Public Cloud Platforms Research Articles

Cloud-Based Remote Real-Time Monitoring and Control System for Spring Fatigue Testing Machine

In recent years, the utilization of cloud technology has witnessed a surge, particularly in the domains of industrial automation and intelligent scenarios. However, the prevailing spring fatigue testing machine is still in the traditional single-machine working mode. In this mode, there are many problems such as low automation of testing equipment, poor experimental site environment, and the need for experimenters to be on duty for a long time. In order to solve the above problems, this paper builds a cloud-based remote monitoring and control system based on the high-temperature constant-force spring fatigue testing machine. The system is based on Browser/Server architecture, and clients can access it anytime and anywhere using a browser in a public network environment. The server is hosted on a public cloud platform and includes website service, data storage service, WebSocket real-time communication service, and remote video monitoring service. Clients can remotely monitor and control the testing machine in real time through the cloud. After experimental verification, the real-time monitoring and control messages delay is 11 ms, and the video monitoring delay is 291 ms, which can meet the actual needs of remote spring fatigue testing. This remote monitoring and control system improves the automation of the spring fatigue testing machine and improves the working environment of the experimenters. In addition, it can be applied to other reliability testing machines in the laboratory, and can further help build a workshop-level remote monitoring and control platform.

A secure authentication and collaborative data-sharing model based on a blockchain network in the cloud

ABSTRACT In cloud computing, various organisations store their confidential information in private or public cloud platforms. The data sharing through the various cloud platforms increases the risk of security. To attain data sharing while sustaining data confidentiality, a secure sharing platform and collaboration model is needed. Hence, this paper introduces the novel secure authentication and collaborative data sharing (SecAuth_CDS) scheme for sharing data on blockchain. The entities involved in the SecAuth_CDS are third parties, data owners, smart contracts and blockchain networks. The security of the authentication process can be boosted by applying various security operations, such as Exclusive OR (EXOR), hashing, encryption and so on. The proposed method assures that the sharing is secure and trustworthy by blockchain. The experimental result reveals that the SecAuth_CDS performed effectively in terms of revenue, memory usage and computation cost with the values of 27, 62.20 MB and 0.003 sec.

A Multiuser, Multisite, and Platform-Independent On-the-Cloud Framework for Interactive Immersion in Holographic XR

Background: The ever-growing extended reality (XR) technologies offer unique tools for the interactive visualization of images with a direct impact on many fields, from bioinformatics to medicine, as well as education and training. However, the accelerated integration of artificial intelligence (AI) into XR applications poses substantial computational processing demands. Additionally, the intricate technical challenges associated with multilocation and multiuser interactions limit the usability and expansion of XR applications. Methods: A cloud deployable framework (Holo-Cloud) as a virtual server on a public cloud platform was designed and tested. The Holo-Cloud hosts FI3D, an augmented reality (AR) platform that renders and visualizes medical 3D imaging data, e.g., MRI images, on AR head-mounted displays and handheld devices. Holo-Cloud aims to overcome challenges by providing on-demand computational resources for location-independent, synergetic, and interactive human-to-image data immersion. Results: We demonstrated that Holo-Cloud is easy to implement, platform-independent, reliable, and secure. Owing to its scalability, Holo-Cloud can immediately adapt to computational needs, delivering adequate processing power for the hosted AR platforms. Conclusion: Holo-Cloud shows the potential to become a standard platform to facilitate the application of interactive XR in medical diagnosis, bioinformatics, and training by providing a robust platform for XR applications.

Evaluation and Challenges of IoT Simulators for Intelligent Transportation System Applications

The Internet-of-Things (IoT) constructs a vast, intricate, and perpetually evolving ecosystem exerting profound societal implications. This labyrinthine nature often culminates in errors that directly impact human lives. A significant domain where this complexity materializes is Intelligent Transportation Systems (ITS). Present tools and methodologies inadequately accommodate the complex task of testing and validation, underscoring the urgency for comprehensive review and enhancement. This study aims to present a broad analysis of existing simulators utilized for ITS simulations. It delves into the role and effectiveness of such simulation tools, highlighting their limitations and proposing research directions. This paper scrutinizes both commercial and research-oriented IoT simulators for ITS, evaluating their features and simulation environment tools. We have detailed various ITS scenarios simulated within these frameworks, intending to gauge their readiness for real-world ITS applications and to elaborate on the challenges involved in ITS infrastructure implementation. The findings suggest that despite numerous simulators aiding the evolution of solutions for IoT challenges in recent years, their utility in actual ITS implementations remain uncertain. Consequently, we explore public cloud platforms offering IoT simulation capabilities, focusing particularly on the capabilities provided by the Amazon Web Services (AWS) IoT simulation for this study. Our research outlines the pressing challenges in this field, while proposing potential solutions and flagging opportunities for further research. This study paves the way towards improving the reliability and accuracy of IoT simulators in the context of ITS, which has immense potential to enhance the quality of human life.

Cloud native Lightweight Slice Orchestration (CLiSO) framework

Cloud-native network functions are becoming promising for 5G and beyond networks. They provide the much needed agility and flexibility that was missing from virtual machines. Though, this paradigm shift of using cloud-native application design principles, containerization, microservices, high resilience, and on-demand scaling has created challenges for legacy orchestration systems. They were designed for handling virtual machine-based network functions. Indeed, network slice orchestration requires interaction with multiple technological domain orchestrators, access, transport, core network, and edge computing. The specifications and existing orchestrators are made on top of the legacy virtual machine based network function orchestration. Hence, this limitation constrains their approach to managing a cloud-native network function. To overcome their challenges, we propose a novel Cloud-native Lightweight Slice Orchestration (CLiSO) framework extending our previously proposed Lightweight edge Slice Orchestration (LeSO) framework. In addition, we present a technology-agnostic and deployment-oriented network slice template. To allow zero-touch management of network slices, our framework provides a concept of Domain Specific Handlers. The framework has been thoroughly evaluated via orchestrating OpenAirInterface container network functions on public and private cloud platforms.

Design Tradeoffs in CXL-Based Memory Pools for Public Cloud Platforms

DRAM is a key driver of performance and cost in public cloud servers. At the same time, a significant amount of DRAM is underutilized due to fragmented use across servers. Emerging interconnects such as CXL offer a path towards improving utilization through memory pooling. However, the design space of CXL-based memory systems is large, with key questions around the size, reach, and topology of the memory pool. At the same time, using pools requires navigating complex design constraints around performance, virtualization, and management. This paper discusses why cloud providers should deploy CXL memory pools, key design constraints, and observations in designing towards practical deployment. We identify configuration examples with significant positive return of investment.

Towards a lightweight task scheduling framework for cloud and edge platform

Mobile devices are becoming ubiquitous in our daily lives, but they have limited computational capacity. Thanks to the advancement in the network infrastructure, task offloading from resource-constrained devices to the near edge and the cloud becomes possible and advantageous. Complete task offloading is now possible to almost limitless computing resources of public cloud platforms. Generally, the edge computing resources support latency-sensitive applications with limited computing resources, while the cloud supports latency-tolerant applications. This paper proposes one lightweight task-scheduling framework from cloud service provider perspective, for applications using both cloud and edge platforms. Here, the challenge is using edge and cloud resources efficiently when necessary. Such decisions have to be made quickly, with a small management overhead. Our framework aims at solving two research questions. They are: (i) How to distribute tasks to the edge resource pools and multi-clouds? (ii) How to manage these resource pools effectively with low overheads? To answer these two questions, we examine the performance of our proposed framework based on Reliable Server Pooling (RSerPool). We have shown via simulations that RSerPool, with the correct usage and configuration of pool member selection policies, can accomplish the cloud/edge setup resource selection task with a small overhead.

A homomorphic approach for security and privacy preservation of Smart Airports

The concept of Smart Airport is emerging in the Aviation sector because of the industry 4.0 revolution. To provide the best facility to the air travelers, most of the airports have enhanced their infrastructure using smart automation and Cloud Services (CS), etc. which play a significant role in increasing the revenue of the aviation industry. As the use of digital technology at airports is increasing, they are becoming progressively vulnerable to cyber-attacks and data breaches. The data collected by the airports is stored in specialized cloud databases, which ensures the availability of the data at any time. However, cloud servers have been compromised several times which leads to a lack of trust by the consumers. To meet the privacy and security objectives of the data collected at the airports and stored in the cloud, this research proposes a Searchable Encryption (SE) scheme based on Partial Homomorphic Encryption (PHE). The proposed scheme allows users to search on the encrypted data stored in the cloud hence enhancing the usability of the encrypted data. In this paper, a Single Keyword Searchable Encryption (SKSE) scheme is presented which is implemented using Paillier Cryptosystem. The proposed scheme comprises of two variants (a) Secure SKSE (b) Efficient SKSE. The Secure SKSE scheme exploits the probabilistic properties of the Paillier cryptosystem, whereas, the Efficient SKSE scheme is lightweight as it is based on the deterministic properties of the Paillier cryptosystem. Both variants of SKSE scheme are deployed on a public cloud platform “Contabo” and tested on the real-world aviation data to assess the performance of both approaches. The security and performance analysis illustrates that the proposed Secure SKSE maintains a high-security level by providing probabilistic encryption and trapdoors. On the other hand, the Efficient SKSE is 84 times faster in terms of performance.

Pocket Diagnosis: Secure Federated Learning Against Poisoning Attack in the Cloud

Federated learning has become prevalent in medical diagnosis due to its effectiveness in training a federated model among multiple health institutions (i.e., Data Islands (DIs)). However, increasingly massive DI-level poisoning attacks have shed light on a vulnerability in federated learning, which inject poisoned data into certain DIs to corrupt the availability of the federated model. Previous works on federated learning have been inadequate in ensuring the privacy of DIs and the availability of the final federated model. In this article, we design a secure federated learning mechanism with multiple keys to prevent DI-level poisoning attacks for medical diagnosis, called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SFAP</small> . Concretely, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SFAP</small> provides privacy-preserving random forest-based federated learning by using the multi-key secure computation, which guarantees the confidentiality of DI-related information. Meanwhile, a secure defense strategy over encrypted locally-submitted models is proposed to resist DI-level poisoning attacks. Finally, our formal security analysis and empirical tests on a public cloud platform demonstrate the security and efficiency of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SFAP</small> as well as its capability of resisting DI-level poisoning attacks.

Latency Analysis of Blockchain-Based SSI Applications

Several revolutionary applications have been built on the distributed ledgers of blockchain (BC) technology. Besides cryptocurrencies, many other application fields can be found in smart systems exploiting smart contracts and Self Sovereign Identity (SSI) management. The Hyperledger Indy platform is a suitable open-source solution for realizing permissioned BC systems for SSI projects. SSI applications usually require short response times from the underlying BC network, which may vary highly depending on the application type, the used BC software, and the actual BC deployment parameters. To support the developers and users of SSI applications, we present a detailed latency analysis of a permissioned BC system built with Indy and Aries. To streamline our experiments, we developed a Python application using containerized Indy and Aries components from official Hyperledger repositories. We deployed our experimental application on multiple virtual machines in the public Google Cloud Platform and on our local, private cloud using a Docker platform with Kubernetes. We evaluated and compared their performance benchmarked by Read and Write latencies. We found that the local Indy ledger reads and writes 30–50%, and 65–85% faster than the Indy ledger running on the Google Cloud Platform, respectively.

Cloud Evidence Tracing System: An integrated forensics investigation system for large-scale public cloud platform

With the rise of cloud computing, many systems are migrating to public cloud platforms. Numerous crimes are committed in the cloud, including the establishment of illegal websites and the storage of illegal data. Using virtualization technology, data can be logically stored in the same virtual host, but also physically distributed across multiple hard drives, clusters, or even countries. In these circumstances, using the traditional forensic method of physical preservation will consume a great deal of resources, which will clog the forensic process. In order to develop an effective cloud investigation solution, two challenges must be overcome. First, the difficulty of collecting data consistently when the VMs (Virtual Machines) involved are deployed across multiple CSPs. Second, the difficulty of keeping track of all the files created during the forensic workflow. We developed CETS (Cloud Evidence Tracing System), which utilizes CSP's existing API to perform a variety of forensic operations including acquisition, preservation, and emulation, as well as data analysis and file management. To evaluate the system, we created three cloud environments in the laboratory, including a forensic target cloud, a preservation cloud, and an emulation cloud, and conducted a series of forensic experiments. CETS was shown to significantly increase the investigator's investigative efficiency and reduce the investigation workflow's resource consumption. Currently, CETS has collected data exceeding 2 PB, rerun more than 2000 virtual hosts, including servers and databases, supported more than 300 investigation cases related to cloud platforms. CETS can be an example system for efficient forensic investigation in large-scale cloud environment.

Construction of a Public Service Cloud Platform for Disabled People Based on the Big Data Management Model of the Internet of Things.

With the continuous development of social economy and the continuous improvement of people's ideological level, the state and social people have given more and more care and assistance to the disadvantaged group of disabled persons. The construction of a public service cloud platform for the disabled is one of the important measures to protect the human rights of the disabled and improve the lives of the disabled. According to the statistics of the China Disabled Persons' Federation, since the twentieth century, with the acceleration of the equalization of basic public services and the establishment of urban and rural social security systems, a practical and effective public service system for the disabled has made leaps and bounds. The construction of the cloud platform for public services for the disabled is an important branch of the construction of the public service system for the disabled. However, due to the late development of the construction of the cloud platform for public services for the disabled in China, it is not perfect, and there are still some problems to be solved. This paper mainly studies the public service cloud platform for the disabled based on the big data management model of the Internet of Things. Based on this research theme, this paper conducts a service response efficiency test for the disabled provided by the disabled public service cloud platform based on the big data management model of the Internet of Things. It concluded that the public service cloud platform for the disabled based on the big data management model of the Internet of Things has improved the response efficiency of providing services for the disabled by 57%.

Cost Effective Cloud Storage Interoperability Between Public Cloud Platforms

With recent advancement in technology, cloud storage became cheaper enabling organizations around the world to store more data on the cloud (texts, images, videos, databases etc.), whereas it’s for a backup, archiving or just storing data streams. New digital laws and regulation (eg. General Data Protection Regulation) require these organizations to change their way of processing or handling data, which results usually in a change of cloud providers or adoption of hybrid architecture or multi-cloud one. With the amount of data stored increasing year after year, it becomes difficult for these organizations to change cloud platforms or cloud provider and migrate their data without thinking about the technical complexity, the time and the huge cost it may incur. This article discusses the data migration and interoperability issues between cloud platforms; the proposed approach provides a simple cost-effective migration that would help organizations save time and money in this process based on a hybrid ontology approach for the brokerage of data transfers.. Keywords-Cloud Computing; Storage; Security; data; migration; cost optimization;

A Cloud-Fog Continuum Computing Architecture for Cyber-Manufacturing Systems

Cyber-Manufacturing System (CMS) is a vision for the factory of the future, where physical components and processes are seamlessly integrated with computing processes to form highly adaptive and responsive manufacturing operations. In CMS, manufacturing resources and capabilities are digitized and shared with users and stakeholders through a local area network (LAN) and the Internet. CMS aims to utilize the manufacturing data obtained during all product lifecycle phases to provide agile and scalable manufacturing solutions. Currently, a centralized cloud-based computing environment supported by the distributed Internet of Things (IoT) devices network is used to enable the typical functionalities—such as manufacturing resource sharing and large-scale manufacturing collaborations. However, facing the explosion of manufacturing data from factory floors, cloud-based computing solutions show limitations in providing low-latency services, performing real-time state analysis, configuring the machines, and controlling other executors in the physical manufacturing end. Furthermore, private production data and technical details cannot be appropriately masked in the public cloud platform. In this research, a Cloud-Fog Continuum Computing Architecture is introduced to better utilize and govern the manufacturing data for manufacturing enterprise stakeholders and customers in CMS. A Hadoop-Raspberry Pi computing system is presented as a proof-of-concept of the proposed continuum computing mechanism to provide machining services in CMS.

MRMondrian: Scalable Multidimensional Anonymisation for Big Data Privacy Preservation

Scalable data processing platforms built on cloud computing becomes increasingly attractive as infrastructure for supporting big data applications. But privacy concerns are one of the major obstacles to making use of public cloud platforms. Multidimensional anonymisation, a global-recoding generalisation scheme for privacy-preserving data publishing, has been a recent focus due to its capability of balancing data obfuscation and usability. Existing multidimensional anonymisation methods suffer from scalability problems when handling big data due to the impractical serial I/O cost. Given the recursive feature of multidimensional anonymisation, parallelisation is an ideal solution to scalability issues. However, it is still a challenge to use existing distributed and parallel paradigms directly for recursive computation. In this paper, we propose a scalable approach for big data multidimensional anonymisation based on MapReduce, a state-of-the-art data processing paradigm. Our basic idea is to partition a data set recursively into smaller partitions using MapReduce until all partitions can fit in the memory of a computing node. A tree indexing structure is proposed to achieve recursive computation. Moreover, we show the applicability of our approach to differential privacy. Experimental results on real-life data demonstrate that our approach can significantly improve the scalability of multidimensional anonymisation over existing methods.

Privacy and security-aware workflow scheduling in a hybrid cloud

Increasing numbers of workflow applications are being deployed on cloud platforms. When deploying workflows in a hybrid cloud environment, additional concerns emerge: 1. privacy-sensitive data and tasks cannot be exposed to a public cloud platform; 2. security and bandwidth of data transmission across cloud platforms should be guaranteed as it goes through the ‘unpredictable’ Internet. This paper investigates how to effectively reduce monetary cost when deploying a workflow in a hybrid cloud under the deadline and privacy constraints. A three-level data privacy and security model integrating hybrid encryption is devised to guarantee privacy and security of workflow applications. Two scheduling heuristics are then proposed to tackle the considered constrained optimization problem: privacy and security-aware list scheduling (PSLS) and simulated annealing (PSSA). PSLS distributes the user-defined deadline to each task and allocates each task to a resource in the hybrid cloud that meets constraints and incurs a minimum cost. PSSA permutes task lists for iterative improvement based on simulated annealing and PSLS. Simulation experiments are conducted and experimental results demonstrate that PSLS generally performs better than four existing algorithms to optimize monetary costs and PSSA achieves an even higher performance at the expense of runtime.

Алгоритмы планирования вычислений с учетом избыточности и неопределенности

Nowadays, the development and use of workflow-based applications (distributed applied software packages) are some of the key challenges in terms of preparing and carrying out large-scale scientific experiments in distributed environments with heterogeneous computing resources. The environment resources can be represented by clusters of personal computers, supercomputers, and private or public cloud platforms and differ in their computational characteristics. Moreover, the composition and characteristics of resources change in dynamics. Therefore, computations planning and resource allocation in the considered environments are important problems. In this regard, we propose new algorithms for computation planning taking into account redundancy and uncertainty in such distributed applied software packages. Compared to other algorithms of a similar purpose, the proposed algorithms use evaluations of workflow execution makespan obtained in the process of continuous integration, delivery, and deployment of applied software. The proposed algorithms provide the construction of redundant problem-solving schemes that allow us to adapt them to the dynamic characteristics of computational resources and improve distributed computing reliability. The algorithms are based on a theory of conceptual modeling computational processes. We demonstrate the process of constructing problem-solving schemes on model examples. In addition, we show the utility in using redundancy for increasing the distributed computing reliability In comparison with some traditional meta-schedulers.

SDCBench: A Benchmark Suite for Workload Colocation and Evaluation in Datacenters

Colocating workloads are commonly used in datacenters to improve server utilization. However, the unpredictable application performance degradation caused by the contention for shared resources makes the problem difficult and limits the efficiency of this approach. This problem has sparked research in hardware and software techniques that focus on enhancing the datacenters’ isolation abilities. There is still lack of a comprehensive benchmark suite to evaluate such techniques. To address this problem, we present SDCBench, a new benchmark suite that is specifically designed for workload colocation and characterization in datacenters. SDCBench includes 16 applications that span a wide range of cloud scenarios, which are carefully selected from the existing benchmarks using the clustering analysis method. SDCBench implements a robust statistical methodology to support workload colocation and proposes a concept of latency entropy for measuring the isolation ability of cloud systems. It enables cloud tenants to understand the performance isolation ability in datacenters and choose their best-fitted cloud services. For cloud providers, it also helps them to improve the quality of service to increase their revenues. Experimental results show that SDCBench can simulate different workload colocation scenarios by generating pressures on multidimensional resources with simple configurations. We also use SDCBench to compare the latency entropies in public cloud platforms such as Huawei Cloud and AWS Cloud and a local prototype system FlameCluster-II; the evaluation results show FlameCluster-II has the best performance isolation ability over these three cloud systems, with 0.99 of experience availability and 0.29 of latency entropy.

Advanced Authentication Mechanisms for Identity and Access Management in Cloud Computing

Identity management is based on the creation and management of user identities for granting access to the cloud resources based on the user attributes. The cloud identity and access management (IAM) grants the authorization to the end-users to perform different actions on the specified cloud resources. The authorizations in the IAM are grouped into roles instead of granting them directly to the end-users. Due to the multiplicity of cloud locations where data resides and due to the lack of a centralized user authority for granting or denying cloud user requests, there must be several security strategies and models to overcome these issues. Another major concern in IAM services is the excessive or the lack of access level to different users with previously granted authorizations. This paper proposes a comprehensive review of security services and threats. Based on the presented services and threats, advanced frameworks for IAM that provide authentication mechanisms in public and private cloud platforms. A threat model has been applied to validate the proposed authentication frameworks with different security threats. The proposed models proved high efficiency in protecting cloud platforms from insider attacks, single sign-on failure, brute force attacks, denial of service, user privacy threats, and data privacy threats.

NASCENT: A Non-Invasive Solution for Detecting Utilization of Servers in Bare-Metal Cloud

Physical servers are available as-a-service in bare-metal public and private cloud platforms, and their demand has been proliferating because of the high levels of privacy and security guarantees they provide to the tenants. This raises the need for efficient management of bare-metal clouds to keep operational costs low such as by reducing energy consumption. For efficiently managing the cloud infrastructure, bare-metal cloud operators need to monitor the utilization of servers. However, the privacy and security concerns prohibit the installation of third-party monitoring agents on the servers; thus, finding the server-utilization becomes a challenge. In this work, we present NASCENT, a scalable machine-learning (ML) based non-invasive solution for finding the utilization of servers without compromising the privacy and security of bare-metal cloud tenants. Our key idea is to infer utilization from various sensor readings accessible via a server’s baseboard management controller (BMC) hardware. We evaluate the proposed solution with three regression based supervised ML algorithms in a Bare-metal-as-a-service (BMaaS) cloud. Our experimental evaluation shows that one of the ML algorithms employed in NASCENT infers the utilization with a root-mean-square error (RMSE) between 2.9 to 9.3 for different workloads. Also, the proposed solution uses minimal memory resources (19 KB) and can even run on BMC hardware which has very limited memory. We also propose a BMaaS cloud architecture that seamlessly integrates automated training and deployment of the ML algorithm in our solution into the life-cycle of bare-metal servers. NASCENT’s codebase can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/iithcandle/dhi-ojas</uri>