/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

Software defined networking: State of the art and research challenges

Plug-and-play information technology (IT) infrastructure has been expanding very rapidly in recent years. With the advent of cloud computing, many ecosystem and business paradigms are encountering potential changes and may be able to eliminate their IT infrastructure maintenance processes. Real-time performance and high availability requirements have induced telecom networks to adopt the new concepts of the cloud model: software-defined networking (SDN) and network function virtualization (NFV). NFV introduces and deploys new network functions in an open and standardized IT environment, while SDN aims to transform the way networks function. SDN and NFV are complementary technologies; they do not depend on each other. However, both concepts can be merged and have the potential to mitigate the challenges of legacy networks. In this paper, our aim is to describe the benefits of using SDN in a multitude of environments such as in data centers, data center networks, and Network as Service offerings. We also present the various challenges facing SDN, from scalability to reliability and security concerns, and discuss existing solutions to these challenges.

Software-Defined Networking: State of the Art and Research Challenges

Software Defined Networking simplifies design, monitoring and management of next generation networks by segregating a legacy network into a centralized control plane and a remotely programmable data plane. The intelligent centralized SDN control plane controls behavior of forwarding devices in processing the incoming packets and provides a bird-eye view of entire network at a single central point. The centralized control provides network programmability and facilitates introduction of adaptive and automatic network control. The SDN control plane can be implemented by using following three deployment models: (i) physically centralized, in which a single SDN controller is configured for a network; (ii) physically distributed but logically centralized, wherein multiple SDN controllers are used to manage a network; and (iii) hybrid, in which both legacy distributed control and centralized SDN control coexist. This manuscript presents all these control plane architectures and discusses various SDN controllers supporting these architectures. We have analyzed more than forty SDN controllers in terms of following performance parameters: scalability, reliability, consistency and security. We have examined the mechanisms used by various SDN controllers to address the said performance parameters and have highlighted the pros and cons associated with each mechanism. In addition to it, this manuscript also highlights number of research challenges and open issues in different SDN control plane architectures.

Scalability, Consistency, Reliability and Security in SDN Controllers: A Survey of Diverse SDN Controllers

Load balance based workflow job scheduling algorithm in distributed cloud

The Internet of Things (IoT) has emerged as a technology capable of connecting heterogeneous nodes/objects, such as people, devices, infrastructure, and makes our daily lives simpler, safer, and fruitful. Being part of a large network of heterogeneous devices, these nodes are typically resource-constrained and became the weakest link to the cyber attacker. Classical encryption techniques have been employed to ensure the data security of the IoT network. However, high-level encryption techniques cannot be employed in IoT devices due to the limitation of resources. In addition, node security is still a challenge for network engineers. Thus, we need to explore a complete solution for IoT networks that can ensure nodes and data security. The rule-based approaches and shallow and deep machine learning algorithms– branches of Artificial Intelligence (AI)– can be employed as countermeasures along with the existing network security protocols. This paper presented a comprehensive layer-wise survey on IoT security threats, and the AI-based security models to impede security threats. Finally, open challenges and future research directions are addressed for the safeguard of the IoT network.

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Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

When deploying a model for object detection, a confidence score threshold is chosen to filter out false positives and ensure that a predicted bounding box has a certain minimum score. To achieve state-of-the-art performance on benchmark datasets, most neural networks use a rather low threshold as a high number of false positives is not penalized by standard evaluation metrics. However, in scenarios of Artificial Intelligence (AI) applications that require high confidence scores (e.g., due to legal requirements or consequences of incorrect detections are severe) or a certain level of model robustness is required, it is unclear which base model to use since they were mainly optimized for benchmark scores. In this paper, we propose a method to find the optimum performance point of a model as a basis for fairer comparison and deeper insights into the trade-offs caused by selecting a confidence score threshold.

Confidence Score: The Forgotten Dimension of Object Detection Performance Evaluation

https://www.techscience.com/cmc/v70n2/44706/pdf

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

Efficient virtualization methodologies constitute the core of cloud computing data center implementation. Clients are attracted to the cloud model by its ability to scale the resources dynamically and the flexibility in payment options that it offers. However, performance hiccups may push them to go back to the buy-and-maintain model. Virtualization plays a key role in the synchronous management of the thousands of servers along with clients' data living on them. To achieve seamless virtualization, cloud providers require a system that performs the function of virtual network provisioning. This includes receiving the cloud client requests and allocating their computational and network resources in a way that guarantees the quality-of-service conditions for clients while maximizing the data center resource utilization and providers' revenue. We introduce a comprehensive system to solve the problem of virtual network mapping for a set of connection requests sent by cloud clients. Connections are collected in time intervals called windows. Consequently, node and link provisioning is performed. Different window size selection schemes are introduced and evaluated. Three schemes to prioritize connections are used, and their effect is assessed. Moreover, a technique dealing with connections spanning over more than a window is introduced. The proposed algorithm is compared with previous work well known in the literature. Simulation results show that the dynamic window size algorithm achieves cloud service providers' objectives in terms of generated revenue, served-connection ratio, resource utilization, and computational overhead. In addition, experimental results show that handling spanning connections independently improves the performance of the system.

Drawing the Cloud Map: Virtual Network Provisioning in Distributed Cloud Computing Data Centers

The coronavirus pandemic (COVID-19) spreads worldwide during the first half of 2020. As is the case for all countries, the Kingdom of Saudi Arabia (KSA), where the number of reported cases reached more than 392 K in the first week of April 2021, was heavily affected by this pandemic. In this study, we introduce a new simulation model to examine the pandemic evolution in two major cities in KSA, namely, Riyadh (the capital city) and Jeddah (the second-largest city). Consequently, this study estimates and predicts the number of cases infected with COVID-19 in the upcoming months. The major advantage of this model is that it is based on real data for KSA, which makes it more realistic. Furthermore, this paper examines the parameters used to understand better and more accurately predict the shape of the infection curve, particularly in KSA. The obtained results show the importance of several parameters in reducing the pandemic spread: the infection rate, the social distance, and the walking distance of individuals. Through this work, we try to raise the awareness of the public and officials about the seriousness of future pandemic waves. In addition, we analyze the current data of the infected cases in KSA using a novel Gaussian curve fitting method. The results show that the expected pandemic curve is flattening, which is recorded in real data of infection. We also propose a new method to predict the new cases. The experimental results on KSA’s updated cases reveal that the proposed method outperforms some current prediction techniques, and therefore, it is more efficient in fighting possible future pandemics.

https://www.mdpi.com/2071-1050/13/9/4888/pdf

Khaled Alhazmi

Papers

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

Confidence Score: The Forgotten Dimension of Object Detection Performance Evaluation

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

Drawing the Cloud Map: Virtual Network Provisioning in Distributed Cloud Computing Data Centers

A Simulation Model for Forecasting COVID-19 Pandemic Spread: Analytical Results Based on the Current Saudi COVID-19 Data