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Software Defined Networking (SDN) is programmable by separation of forwarding control through the centralization of the controller. The controller plays the role of the 'brain' that dictates the intelligent part of SDN technology. Various versions of SDN controllers exist as a response to the diverse demands and functions expected of them. There are several SDN controllers available in the open market besides a large number of commercial controllers; some are developed to meet carrier-grade service levels and one of the recent trends in open-source SDN controllers is the Open Network Operating System (ONOS). This paper presents a comparative study between open source SDN controllers, which are known as Network Controller Platform (NOX), Python-based Network Controller (POX), component-based SDN framework (Ryu), Java-based OpenFlow controller (Floodlight), OpenDayLight (ODL) and ONOS. The discussion is further extended into ONOS architecture, as well as, the evolution of ONOS controllers. This article will review use cases based on ONOS controllers in several application deployments. Moreover, the opportunities and challenges of open source SDN controllers will be discussed, exploring carrier-grade ONOS for future real-world deployments, ONOS unique features and identifying the suitable choice of SDN controller for service providers. In addition, we attempt to provide answers to several critical questions relating to the implications of the open-source nature of SDN controllers regarding vendor lock-in, interoperability, and standards compliance, Similarly, real-world use cases of organizations using open-source SDN are highlighted and how the open-source community contributes to the development of SDN controllers. Furthermore, challenges faced by open-source projects, and considerations when choosing an open-source SDN controller are underscored. Then the role of Artificial Intelligence (AI) and Machine Learning (ML) in the evolution of open-source SDN controllers in light of recent research is indicated. In addition, the challenges and limitations associated with deploying open-source SDN controllers in production networks, how can they be mitigated, and finally how open-source SDN controllers handle network security and ensure that network configurations and policies are robust and resilient are presented. Potential opportunities and challenges for future Open SDN deployment are outlined to conclude the article. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence has created a moment in history where human beings have begin to closely interact with artificial intelligence (AI) tools, putting policymakers in a position to restrict or legislate such tools. One particular example of such a tool is ChatGPT which is the first and world's most popular multipurpose generative AI tool. This study aims to put forward a policy-making framework of generative artificial intelligence based on the risk, reward, and resilience framework. A systematic search was conducted, by using carefully chosen keywords, excluding non-English content, conference articles, book chapters, and editorials. Published research were filtered based on their relevance to ChatGPT ethics, yielding a total of 41 articles. Key elements surrounding ChatGPT concerns and motivations were systematically deduced and classified under the risk, reward, and resilience categories to serve as ingredients for the proposed decision-making framework. The decisionmaking process and rules were developed as a primer to help policymakers navigate decision-making conundrums. Then, the framework was practically tailored towards some of the concerns surrounding ChatGPT in the context of higher education. In the case of the interconnection between risk and reward, the findings show that providing students with access to ChatGPT presents an opportunity for increased efficiency in tasks such as text summarization and workload reduction. However, this exposes them to risks such as plagiarism and cheating. Similarly, pursuing certain opportunities such as accessing vast amounts of information, can lead to rewards, but it also introduces risks like misinformation and copyright issues. Likewise, focusing on specific capabilities of ChatGPT, such as developing tools to detect plagiarism and misinformation, may enhance resilience in some areas (e.g., academic integrity). However, it may also create vulnerabilities in other domains, such as the digital divide, educational equity, and job losses. Furthermore, the finding indicates secondorder effects of legislation regarding ChatGPT which have implications both positively and negatively. One potential effect is a decrease in rewards due to the limitations imposed by the legislation, which may hinder individuals from fully capitalizing on the opportunities provided by ChatGPT. Hence, the risk, reward, and resilience framework provides a comprehensive and flexible decision-making model that allows policymakers and in this use case, higher education institutions to navigate the complexities and trade-offs associated with ChatGPT, which have theoretical and practical implications for the future. [ABSTRACT FROM AUTHOR]