Network Security Analytics on the Cloud: Public vs. Private Case

In cloud computing, private cloud tends to exhibit high controllability but lack scalability, whereas public cloud is just the opposite. The hybrid cloud formed by combining them can effectively balance controllability and scalability, and has been widely adopted in industry for executing workflows. The network connecting public and private clouds goes through Internet and its bandwidth is rather limited in comparison with that inside clouds. Hence, cross-cloud data transmission can become a bottleneck of executing workflows in such environment. Moreover, multiple data communications may contend for bandwidth resources, thereby incurring delay. To address these concerns, this work establishes a scheduling model for workflow execution across public and private clouds, where a queueing mode is innovatively employed to address potential network communication contention. A contention-cognizant list scheduling (CCLS) heuristic equipped with task duplication is devised to minimize workflow makespan. It adopts a novel task sorting attribute to schedule tasks and cross-cloud data communications by using available computation and communication resources, and employs task duplication to obviate the needs for certain data communications. Experiments are conducted with realistic workflows and diverse settings, and the results verify the superiority of CCLS over the existing ones as it can always achieve the best makespan <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —A crucial challenge for workflow scheduling across public and private clouds is that the cross-cloud bandwidth resource is relatively limited and multiple data communications may contend for it in practice. However, this communication contention issue has largely been neglected in existing investigations. To advance the state of the art, this paper proposes a communication contention-cognizant scheduling approach based on a queueing mode to minimize the makespan for workflow execution across public and private clouds. The proposed approach can be readily put into use and experimental results show that it performs better than traditional scheduling approaches that fail to consider communication contention.

The development of cloud computing technology has been continuously growing since its invention and has attracted the attention of many researchers in the academia and the industry, particularly during the recent years. The majority of organizations, whether large corporate businesses or typical small companies, are moving towards employing this cutting edge technology. Using private cloud provides low cost and privacy for workflow applications execution. However, an organization’s requirements to high performance resources and high capacity storage devices lead them to utilize public clouds. Public cloud leases information technology services in the form of small units and in larger scale compared to private cloud, but this model is potentially exposed to the risk of data breach and is less secure in comparison to a pure private cloud environment. The combination of public and private clouds is known as hybrid cloud, where workflow tasks can be executed on resources residing on either public or private clouds. The objective of this paper is to present a scheduling algorithm for maintaining data privacy in workflow applications, such that the budget is minimized, while the makespan limitation imposed by the user is satisfied.

Cloud computing is one of the emerging technologies in last decade; this technology provides a service model to organizations and public users. Organization users and developers, they start and maintain their organization without any hardware and software infrastructure they can develop their company with the help of cloud technology. Cloud deployment is categorized into three types, private, public and hybrid cloud environment and here public and private cloud more secure comparatively hybrid cloud because when data is moving from private to public cloud security problem is occur. Data communication is most important task in a network environment so the proposed model is focus on data security in hybrid cloud environment. In a hybrid cloud the participation of private cloud and its nodes very important after that the data transmission is depends on active node, In this node selection is based on genetic algorithm. The result of proposed model is going to compare with private and public cloud security parameters.

Besides well-known benefits, commodity cloud storage also raises concerns that include security, reliability, and consistency. We present Hybris key-value store, the first robust hybrid cloud storage system, aiming at addressing these concerns leveraging both private and public cloud resources. Hybris robustly replicates metadata on trusted private premises (private cloud), separately from data, which are dispersed (using replication or erasure coding) across multiple untrusted public clouds. Hybris maintains metadata stored on private premises at the order of few dozens of bytes per key, avoiding the scalability bottleneck at the private cloud. In turn, the hybrid design allows Hybris to efficiently and robustly tolerate cloud outages but also potential malice in clouds without overhead. Namely, to tolerate up to f malicious clouds, in the common case of the Hybris variant with data replication, writes replicate data across f +1 clouds, whereas reads involve a single cloud. In the worst case, only up to f additional clouds are used. This is considerably better than earlier multi-cloud storage systems that required costly 3 f +1 clouds to mask f potentially malicious clouds. Finally, Hybris leverages strong metadata consistency to guarantee to Hybris applications strong data consistency without any modifications to the eventually consistent public clouds. We implemented Hybris in Java and evaluated it using a series of micro and macro-benchmarks. Our results show that Hybris significantly outperforms comparable multi-cloud storage systems and approaches the performance of bare-bone commodity public cloud storage.

Cloud computing has caused a revolution in how companies do business giving them flexibility, scalability, and cost-efficiency like never before. It lets organizations use computing resources through the internet instead of relying on the usual in-house setup making cloud computing a key part of today's IT plans. From new startups to big global companies, businesses are using cloud solutions more and more to boost their ability to adapt cut down on running costs, and come up with new ideas. The cloud ecosystem is big and varied, with different deployment models and service types to suit various business needs. Public, private, and hybrid clouds are the three main deployment models each with its own pros and cons. public clouds, like those from Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform, offer affordable scalability and global reach. Private clouds, on the other hand, are for organizations that have strict security or compliance needs giving them dedicated environments. Hybrid clouds mix the best parts of both letting businesses find a balance between security and scalability by combining private and public cloud resources

E-healthcare is an emerging and promising healthcare system to meet the increasing medical demand from aging population. It requires extensive data storage, pervasive data access, and reliable computing resources to support the real-time communication of critical health information and the real-time diagnosis. Recently, cloud computing, including public cloud and private cloud, with both scalability and accessibility is proposed to be integrated in the e-healthcare system. However, in meeting stringent medical requirements, private clouds lack necessary reliability, whereas public clouds suffer from communication delay. In this paper, we first introduce a cooperation framework to address the distinct challenger facing different clouds. It is inspired by the fact that private clouds are geographically deployed and public clouds can be regarded to possess infinite computing resources. In our framework, private clouds are designed to serve parts of local requests to public clouds, and rewarded by receiving help with excess requests. We adopt stochastic control theory to address the failure minimization issues for private clouds under random demand process. We prove the optimality of a policy constructed through recursion. Numerical and simulation results are presented to demonstrate that our proposed scheme can improve the reliability of private clouds, as well as reduce average delay of public clouds.

This book develops an integrated scholarly framework for understanding how graph neural networks and transformer architectures can be combined to strengthen advanced cyber threat detection and network security under conditions of scale, uncertainty, and adversarial adaptation. It argues that contemporary defence environments require models capable of representing topology, temporal dependence, semantic context, and operational uncertainty simultaneously, rather than treating logs, packets, flows, identities, and infrastructure states as isolated variables. The manuscript therefore moves from foundational epistemic questions and research design principles to statistical explanation, causal analysis, predictive machine learning, scalable data engineering, reproducibility, MLOps, and governance. Across the chapters, attention is given to the full analytical lifecycle: threat modelling, representation learning, evaluation design, trustworthiness, deployment, policy translation, and sectoral implementation. Conceptual diagrams, methodological tables, mathematical formulations, algorithmic sketches, and governance frameworks are embedded throughout the text to support researchers, practitioners, and policymakers working in heterogeneous contexts across South Asia, Europe, Africa, and the Americas. The central contribution of the book is to show that secure and accountable cyber analytics emerges not from isolated algorithmic performance gains, but from coherent alignment among data architecture, inference strategy, operational workflow, human oversight, and institutional governance. In that sense, the text is designed as both a research monograph and a strategic reference handbook for long-horizon cyber defence innovation. Keywords graph neural networks, transformers, cyber threat detection, network security, adversarial learning, anomaly detection, causal inference, statistical modeling, trustworthiness, explainable AI, reproducibility, MLOps, threat intelligence, attack graphs, intrusion detection, zero-trust architecture, cyber governance, scalable analytics, security operations centers, representation learning, uncertainty quantification, risk modeling, privacy-preserving analytics, policy

Publicly Verifiable Computation (PVC) enables computationally weak trusted sources to outsource several computations to some more powerful public untrusted clouds. On issuing a query, the public cloud replies the result of the function evaluation with a witness vouching for correctness of computation. This primitive requires high efficiency and public verifiability. However, existing PVC constructions all request trusted sources to know delegated function beforehand, and thus it fails to meet diverse requirements, especially outsourced target unknown need to be jointly computed among different entities in a privacy-preserving manner. To strengthen current PVC’s flexibility, we proposed a new primitive called Secure Collaborative PVC ( ${\mathcal {SCPVC}}$ ), where TTP is responsible only for initializing system parameter and publishing some information in its bulletin. After some rounds, the public cloud owns lots of functions outsourced in PVC ways. The private cloud works out an algebraic operation structure ${\mathcal {L}}$ , which involves some functions provided by public cloud and himself. Based on ${\mathcal {L}}$ , they jointly perform the protocol to generate the target function. At the end of the protocol, the public cloud obtains target function while not disclosing respective secrets. Due to the misbehavior of the public cloud, this mechanism allows the private cloud to check the integrity of target function and any client to verify the correctness of results. Our scheme without jointly computing is a typical existing PVC scheme. Therefore, our protocol is compatible with the prevailing publicly verifiable computation Scheme. Before investigating ${\mathcal {SCPVC}}$ , we tailored two secure two-party polynomial computation protocols using 1-out-of- ${l}$ Oblivious Transfer protocol as the main building block to $ {\mathcal {SCPVC}}$ . More preciously, polynomial multiplication protocol transforms two polynomials multiplication into another two addition such that the result of sum is equal to the result of multiplication. Similarly, polynomial addition protocol is as same as multiplication protocol converts two polynomials addition into another two multiplication.

After years of development, cloud computing has developed into public cloud, private cloud, community cloud, hybrid cloud and other cloud forms. However, the government, financial and other industries have more concerns about security, operation and maintenance, cost and other issues in the process of cloud. In this regard, the industry, academia and other parties have launched the frontier exploration, give different solutions. To meet users’ new needs, we proposed a new model that combines the advantages of public and private clouds. It is called proprietary cloud. Proprietary cloud provides users with more efficient management by incorporating years of experience of public cloud providers. On the one hand, it meets the rigid needs of users’ independent control and exclusive resources, on the other hand, it reduces the maintenance cost and downtime probability of users, and achieves the desired effect of users. It mainly includes self-built model and exclusive model deployment modes. The main difference is whether to accept public cloud management. This paper introduces and analyzes proprietary cloud in detail from the aspects of deployment architecture, mode differences and common deployment patterns. It concludes that proprietary cloud is an indispensable cloud computing deployment pattern in the future.KeywordsCloud computingDigital transformationProprietary cloud

With emergence in cloud computing there was a huge demand for the data center technology and the operating system which can handle the data center. Increasing demand of infrastructure services leading the organizations to move towards Cloud. The aim is to provide an opportunity to the industry to build a hosting architecture, massively scalable which is completely open source, and to provide a solution to manage their on premises datacenters or private cloud and public cloud data centers simultaneously. When we talk about the combination of private as well as public cloud workloads, here term comes into existence i.e. Hybrid Cloud. To build and manage the Hybrid cloud, Openstack is the open source solution available in market. Here we will be discussing the whole concept of OpenStack in detail, It's architecture, functionalities and how we setup it in our environment tested different use cases.

Deadline-constrained cost-aware workflow scheduling in hybrid cloud

Security and privacy concerns are increasingly important when massive data processing and transferring becomes a reality in the era of the Sixth Generation (6G) Networks. Under this circumstance, it becomes a trend that the enterprises tend to host their data and services on private clouds dedicated to their own use, rather than the public cloud services. However, in contrary to the well-investigated total cost of ownership (TCO) for public clouds, the analytic research on the cost of purchase and operation for private clouds is still a blank. In this work, we first review the state-of-the-art TCO literature to summarize the models, tools, and cost optimization techniques for public clouds. Based on our survey, we envision the TCO modeling and optimization for private clouds by comparing the differences of features between public and private clouds.

Cloud computing is a Service Oriented Architecture which reduced information technology overhead for the end-user and provide great flexibility, reduced total cost of ownership, on-demand services and many other things [1]. Hence it delivered all IT related capability as services rather than product .Services on cloud are divided into three broad categories: Software as a Service, Infrastructure as a Service & Platform as a Service. Same as services cloud is also classified as Private Cloud, Public Cloud & Hybrid Cloud. Private cloud is increasingly become popular between every private organization either large or small wants .To deploy public or private cloud there are many open source software available some are Eucalyptus, Nimbus, OpenStack, Open Nebula Cloud stack and Amazon Web Services .This paper, I provide a study of the main open source software such as Eucalyptus, OpenStack and OpenNebula for cloud implementation. It is believe that the comparison presented in this paper would benefit developers in selecting best open source Software.

Demand Response (DR) is often regarded as the killer application of the emerging smart grid. The ever-growing Internet population and energy consumption makes data centers auspicious candidates with great potential for DR. Besides commercial data centers and large server farms, small data centers scattered in academic or research environments consume considerable amount of energy. Unlike commercial data centers that are mission critical and must meet rigorous reliability and availability requirements, small data centers on campus are typically not mission critical. Such data centers are the most likely prospects for participating in DR programs. Different from the long term effort required for improving energy efficiency, DR programs expect participants to temporarily reduce their power demand on some short periods. The related information is still inadequate for organizations maintaining many small data centers, since the existing studies usually focus on energy efficiency issues for the large dedicated server farms. This study examines the power consumption recorded by smart meters from a small data center, and identifies the limitations and potential for small data centers participating in DR programs.

Cloud computing technology is increasingly important in the industry with the promise of increased performance, dynamic resource allocation capabilities and provides a convincing opportunity for organizations to outsource their IT infrastructure under the pay-per-use model offered by many public cloud providers. But there are concerns when selecting between the private cloud and the public cloud that must be taken into account. Many SME enterprises with scarce in-house IT support and limited knowledge about different cloud technologies encounter difficulties in making the most appropriate choice on 'buying' a private cloud or 'renting' a public cloud service. Through a number of interviews with potential cloud adopters from the industry, this paper identifies the most relevant factors concerning cloud adoption and develops a cloud deployment model assessment method called Cloud Deployment Selection Model (CDSM). The model has been validated in real case studies, and recommendations derived have been compared with real adoption cases. Result shows it is able to accurately recommend a suitable cloud deployment model based on the factors identified from many SME organizations, an important tool for SMEs to decide between the private or the public cloud solution.