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The IT industry is advancing rapidly, with virtually every branch of modern computing experiencing swift development. Concepts such as Cloud Computing and Artificial Intelligence no longer surprise anyone. Recently, Software Defined Networks (SDN) have been gaining significant popularity. This innovative approach to computer networks allows for greater flexibility and is, therefore, much more well-known in the world of cloud computing than in traditional network implementations. This paper introduces the concept of SDN and Network Functions Virtualization (NFV) and outlines all the challenges and security issues associated with the cloud environment. The dynamic nature of the IT landscape requires constant adaptation to emerging technologies, and SDN represents a noteworthy evolution in the realm of computer networking. Platforms such as SDN and open-source tools enabling the creation of private cloud environments such as OpenStack or OpenNebula were compared. At the same time, aspects like security, network performance, flexibility, and scalability were analyzed. Based on the prior analysis, a comprehensive cloud environment was built using the OpenStack solution and SDN - OpenDaylight was deployed. Additional tests conducted on the OpenStack cloud, both with and without SDN, demonstrated the superiority of SDN implementation in the cloud. [ABSTRACT FROM AUTHOR]

Traditional auto-scaling approaches are conceived as reactive automations, typically triggered when predefined thresholds are breached by resource consumption metrics. Managing such rules at scale is cumbersome, especially when resources require non-negligible time to be instantiated. This paper introduces an architecture for predictive cloud operations, which enables orchestrators to apply time-series forecasting techniques to estimate the evolution of relevant metrics and take decisions based on the predicted state of the system. In this way, they can anticipate load peaks and trigger appropriate scaling actions in advance, such that new resources are available when needed. The proposed architecture is implemented in OpenStack, extending the monitoring capabilities of Monasca by injecting short-term forecasts of standard metrics. We use our architecture to implement predictive scaling policies leveraging on linear regression, autoregressive integrated moving average, feed-forward, and recurrent neural networks (RNN). Then, we evaluate their performance on a synthetic workload, comparing them to those of a traditional policy. To assess the ability of the different models to generalize to unseen patterns, we also evaluate them on traces from a real content delivery network (CDN) workload. In particular, the RNN model exhibites the best overall performance in terms of prediction error, observed client-side response latency, and forecasting overhead. The implementation of our architecture is open-source.

Unlike traditional networks, Software-defined networks (SDNs) provide an overall view and centralized control of all the devices in the network. SDNs enable the network administrator to implement the network policy by programming applications on top of the SDN controller using generic APIs. One or more controller instances can be deployed to administer the data flow by maintaining unified control of the entire network. The controller is expected to respond quickly to queries from forwarding devices. Presuming quick responses from the controller while enforcing a complex security mechanism is unreasonable. In this paper, the authors propose a unique, adaptive, lightweight, yet efficient technique called ISOLATOR to mitigate the effect of insider attacks and malfunctioning of distributed applications in an SDN-enabled cloud. The proposed security application upon detecting any suspicious activity by a virtual machine, isolates it by removing the interface to its respective shared network and reconnects it through a restricted network operating in a highly selective mode. By subjecting the data traffic to deep packet inspection, the restricted network searches for a match with a known worm pattern. The application is programmed for the OpenDayLight controller and the results show a significant improvement in identifying malicious activities with minimal latency and computational cost. [ABSTRACT FROM AUTHOR]

Cyber Range is an experimental platform based on virtualization technology to construct a controlled simulation environment, providing a real-world simulation environment for cybersecurity personnel to conduct various practical exercises. The problem is that generating a virtual environment satisfying the requirements is labor-intensive and time-consuming. To resolve the above problem, this paper proposes a system to automate the deployment of a cyber range. In our method, the first step is to collect virtual machines (VMs) related to cybersecurity and extract relevant features. Then, machine learning is used to classify VMs to reduce the cost of manual VMs selection. Lastly, leveraging the popular OpenStack cloud platform as the deployment platform enhances the applicability of the cyber range. When it comes time to deploy a virtual environment, the instructor only needs to provide some brief description information of a virtual environment. Then, the system will automatically parse the description file to complete the automated deployment of the virtual environment. This system has been successfully applied to the cyber range of Sichuan University for daily teaching tasks. [ABSTRACT FROM AUTHOR]

The increasing complexity of cloud computing has prompted a greater emphasis on protecting the privacy, integrity, and security of data stored and processed in the cloud. Data privacy is safeguarded through access control, but existing models such as Role Based Access Control (RBAC) and Attribute Based Access Control (ABAC) rely on a centralized server. If this server is compromised, it poses significant risks to data security. To address this issue, there is a need for Distributed ABAC (DABAC) system for OpenStack services based on blockchain. The unique features of blockchain enables access control systems which ensures data integrity and privacy. Additionally, blockchain offers a level of transparency for both the resource owner and a user. In this work, we propose a smart contract based ABAC system. We implemented the proposed work using an Ethereum blockchain and OpenStack cloud. Furthermore, we evaluated two consensus algorithms for scalability analysis of DABC mechanism. The results demonstrate that DABAC performs better than ABAC in ensuring fine-grade access control with proof of stake consensus algorithm providing better scalability. [ABSTRACT FROM AUTHOR]

Modern software systems are designed to be deployed in different configured environments (e.g., permissions, virtual resources, network connections) and adapted at runtime to different situations (e.g., memory limits, enabling/disabling features, database credentials). Such a configuration during the deployment and runtime of a software system is implemented via a set of configuration files, which together constitute what we refer to as a "configuration system." Recent research efforts investigated the evolution and maintenance of configuration files. However, they merely focused on a limited part of the configuration system (e.g., specific infrastructure configuration files or Dockerfiles), and their results do not generalize to the whole configuration system. To cope with such a limitation, we aim to better capture and understand what files constitute a configuration system. To do so, we leverage an open card sort technique to qualitatively study 1,756 configuration files from OpenStack, a large and widely studied open source software ecosystem. Our investigation reveals the existence of nine types of configuration files, which cover the creation of the infrastructure on top of which OpenStack will be deployed, along with other types of configuration files used to customize OpenStack after its deployment. These configuration files are interconnected while being used at different deployment stages. For instance, we observe specific configuration files used during the deployment stage to create other configuration files that are used in the runtime stage. We also observe that identifying and classifying these types of files is not straightforward, as five out of the nine types can be written in similar programming languages (e.g., Python and Bash) as regular source code files. We also found that the same file extensions (e.g., Yaml) can be used for different configuration types, making it difficult to identify and classify configuration files. Thus, we first leverage a machine learning model to identify configuration from non-configuration files, which achieved a median area under the curve (AUC) of 0.91, a median Brier score of 0.12, a median precision of 0.86, and a median recall of 0.83. Thereafter, we leverage a multi-class classification model to classify configuration files based on the nine configuration types. Our multi-class classification model achieved a median weighted AUC of 0.92, a median Brier score of 0.04, a median weighted precision of 0.84, and a median weighted recall of 0.82. Our analysis also shows that with only 100 labeled configuration and non-configuration files, our model reached a median AUC higher than 0.69. Furthermore, our configuration model requires a minimum of 100 configuration files to reach a median weighted AUC higher than 0.75. [ABSTRACT FROM AUTHOR]

Trust is a critical element when it comes to news articles, and an important problem is how to ensure trust in the published information on news websites. First, this paper describes the inner workings of a proposed news-retrieval and aggregation architecture employed by a blockchain-based solution for fighting disinformation; this includes a comparison between existing information retrieval solutions. The decentralized nature of the solution is achieved by separating the crawling (i.e., extracting the web page links) from the scraping (i.e., extracting the article information) and having third-party actors extract the data. A majority-rule mechanism is used to determine the correctness of the information, and the blockchain network is used for traceability. Second, the steps needed to deploy the distributed components in a cloud environment seamlessly are discussed in detail, with a special focus on the open-source OpenStack cloud solution. Lastly, novel methods for achieving a truly decentralized architecture based on community input and blockchain technology are presented, thus ensuring maximum trust and transparency in the system. The results obtained by testing the proposed news-retrieval system are presented, and the optimizations that can be made are discussed based on the crawling and scraping test results. [ABSTRACT FROM AUTHOR]

On-demand provisioning of computing resources using the network over the Cloud consumes a tremendous amount of energy. The energy consumed does not scale out efficiently with the computational use of the resources. A large portion accounts for the power consumption to drive the hardware and cooling facilities for the hardware resources. Nowadays, energy and power consumption have become the most critical concerns in data centers. This work highlights a novel technique to achieve energy efficiency through server consolidation to address the abovementioned situations by effectively scheduling Virtual Machines (VMs) onto the servers and rescheduling active VMs upon deletion of existing VMs through cold migration. An advanced technology, i.e. Wake on LAN (WoL), is employed through power wake for dynamic and wake up of the servers on private Cloud. Compared to existing models, the proposed approach's maximum estimated energy and power saved are experimentally verified to be 54% and 50%, respectively. [ABSTRACT FROM AUTHOR]

Abstract With the development of cloud-edge collaborative computing, cloud computing has become crucial in data analysis and data processing. OpenStack and Docker are important components of cloud computing, and the integration of the two has always attracted widespread attention in industry. The scheduling mechanism adopted by the existing fusion solution uses a uniform resource weight for all containers, and the computing nodes resources on the cloud platform is unbalanced under differentiated resource requirements of the containers. Therefore, considering different network communication qualities, a load-balancing Docker scheduling mechanism based on OpenStack is proposed to meet the needs of various resources (CPU, memory, disk, and bandwidth) of containers. This mechanism uses the precise limitation strategy for container resources and a centralized strategy for container resources as the scheduling basis, and it generates exclusive weights for containers through a filtering stage, a weighing stage based on weight adaptation, and a non-uniform memory access (NUMA) lean stage. The experimental results show that, compared with Nova-docker and Yun, the proposed mechanism reduces the resource load imbalance within a node by 57.35% and 59.00% on average, and the average imbalance between nodes is reduced by 53.53% and 50.90%. This mechanism can also achieve better load balancing without regard to bandwidth.

This study introduces a multi-cloud model that combines private and public cloud services for processing and managing satellite images. The multi-cloud service is established by incorporating private clouds within organizations and integrating them with external public cloud services to utilize the data. Private clouds can maintain data security within an organization or between organizations, while public clouds offer easy processing options for general users with access accounts. The model for the private cloud service utilizes open-source OpenStack software to create virtual machines, allowing users to manage analysis ready data (ARD) of the Korea Multi-Purpose Satellite (KOMPSAT)-3/3A images simultaneously. The public cloud service through Amazon Web Services (AWS) offers four services and uses the Open Data Cube (ODC) to manage data and provide web-based time-series visualization and processing. The model utilizes OpenStack to create virtual machines, and the public cloud service through AWS offers various services using ODC to manage data. A system that handles large amounts of satellite imagery in a multi-cloud environment has benefits such as improved availability, cost savings through open-source, and enhanced scalability. We present a prototyped utilization model that can be used with the ODC user interface (UI) that applies the proposed multi-cloud model. The multi-cloud model of this study can be applied to constructing a country-scale data cube system, that deals with large-scale satellite image data. It can also be applied to systems that need to be built with data that is tailored to a specific user's needs at any institution. [ABSTRACT FROM AUTHOR]

Cloud computing platforms like OpenStack use orchestration as a crucial component in the deployment and administration of cloud services. For the deployment and management of virtualized resources, these platforms mainly rely on orchestration techniques. Cloud orchestration services like Service Function Chaining (SFC) have benefited from lower costs and increased scalability for cloud computing platforms thanks to the introduction of technologies like Software-Defined Networking (SDN) and Network Function Virtualization (NFV). The use of gRPC APIs and an SDN switch with P4 support is described in this research as an innovative method for enhancing SFC orchestration service in OpenStack computing platforms. The proposed approach uses a P4 enabled SDN switch and gRPC APIs to enhance network performance in data plane communication with the underlying infrastructure and performance in terms of service creation time between the SFC orchestration services. Our evaluation's findings demonstrate that this strategy greatly boosts the orchestration service's performance, making it more appropriate for usage in extensive cloud-based deployments. Additionally, the possibility of combining gRPC APIs and P4 switch to improve the efficiency and flexibility of orchestration service in cloud computing platforms is demonstrated by the performance analysis of the proposed approach on numerous platforms across diverse use cases. [ABSTRACT FROM AUTHOR]

Cloud computing has become a leading technology for IT infrastructure, with many companies migrating their services to cloud servers in recent years. As cloud services continue to expand, the issue of cloud monitoring has become increasingly important. One important metric to monitor is CPU steal time, which measures the amount of time a virtual CPU waits for the actual CPU. In this study, we focus on the impact of CPU steal time on virtual machine performance and the potential problems that can arise. We implement our work using an OpenStack-based cloud environment and investigate intrusive and non-intrusive monitoring methods. Our analysis provides insights into the importance of CPU steal time monitoring and its impact on cloud performance. [ABSTRACT FROM AUTHOR]

Effective scheduling algorithms are needed in the cloud paradigm to leverage services to customers seamlessly while minimizing the makespan, energy consumption and SLA violations. The ineffective scheduling of resources while not considering the suitability of tasks will affect the quality of service of the cloud provider, and much more energy will be consumed in the running of tasks by the inefficient provisioning of resources, thereby taking an enormous amount of time to process tasks, which affects the makespan. Minimizing SLA violations is an important aspect that needs to be addressed as it impacts the makespans, energy consumption, and also the quality of service in a cloud environment. Many existing studies have solved task-scheduling problems, and those algorithms gave near-optimal solutions from their perspective. In this manuscript, we developed a novel task-scheduling algorithm that considers the task priorities coming onto the cloud platform, calculates their task VM priorities, and feeds them to the scheduler. Then, the scheduler will choose appropriate tasks for the VMs based on the calculated priorities. To model this scheduling algorithm, we used the cat swarm optimization algorithm, which was inspired by the behavior of cats. It was implemented on the Cloudsim tool and OpenStack cloud platform. Extensive experimentation was carried out using real-time workloads. When compared to the baseline PSO, ACO and RATS-HM approaches and from the results, it is evident that our proposed approach outperforms all of the baseline algorithms in view of the above-mentioned parameters. [ABSTRACT FROM AUTHOR]

When operating large cloud computing infrastructures, ensuring healthiness of physical resources and software components is of paramount importance to meet the demanding service levels expected by customers. This is only possible using automations that can detect anomalies and alert the on-call personnel, or trigger healing procedures. In production-grade deployments, such automations are generally based on static thresholds or predefined pattern-matching rules, checked against relevant metrics and logs. Defining and maintaining them is cumbersome and, as the infrastructure grows, they need continuous adjustments. To tackle this problem, we propose an intelligent automation system for cloud operations that learns, from what operators have done in the past, what actions should be applied in response to the observed anomalies. Such system is designed to operate elastic groups of cloud instances realizing typical (replicated) cloud services. The mechanism is based on a 2-phase machine learning pipeline, composed of: a first, lighter, model that automatically detects anomalous patterns, based on past observations of the normal behavior, causing activation of the second, more involved, model; this is a model that recommends specific corrective actions, based on historical operational data reporting the actions applied to heal the faulty components. The approach was validated on an OpenStack deployment, where we deployed both a synthetic application and a multi-node Cassandra NoSQL data-store, and injected different types of anomalies while these systems were exercised using synthetic workloads. For both applications, we obtained a remarkable accuracy (mostly beyond 90%, and also going beyond 95% in some cases), for the anomaly detection and corrective action recommendation tasks, by applying the models on the respective test sets. This allows us to conclude that the presented mechanism constitutes an efficient and effective technique to help operating cloud services in presence of a number of faults, albeit the types and heterogeneity of faulty conditions might be expanded in future evolutions of the framework. The implementation and the material needed to reproduce our results are available under an open-source license.

With the development of cloud-edge collaborative computing, cloud computing has become crucial in data analysis and data processing. OpenStack and Docker are important components of cloud computing, and the integration of the two has always attracted widespread attention in industry. The scheduling mechanism adopted by the existing fusion solution uses a uniform resource weight for all containers, and the computing nodes resources on the cloud platform is unbalanced under differentiated resource requirements of the containers. Therefore, considering different network communication qualities, a load-balancing Docker scheduling mechanism based on OpenStack is proposed to meet the needs of various resources (CPU, memory, disk, and bandwidth) of containers. This mechanism uses the precise limitation strategy for container resources and a centralized strategy for container resources as the scheduling basis, and it generates exclusive weights for containers through a filtering stage, a weighing stage based on weight adaptation, and a non-uniform memory access (NUMA) lean stage. The experimental results show that, compared with Nova-docker and Yun, the proposed mechanism reduces the resource load imbalance within a node by 57.35% and 59.00% on average, and the average imbalance between nodes is reduced by 53.53% and 50.90%. This mechanism can also achieve better load balancing without regard to bandwidth. [ABSTRACT FROM AUTHOR]

Now days the usage of cloud environment is rapidly increasing in all the fields to run applications in virtual machines instead of physical hardware based machine. This increases the service availability and also reduces the cost. The usage of openstack cloud environment is also increasing both in academics and industry as it provides open source cloud services to run the application both for research and for production environment. One of the challenges in cloud environment is that the detection and prediction of the anomalies before they occur. In the traditional approach, the anomalies are detected manually by keeping track of threshold level and heartbeat. The recent research is happening on using machine learning techniques in detecting the anomalies before they occur. In this paper, we propose a model for anomaly detection in openstack cloud environment. In the proposed model, we used Stacked and Bidirectional LSTM models to build the neural network. For the experiment the data is collected from openstack using collectd. The collected data sets 10 features and class label. Using LSTM neural network, we were able to detect the anomalies in openstack environment. The proposed model achieved the detection accuracy of 94.61% for training set and 93.98% for the test set using binary cross entropy function as a loss function. [ABSTRACT FROM AUTHOR]

Spatial Data Infrastructure (SDI) is considered as a holistic framework for effective collection, integration, discovery, sharing and delivery on a common platform for better utilization of multi-source geospatial data by global community, thereby, resulting in increased awareness of the use of geospatial data and the cooperation between decision-makers and stakeholders. Cloud computing technology can provide convenient and on-demand network access to shared computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction, and has been widely used in different applications. However, much research could not be found related to adoption of cloud computing in development of SDI at various levels from local to country. The present research is a step forward to fill this research gap by development of an open, interoperable and efficient framework for implementation of SDI at City level (acronym GeoCloud4SDI) for Prayagraj city (India) and deploying the same on OpenStack private cloud. Accordingly, multi-tier client server web GIS based SDI architecture and cloud enabled SDI services and workflow using load balancing and elastic computing architecture is developed. The novel framework for GeoCloud4SDI is developed using four layers, namely, (i) physical layer, (ii) cloud services layer, (iii) geospatial services layer and (iv) client layer. The physical layer and the cloud services layer combined together enables the access of virtualized computing resources from the cloud to enhance the performance of City level SDI. Geospatial services layer provides two different functionalities, namely, (a) management, retrieval and access of spatial data and metadata, and (b) retrieval of cloud services from cloud service layer. The performance of GeoCloud4SDI is examined by cloud enabled load balancing and elastic computing framework and the results show the response time reduced up to 50.43% along with 1.95 s performance gain for 1000 concurrent requests. The developed and implemented cloud enabled framework of GeoCloud4SDI is built via Open Geospatial Consortium (OGC) compliant interoperable service standards using Open Source Software (OSS), thereby ensuring standardisation and interoperability, and can also be adopted for other cities in India and the world. [ABSTRACT FROM AUTHOR]

With the advent of technology and the development of more complex software systems, the size of logs generated by these systems has increasingly risen so that the anomaly detection for remediating common errors has been more difficult than ever. The cloud emergence in the information technology (IT) industry has led to the immigration of enterprises toward it, which has extended the application of cloud management stacks such as OpenStack. By using the OpenStack platform, users can access resource infrastructure and manage virtual machines (VMs). The anomaly detection in OpenStack logs is not realized conveniently due to the substantial size of logs, and it is required to automate this process. Since there is no appropriate open‐source dataset for OpenStack logs, we have generated 25,000 logs by injecting three types of anomalies to propose a more efficient technique in terms of performance and time in detecting anomalies in OpenStack logs relative to recent studies by proper OpenStack log parsing and analyzing these logs by data mining algorithms. To this end, compared to the previous research study, we could improve the anomaly detection performance in terms of F1 score, recall, and precision by 9%, 4%, and 14%, respectively, and decrease the running time relative to the log size by at least 30 s. [ABSTRACT FROM AUTHOR]

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Trust is a critical element when it comes to news articles, and an important problem is how to ensure trust in the published information on news websites. First, this paper describes the inner workings of a proposed news-retrieval and aggregation architecture employed by a blockchain-based solution for fighting disinformation; this includes a comparison between existing information retrieval solutions. The decentralized nature of the solution is achieved by separating the crawling (i.e., extracting the web page links) from the scraping (i.e., extracting the article information) and having third-party actors extract the data. A majority-rule mechanism is used to determine the correctness of the information, and the blockchain network is used for traceability. Second, the steps needed to deploy the distributed components in a cloud environment seamlessly are discussed in detail, with a special focus on the open-source OpenStack cloud solution. Lastly, novel methods for achieving a truly decentralized architecture based on community input and blockchain technology are presented, thus ensuring maximum trust and transparency in the system. The results obtained by testing the proposed news-retrieval system are presented, and the optimizations that can be made are discussed based on the crawling and scraping test results.

With the rapid development of the IoT (Internet-of-Things), additional smart gadgets may be associated with the Internet, significantly enhancing data transfer and communication. Software-Defined Networking (SDN) is known as a new model that separates the control plane and the data plane, and is anticipated as a favorable solution for implementing Blockchain, to offer the scalability and adaptability required for IoT. The scalability of the network rises in direct proportion to the users’ enhanced privacy on the network. Blockchain and SDN are two top innovations utilized to create secure network architectures and provide trustworthy data transmission. They offer a strong and trustworthy platform to deal with dangers and problems, including security, privacy, adaptability, scalability, and secrecy. Unfortunately, the attackers can still inject traffic to disrupt a blockchain node’s regular functions. This study provides an optimized Blockchain-based SD IoT architecture for smart networks that is safe and energy-efficient. In this work, it is concentrated on blockchain-based SDN and creates an SDN-Blockchain Classifier. This IDS-based security tool provides a trust-based classifier by handling and reducing harmful traffic through traffic fusion and aggregation. Finally, it is concluded by evaluating the proposed framework SDN-Blockchain Classifier performance against MAC flooding attack in a simulation setting and demonstrating that it can attain optimized average throughput, response time, packet loss of crossing domain path, energy efficiency, end-to-end delay, file transfer operation, energy consumption, and CPU utilization compared to the baselines taken into consideration, thereby achieving efficacy and also security in the proposed smart network.

Resource management in cloud infrastructure is one of the key elements of quality of services provided by the cloud service providers. Resource management has its taxonomy, which includes discovery of resources, selection of resources, allocation of resources, pricing of resources, disaster management, and monitoring of resources. Specifically, monitoring provides the means of knowing the status and availability of the physical resources and services within the cloud infrastructure. This results in making “monitoring of resources” one of the key aspects of the cloud resource management taxonomy. However, managing the resources in a secure and scalable manner is not easy, particularly when considering a federated cloud environment. A federated cloud is used and shared by many multi-cloud tenants and at various cloud software stack levels. As a result, there is a need to reconcile all the tenants’ diverse monitoring requirements. To cover all aspects relating to the monitoring of resources in a federated cloud environment, we present the FEDerated Architecture for Resource manaGement and mOnitoring in cloudS Version 1.0 (FEDARGOS-V1), a cloud resource monitoring architecture for federated cloud infrastructures. The architecture focuses mainly on the ability to access information while monitoring services for early identification of resource constraints within short time intervals in federated cloud platforms. The monitoring architecture was deployed in a real-time OpenStack-based FEDerated GENomic (FEDGEN) cloud testbed. We present experimental results in order to evaluate our design and compare it both qualitatively and quantitatively to a number of existing Cloud monitoring systems that are similar to ours. The architecture provided here can be deployed in private or public federated cloud infrastructures for faster and more scalable resource monitoring.