Addressing Compatibility Challenges in Multi-Cloud Services: A Markov Chain-Based Service Recommendation Framework

In this article, we discuss interaction issues about conflicts and interferences between various context information and services in the intelligent ubiquitous home environment. Context information conflicts occur when classifying services based on various sensing information. Service interactions are generated when services are applied in different controls simultaneously for the same device. In order to solve the problem, the paper utilizes the context-aware based service compatibility and interaction solving mechanism in intelligent ubiquitous home

Most Threat Analysis (TA) techniques analyze threats to targeted assets (e.g., components, services) by considering static interconnections among them. However, in dynamic environments, e.g., the Cloud, resources can instantiate, migrate across physical hosts, or decommission to provide rapid resource elasticity to its users. Existing TA techniques are not capable of addressing such requirements. Moreover, complex multi-layer/multi-asset attacks on Cloud systems are increasing, e.g., the Equifax data breach; thus, TA approaches must be able to analyze them. This paper proposes ThreatPro, which supports dynamic interconnections and analysis of multi-layer attacks in the Cloud. ThreatPro facilitates threat analysis by developing a technology-agnostic information flow model, representing the Cloud's functionality through conditional transitions. The model establishes the basis to capture the multi-layer and dynamic interconnections during the life cycle of a Virtual Machine. ThreatPro contributes to (1) enabling the exploration of a threat's behavior and its propagation across the Cloud, and (2) assessing the security of the Cloud by analyzing the impact of multiple threats across various operational layers/assets. Using public information on threats from the National Vulnerability Database, we validate ThreatPro's capabilities, i.e., identify and trace actual Cloud attacks and speculatively postulate alternate potential attack paths.

Web services are self-contained software components that support business process automation over the Internet, and mashup is a popular technique that creates value-added service compositions to fulfill complicated business requirements. For mashup developers, looking for desired component services from a sea of service candidates is often challenging. Therefore, web service recommendation has become a highly demanding technique. Traditional approaches, however, mostly rely on static and potentially subjectively described texts offered by service providers. In this paper, we propose a novel way of dynamically reconstructing objective service profiles based on mashup descriptions, which carry historical information of how services are used in mashups. Our key idea is to leverage mashup descriptions and structures to discover important word features of services and bridge the vocabulary gap between mashup developers and service providers. Specifically, we jointly model mashup descriptions and component service using author topic model in order to reconstruct service profiles. Exploiting word features derived from the reconstructed service profiles, a new service recommendation algorithm is developed. Experiments over a real-world data set from ProgrammableWeb.com demonstrate that our proposed service recommendation algorithm is effective and outperforms the state-of-the-art methods. Note to Practitioners —Service recommendation accuracy for mashup creation is often limited due to poor quality of service descriptions. Mashup descriptions contain valuable information about functions and features of its component services, which can be leveraged to enhance descriptive quality of original service profiles. Based on the assumption, this paper proposes a novel two-phase service recommendation framework to facilitate mashup creation. Specifically, our approach reconstructs service profiles by extracting appropriate words from historical mashup descriptions. Then, a novel service recommendation algorithm is developed by exploiting popularity and relevance measures hidden in the reconstructed profiles. Moreover, we propose the rules of dominant words discovery and employ it to further refine our algorithm.

The nature of cloud environments makes them vulnerable to data breaches and prohibited access. Most of the time, conventional anomaly detection techniques are not scalable and inaccurate. Using advanced statistical techniques such as S-H-ESD, GMM, and machine learning algorithms like XGBoost will improve anomaly detection in dynamic cloud environments with Trusted Execution Environments (TEEs) providing impenetrable security. The traditional anomaly detection was often inadequate and lack of ccalability. By using advanced integration methods like GMM, S-H-ESD and XGBoost are effective in identifying the anamoly detection in cloud environment with Trusted Execution Environments (TEEs) providing solid security. TEEs collect data, preprocess it and safely analyze it. Performance measures were evaluated using precision, recall, latency, and F1-score measurements. This model executes well by exhibiting 95 % detection capability and a low 5 % false positive ratio. Latencies decrease to a lower value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{1 0 ~ m s}$</tex>, and with its effective amalgamation with complex algorithms with secure execution, anomalies get identified with considerable precision (93 %) and an outstanding recall ratio of 96 %. This proposed method addresses cloud security concerns for evolving cloud services by integrating the machine learning and statistical tools with secure hardware. Overall, the system offers high scalability, tamper-proof architecture, and high accuracy, which makes it an ideal choice for modern cloud systems.

Modern cloud infrastructures are increasingly complex, driven by heterogeneity, disaggregation, and dynamic service composition—exposing critical limits in traditional attestation models. These models struggle to scale when trust must span multiple domains and elastic services. We present scale-out attestation, a paradigm decoupling platform trust verification from app-level attestation. Our design introduces a recursive attestation framework leveraging abstract service identities and trusted deployment workflows: a single infrastructure agent verifies platforms via abstract policies, while services derive instance-agnostic identities enabling secure recursive dependency attestation. We implement the system on FractOS, a distributed OS for disaggregated data centers, and plan to extend Confidential Containers for practical deployment. Evaluation shows strong security with minimal overhead, enabling scalable confidential computing across heterogeneous and dynamic cloud environments.

Cloud Clusters consists of various collections of files which are being accessed by multiple users of Cloud. The users are managed as a group and the association of the user to a particular group is dynamic in nature. Every group has a manager who handles the membership of a user to a particular group by issuing keys for encryption and decryption. Due to the dynamic nature of a user he/she may leave the group very frequently. But an attempt can be made by the user who has recently left the group to access a file maintained by that group. Key distribution becomes a critical issue while the behavior of the user is dynamic. Existing techniques to manage the users of group in terms of security and key distribution has been investigated so that to arrive at an objective to identify the scopes to increase security and key management scheme in cloud. The usage of various key combinations to measure the strength of security and efficiency of user management in dynamic cloud environment has been investigated.

An increasing number of services are being offered online, which leads to great difficulties in selecting appropriate services during mashup development. There have been many service recommendation studies and achieved remarkable results to alleviate the issue of service selection challenge. However, they are limited to suggesting services only for a single round or the next round, and ignore the interactive nature in real-world service recommendation scenarios. As a result, existing methods can’t capture developers’ shifting requirements and obtain the long-term optimal recommendation performance over the whole recommendation process. In this paper, we propose a deep reinforcement learning based interactive service recommendation model (RLISR) to tackle this problem. Specifically, we formulate interaction service recommendation as a multi-round decision-making process, and design a reinforcement learning framework to enable the interactions between mashup developers and service recommender systems. First, we propose a knowledge-graph-based state representation modeling method, wherein we consider both the positive and negative feedbacks of developers. Then, we design an informative reward function from the perspective of boosting recommendation accuracy and reducing the number of recommendation rounds. Finally, we adopt a cascading Q-networks model to cope with the enormous combinational candidate space and learn an optimal recommendation policy. Extensive experiments conducted on a real-world dataset validate the effectiveness of the proposed approach compared to the state-of-the-art service recommendation approaches.

Under the open, distributed and dynamic cloud environment, entities interactions make it more complex to build up the trust relationship. Recently, trust has been recognized as an important factor to improve cloud security and becomes a research hotspot. In this paper, we proposes a new multi-level trust model to manage cloud entities' relationship from the view of 'virtual organization-sub domain-entity', and measure trust relationships in a numerical way. By keeping updating the trust information, the model can identify different entity types and defense bad entities' behaviors, optimizes resource allocation. The results show that this model is efficient to identify malicious entities and improves the ratio of service request response. So this model has good defense ability and is able to provide a better secure cloud environment.

NoSQL and relational databases have been integrated in response to the increasing demand for scalable and efficient data management in hybrid cloud environments. The differences in data structures and query processing methods between these databases present both challenges and opportunities when designing an optimized hybrid system. This study explores the integration of NoSQL and relational databases to maximize data storage, retrieval, and processing efficiency across multiple cloud systems. NoSQL databases excel in handling unstructured and semi-structured data, offering flexibility and scalability, whereas relational databases provide robust consistency and structured query capabilities. Despite the wide availability of relational databases, certain applications require the dynamic adaptability of NoSQL systems, making integration a viable solution. The research evaluates key performance parameters, including query execution speed, scalability, data consistency, and resource utilization. Cloudsim is used for simulation, allowing for an in-depth comparison between standalone and hybrid database models. Experimental results indicate that the proposed hybrid model improves query performance by 30%, reduces latency by 20%, and enhances scalability by 40% compared to using relational or NoSQL databases alone. The novelty of this approach lies in its ability to overcome the limitations of each database type by their strengths in a dynamic cloud environment. The results highlight the effectiveness of hybrid database integration in optimizing cloud-based data management while ensuring seamless operation, adaptability, and resource efficiency.

The increasing complexity and dynamism of cloud environments have introduced significant cybersecurity challenges, necessitating advanced risk assessment methodologies capable of handling uncertainty and evolving threats. Traditional risk management frameworks often rely on static and rule-based mechanisms, which lack adaptability in dynamic cloud ecosystems. To address these limitations, this chapter explores the integration of fuzzy logic and evolutionary computation for adaptive cyber risk management in cloud environments. Fuzzy logic provides a powerful framework for modeling imprecise security parameters and uncertainty in threat landscapes, enabling more flexible and context-aware risk assessment. Meanwhile, evolutionary computation offers an adaptive mechanism to optimize cybersecurity strategies through heuristic learning and intelligent decision-making. The chapter presents a hybrid risk assessment framework that leverages fuzzy inference systems to quantify risk levels and evolutionary algorithms to dynamically optimize security controls, it examines the scalability of fuzzy-evolutionary approaches in large-scale cloud infrastructures and their effectiveness in mitigating real-time cyber threats, such as zero-day attacks, insider threats, and advanced persistent threats. The potential integration of explainable AI (XAI), deep learning, and quantum computing in enhancing fuzzy-based risk assessment models is also discussed. This research contributes to the advancement of self-learning, adaptive cyber defense mechanisms capable of proactively mitigating risks in multi-cloud and hybrid-cloud environments. The proposed framework ensures improved threat intelligence, automated risk prioritization, and enhanced decision transparency, offering a robust solution for next-generation cloud security.

This study presents a novel malware detection framework for cloud infrastructures that harnesses the power of Convolutional Neural Networks (CNNs) to achieve real-time threat identification with superior accuracy and speed. Our approach begins with the collection and meticulous preprocessing of heterogeneous cloud log data, followed by advanced feature engineering to extract meaningful patterns indicative of malicious activity. The CNN model automatically learns hierarchical representations from this high-dimensional data, resulting in a detection system that achieves an accuracy of 98.2%, a precision of 97.5%, a recall of 98.0%, and an F1-score of 97.8%. In addition, the model operates with a low latency of 12 ms, a critical factor for timely threat mitigation in dynamic cloud environments. Comparative analysis against Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Random Forest classifiers reveals that the CNN not only outperforms these models in key performance metrics but also maintains a significant advantage in processing speed. These findings highlight the potential of CNN-based approaches to enhance cybersecurity defenses, offering a scalable and efficient solution for detecting evolving malware threats in cloud infrastructures.

Nowadays, Cloud Computing acts a major role in every field. These days, more large data centers are in service and many small cloud data centers are enlarging all over the universe. Cloud Computing is a catchword in the domain of HPC and offers on-demand services to the resources on the internet. The VMs (Virtual Machines) specified in the cloud data centres may have different specifications and instable resource usage, which causes imbalanced resource utilization within servers. Thus, it leads to performance degradation. Hence to achieve efficient selection of VM, these challenges must be addressed and solved by using meta-heuristics algorithms. In order to process the data, the VMs are placed on the PMs (Physical Machines). There will be multiple and dynamic request of input in the IaaS(Infrastructure as a Service) framework, hence the system’s responsibility is to create a VMs without knowing the types of tasks. Therefore, the fixed tasks scheduling is not right for this system. The most important research area that needs to be addressed is its performance in scheduling. The best and optimal solution is to find out in the cloud environment. Metaheuristics-based algorithms provide the near-optimal solution. In this paper, we proposed an Improved Particle Swarm Optimization algorithm to reduce the makespan and improve the throughput. We have compared our results with adaptive three-threshold energy-aware (ATEA) algorithm and PSO. The investigational results display the proposed Improved PSO algorithm will schedule and balance the load in the dynamic cloud environment better than the other approaches.

Time series forecasting is important for cloud computing because it helps keep services running, ensures that Quality of Service (QoS) is maintained at a high level even when workloads change, and makes sure that resources are used well. In the actual world, cloud workloads do not stay the same all the time. Rather, they evolve throughout time, exhibiting cyclical trends, nonlinear fluctuations, and periods of elevated demand. This complicates the utilization of conventional forecasting methodologies. This study presents a hybrid ensemble-based approach for short-term cloud workload prediction that enhances adaptability through workload-aware modeling to tackle this issue. The method employs feature extraction and K-means clustering, complemented by a dynamically weighted ensemble of Prophet, Long Short-Term Memory (LSTM), and Linear Regression models. NASA HTTP server logs and ClarkNet WWW datasets are used to test the architecture with different window sizes and prediction horizons. Experimental results indicate that modest observation windows and short-term perspectives yield the optimal correlation between response time and prediction stability. On the other hand, the hybrid ensemble always does better than single models in different types of workloads. These results show how effectively the suggested technique works for proactive provisioning of cloud resources and reliable auto-scaling in environments that are cloud-native.

Cloud computing is one among the emerging platforms in business, IT enterprise and mobile computing applications. Resources like Software, CPU, Memory and I/O devices etc. are utilized and charged as per the usage, instead of buying it. A Proper and efficient resource allocation in this dynamic cloud environment becomes the challenging task due to drastic increment in cloud usage. Various promising technologies have been developed to improve the efficiency of resource allocation process. But still there is some incompetency in terms of task scheduling and power consumption, when the system gets overloaded. So an energy efficient task scheduling algorithm is required to improve the efficiency of resource allocation process. In this paper an improved task scheduling and an optimal power minimization approach is proposed for efficient dynamic resource allocation process. Using prediction mechanism and dynamic resource table updating algorithm, efficiency of resource allocation in terms of task completion and response time is achieved. This framework brings an efficient result in terms of power reduction since it reduces the power consumption in data centers. The proposed approach gives accurate values for updating resource table. An efficient resource allocation is achieved by an improved task scheduling technique and reduced power consumption approach. The Simulation result gives 8% better results when comparing to other existing methods.

Web technologies along with the virtualization ofcomputation and storage resources have allowed extensive flexibilityin the development, deployment and delivery of softwaresolutions. On one hand, this flexibility has allowed engineersto commission and repurpose resources on demand and atwill. On the other hand, the flexibility has given rise to newbusiness models and interesting implications in the economics ofsoftware ecosystems. In this work, we propose a model to capturethe technical and economic transactions within a web softwareecosystem deployed on a dynamic cloud environment. Next, wedemonstrate the use of such model to define rules for a moreeconomically efficient adaptation strategy for web applicationsas their incoming traffic fluctuates.