Exploring the Power of XAI for Cloud System Architecture Evaluation


Explainable AI (XAI) enhances transparency and understanding in cloud architecture evaluation by providing detailed explanations of decisions. XAI improves efficiency, trust, and decision-making in complex cloud-based systems, benefiting organizations and users alike.

Explainable AI (XAI) is a sub-domain of Artificial Intelligence (AI) that focuses on enabling machine learning models to provide transparent and more understandable explanations of the decisions and actions taken. XAI aims to improve human cognition to understand why and how the model made a particular decision and what factors were considered to reach the decision. As a result, people’s trust in AI systems increases as they know the reasons for the predictions and the decisions made. In contrast to conventional AI systems, which behave like black boxes, XAI provides a detailed explanation of how the system arrived at a particular conclusion, thus enhancing transparency. Besides several domains, such as healthcare and finance, this transparency in explanations is extremely useful in evaluating the architecture of cloud systems. The cloud, by design, is a complex architecture and is, therefore, difficult to understand and comprehend.

Evaluation of cloud architecture involves assessing the design and performance of a cloud-based system in terms of scalability, reliability, and security. By providing more profound insights into the inner workings of models and systems, XAI can contribute to improving the efficiency of the evaluation process of cloud architectures. Furthermore, it also enhances the accuracy of evaluations.

The application of XAI can assist in understanding how cloud-based systems work, what factors are crucial to their performance, and how they can be optimized. This process identifies issues in cloud-based systems and highlights areas for improvement, resulting in efficient and effective cloud-based systems.

Benefits of XAI in cloud architecture evaluation

The purpose of evaluating cloud architecture is to assess the design and performance of cloud-based systems, which are inherently complex and challenging to understand. XAI brings the following benefits to cloud architecture evaluation:

Increased transparency

Through XAI, cloud-based systems’ inner workings and details become more transparent and understandable. This helps understand the working of any cloud system and provides the opportunity to make more informed decisions based on the available explanations about these systems.

Better insights

Patterns and relationships in large and complex datasets pertinent to cloud systems (for example, performance, user behavior, resource utilization, and several other metrics) can be easily understood through XAI, which otherwise are difficult to be comprehended by humans. XAI models can analyze large volumes of data to provide insights into the crucial factors impacting the performance of cloud-based systems. The information can subsequently be utilized well in time to optimize cloud systems’ design and enhance their performance before the issues become complex and grave.

Increased efficiency

The cloud architecture evaluation process can be automated with XAI’s help, leading to greater process efficiency and reduced evaluation costs.

Enhanced trust in cloud-based systems

Due to the transparent and interpretable explanations of the actions and decisions of the cloud-based systems, trust in cloud systems also increases. This also leads to a greater sense of control and understanding, which can ultimately increase the adoption and usage of the system.

XAI techniques for cloud architecture evaluation

Recently, the XAI techniques have appeared as effective and powerful tools for cloud architecture evaluation to enable enterprises to make more knowledgeable decisions about the design, performance, and security of cloud-based applications. Several such techniques are in place, and a few are briefly discussed below:

The decision tree is a popular machine-learning approach that uses a tree-like representation of the decision-making process to provide explanations. As a result, data scientists can visualize the factors that influence a particular decision and trace the decision-making process entirely. For cloud architecture evaluation, decision trees help identify and visualize various critical factors that are crucial for the performance of cloud-based systems and help organizations optimize the architectures to meet performance and scalability requirements.

Another popular XAI technique to evaluate cloud architecture is the neural networks consisting of interconnected neurons. Neural networks help identify the complex relationships and patterns in different cloud-related datasets. Due to their capability of effectively analyzing these datasets, they are regarded as powerful evaluation tools.

To evaluate cloud architecture, rule-based systems are also being employed. Rule-based systems rely on rules for making decisions based on predefined criteria. Rule-based systems are beneficial in verifying the compliance of cloud systems in terms of security and privacy protection against the defined rules. As a result, organizations can take measures proactively to mitigate the risks and to ensure compliance with the rules and regulatory standards.

In addition, fuzzy logic can also be used with XAI techniques to help represent imprecise or uncertain information while evaluating cloud architecture. For instance, rather than measuring factors, such as response time and availability, fuzzy logic can consider the perception of cloud users, for example, satisfaction with a particular application and strategize accordingly.

Likewise, Bayesian networks, which are probabilistic models, can also be used to explain the inner details of the cloud architecture and to understand how the system makes decisions. Bayesian networks represent the relationships in the form of a graphical network. Their same quality can be exploited in cloud architecture evaluation, for example, to understand how a component’s failure impacts the entire system’s performance. As a result, cloud service providers can identify potential weaknesses and risk areas and develop strategies to handle or mitigate them. The Bayesian networks alone may not be able to perform the interpretability task and should be used together with some other techniques to work effectively.

In general, the significance of XAI techniques in cloud architecture evaluation is increasing as the operations of cloud-based systems become more complex and critical for business operations. Therefore, with the help of these techniques, cloud service providers can take deeper insights into the behavior and performance of cloud systems and make decisions accordingly.

Examples of organizations already using XAI for cloud architecture evaluation

Several large-scale organizations already use XAI to evaluate and optimize their cloud architectures. For example, IBM has developed a machine learning-based tool called IBM Watson XAI to evaluate cloud architectures. Besides providing transparent explanations, the tool has fairness and accuracy features that ensure that the evaluations performed are fair and accurate. The tool reasonably explains the decision if a particular design is not recommended. Moreover, the tool can be integrated with other services, allowing developers to incorporate XAI into their solutions.

Microsoft is another organization that uses XAI for cloud architecture. A tool called Azure Well-Architected Review has been developed to evaluate the architectures based on the Azure platform. The tool helps developers understand the reasons for certain recommended adjustments by providing explanations. Moreover, it also analyzes the impact of the suggested modifications on the overall cloud architecture.

Challenges and limitations of XAI for cloud architecture evaluation

Despite the effectiveness of XAI in evaluating cloud architectures, several challenges and limitations of these systems require further attention. A few are briefly outlined below:

Due to the high architectural complexity of the cloud, it may be a challenge for the XAI to effectively and correctly explain the factors contributing to particular decisions. Another area for improvement for XAI is data quality. Data quality is a widespread issue of AI systems and is also challenging for the XAI. To perform well, XAI systems must be trained on high-quality datasets and represent diverse scenarios. For instance, suppose an AI system aims to optimize an organization’s cloud resource allocation and utilization. The training of the system is performed on a subset of the historical data of a few departments whose resource usage patterns are identical. As a result, the system will be biased, and it may be challenging for the system to explain the reason for the system’s biasedness since the bias is rooted in the data used for training. Moreover, developing XAI-based architectural evaluation tools is expensive and may require specialized hardware and software. Hence, the cost is also among one of the challenges. Finally, the lack of standard XAI methods and techniques also makes it difficult to compare and evaluate different systems and hence, is a challenge.


XAI can be a valuable resource for cloud architecture evaluation. Due to its various benefits, such as better insights into the systems’ architectures and enhanced transparency, XAI is helpful for organizations and cloud service providers in effective decision-making and re-strategizing their operational policies. To take the maximum benefits of the capabilities of XAI, certain best practices, such as using high-quality data, selecting appropriate XAI techniques, and rigorous validation and interpretation of the results to ensure the accuracy and absence of bias, must be followed.


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Assad Abbas
Tenured Associate Professor

Dr Assad Abbas received his PhD from North Dakota State University (NDSU), USA. He is a tenured Associate Professor in the Department of Computer Science at COMSATS University Islamabad (CUI), Islamabad campus, Pakistan. Dr. Abbas has been associated with COMSATS since 2004. His research interests are mainly but not limited to smart health, big data analytics, recommender systems, patent analytics and social network analysis. His research has been published in several prestigious journals, including IEEE Transactions on Cybernetics, IEEE Transactions on Cloud Computing, IEEE Transactions on Dependable and Secure Computing, IEEE Systems Journal, IEEE Journal of Biomedical and Health Informatics,…