Mara Nikolaidou

Cloud computing has shifted organisational security spending from predictable capital expenditures to variable, usage-based operational costs, yet academic research continues to treat cloud security and cloud economics as separate domains. This paper presents a SLR of 59 peer-reviewed journal articles (2021–2025), following a three-phase PRISMA-guided methodology, to examine how cloud security threats, controls, and governance practices generate measurable cost obligations and investment requirements. The findings reveal three persistent cost patterns, cost-centric attacks, service disruption and recovery expenditures, and defence overhead from encryption, AI-driven detection, and DevSecOps practices. They show that the financial burden varies substantially across sectors, with banking and healthcare facing the heaviest compliance costs and SMEs operating under strict affordability constraints. A four category cost taxonomy is proposed covering compute overhead, governance and compliance expenditure, operational defence costs, and post-incident recovery. Persistent gaps include the absence of explicit cost modelling frameworks, insufficient quantification of governance costs under NIS2, DORA, and the Cyber Resilience Act, and a lack of longitudinal empirical evidence.

The achievement of organizational interoperability is a crucial factor for the successful digital transformation and a catalyst for Digital Decade's 2030 main targets. The aim of this article is to develop and test a new conceptual model of organizational interoperability assessment. A new model to assess in an effective, holistic way the organizational interoperability of digital services has been developed, using a mixed qualitative and quantitative approach, based on literature research and feedback from experts in the field, composing a valid and effective model for evaluating the achievement of organizational interoperability. The developed model consists of 9 factors and 34 variables and is evaluated in terms of statistical reliability, relevance, and coherence of factors and variables. The evaluation of the model in terms of reliability and internal consistency is achieved through data collected from a sample survey responded to by 112 domain experts. The collected data analyzed with the aid of descriptive and inductive statistical methods such as Cronbach's Alpha coefficient and Factor analysis, from which resulted alpha= 0.869 for the total interoperability scale, which is satisfactory enough, as well as the total variance explained from the Factor Analysis found to be 60% or higher, resulted in a well-established model in statistical terms.

To maximize the business value of the cloud, the cost of cloud solutions is explored alongside technical quality and performance. To enable this form of exploration engineering, finance and business teams collaborate in the context of FinOps, the operational framework that provides the required decision-making. Prominent providers, such as Google and Microsoft, provide FinOps to their customers, integrating cost factors when designing a cloud solution. However, different providers apply different pricing policies for their products, and these policies also change through time. Therefore, there are numerous efforts to explore price evolution through time for different cloud products applying different decision-making methods using different datasets. In an effort to establish a systematic approach to support decision-making on alternative pricing policies for cloud services and compare them across services and providers, the CloudPricingOps framework is proposed in this paper. It constitutes a decision support system that provides alternative decision-making methods, such as hedonic models, time series, and clustering and machine learning techniques, to deal with problems related to cloud product pricing policy analysis, comparison and prediction. It also constitutes a systematic method of discrete steps to integrate additional decision-making methods to deal with these problems and input datasets to be used regardless of the method or the cloud pricing problem that needs to be solved. Two discrete examples based on real data are also presented in this paper to demonstrate the usefulness of the CloudPricingOps framework for cloud engineering and business teams.

To maximise the business value of the cloud the cost of cloud solutions is explored together with technical quality and performance. To enable such exploration engineering, finance and business teams collaborate in the context of FinOps, the operational framework that provides such sort of decision making. Prominent providers, such as Google and Microsoft provide FinOps to their customers, integrating cost factors when designing a cloud solution. However, different providers apply different pricing policies for their products, while these policies also adjust through time. To this end, there are numerous efforts to explore price evolution through time for different cloud products applying different decision-making methods using different data-sets. In an effort to establish a systematic approach to support decision making on alternative pricing policies for cloud services and compare them across services and providers the CloudPricingOps framework is proposed in the paper. It constitutes a decision support system that provides alternative decision making methods, such as hedonic models, time series, clustering and machine learning techniques, to deal with problems related cloud product pricing policy analysis, comparison and prediction. It also constitutes a systematic method of discrete steps to integrate additional decision making methods to deal with these problems and input datasets to be used independently of method or cloud pricing problem to solve. To discrete examples based on real-data are also presented in the paper to demonstrate the usefulness of CloudPricingOps framework for cloud engineering and business teams.

Cloud providers offer various purchasing options to enable users to tailor their costs according to their specific requirements, including on-demand, reserved instances, and spot instances. On-demand and spot instances satisfy short-term workloads, whereas reserved instances fulfill long-term instances. However, there are workloads that fall outside of either long-term or short-term categories. Consequently, there is a notable absence of services specifically tailored for medium-term workloads. On-demand services, while offering flexibility, often come with high costs. Spot instances, though cost-effective, carry the risk of termination. Reserved instances, while stable and less expensive, may have a remaining period that extends beyond the duration of users’ tasks. This gap underscores the need for solutions that address the unique requirements and challenges associated with medium-term workloads in the cloud computing landscape. This paper introduces a new cloud broker that introduces IaaS services for medium-term workloads. On one hand, this broker strategically reserves resources from providers, and on the other hand, it interacts with users. Its interaction with users is twofold. It collects users’ preferences regarding commitment term for medium-term workloads and then transforms the leased resources based on commitment term, aligning with the requirements of most users. To ensure profitability, the broker sells these services utilizing an auction algorithm. Hence, in this paper, an auction algorithm is introduced and developed, which treats cloud services as virtual assets and integrates the depreciation over time. The findings affirm the lack of services that fulfill medium workloads while ensuring the financial viabilty and profitability of the broker, given that the estimated return on investment (ROI) is acceptable.

Fiber-to-the-home (FTTH) technology is a promising solution for providing advanced service delivery to end-users, but its implementation requires substantial capital expenditures. To minimize investment risks and aid decision-making for access service providers, we propose a game theoretic framework based on a modified Nash genetic algorithm. We illustrate how this framework can be applied to analyze the competition between access providers offering a flat-rate FTTH service on multiple geographical areas. Each provider determines its price for all areas simultaneously and decides whether to invest on a particular area depending on the anticipated revenues. Two distinct demand models are adopted to describe different types of consumer behavior. A solution engine, based on a modified mixed-variable Nash genetic algorithm is implemented under an open-source license. The significance and practical implications of the equilibrium points obtained for both single and multiple area games are discussed. The proposed framework and the solution engine developed, aid both providers and regulatory bodies to analyze competitive environments. They can also be used to implement decision support tools for similar problems as well. The paper concludes by pointing out further research directions in this context.