have access to updated ICT services through the cloud, without having the burden of maintaining ICT infrastructure in-house. Based on this interpretation, this study analysed factors affecting cloud computing adoption amongst SMMEs, by use of a Conceptual Research Model based on the Technology-Organization-Environment (TOE) framework. This was informed by a survey distributed to SMMEs within the Ngaka Modiri Molema and Bojanala Platinum Districts of the North West Province. The results of this study will assist SMMEs to make informed decisions on adopting cloud computing practices in their organisations.
•An ensured cloud storage service over client's virtual machine processing services is guaranteed.•Virtual machines and subsystems are even more powerful and useful by the proposals.•Mitigation of ...private cloud computing systems resources is done.•Improvement of Aggregated Migration Energy Consumption is obvious.
This paper proposes an Internet of Everything (IoE) based private multi-data center cloud architecture framework to utilize the merits of multi-data centers. The inclusive security structure for the entire cloud data center presented here is to minimize the potential vulnerabilities from the adoption of the cloud. This work would leverage the novelty in allocating the distribution of data smartly and in a reliable manner to have the network for private cloud data centers. On the other hand, the architecture must have the ability to make out an enterprise's data and association applications in the cloud using the facility of contributing the opportunity. To overcome these shortcomings, a "Facility Overhead-aware multi-objective Dynamic Private Cloud Data Center Algorithm" (FOMDPC) is proposed with the merits of migration payload awareness, dynamic load balancing methodology, and facility infrastructure costs. The factors to be considered to determine the efficiency are time complexity and memory overhead. In addition, aggregated migration energy consumption and aggregated SLA violations are also taken into account.
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Cloud computing is an innovative and promising paradigm that is leading to remarkable changes in the way in which hardware and software are designed and purchased, as well as how IT systems are ...managed. However, the Cloud is a risky paradigm. For instance, the use of Cloud services, which usually are external assets to their consumers, implies unprecedented risks that must be taken into account.
In this paper, we propose the involvement of the risk management discipline into the Cloud computing realm. We present a risk management approach led by business-level objectives (BLOs) of Cloud organizations. Its main goal is to assist in business-driven self-managed Cloud providers, by facing uncertainties always present in their internal decision-making processes. Our Cloud-aware risk management method includes a SEmi-quantitative BLO-driven Cloud Risk Assessment (SEBCRA) as the core subprocess. Its aim is to constantly rank and prioritize risks affecting the governing business-level goals.
In addition, we present, as a use case, a PaaS provider that incorporates our risk management approach to enhance the achievement of two BLOs, i.e. maximization of profit and customer satisfaction. In particular, it can manage–identify, assess, and treat–the most critical Cloud infrastructure-level risks, i.e. provisioning its private Cloud, either under- or over-provisioning, as well as resource failures. We present some risk treatment responses to face these risks and we evaluate their impact on the above-mentioned BLOs. Our results show that the best responses to address risks may change over time depending on the current provider’s status. As a result, an adaptive management of risks should be considered as a mandatory process for Cloud providers to ensure their success in the ever-growing worldwide ecosystem of Clouds.
► We present a state of the art of risk management and assessment methods, and Cloud-related risks. ► We introduce a semi-quantitative BLO-driven risk assessment to drive risk management in the Cloud. ► We present the business-driven management of infrastructure risks in PaaS providers as a use case. ► We demonstrate that a Cloud provider can maximize its profit and client satisfaction by treating risks.
Abstract
Data processing or data analytics is the common functionality that is attached to most of the real world applications. The amount of processing required for data oriented tasks or jobs are ...quiet high. To resolve the processing issue the most common approach deployed is by using a high performance cluster. Setting a cluster over real time infrastructure leads to a very expensive solution. A Cloud based infrastructure remains as an ideal support for setting up a cluster. Managing the cluster over a Cloud is a challenging task as allocation of infrastructure based on the task schedule is a critical parameter. The proposed mathematical model introduces a strategy to allocate the infrastructure and manage the load of the cluster based on Queuing model. The experimental setup is made on top of private cloud and Hadoop based data processing jobs are tested. The proposed data oriented resource optimizer enhances the performance of the cluster by balancing the increased load due to data processing jobs. The result shows the enhanced improvement in performance compared to default resource manager.
This research explores the optimization of firewall systems within private cloud environments, specifically focusing on a 30-day evaluation of the Omni-Secure Firewall. Employing a multi-metric ...approach, the study introduces an innovative effectiveness metric (E) that amalgamates precision, recall, and redundancy considerations. The evaluation spans various machine learning models, including random forest, support vector machines, neural networks, k-nearest neighbors, decision tree, stochastic gradient descent, naive Bayes, logistic regression, gradient boosting, and AdaBoost. Benchmarking against service level agreement (SLA) metrics showcases the Omni-Secure Firewall’s commendable performance in meeting predefined targets. Noteworthy metrics include acceptable availability, target response time, efficient incident resolution, robust event detection, a low false-positive rate, and zero data-loss incidents, enhancing the system’s reliability and security, as well as user satisfaction. Performance metrics such as prediction latency, CPU usage, and memory consumption further highlight the system’s functionality, efficiency, and scalability within private cloud environments. The introduction of the effectiveness metric (E) provides a holistic assessment based on organizational priorities, considering precision, recall, F1 score, throughput, mitigation time, rule latency, and redundancy. Evaluation across machine learning models reveals variations, with random forest and support vector machines exhibiting notably high accuracy and balanced precision and recall. In conclusion, while the Omni-Secure Firewall System demonstrates potential, inconsistencies across machine learning models underscore the need for optimization. The dynamic nature of private cloud environments necessitates continuous monitoring and adjustment of security systems to fully realize benefits while safeguarding sensitive data and applications. The significance of this study lies in providing insights into optimizing firewall systems for private cloud environments, offering a framework for holistic security assessment and emphasizing the need for robust, reliable firewall systems in the dynamic landscape of private clouds. Study limitations, including the need for real-world validation and exploration of advanced machine learning models, set the stage for future research directions.
Abstract
Cloud computing is becoming a popular approach to solve many serious problematic issues since its introduction in 2000. Cloud computing includes data storage in addition to software and ...hardware sharing via using network infrastructure, computers, and other resources. However, the educational centers are still considered conventional and limited in terms of their existing hardware infrastructure. A case study of cloud computing deployment in the University of Diyala-ICC (Internet and Computer Center)-will be used to illustrate the situation. This study aims to develop a basic cloud-based design for improving the efficiency of ICC laboratories, as an educational center in the University of Diyala to make it a study and research center of cloud computing for computer science students in the university. The proposed design is built via using “Software as a Service” (SaaS) model. Cloud computing in ICC laboratories is designed using Private Cloud Computing. The proposed design provided flexibility to ICC and allows to improve the capabilities of computer network and helps in managing their resources easily.
Personal Cloud P2P Upra, Ravi; Chaisricharoen, Roungsan; Yaibuates, Mayoon
Wireless personal communications,
08/2021, Volume:
119, Issue:
3
Journal Article
Peer reviewed
Small and medium sized businesses have lacked of good and automate backup system. Data storages are unreliable and tend to fail without any warning. Whereas, personal computers are already equipped ...with terabytes of hard drives. This is enormous space for personal or business use. Under normal working conditions, more than half of total storage space is not being used. It is scattered in all connected devices. This paper proposes pooling unused storage resources to create a personal private cloud. The process will be transparent to the users by having a reliable drive with an auto backup built in. The proposed system solution uses a replica-based model in which three sets of the same information would distribute and keep in three different nodes. All the participated nodes are peer-to-peer (P2P) and are decentralized. Therefore, private cloud system can support small or medium businesses as backup system or business sharing drives without adding any cost.
Mobile cloud computing has introduced offloading to save the battery life of mobile devices. In mobile cloud computing optimization of power and delay for offloading has become a vital research ...scope. However, migration of the storage and computation from the mobile device to the remote cloud server enhances the delay and power consumption. To overcome this difficulty, cloudlet comes which is located nearby the mobile device. Since the cloudlet may not be able to fulfill all the offloading requests, sometimes remote public cloud server is used for the same. As a result the power and delay consumptions are increased. For solving this difficulty, private cloud server is used in our scheme along with the cloudlet and public cloud server. In this paper multilevel full and partial offloading strategies are proposed based on cloudlet, private and public cloud servers. The power and delay consumption in the proposed methods are determined and compared with the existing offloading methods. The theoretical and experimental analyses demonstrate that the proposed multilevel offloading methods are power and delay efficient. The simulation results show that the proposed multilevel full and partial offloading strategies reduce the power consumption by approximately 8–9% and 20% respectively than the existing methods.
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