Distributed Cloud Services Research Articles

An approach for multipath optimal selection of network service combinations based on golden eagle optimizer with double learning strategies

The traditional optimal-path algorithm can address a single constraint in small and straightforward networks. However, in complex multipath distributed cloud services, the network nodes no longer exhibit singular or deterministic path characteristics. It requires the optimal paths that not only determines the shortest routes, but also combine the safety, speed, and enhanced service quality across multiple service nodes in the network topology. The Golden Eagle Optimization Algorithm (GEO) is specialized for optimizing these network service combinations. On this basis, the Golden Eagle Optimizer with Double Learning Strategies (GEO-DLS) resolved the multipath optimal service selection issues within intricate network environments. The algorithm modeled the hunting tactics of wild golden eagles, efficiently targeting the best prey in minimal time by dynamically adjusting two critical components, such as the attack and cruising strategies. In GEO-DLS, the enhanced GEO significantly broadened the search scope for food sources by using personalized learning and mirror reflection techniques. These advancements notably enhanced the GEO search capabilities and improved the solution accuracy. Key contribution include GEO-DLS can converge to the optimal solution faster by optimizing the search strategy and parameter settings. This means that in the problem of network service composition, algorithms can quickly find the optimal path that meets the quality of service (QoS) requirements. To validate the effectiveness of GEO, a set of ten standard benchmark functions was utilized to evaluate its performance. The results from these evaluations consistently presented its superior performance in tackling optimization challenges compared to other five metaheuristic algorithms and five enhanced algorithms.

PDHSMICK: A partial key-based secure EHRs with distributed cloud for healthcare systems applying MI-CRYSTALS-Kyber

Healthcare, which raises several challenges regarding security and privacy, is the largest sector of society dealing with patients’ health data. Numerous approaches have been developed regarding healthcare; however, these approaches lead to high response time and leakage of transaction privacy. Thus, this work proposes PDHSMICK, a partial key-based secure EHRs with distributed cloud for Healthcare Systems applying Mimetic Interpolation-based CRYSTALS-Kyber (MI-CRYSTAL-Kyber). Primarily, the patient and doctor register with the system. After that, the patient logs in and books an appointment with the doctor. Then, consultation is done, and their details are sent to the Third-Party Auditor (TPA), where they store the information in the log file. Afterwards, the patient verifies the log file and then uploads the information to the concerned Cloud Server (CS). During uploading, the files are split, and the attributes from the files and Distributed Cloud Server (DCS) are extracted. Then, by utilizing Interpolation-based Pearson Correlation Coefficient (I-PCC), a particular DCS is selected. After that, for authorization purposes, a hash code and digital signature are generated by utilizing Exponential based Extensible Output Functions (EX-EOF) and Double Mod-Digital Signature Algorithm (DM-DSA). Concurrently, the files are encrypted by employing MI-CRYSTAL-Kyber techniques. Lastly, the doctor logs on to the system, and hash code and digital signature verification are performed. The system allows the doctor to download the data if the verification matches. Also, the experimental assessment was validated, which shows the proposed technique’s efficacy.

The construction of blockchain platform for engineering museums based on vectorized image processing technology

Abstract The characteristics of blockchain such as tamper-evident and distributed can effectively solve the increasingly prominent security and privacy issues in engineering pavilions, however, the current throughput of mainstream blockchain platforms is far from meeting the demand for rapid chain-up of massive data in engineering pavilions, and the high redundancy storage mechanism adopted by traditional blockchain cannot be applied to engineering pavilion scenarios. To address the above problems, based on vectorized image processing technology, a hierarchical blockchain architecture is first proposed to store block data in multiple distributed cloud servers and edge servers in a hierarchical manner to cope with the growing data volume in the industrial Internet. Then based on the topology between edge servers, a blockchain network slicing algorithm is designed based on an improved complex network association partitioning algorithm, which improves the blockchain network throughput while shortening the slicing time. Finally, the impact of block broadcast time on throughput is demonstrated by formalizing the block broadcast process as a spanning tree, based on which a spanning tree-based intra-slice master node selection algorithm is proposed to further improve the throughput of the blockchain network. The analysis and experimental results show that the proposed scheme effectively reduces the partitioning time of the blockchain network and the broadcast time of the blocks within each partition. Compared with the classical association partitioning algorithm, this paper's partitioning algorithm can reduce the partitioning time by about 36% without sacrificing the quality of partitioning when partitioning a large-scale network.

Game-Theoretic Strategies for Cyber-Physical Infrastructures Under Component Disruptions

Networked infrastructures of recursively defined systems composed of discrete cyber and physical components are considered. The components of basic systems at the finest levels can be disrupted by cyber or physical means, and can be reinforced to survive at certain costs. A problem of ensuring the infrastructure performance is formulated as a game between a provider and an attacker, who probabilistically choose components to reinforce and attack, respectively. The disruptions of this infrastructure are characterized using the aggregate failure correlation function that specifies the conditional failure probability of the infrastructure given the failure of an individual system at that level. The survival probabilities of basic systems satisfy simple product-form, first-order differential equations expressed in terms of the multiplier functions. The utility functions of the provider and attacker are composed of the reward and cost terms, both expressed in terms of the component reinforcement and attack probabilities. The Nash equilibrium of this game is characterized, along with the sensitivity functions of the survival probabilities of basic systems that highlight their dependence individually on the cost-benefit terms, the correlation functions, and the multiplier functions. These results are illustrated using simplified models of a distributed cloud servers infrastructure, a 5G data network infrastructure, a high performance computing federation, and a smart energy grid infrastructure.

A task scheduling algorithm with deadline constraints for distributed clouds in smart cities.

Computing technologies and 5G are helpful for the development of smart cities. Cloud computing has become an essential smart city technology. With artificial intelligence technologies, it can be used to integrate data from various devices, such as sensors and cameras, over the network in a smart city for management of the infrastructure and processing of Internet of Things (IoT) data. Cloud computing platforms provide services to users. Task scheduling in the cloud environment is an important technology to shorten computing time and reduce user cost, and thus has many important applications. Recently, a hierarchical distributed cloud service network model for the smart city has been proposed where distributed (micro) clouds, and core clouds are considered to achieve a better network architecture. Task scheduling in the model has attracted many researchers. In this article, we study a task scheduling problem with deadline constraints in the distributed cloud model and aim to reduce the communication network's data load and provide low-latency services from the cloud server in the local area, hence promoting the efficiency of cloud computing services for local users. To solve the task scheduling problem efficiently, we present an efficient local search algorithm to solve the problem. In the algorithm, a greedy search strategy is proposed to improve the current solutions iteratively. Moreover, randomized methods are used in selecting tasks and virtual machines for reassigning tasks. We carried out extensive computational experiments to evaluate the performance of our algorithm and compared experimental results with Swarm-based approaches, such as GA and PSO. The comparative results show that the proposed local search algorithm performs better than the comparative algorithms on the task scheduling problem.

Enhancing Cloud Forensic Investigation System in Distributed Cloud Computing Using DK-CP-ECC Algorithm and EK-ANFIS

The investigation as well as recovery of data gathered as of digital devices associated with computer crime is involved in Digital Forensics (DF). In a distributed Cloud Server (CS), DF investigation is more complicated (during collecting, preserving, and reporting the evidence) as well as insecure during gathering evidence as of the cloud sources. Centered on the DF investigation system, numerous works were performed. However, lots of challenges still remain that bring about cybercrime. The work developed a robust cloud forensic investigation system centered upon distributed Cloud Computing (CC) for conquering the challenges. It is framed into the ‘3’ phase (i.e.) originally, Group Key Generation (GKG) phase that enables the authorized user to upload or download the evidence for maintaining the evidence’s trustworthiness. Distributed Key Cipher Policy Elliptic Curve Cryptography (DK-CP-ECC) algorithm performed the Secure Data Transfer (SDT) phase. It aids in maintaining the evidence’s privacy together with confidentiality. Exponential Membership Function Adaptive Neuro-Fuzzy Interference System (EK-ANFIS) carries out the CS selection with the aid of a deer hunting genetic algorithm that evades the reporting issues and renders secure evidence storage. 97% Security Level (SL) is obtained by the proposed work that is better analogized to the prevailing frameworks.

IPORM: A Resource Management Scheme for Fog-Enabled Cloud Environments: An Improved Political Optimizer-Based Approach

In recent years, with the rapid growth of the internet of things (IoT), the number of devices connected to the network has increased significantly. The rising trend of mass data that objects send to the network has become a big challenge for cloud networks. The next generation IoT applications require low delay and real-time responses. Hence, IoT applications need a new processing paradigm to be able to access distributed cloud services at the network edge. In recent years, fog computing has emerged as a distributed computing model compatible with heterogeneous environments that can provide cloud services with low delay at the network edge. Meanwhile, services provided by the IoT suffer from varying workload changes over time, and approaches are needed to automatically provision resources and satisfy quality of service (QoS) requirements. With this motivation, this study presents an improved political optimizer-based resource management scheme for fog-enabled cloud environments, which is named IPORM. IPORM seeks to efficiently allocate resources to IoT requests with the purpose of maximizing resource utilization. In addition, other objectives such as energy, bandwidth, and delay are also included in IPORM. Simulation results show that our scheme significantly improves network performance in terms of various metrics compared to its counterparts.

IoTaaS: Drone-Based Internet of Things as a Service Framework for Smart Cities

The Internet of Things (IoT) offers new services in the context of smart cities through digital devices embedded with sensing, computation, and communication capabilities. The IoT devices enhance the smart city vision by employing advanced communication and computation technologies for smart city administrations. The IoT-based smart city applications require many IoT devices and gateways to be deployed at different city points. Heterogeneous sensing devices, placing smart devices in a constrained or physically inaccessible area, and large urban areas to monitor together make IoT node deployment and sensing management tasks difficult, time-consuming, and expensive. Additionally, certain tasks may require smart devices to be deployed for a very short period of time to sense and report contextual information, making it economically infeasible to purchase the devices. In this regard, we propose a drone-based IoT as a Service (IoTaaS) framework that enables the dynamic provisioning or deployment of IoT devices using drones. IoTaaS allows IoT devices and gateways to be mounted on drones and provides a distributed cloud service by placing the IoT devices in an area according to the requirements specified by a user. We also provide an economic analysis for operating such drone-based IoT services. A proof-of-concept implementation of IoTaaS for smart agriculture and air pollution monitoring applications shows that IoTaaS can reduce setup costs and increase the usage of IoT devices.

Remote Data Auditing in a Cloud Computing Environment

In the current paradigms of information technology, cloud computing is the most essential kind of computer service. It satisfies the need for high-volume customers, flexible computing capabilities for a range of applications like as database archiving and business analytics, and the requirement for extra computer resources to provide a financial value for cloud providers. The purpose of this investigation is to assess the viability of doing data audits remotely inside a cloud computing setting. There includes discussion of the theory behind cloud computing and distributed storage systems, as well as the method of remote data auditing. In this research, it is mentioned to safeguard the data that is outsourced and stored in cloud servers. There are four different techniques of remote data auditing procedures that are presented here for distributed cloud services. There are several difficulties associated with data audit methods; however, these difficulties may be overcome by using a variety of techniques, such as the Boneh-Lynn-Shacham signature or the automated blocker protocol. In addition to that, other difficulties associated with distributed-based remote data auditing solutions are discussed. In addition, a variety of approaches might be researched further for further examination in order to find answers to these impending problems.

Secure Data Deduplication and Data Portability in Distributed Cloud Server Using Hash Chaining and LF-WDO

Secure Data Deduplication and Data Portability in Distributed Cloud Server Using Hash Chaining and LF-WDO

Learning-Based Multi Attribute Network Selection in Heterogeneous Wireless Access

The rise of heterogeneous networks including macro, micro, pico, femto, and WLAN presents new challenges in optimizing user’s access to the networks. To fully utilize the capacity of such rich field of heterogeneous wireless connectivity, mobile devices should be able to select Radio Access Network (RAN) for their connection depending on their need. The proposed algorithm here is based on an autonomous agent at the mobile node and assisted by integration of distributed cloud services, e.g. the edge cloud. The selection of RAN is based on a Multi Attribute Decision Making process combined with the Reinforcement Learning that reinforces historical data collected from the edge cloud. Mobile agent is responsible for collecting data, executing the selection algorithm, possibly offloading the parts of the execution to the edge cloud, and providing feedback to the edge cloud after termination of the connection. The ultimate aim is to the design of RAN selection algorithm owned by autonomous and intelligent user agents that can significantly improve the user’s experience in terms of network coverage, data rate, latency, and battery lifetime. Through extensive simulation scenarios, we demonstrate the stability and precision of our proposed algorithm.

Secure Data Deduplication and Data Portability in Distributed Cloud Server Using Hash Chaining and Lf-Wdo

An application of Cloud Computing (CC) technologies for interconnecting data as well as applications served as of manifold geographic locations is the distributed cloud. For distributed CC, managing a vast measure of data along with maintaining security are vital aspects. Though lots of techniques were introduced recently to manage and protect data on the distributed cloud, those have not rendered any desirable security. Thus, this paper proposes a secure Data Deduplication (DD) and Data Portability (DP) for a distributed cloud environment. To enhance the user data’s security, primarily, the inputted files are bifurcated into blocks and after that, an appropriate Cloud Server (CS) is chosen utilizing Hybrid Forest Genetic Algorithm (HFGA) centred upon split file features together with CS features. In a DD phase, the Whirlpool algorithm converts the split data into Hash Code (HC), and then, the hash chaining technique securely removes the duplicated data. Then, that split data is comp ressed, encrypted, together with amassed in the selected CS. Next, the Levy Flight – Wind Driven Optimization (LF-WDO) performs the DP to enhance the cloud data’s security. Investigational outcomes exhibit the proposed work's potential.

Green Construction Evaluation System Based on BIM Distributed Cloud Service

Based on the BIM cloud service, in the green construction evaluation system, this paper introduces the sub-module and the overall architecture. In the sub-module, it includes auxiliary evaluation system, load balancing architecture, and data service. It is an effective method to store data. Through the conversion and integration of data storage methods of various computer protocols, the traditional green construction evaluation methods can be efficiently and accurately evaluated by cloud computing servers. At the same time, the database can hold a large amount of data and continue to expand.

Time-constrained strong multi-designated verifier signature suitable for Internet of things–based collaborative fog computing systems

Fog computing is viewed as an extended technique of cloud computing. In Internet of things–based collaborative fog computing systems, a fog node aggregating lots of data from Internet of things devices has to transmit the information to distributed cloud servers that will collaboratively verify it based on some predefined auditing policy. However, compromised fog nodes controlled by an adversary might inject bogus data to cheat or confuse remote servers. It also causes the waste of communication and computation resources. To further control the lifetime of signing capability for fog nodes, an appropriate mechanism is crucial. In this article, the author proposes a time-constrained strong multi-designated verifier signature scheme to meet the above requirement. In particular, a conventional non-delegatable strong multi-designated verifier signature scheme with low computation is first given. Based on its constructions, we show how to transform it into a time-constrained variant. The unforgeability of the proposed schemes is formally proved based on the famous elliptic curve discrete logarithm assumption. The security requirement of strong signer ambiguity for our substantial constructions is also analyzed by utilizing the intractable assumption of decisional Diffie–Hellman. Moreover, some comparisons in terms of the signature size and computational costs for involved entities among related mechanisms are made.

Faster and efficient cloud-server-aided data de-duplication scheme with an authenticated key agreement for Industrial Internet-of-Things

The volume of the outsourced IIoT data to the cloud servers is exponentially increasing due to the increase in the number of IIoT devices and duplicity in their outsourced data. This has therefore increased the cost and storage overheads in IIoT and cloud server, respectively. Aside from this, outsourcing IIoT data to semi-trusted distributed cloud servers increases the security challenges in IIoT. Although, many encryption techniques had been proposed to perform secure de-duplication over outsourced data in the cloud server. However, some of these schemes are computational intensive, do not allow data searching over the ciphertext, and could not enforce necessary security requirements especially Inside Keyword Guess Attack (IKGA). In this paper, we propose a novel faster server-aided data de-duplication scheme with an efficient authenticated key agreement. The scheme enforces data singularity, confidentiality, integrity in the IIoT and allows secure data searching over the ciphertext in the semi-trusted cloud server. The effectiveness of the scheme revolves around its capability to generate a tokenized-tag and trapdoor for every uploaded data on the server.

Automated Security Assessment for IDaaS Framework

Nowadays, we are moving quickly to a new Identity concept due to the cloud computing paradigm called Identity as a Service (IDaaS). However, the one Identity adoption for all services access does not bring only good news. Hackers are increasing more and more their attacks based Identity theft. This means that the security of Identity itself becomes a threat vector. Therefore, this paper focuses on the concept of using Virtual Identity ( $$V_{ID}$$ ) under the framework of IDaaS. This IDaaS is well known for Software as a Service (SaaS) cloud deployment model authentication. It can be delivered by a a third party Identity providers for the whole identity management approach including the creation process, the authentication mechanism and the identity privacy assurance level. Moreover, the proposed $$V_{ID}$$ mechanism for IDaaS framework is considered as a new realization for anonymous Single Sign On (SSO) in this distributed cloud services environments. Actually, we proposed the $$V_{ID}$$ creation framework using Elliptic Curve Cryptography (ECC). After we designed the two approaches either Identity Based Encryption (IBE) or Pseudonym Based Encryption (PBE), we implemented them by MIRACL security library. In order to judge on our solutions security measure, we used the (AVISPA) tool to assess the IBE and PBE protocols vulnerabilities. AVISPA: Automated Validation of Internet Security Protocols and Applications uses a group of applications to build and analyze the formal models of many known or designed security protocols. Through its language structure, we built our communication protocols in a descriptive way. The analysis of our $$V_{ID}$$ proposed approaches based on IBE and PBE using AVISPA back-ends indicated that both of them are safe (i.e. no attacks found). So, the $$V_{ID}$$ proposed approaches based on IBE and PBE are suitable and scalable enough to secure the anonymous communication in cloud services environment comparing to the state of the art solutions.

5G network-oriented hierarchical distributed cloud computing system resource optimization scheduling and allocation

As the core technology of the next generation mobile communication system, the development of 5G key technologies needs to be able to efficiently and effectively support massive data services. Aiming at the impact of massive data traffic on mobile communication networks in 5G communication systems, this paper proposes a 5G-oriented hierarchical distributed cloud service mobile communication system architecture. The model consists of a cloud access layer, a distributed micro-cloud system, and a core cloud data center. The distributed micro cloud system consists of multiple micro clouds that are deployed to the edge of the network. The service content in the core cloud data center can be deployed and cached to the local micro cloud server in advance to reduce repeated redundant transmission of user requested content in the network. Aiming at the problem of how to determine the migration object when dynamically optimizing the resource structure, a heuristic function-based dynamic optimization algorithm for cloud resources is proposed. The experimental results show that the dynamic expansion algorithm of cloud resources based on dynamic programming ideas can better improve the performance of virtual resources, and the dynamic optimization algorithm of cloud resources based on heuristic functions can effectively and quickly optimize the resource structure, thereby improving the operating efficiency of user virtual machine groups. An efficient resource allocation scheme based on cooperative Q (Quality) learning is proposed. The environmental knowledge obtained by the base station learning and exchanging information is used for distributed resource block allocation. This resource allocation scheme can obtain the optimal resource allocation strategy in a short learning time, and can terminate the learning process at any time according to the delay requirements of different services. Compared with traditional resource allocation schemes, it can effectively improve system throughput.

Data Migration System Over Distributed Cloud Environment with Internet of Things for Smart Communications

Data Migration (DM) is a significant event for an organization in the revolution of Internet of Things (IoT) and Smart City. The total service providers augmented dramatically since, the data generation through IoT in cloud is enormous. Furthermore, the cloud is turning out to be the tools of option for additional cloud storage services along IoT for Smart city. Nevertheless, as more private information/data is transferred to the cloud via social media sites like Baidu WangPan, DropBox, etc., data security and also the privacy issues are questioned. To beat such drawbacks, this paper proposed a security enhancement for the DM system over the cloud environment for Smart City. This work is partitioned into ‘2’ phases where Phase I is a storage phase and Phase II is a retrieval phase. In the storage phase, the data is compressed using MHA and then the data is encrypted utilizing MHECC algorithm for securing data for Smart City. Subsequently, some of the data-related features are extracted. After feature extraction (FE), the distributed cloud server is selected using MGWO. Then, DM is performed dynamically on various cloud servers (CS) using ANFIS. The retrieval phase is the opposite process of the storage phase. In the retrieval phase, the data is decrypted using the same MHECC algorithm and then, the decrypted data is decompressed utilizing the same MHA algorithm. After that, row transposition is performed on both component A and component B. Finally, the components are summed up, and the original data is obtained. Experimental results contrasted the proposed and the prevailing systems centered on key generation time, encryption time, decryption time, security, and threshold.

Secure data transmission on a distributed cloud server with the help of HMCA and data encryption using optimized CP-ABE-ECC

The cloud computing (CC) model is diverse as well as has broad phenomena because of which it is being preferred widely. Due to security challenges, the user cannot utilize CC to its full potentials. Storing their sensitive data in an insecure place is not even an option for these users. Many techniques were posited for securely amassing the data in the cloud; however, during massive transmission, none of them maintain end-to-end security. And, prevailing methods are also unable to solve the key intricacy and avoid key secrecy expose. Thus, for trouncing these disadvantages and also assuring the data transmission (DT) as of the source–destination, a secure DT scheme with the help of a distributed cloud server (CS) and data deduplication is proposed. The uploaded user data can well be amassed in disparate CS to ameliorate the user data’s security. It can be done by, first, extracting the features of the user data along with the CS. After that, the Hybrid Meerkat clan algorithm (HMCA) chose the CS, which is optimally centered on the features. Next, the SHA512 algorithm performs the data deduplication of the input user data. Subsequent to data deduplication, the input file is compressed and encrypted using a two-stage lempel–ziv algorithm and optimized CP-ABE-ECC algorithm. Lastly, the encrypted file is stored in the chosen CS. The experimentation is implemented to analyze the proposed method’s performance. The outcomes exhibited that the proposed work performed better contrasted to other prevailing algorithms.

Resource optimization scheduling and allocation for hierarchical distributed cloud service system in smart city

With the support of 5G system, the hierarchical distributed cloud service network model (HDCSN) is proposed. The model consists of three levels: Access Cloud + Distributed Micro Cloud + Core Cloud, which meets the basic requirements of IMT-2020 (5G) and 3GPP for network system architecture. On the basis of access cloud, the distributed micro-cloud system of Smart City is deployed to migrate the service capabilities of the remote core cloud server to the local area. Users can obtain high-quality low-latency application services from the micro-cloud server in the local area. A resource description model based on resource graph and hierarchical resource vector is established, which enriches the Smart City service mode of IaaS cloud platform and provides a structural basis for optimal scheduling of cluster resources. A multi-node system resource graph scheduling and allocation algorithm VCE-PSO based on particle swarm optimization is proposed to optimize the response speed and resource efficiency of multi-node collaborative scheduling. The example shows that the above key technologies significantly improve 5G. The scheduling optimization and utilization efficiency of various resources in the hierarchical distributed cloud service for the Smart City effectively reduces the response time of the tenant resource request and optimizes the performance of system resource scheduling on the cloud platform.