Pub Date : 2024-09-17DOI: 10.1186/s13677-024-00695-9
Prateek Yadav, Subrat Kar
The massive data demand requires content distribution networks (CDNs) to use evolving techniques for efficient content distribution with guaranteed quality of service (QoS). The distributed fog-based CDN model, with optimal fog node placements, is a suggested aproach by researchers to meet this demand. While many studies have focused on improving QoS by optimizing fog node placement, they have rarely considered the impact on content distribution, affected by placement, usage changes, and delivery rates. Therefore, the practical approach to fog node placement for CDN services must examine its impact on content distribution. Further, current research on fog-based CDN lacks formal methods to address key challenges: R1) strategic placement of fog nodes to process end-user requests; R2) construction of a content distribution path with guaranteed QoS; R3) cost minimization of building a fog-based CDN model. We construct this as a joint optimization problem by considering four parameters: geographical regions, open public Wi-Fi access points (OPWAPs) locations, QoS, and cost to achieve research objectives R1–R3. As a solution, we propose a dual-step framework. First, a heuristic for optimal fog node placement based on geographic regions and OPWAP locations is proposed. Second, we propose two algorithms, Greedy Performance-based Node Selection (GPDS) and Greedy Fog Node Selection algorithm (GFNSA), for selecting fog nodes, minimizing the cost of building a fog-based CDN while achieving optimal content distribution paths. The results demonstrate that the proposed methods outperform the baseline techniques and provide near-optimal solutions to the problem.
{"title":"A cost-efficient content distribution optimization model for fog-based content delivery networks","authors":"Prateek Yadav, Subrat Kar","doi":"10.1186/s13677-024-00695-9","DOIUrl":"https://doi.org/10.1186/s13677-024-00695-9","url":null,"abstract":"The massive data demand requires content distribution networks (CDNs) to use evolving techniques for efficient content distribution with guaranteed quality of service (QoS). The distributed fog-based CDN model, with optimal fog node placements, is a suggested aproach by researchers to meet this demand. While many studies have focused on improving QoS by optimizing fog node placement, they have rarely considered the impact on content distribution, affected by placement, usage changes, and delivery rates. Therefore, the practical approach to fog node placement for CDN services must examine its impact on content distribution. Further, current research on fog-based CDN lacks formal methods to address key challenges: R1) strategic placement of fog nodes to process end-user requests; R2) construction of a content distribution path with guaranteed QoS; R3) cost minimization of building a fog-based CDN model. We construct this as a joint optimization problem by considering four parameters: geographical regions, open public Wi-Fi access points (OPWAPs) locations, QoS, and cost to achieve research objectives R1–R3. As a solution, we propose a dual-step framework. First, a heuristic for optimal fog node placement based on geographic regions and OPWAP locations is proposed. Second, we propose two algorithms, Greedy Performance-based Node Selection (GPDS) and Greedy Fog Node Selection algorithm (GFNSA), for selecting fog nodes, minimizing the cost of building a fog-based CDN while achieving optimal content distribution paths. The results demonstrate that the proposed methods outperform the baseline techniques and provide near-optimal solutions to the problem.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.1186/s13677-024-00703-y
Kan Ni, Subrota Kumar Mondal, H M Dipu Kabir, Tian Tan, Hong-Ning Dai
Serverless computing is one of the recent compelling paradigms in cloud computing. Serverless computing can quickly run user applications and services regardless of the underlying server architecture. Despite the availability of several commercial and open-source serverless platforms, there are still some open issues and challenges to address. One of the key concerns in serverless computing platforms is security. Therefore, in this paper, we present a multi-layer abstract model of serverless computing for an security investigation. We conduct a quantitative analysis of security risks for each layer. We observe that the Attack Tree and Attack-Defense Tree methodologies are viable approaches in this regard. Consequently, we make use of the Attack Tree and the Attack-Defense Tree to quantify the security risks and countermeasures of serverless computing. We also propose a novel measure called the Relative Risk Matrix (RRM) to quantify the probability of attack success. Stakeholders including application developers, researchers, and cloud providers can potentially apply these findings and implications to better understand and further enhance the security of serverless computing.
{"title":"Toward security quantification of serverless computing","authors":"Kan Ni, Subrota Kumar Mondal, H M Dipu Kabir, Tian Tan, Hong-Ning Dai","doi":"10.1186/s13677-024-00703-y","DOIUrl":"https://doi.org/10.1186/s13677-024-00703-y","url":null,"abstract":"Serverless computing is one of the recent compelling paradigms in cloud computing. Serverless computing can quickly run user applications and services regardless of the underlying server architecture. Despite the availability of several commercial and open-source serverless platforms, there are still some open issues and challenges to address. One of the key concerns in serverless computing platforms is security. Therefore, in this paper, we present a multi-layer abstract model of serverless computing for an security investigation. We conduct a quantitative analysis of security risks for each layer. We observe that the Attack Tree and Attack-Defense Tree methodologies are viable approaches in this regard. Consequently, we make use of the Attack Tree and the Attack-Defense Tree to quantify the security risks and countermeasures of serverless computing. We also propose a novel measure called the Relative Risk Matrix (RRM) to quantify the probability of attack success. Stakeholders including application developers, researchers, and cloud providers can potentially apply these findings and implications to better understand and further enhance the security of serverless computing.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The security and privacy of medical images are crucial due to their sensitive nature and the potential for severe consequences from unauthorized modifications, including data breaches and inaccurate diagnoses. This paper introduces a method for lossless medical image retrieval from encrypted images stored on third-party clouds. The proposed approach employs a symmetric integrity-centric image encryption scheme, leveraging multiple chaotic maps and cryptographic hash techniques, to ensure lossless image reconstruction. Medical images are first encrypted by the image owners and converted into hashcodes encapsulating essential features using a deep hashing technique with the ConvNeXt network as the backbone in parallel. To ensure index privacy, these hashcodes are encrypted in a searchable manner. The encrypted medical images, along with a secure index, are subsequently uploaded to cloud storage. Authorized medical image users can request similar medical images for diagnostic purposes by submitting a query image, from which a search trapdoor is generated and sent to the cloud. The retrieval process involves a secure similar image search over the encrypted indexes, followed by decryption along with integrity verification of the retrieved images. The proposed method has been rigorously tested on three standard medical datasets, demonstrating an improvement of 5-20% in retrieval accuracy compared to standard baselines. Formal security analysis and experimental results indicate that the proposed scheme offers enhanced security and retrieval accuracy, making it an effective solution for the encrypted storage and secure retrieval of medical image data.
{"title":"SMedIR: secure medical image retrieval framework with ConvNeXt-based indexing and searchable encryption in the cloud","authors":"Arun Amaithi Rajan, Vetriselvi V, Mayank Raikwar, Reshma Balaraman","doi":"10.1186/s13677-024-00702-z","DOIUrl":"https://doi.org/10.1186/s13677-024-00702-z","url":null,"abstract":"The security and privacy of medical images are crucial due to their sensitive nature and the potential for severe consequences from unauthorized modifications, including data breaches and inaccurate diagnoses. This paper introduces a method for lossless medical image retrieval from encrypted images stored on third-party clouds. The proposed approach employs a symmetric integrity-centric image encryption scheme, leveraging multiple chaotic maps and cryptographic hash techniques, to ensure lossless image reconstruction. Medical images are first encrypted by the image owners and converted into hashcodes encapsulating essential features using a deep hashing technique with the ConvNeXt network as the backbone in parallel. To ensure index privacy, these hashcodes are encrypted in a searchable manner. The encrypted medical images, along with a secure index, are subsequently uploaded to cloud storage. Authorized medical image users can request similar medical images for diagnostic purposes by submitting a query image, from which a search trapdoor is generated and sent to the cloud. The retrieval process involves a secure similar image search over the encrypted indexes, followed by decryption along with integrity verification of the retrieved images. The proposed method has been rigorously tested on three standard medical datasets, demonstrating an improvement of 5-20% in retrieval accuracy compared to standard baselines. Formal security analysis and experimental results indicate that the proposed scheme offers enhanced security and retrieval accuracy, making it an effective solution for the encrypted storage and secure retrieval of medical image data.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.1186/s13677-024-00704-x
Li Ma, Binbin Duan, Bo Zhang, Yang Li, Yingxun Fu, Dongchao Ma
Edge computing nodes close to the perception layer of IoT systems are susceptible to data leaks and unauthorized access. To address these security concerns, this paper proposes a trusted IoT data sharing method based on secure multi-party computation (SMC). By running a reliable third-party blockchain service at edge computing nodes, the data computation relationships between IoT devices in the perception layer are registered in blockchain smart contracts. This constructs a publicly verifiable IoT data sharing method combining on-chain audit verification and off-chain SMC. Furthermore, a Bloom filter is maintained at the on-chain smart contract layer to track the trust status of IoT devices in the perception layer, filtering out non-trustworthy device requests and enabling secure data sharing among trusted devices. Comparative analysis and performance tests demonstrate the proposed method’s high computational efficiency for IoT device nodes.
{"title":"A trusted IoT data sharing method based on secure multi-party computation","authors":"Li Ma, Binbin Duan, Bo Zhang, Yang Li, Yingxun Fu, Dongchao Ma","doi":"10.1186/s13677-024-00704-x","DOIUrl":"https://doi.org/10.1186/s13677-024-00704-x","url":null,"abstract":"Edge computing nodes close to the perception layer of IoT systems are susceptible to data leaks and unauthorized access. To address these security concerns, this paper proposes a trusted IoT data sharing method based on secure multi-party computation (SMC). By running a reliable third-party blockchain service at edge computing nodes, the data computation relationships between IoT devices in the perception layer are registered in blockchain smart contracts. This constructs a publicly verifiable IoT data sharing method combining on-chain audit verification and off-chain SMC. Furthermore, a Bloom filter is maintained at the on-chain smart contract layer to track the trust status of IoT devices in the perception layer, filtering out non-trustworthy device requests and enabling secure data sharing among trusted devices. Comparative analysis and performance tests demonstrate the proposed method’s high computational efficiency for IoT device nodes.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1186/s13677-024-00679-9
Lei Zhang, Shaoming Zhu, Shen Su, Xiaofeng Chen, Yan Yang, Bing Zhou
With the support of our government’s commitment to the energy sector, the installed capacity of wind power will continue to grow. However, due to the instability of wind power, accurate prediction of wind power output is essential for effective grid dispatch. In addition, data privacy and protection have become paramount in today’s society. Traditional wind forecasting methods rely on centralized data, which raises concerns about data privacy and data silos. To address these challenges, we propose a hybrid approach that combines federated learning and deep learning for wind power forecasting. In our proposed method, we use a bidirectional long short-term memory (BILSTM) neural network as the basic prediction model to improve the prediction accuracy. Then, the model is integrated into the federated learning framework to form the Fed-BILSTM prediction method. In addition, we have introduced cloud computing technology into the Fed-BILSTM method, using cloud resources for model training and parameter update. Participants share model parameters instead of sharing raw data, which solves data privacy concerns. We compared Fed-BILSTM with traditional forecasting methods. Experimental results show that the proposed Fed-BILSTM is better than the traditional prediction method in terms of prediction accuracy. What’s more, Fed-BILSTM can effectively protect data privacy compared to traditional centralized forecasting methods while ensuring prediction performance.
{"title":"Wind power prediction method based on cloud computing and data privacy protection","authors":"Lei Zhang, Shaoming Zhu, Shen Su, Xiaofeng Chen, Yan Yang, Bing Zhou","doi":"10.1186/s13677-024-00679-9","DOIUrl":"https://doi.org/10.1186/s13677-024-00679-9","url":null,"abstract":"With the support of our government’s commitment to the energy sector, the installed capacity of wind power will continue to grow. However, due to the instability of wind power, accurate prediction of wind power output is essential for effective grid dispatch. In addition, data privacy and protection have become paramount in today’s society. Traditional wind forecasting methods rely on centralized data, which raises concerns about data privacy and data silos. To address these challenges, we propose a hybrid approach that combines federated learning and deep learning for wind power forecasting. In our proposed method, we use a bidirectional long short-term memory (BILSTM) neural network as the basic prediction model to improve the prediction accuracy. Then, the model is integrated into the federated learning framework to form the Fed-BILSTM prediction method. In addition, we have introduced cloud computing technology into the Fed-BILSTM method, using cloud resources for model training and parameter update. Participants share model parameters instead of sharing raw data, which solves data privacy concerns. We compared Fed-BILSTM with traditional forecasting methods. Experimental results show that the proposed Fed-BILSTM is better than the traditional prediction method in terms of prediction accuracy. What’s more, Fed-BILSTM can effectively protect data privacy compared to traditional centralized forecasting methods while ensuring prediction performance.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The convergence of artificial intelligence and in-vehicle wireless communication technologies, promises to fulfill the pressing communication needs of the Internet of Vehicles (IoV) while promoting the development of vehicle applications. However, making real-time dependency-aware task offloading decisions is difficult due to the high mobility of vehicles and the dynamic nature of the network environment. This leads to additional application computation time and energy consumption, increasing the risk of offloading failures for computationally intensive and latency-sensitive applications. In this paper, an offloading strategy for vehicle applications that jointly considers latency and energy consumption in the base station cooperative computing model is proposed. Firstly, we establish a collaborative offloading model involving multiple vehicles, multiple base stations, and multiple edge servers. Transferring vehicular applications to the application queue of edge servers and prioritizing them based on their completion deadlines. Secondly, each vehicular application is modeled as a directed acyclic graph (DAG) task with data dependency relationships. Subsequently, we propose a task offloading method based on task dependency awareness in deep reinforcement learning (DAG-DQN). Tasks are assigned to edge servers at different base stations, and edge servers collaborate to process tasks, minimizing vehicle application completion time and reducing edge server energy consumption. Finally, simulation results show that compared with the heuristic method, our proposed DAG-DQN method reduces task completion time by 16%, reduces system energy consumption by 19%, and improves decision-making efficiency by 70%.
{"title":"Dependency-aware online task offloading based on deep reinforcement learning for IoV","authors":"Chunhong Liu, Huaichen Wang, Mengdi Zhao, Jialei Liu, Xiaoyan Zhao, Peiyan Yuan","doi":"10.1186/s13677-024-00701-0","DOIUrl":"https://doi.org/10.1186/s13677-024-00701-0","url":null,"abstract":"The convergence of artificial intelligence and in-vehicle wireless communication technologies, promises to fulfill the pressing communication needs of the Internet of Vehicles (IoV) while promoting the development of vehicle applications. However, making real-time dependency-aware task offloading decisions is difficult due to the high mobility of vehicles and the dynamic nature of the network environment. This leads to additional application computation time and energy consumption, increasing the risk of offloading failures for computationally intensive and latency-sensitive applications. In this paper, an offloading strategy for vehicle applications that jointly considers latency and energy consumption in the base station cooperative computing model is proposed. Firstly, we establish a collaborative offloading model involving multiple vehicles, multiple base stations, and multiple edge servers. Transferring vehicular applications to the application queue of edge servers and prioritizing them based on their completion deadlines. Secondly, each vehicular application is modeled as a directed acyclic graph (DAG) task with data dependency relationships. Subsequently, we propose a task offloading method based on task dependency awareness in deep reinforcement learning (DAG-DQN). Tasks are assigned to edge servers at different base stations, and edge servers collaborate to process tasks, minimizing vehicle application completion time and reducing edge server energy consumption. Finally, simulation results show that compared with the heuristic method, our proposed DAG-DQN method reduces task completion time by 16%, reduces system energy consumption by 19%, and improves decision-making efficiency by 70%.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1186/s13677-024-00696-8
Naif Alsharabi, Jalel Ktari, Tarek Frikha, Abdulaziz Alayba, Abdullah J. Alzahrani, Amr jadi, Habib Hamam
This paper proposes a total fusion of blockchain and AI tech for tomorrow’s viable, rich in diversity and transparent fisheries. It outlines the main goal of tackling overfishing challenges due to lack of transparency and biodiversity depletion in the fisheries sector. With the use of blockchain technology, we can ensure that all fishery products are safely traced from their harvest up to when they get to the market— at the same time, AI algorithms are used in monitoring fish populations and predicting them plus decision-making processes which should be enhanced thus promoting bio-diversity and ensuring sustainability of fish stocks. Results show promise on using both technologies together: improving sustainability plus transparency in fisheries which would promote more fish biodiversity, while others including using an artificial intelligence system have not been confirmed yet by observations. The conclusion underscores the transformative nature of these technologies as having great implications towards fisheries management; this implies that there is a need for future observational studies aimed at validating such other findings.
{"title":"Using blockchain and AI technologies for sustainable, biodiverse, and transparent fisheries of the future","authors":"Naif Alsharabi, Jalel Ktari, Tarek Frikha, Abdulaziz Alayba, Abdullah J. Alzahrani, Amr jadi, Habib Hamam","doi":"10.1186/s13677-024-00696-8","DOIUrl":"https://doi.org/10.1186/s13677-024-00696-8","url":null,"abstract":"This paper proposes a total fusion of blockchain and AI tech for tomorrow’s viable, rich in diversity and transparent fisheries. It outlines the main goal of tackling overfishing challenges due to lack of transparency and biodiversity depletion in the fisheries sector. With the use of blockchain technology, we can ensure that all fishery products are safely traced from their harvest up to when they get to the market— at the same time, AI algorithms are used in monitoring fish populations and predicting them plus decision-making processes which should be enhanced thus promoting bio-diversity and ensuring sustainability of fish stocks. Results show promise on using both technologies together: improving sustainability plus transparency in fisheries which would promote more fish biodiversity, while others including using an artificial intelligence system have not been confirmed yet by observations. The conclusion underscores the transformative nature of these technologies as having great implications towards fisheries management; this implies that there is a need for future observational studies aimed at validating such other findings.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-21DOI: 10.1186/s13677-024-00694-w
Jomina John, John Singh K
Cloud computing has become integral to modern computing infrastructure, offering scalability, flexibility, and cost-effectiveness. Trust is a critical aspect of cloud computing, influencing user decisions in selecting Cloud Service Providers (CSPs). This paper provides a comprehensive review of existing trust models in cloud computing, including agreement-based, SLA-based, certificate-based, feedback-based, domain-based, prediction-based, and reputation-based models. Building on this foundation, we propose a novel methodology for creating a trust model in cloud computing using digital twins for CSPs. The digital twin is augmented with a fuzzy inference system, which computes the trust score of a CSP based on trust-related parameters. The architecture of the digital twin with the fuzzy inference system is detailed, outlining how it processes security parameter values obtained through penetration testing mechanisms. These parameter values are transformed into crisp values using a linear ridge regression function and then fed into the fuzzy inference system to calculate a final trust score for the CSP. The paper also presents the outputs of the fuzzy inference system, demonstrating how different security parameter inputs yield various trust scores. This methodology provides a robust framework for assessing CSP trustworthiness and enhancing decision-making processes in cloud service selection.
{"title":"Predictive digital twin driven trust model for cloud service providers with Fuzzy inferred trust score calculation","authors":"Jomina John, John Singh K","doi":"10.1186/s13677-024-00694-w","DOIUrl":"https://doi.org/10.1186/s13677-024-00694-w","url":null,"abstract":"Cloud computing has become integral to modern computing infrastructure, offering scalability, flexibility, and cost-effectiveness. Trust is a critical aspect of cloud computing, influencing user decisions in selecting Cloud Service Providers (CSPs). This paper provides a comprehensive review of existing trust models in cloud computing, including agreement-based, SLA-based, certificate-based, feedback-based, domain-based, prediction-based, and reputation-based models. Building on this foundation, we propose a novel methodology for creating a trust model in cloud computing using digital twins for CSPs. The digital twin is augmented with a fuzzy inference system, which computes the trust score of a CSP based on trust-related parameters. The architecture of the digital twin with the fuzzy inference system is detailed, outlining how it processes security parameter values obtained through penetration testing mechanisms. These parameter values are transformed into crisp values using a linear ridge regression function and then fed into the fuzzy inference system to calculate a final trust score for the CSP. The paper also presents the outputs of the fuzzy inference system, demonstrating how different security parameter inputs yield various trust scores. This methodology provides a robust framework for assessing CSP trustworthiness and enhancing decision-making processes in cloud service selection.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Load prediction tasks aim to predict the dynamic trend of future load based on historical performance sequences, which are crucial for cloud platforms to make timely and reasonable task scheduling. However, existing prediction models are limited while capturing complicated temporal patterns from the load sequences. Besides, the frequently adopted global weighting strategy (e.g., the self-attention mechanism) in temporal modeling schemes has quadratic computational complexity, hindering the immediate response of cloud servers in complex real-time scenarios. To address the above limitations, we propose a Wavelet decomposition-enhanced External Transformer (WETformer) to provide accurate yet efficient load prediction for cloud servers. Specifically, we first incorporate discrete wavelet transform to progressively extract long-term trends, highlighting the intrinsic attributes of temporal sequences. Then, we propose a lightweight multi-head External Attention (EA) mechanism to simultaneously consider the inter-element relationships within load sequences and the correlations across different sequences. Such an external component has linear computational complexity, mitigating the encoding redundancy prevalent and enhancing prediction efficiency. Extensive experiments conducted on Alibaba Cloud’s cluster tracking dataset demonstrate that WETformer achieves superior prediction accuracy and the shortest inference time compared to several state-of-the-art baseline methods.
{"title":"When wavelet decomposition meets external attention: a lightweight cloud server load prediction model","authors":"Zhen Zhang, Chen Xu, Jinyu Zhang, Zhe Zhu, Shaohua Xu","doi":"10.1186/s13677-024-00698-6","DOIUrl":"https://doi.org/10.1186/s13677-024-00698-6","url":null,"abstract":"Load prediction tasks aim to predict the dynamic trend of future load based on historical performance sequences, which are crucial for cloud platforms to make timely and reasonable task scheduling. However, existing prediction models are limited while capturing complicated temporal patterns from the load sequences. Besides, the frequently adopted global weighting strategy (e.g., the self-attention mechanism) in temporal modeling schemes has quadratic computational complexity, hindering the immediate response of cloud servers in complex real-time scenarios. To address the above limitations, we propose a Wavelet decomposition-enhanced External Transformer (WETformer) to provide accurate yet efficient load prediction for cloud servers. Specifically, we first incorporate discrete wavelet transform to progressively extract long-term trends, highlighting the intrinsic attributes of temporal sequences. Then, we propose a lightweight multi-head External Attention (EA) mechanism to simultaneously consider the inter-element relationships within load sequences and the correlations across different sequences. Such an external component has linear computational complexity, mitigating the encoding redundancy prevalent and enhancing prediction efficiency. Extensive experiments conducted on Alibaba Cloud’s cluster tracking dataset demonstrate that WETformer achieves superior prediction accuracy and the shortest inference time compared to several state-of-the-art baseline methods.","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-19DOI: 10.1186/s13677-024-00690-0
Syed Imran Akhtar, Abdul Rauf, Haider Abbas, Muhammad Faisal Amjad, Ifra Batool
Cloud-based digital twins use real-time data from various data sources to simulate the behavior and performance of their physical counterparts, enabling monitoring and analysis. However, one restraining factor in the use of cloud computing for digital twins is its users’ concerns about the security of their data. This data may be located anywhere in the cloud, with very limited control of the user to ensure its security. Cloud-based digital twins provide opportunities for researchers to collaborate yet security of such digital twins requires measures specific to cloud computing. To overcome this shortcoming, we need to devise a mechanism that not only ensures essential security safeguards but also computes a Trustworthiness value for Cloud Service Providers (CSP). This would give confidence to cloud users and enable them to choose the right CSP for their data-related interaction. This research proposes a solution, whereby the Trustworthiness of CSPs is calculated based on their Compliance with data security controls, User Feedback, and Auditor Rating. Two additional factors, Accuracy of Compliance Measurement and Control Significance Factor have been built in, to cater for other nonstandard conditions. Our implementation of Data Security Compliance Monitor and Data Trust as a Service, along with three CSPs, each with ten different settings, has supported our proposition through the devised formula. Experimental outcomes show changes in the trustworthiness value with changes in compliance level, user feedback and auditor rating. CSPs with better compliance have better trustworthiness values. However, if the Accuracy of Compliance Measurement and Control Significance Factor are low the trustworthiness is also proportionately less. This creates a balance and realism in our calculations. This model is unique and will help in creating users’ trust in cloud-based digital twins.
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