Search

The IT industry is advancing rapidly, with virtually every branch of modern computing experiencing swift development. Concepts such as Cloud Computing and Artificial Intelligence no longer surprise anyone. Recently, Software Defined Networks (SDN) have been gaining significant popularity. This innovative approach to computer networks allows for greater flexibility and is, therefore, much more well-known in the world of cloud computing than in traditional network implementations. This paper introduces the concept of SDN and Network Functions Virtualization (NFV) and outlines all the challenges and security issues associated with the cloud environment. The dynamic nature of the IT landscape requires constant adaptation to emerging technologies, and SDN represents a noteworthy evolution in the realm of computer networking. Platforms such as SDN and open-source tools enabling the creation of private cloud environments such as OpenStack or OpenNebula were compared. At the same time, aspects like security, network performance, flexibility, and scalability were analyzed. Based on the prior analysis, a comprehensive cloud environment was built using the OpenStack solution and SDN - OpenDaylight was deployed. Additional tests conducted on the OpenStack cloud, both with and without SDN, demonstrated the superiority of SDN implementation in the cloud. [ABSTRACT FROM AUTHOR]

Traditional auto-scaling approaches are conceived as reactive automations, typically triggered when predefined thresholds are breached by resource consumption metrics. Managing such rules at scale is cumbersome, especially when resources require non-negligible time to be instantiated. This paper introduces an architecture for predictive cloud operations, which enables orchestrators to apply time-series forecasting techniques to estimate the evolution of relevant metrics and take decisions based on the predicted state of the system. In this way, they can anticipate load peaks and trigger appropriate scaling actions in advance, such that new resources are available when needed. The proposed architecture is implemented in OpenStack, extending the monitoring capabilities of Monasca by injecting short-term forecasts of standard metrics. We use our architecture to implement predictive scaling policies leveraging on linear regression, autoregressive integrated moving average, feed-forward, and recurrent neural networks (RNN). Then, we evaluate their performance on a synthetic workload, comparing them to those of a traditional policy. To assess the ability of the different models to generalize to unseen patterns, we also evaluate them on traces from a real content delivery network (CDN) workload. In particular, the RNN model exhibites the best overall performance in terms of prediction error, observed client-side response latency, and forecasting overhead. The implementation of our architecture is open-source.

The two most prominent cloud computing and container management solutions are OpenStack and CapRover. CapRover focuses on simplifying the deployment and management of web applications, while OpenStack is a comprehensive cloud computing platform that offers a range of services for building and managing cloud infrastructure. The paper explores the architectural underpinnings, performance, security, usability, and extensibility, giving us insights into how well-suited they are for different applications. Use cases from the real world are used to demonstrate its applicability. The research gives decision-makers the ability to choose the best platform for their infrastructure management requirements, which advances the field of cloud computing and containerization. [ABSTRACT FROM AUTHOR]

The design, execution, and advantages of creating a private cloud infrastructure using OpenStack within the context of educational institutions are thoroughly examined in this research study. IT solutions that are dependable, scalable, and economical are needed in today's educational environment to support a variety of academic and administrative purposes. The purpose of this study is to investigate how using OpenStack as a private cloud solution might meet these needs and improve the broader institutional IT ecosystem. [ABSTRACT FROM AUTHOR]

Unlike traditional networks, Software-defined networks (SDNs) provide an overall view and centralized control of all the devices in the network. SDNs enable the network administrator to implement the network policy by programming applications on top of the SDN controller using generic APIs. One or more controller instances can be deployed to administer the data flow by maintaining unified control of the entire network. The controller is expected to respond quickly to queries from forwarding devices. Presuming quick responses from the controller while enforcing a complex security mechanism is unreasonable. In this paper, the authors propose a unique, adaptive, lightweight, yet efficient technique called ISOLATOR to mitigate the effect of insider attacks and malfunctioning of distributed applications in an SDN-enabled cloud. The proposed security application upon detecting any suspicious activity by a virtual machine, isolates it by removing the interface to its respective shared network and reconnects it through a restricted network operating in a highly selective mode. By subjecting the data traffic to deep packet inspection, the restricted network searches for a match with a known worm pattern. The application is programmed for the OpenDayLight controller and the results show a significant improvement in identifying malicious activities with minimal latency and computational cost. [ABSTRACT FROM AUTHOR]

Cyber Range is an experimental platform based on virtualization technology to construct a controlled simulation environment, providing a real-world simulation environment for cybersecurity personnel to conduct various practical exercises. The problem is that generating a virtual environment satisfying the requirements is labor-intensive and time-consuming. To resolve the above problem, this paper proposes a system to automate the deployment of a cyber range. In our method, the first step is to collect virtual machines (VMs) related to cybersecurity and extract relevant features. Then, machine learning is used to classify VMs to reduce the cost of manual VMs selection. Lastly, leveraging the popular OpenStack cloud platform as the deployment platform enhances the applicability of the cyber range. When it comes time to deploy a virtual environment, the instructor only needs to provide some brief description information of a virtual environment. Then, the system will automatically parse the description file to complete the automated deployment of the virtual environment. This system has been successfully applied to the cyber range of Sichuan University for daily teaching tasks. [ABSTRACT FROM AUTHOR]

The increasing complexity of cloud computing has prompted a greater emphasis on protecting the privacy, integrity, and security of data stored and processed in the cloud. Data privacy is safeguarded through access control, but existing models such as Role Based Access Control (RBAC) and Attribute Based Access Control (ABAC) rely on a centralized server. If this server is compromised, it poses significant risks to data security. To address this issue, there is a need for Distributed ABAC (DABAC) system for OpenStack services based on blockchain. The unique features of blockchain enables access control systems which ensures data integrity and privacy. Additionally, blockchain offers a level of transparency for both the resource owner and a user. In this work, we propose a smart contract based ABAC system. We implemented the proposed work using an Ethereum blockchain and OpenStack cloud. Furthermore, we evaluated two consensus algorithms for scalability analysis of DABC mechanism. The results demonstrate that DABAC performs better than ABAC in ensuring fine-grade access control with proof of stake consensus algorithm providing better scalability. [ABSTRACT FROM AUTHOR]

Modern software systems are designed to be deployed in different configured environments (e.g., permissions, virtual resources, network connections) and adapted at runtime to different situations (e.g., memory limits, enabling/disabling features, database credentials). Such a configuration during the deployment and runtime of a software system is implemented via a set of configuration files, which together constitute what we refer to as a "configuration system." Recent research efforts investigated the evolution and maintenance of configuration files. However, they merely focused on a limited part of the configuration system (e.g., specific infrastructure configuration files or Dockerfiles), and their results do not generalize to the whole configuration system. To cope with such a limitation, we aim to better capture and understand what files constitute a configuration system. To do so, we leverage an open card sort technique to qualitatively study 1,756 configuration files from OpenStack, a large and widely studied open source software ecosystem. Our investigation reveals the existence of nine types of configuration files, which cover the creation of the infrastructure on top of which OpenStack will be deployed, along with other types of configuration files used to customize OpenStack after its deployment. These configuration files are interconnected while being used at different deployment stages. For instance, we observe specific configuration files used during the deployment stage to create other configuration files that are used in the runtime stage. We also observe that identifying and classifying these types of files is not straightforward, as five out of the nine types can be written in similar programming languages (e.g., Python and Bash) as regular source code files. We also found that the same file extensions (e.g., Yaml) can be used for different configuration types, making it difficult to identify and classify configuration files. Thus, we first leverage a machine learning model to identify configuration from non-configuration files, which achieved a median area under the curve (AUC) of 0.91, a median Brier score of 0.12, a median precision of 0.86, and a median recall of 0.83. Thereafter, we leverage a multi-class classification model to classify configuration files based on the nine configuration types. Our multi-class classification model achieved a median weighted AUC of 0.92, a median Brier score of 0.04, a median weighted precision of 0.84, and a median weighted recall of 0.82. Our analysis also shows that with only 100 labeled configuration and non-configuration files, our model reached a median AUC higher than 0.69. Furthermore, our configuration model requires a minimum of 100 configuration files to reach a median weighted AUC higher than 0.75. [ABSTRACT FROM AUTHOR]