Future Generation Computer Systems-The International Journal of Escience最新文献

{"title":"Skip index: Supporting efficient inter-block queries and query authentication on the blockchain","authors":"Matteo Loporchio,&nbsp;Anna Bernasconi,&nbsp;Damiano Di Francesco Maesa,&nbsp;Laura Ricci","doi":"10.1016/j.future.2024.107556","DOIUrl":"10.1016/j.future.2024.107556","url":null,"abstract":"<div><div>Decentralized applications, the driving force behind the new Web3 paradigm, require continuous access to blockchain data. Their adoption, however, is hindered by the constantly increasing size of blockchains and the sequential scan nature of their read operations, which introduce a clear inefficiency bottleneck. Also, the growing amount of data recorded on the blockchain makes resource-constrained light nodes dependent on untrusted full nodes for fetching information, with a consequent need for query authentication protocols ensuring result integrity. Motivated by these reasons, in this paper we propose the skip index, an indexing data structure that allows users to quickly retrieve information simultaneously from multiple blocks of a blockchain. Our solution is also designed to be used as an authenticated data structure to guarantee the integrity of query results for light nodes. We discuss the theoretical properties of skip indices, propose efficient algorithms for their construction and querying, and detail their computational complexity. Finally, we assess the effectiveness of our proposal through an experimental evaluation on the Ethereum blockchain. As a reference use case, we focus on the popular CryptoKitties application and simulate a scenario where users seek to retrieve the events generated by the service. Our experimental results suggest that the use of skip indices offers a constant multiplicative speedup, thanks to search times that are at most logarithmic within a chosen search window. This allows to reduce the number of visited blocks by up to two orders of magnitude if compared to the naive sequential approach currently in use.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107556"},"PeriodicalIF":6.2,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EASL: Enhanced append-only skip list index for agile block data retrieval on blockchain","authors":"Jared Newell,&nbsp;Sabih ur Rehman,&nbsp;Quazi Mamun,&nbsp;Md Zahidul Islam","doi":"10.1016/j.future.2024.107554","DOIUrl":"10.1016/j.future.2024.107554","url":null,"abstract":"<div><div>The weakness of blockchain is widely recognised as the linear, temporal cost required for retrieving data due to the sequential structure of data blocks. To address this, conventional approaches have relied on database indexing techniques applied to each individual replica copy of a blockchain. However, this only partially addresses the problem, because if the index is not distributed it is not available for devices in the blockchain network. If an index is to be incorporated and distributed within blockchain, the unique attribute of immutability necessitates a more innovative approach. To that end, we propose an Enhanced Append-only Skip List (EASL). This specialised indexing technique utilises binary search with skip lists in blockchain, resulting in a sublinear cost for data retrieval. The EASL indexing technique is maintained by each newly appended blockchain block and offers enhanced readability and robustness using an explicitly recorded index structure. Our proposed technique is 42% more efficient in computing and 60% more efficient in storage consumption than its predecessor, the Deterministic Append-only Skip List (DASL) indexing technique. This is achieved through agile data retrieval, resulting in energy cost savings from less computational effort to maintain the index, and less network bandwidth to retrieve blockchain data. The code for the proposed technique is publicly available on GitHub {<span><span>https://github.com/jarednewell/EASL/</span><svg><path></path></svg></span>}, to expedite future research and encourage the practical application of this effectual data index.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107554"},"PeriodicalIF":6.2,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Event log extraction methodology for Ethereum applications","authors":"Andrea Morichetta,&nbsp;Yuri Paoloni,&nbsp;Barbara Re","doi":"10.1016/j.future.2024.107566","DOIUrl":"10.1016/j.future.2024.107566","url":null,"abstract":"<div><div>The adoption of smart contracts in decentralized blockchain-based applications enables reliable and certified audits. These audits allow the extraction of valuable information from blockchains, which can be used to reconstruct the execution of the application and facilitate advanced analyses. One of the most commonly used techniques in this context is process mining, which leverages event logs to trace and accurately represent the process execution of applications. However, extracting execution data from blockchains poses significant challenges, and the current methodologies developed have some limitations. Most approaches are tailored to specific use cases, requiring that analysis techniques are defined during the smart contract’s development. Other techniques are applied a posteriori, relying on blockchain events that often lack a standardized format. This absence of standardization requires complex processing steps to correlate logs with the executed actions and such approaches are not universally applicable to all smart contracts on the blockchain, further limiting their scope. Lastly, none of the existing techniques can extract information from event logs embedded in internal transactions of smart contracts.</div><div>To address these limitations, we propose EveLog an application-agnostic methodology that can be applied to any EVM-compatible application without predefined constraints. Its primary goal is to extract information from smart contracts, capturing both public and internal transactions, and organizing the results into a structured XES event log. The EveLog methodology consists of five key steps: (i) extraction of data from smart contract transactions, (ii) decoding raw data, (iii) selection of sorting criteria, (iv) construction of traces, and (v) generation of the XES event log. EveLog has been implemented in a client–server application and tested on existing solutions, specifically the CryptoKitties application, a blockchain-based game on the Ethereum blockchain. The study was conducted using 12,996 blocks, including over 8000 real transactions from the Ethereum mainnet.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107566"},"PeriodicalIF":6.2,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Confidential outsourced support vector machine learning based on well-separated structure","authors":"Guoqiang Deng ,&nbsp;Min Tang ,&nbsp;Zengyi Huang ,&nbsp;Yuhao Zhang ,&nbsp;Yuxing Xi","doi":"10.1016/j.future.2024.107564","DOIUrl":"10.1016/j.future.2024.107564","url":null,"abstract":"<div><div>Support Vector Machine (SVM) has revolutionized various domains and achieved remarkable successes. This progress relies on subtle algorithms and more on large training samples. However, the massive data collection introduces security concerns. To facilitate secure integration of data efficiently for building an accurate SVM classifier, we present a non-interactive protocol for privacy-preserving SVM, named <em>NPSVMT</em>. Specifically, we define a new well-separated structure for computing gradients that can decouple the fusion matter between user data and model parameters, allowing data providers to outsource the collaborative learning task to the cloud. As a result, <em>NPSVMT</em> is capable of removing the multiple communications and eliminating the straggler’s effect (waiting for the last), thereby going beyond those developed with interactive methods, e.g., federated learning. To further decrease the data traffic, we introduce a high-efficient coding method to compress and parse training data. In addition, unlike outsourced schemes based on homomorphic encryption or secret sharing, <em>NPSVMT</em> exploits functional encryption to maintain the data confidentiality, achieving dropout-tolerant secure aggregation. The implementations verify that <em>NPSVMT</em> is faster by orders of magnitude than the existing privacy-preserving SVM schemes on benchmark datasets.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107564"},"PeriodicalIF":6.2,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FedDA: Resource-adaptive federated learning with dual-alignment aggregation optimization for heterogeneous edge devices","authors":"Shaohua Cao ,&nbsp;Huixin Wu ,&nbsp;Xiwen Wu ,&nbsp;Ruhui Ma ,&nbsp;Danxin Wang ,&nbsp;Zhu Han ,&nbsp;Weishan Zhang","doi":"10.1016/j.future.2024.107551","DOIUrl":"10.1016/j.future.2024.107551","url":null,"abstract":"<div><div>Federated learning (FL) is an emerging distributed learning paradigm that allows multiple clients to collaborate on training a global model without sharing their local data. However, in practical heterogeneous edge device scenarios, FL faces the challenges of system heterogeneity and data heterogeneity, which leads to unfair participation and degraded global model performance. In this paper, we introduce FedDA, a resource-adaptive FL framework, which adapts to the client’s computing resources by assigning heterogeneous models of different sizes. To improve the performance of heterogeneous model aggregation and adjust to non-independent and identically distributed (non-i.i.d.) data, we propose a dual-alignment aggregation optimization method, i.e., parameter feature space alignment and output space alignment. Specifically, FedDA exploits the permutation symmetry property of weight space to permutate the model parameter positions via an adaptive layer-wise matching method, thereby aligning models with significant deviations in parameter feature space. FedDA mitigates the imbalance in parameter quantity between smaller and larger models through parameter expansion. Additionally, FedDA maps client labels into a uniform embedding space through output space alignment, thus reducing model parameter deviations due to non-i.i.d. data without additional client-side computing overhead. We evaluate the performance of FedDA on benchmark datasets, including FashionMNIST, CIFAR10, CIFAR100 and AGNews. Experimental results demonstrate that FedDA achieves up to 8.71% improvement in model accuracy compared to baseline methods, highlighting its effectiveness in addressing the challenges of heterogeneity.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"163 ","pages":"Article 107551"},"PeriodicalIF":6.2,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FeL-MAR: Federated learning based multi resident activity recognition in IoT enabled smart homes","authors":"Abisek Dahal ,&nbsp;Soumen Moulik ,&nbsp;Rohan Mukherjee","doi":"10.1016/j.future.2024.107552","DOIUrl":"10.1016/j.future.2024.107552","url":null,"abstract":"<div><div>This study explores and proposes the use of a Federated Learning (FL) based approach for recognizing multi-resident activities in smart homes utilizing a diverse array of data collected from Internet of Things (IoT) sensors. FL model is pivotal in ensuring the utmost privacy of user data fostering decentralized learning environments and allowing individual residents to retain control over their sensitive information. The main objective of this paper is to accurately recognize and interpret individual activities by allowing them to maintain sovereignty over their confidential information. This will help to provide a services that enrich assisted living experiences within the smart homes. The proposed system is designed to be adaptable learning from the multi-residential behaviors to predict and respond intelligently to the residents needs and preferences promoting a harmonious and sustainable living environment while maintaining privacy, confidentiality and control over the data collected from sensors. The proposed FeL-MAR model demonstrates superior performance in activity recognition within multi-resident smart homes, outperforming other models with its high accuracy and precision while maintaining user privacy. It suggest an effective use of FL and IoT sensors marks a significant advancement in smart home technologies enhancing both efficiency and user experience without compromising data security.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"163 ","pages":"Article 107552"},"PeriodicalIF":6.2,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142551981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LGTDA: Bandwidth exhaustion attack on Ethereum via dust transactions","authors":"Qunhong Sun ,&nbsp;Chang Wang ,&nbsp;Yifan Hu ,&nbsp;Shen Su ,&nbsp;Ting Cui","doi":"10.1016/j.future.2024.107549","DOIUrl":"10.1016/j.future.2024.107549","url":null,"abstract":"<div><div>Dust attacks typically involve sending a large number of low-value transactions to numerous addresses, aiming to facilitate transaction tracking and undermine privacy, while simultaneously disrupting the market and increasing transaction delays. These transactions not only impact the network but also incur significant costs. This paper introduces a low-cost attack method called LGTDA, which achieves network partitioning through dust attacks. This method hinders block synchronization by consuming node bandwidth, leading to denial of service (DoS) for nodes and eventually causing large-scale network partitioning. In LGTDA, the attacker does not need to have real control over the nodes in the network, nor is there a requirement for the number of peer connections to the nodes; the attack can even be initiated by simply invoking RPC services to send transactions. Under the condition of ensuring the validity of the attack transactions, the LGTDA attack sends a large volume of low-value, high-frequency dust transactions to the network, relying on nodes for global broadcasting. This sustained attack can significantly impede the growth of block heights among nodes, resulting in network partitioning. We discuss the implications of the LGTDA attack, including its destructive capability, low cost, and ease of execution. Additionally, we analyze the limitations of this attack. Compared to grid lighting attacks, the LGTDA attack has a broader impact range and is not limited by the positional relationship with the victim node. Through experimental validation in a controlled environment, we confirm the effectiveness of this attack.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"163 ","pages":"Article 107549"},"PeriodicalIF":6.2,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlating node centrality metrics with node resilience in self-healing systems with limited neighbourhood information","authors":"Arles Rodríguez ,&nbsp;Ada Diaconescu ,&nbsp;Johan Rodríguez ,&nbsp;Jonatan Gómez","doi":"10.1016/j.future.2024.107553","DOIUrl":"10.1016/j.future.2024.107553","url":null,"abstract":"<div><div>Resilient systems must self-heal their components and connections to maintain their topology and function when failures occur. This ability becomes essential to many networked and distributed systems, e.g., virtualisation platforms, cloud services, microservice architectures and decentralised algorithms. This paper builds upon a self-healing approach where failed nodes are recreated and reconnected automatically based on topology information, which is maintained within each node’s neighbourhood. The paper proposes two novel contributions. First, it offers a generic method for establishing the minimum size of a network neighbourhood to be known by each node in order to recover the system’s component interconnection topology under a certain probability of node failure. This improves the previous proposal by reducing resource consumption, as only local information is communication and stored. Second, it adopts analysis techniques from complex networks theory to correlate a node’s recovery probability with its closeness centrality within the self-healing system. This allows strengthening a system’s resilience by analysing its topological characteristics and rewiring weakly-connected nodes. These contributions are supported by extensive simulation experiments on different systems with various topological characteristics. Obtained results confirm that nodes which propagate their topology information to more neighbours are more likely to be recovered; while requiring more resources. The proposed contributions can help practitioners to: identify the most fragile nodes in their distributed systems; consider corrective measures by increasing each node’s connectivity; and, establish a suitable compromise between system resilience and costs.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"163 ","pages":"Article 107553"},"PeriodicalIF":6.2,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Bagging with Nested Rotations (FBNR) for anomaly detection in multivariate time series","authors":"Anastasios Iliopoulos,&nbsp;John Violos,&nbsp;Christos Diou,&nbsp;Iraklis Varlamis","doi":"10.1016/j.future.2024.107545","DOIUrl":"10.1016/j.future.2024.107545","url":null,"abstract":"<div><div>Detecting anomalies in multivariate time series poses a significant challenge across various domains. The infrequent occurrence of anomalies in real-world data, as well as the lack of a large number of annotated samples, makes it a complex task for classification algorithms. Deep Neural Network approaches, based on Long Short-Term Memory (LSTMs), Autoencoders, and Variational Autoencoders (VAEs), among others, prove effective with handling imbalanced data. However, the same does not follow when such algorithms are applied on multivariate time-series, as their performance degrades significantly. Our main hypothesis is that the above is due to anomalies stemming from a small subset of the feature set. To mitigate the above issues in the multivariate setting, we propose forming an ensemble of base models by combining different feature selection and transformation techniques. The proposed processing pipeline includes applying a Feature Bagging techniques on multiple individual models, which considers separate feature subsets for each specific model. These subsets are then partitioned and transformed using multiple nested rotations derived from Principal Component Analysis (PCA). This approach aims to identify anomalies that arise from only a small portion of the feature set while also introduces diversity by transforming the subspaces. Each model provides an anomaly score, which are then aggregated, via an unsupervised decision fusion model. A semi-supervised fusion model was also explored, in which a Logistic Regressor was applied on the individual model outputs. The proposed methodology is evaluated on the Skoltech Anomaly Benchmark (SKAB), containing multivariate time series related to water flow in a closed circuit, as well as the Server Machine Dataset (SMD), which was collected from a large Internet company. The experimental results reveal that the proposed ensemble technique surpasses state-of-the-art algorithms. The unsupervised approach demonstrated a performance improvement of 2% for SKAB and 3% for SMD, compared to the baseline models. In the semi-supervised approach, the proposed method achieved a minimum of 10% improvement in terms of anomaly detection accuracy.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"163 ","pages":"Article 107545"},"PeriodicalIF":6.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing interconnection network topology for chiplet-based systems: An automated design framework","authors":"Zhipeng Cao ,&nbsp;Qinrang Liu ,&nbsp;Zhiquan Wan ,&nbsp;Wenbo Zhang ,&nbsp;Ke Song ,&nbsp;Wenbin Liu","doi":"10.1016/j.future.2024.107547","DOIUrl":"10.1016/j.future.2024.107547","url":null,"abstract":"<div><div>Chiplet-based systems integrate discrete chips on an interposer and use the interconnection network to enable communication between different components. The topology of the interconnection network poses a significant challenge to overall performance, as it can greatly affect both latency and throughput. However, the design of the interconnection network topology is not currently automated. They rely heavily on expert knowledge and fail to deliver optimal performance. To this end, we propose an automated design framework for chiplet interconnection network topology, called CINT-AD. To implement CINT-AD, we first investigate topology-related properties from the perspective of design constraints and structural symmetry. Then, using these properties, we develop an automated framework to generate the topology for interposer interconnections between different chiplets. A deadlock-free routing scheme is proposed for the topologies generated by CINT-AD to fully utilize the resources of the interconnection network. Experimental results show that CINT-AD achieves lower average latency and higher throughput compared to existing state-of-the-art topologies. Furthermore, power and area analysis show that the overhead of CINT-AD is negligible.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"163 ","pages":"Article 107547"},"PeriodicalIF":6.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}