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Reliable federated learning based on dual-reputation reverse auction mechanism in Internet of Things Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-15 Yuncan Tang, Yongquan Liang, Yang Liu, Jinquan Zhang, Lina Ni, Liang Qi
Federated learning, a promising distributed machine learning paradigm, has been used in various Internet of Things (IoT) environments to solve isolated data island issues and protect data privacy. However, since the central server in federated learning cannot detect the local training process of the client, it is vulnerable to adversarial attacks against its security and privacy by malicious clients
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Digital twin framework for smart greenhouse management using next-gen mobile networks and machine learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-13 Hameedur Rahman, Uzair Muzamil Shah, Syed Morsleen Riaz, Kashif Kifayat, Syed Atif Moqurrab, Joon Yoo
Due to the increase in world population, arable land has been reduced. Consequently, the concept of urban greenhouses is on the rise. Smart greenhouses need to monitor physical parameters for the healthy growth of plants from remote locations. A digital twin is a representation of physical assets in the digital world, and this emerging technology has opened up opportunities for efficient system development
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Software Quality Assurance as a Service: Encompassing the quality assessment of software and services Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-13 Samuel Bernardo, Pablo Orviz, Mario David, Jorge Gomes, David Arce, Diana Naranjo, Ignacio Blanquer, Isabel Campos, Germán Moltó, Joao Pina
This paper introduces the Software Quality Assurance as a Service (SQAaaS) concept and it describes an open-source implementation of a comprehensive platform that supports the automated assessment of specific quality metrics for software and services, defined as a set of baseline requirements. The platform is openly accessible, focuses on research software and open science, and promotes best practices
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Hybrid learning of predictive mobile-edge computation offloading under differently-aged network states Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-12 Chenshan Ren, Wei Song, Xinchen Lyu
By offloading computationally demanding applications to edge servers, mobile edge computing (MEC) can alleviate the stringent hardware requirements and save energy consumption of resource-restrained devices. Mobile edge computation offloading (MECO, i.e., optimizing computation offloading and resource allocation) is critical to the performance of MEC. However, the existing study typically assumed the
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Multi-GPU work sharing in a task-based dataflow programming model Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-12 Joseph John, Josh Milthorpe, Thomas Herault, George Bosilca
Today, multi-GPU computing nodes are the mainstay of most high-performance computing systems. Despite significant progress in programmability, building an application that efficiently utilizes all the GPUs in a computing node is still a significant challenge, especially using the existing shared-memory and message-passing paradigms. In this aspect, the task-based dataflow programming model has emerged
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PPSFL: Privacy-Preserving Split Federated Learning for heterogeneous data in edge-based Internet of Things Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-11 Jiali Zheng, Yixin Chen, Qijia Lai
With the rapid increase in the number of Internet of Things (IoT) devices and the amount of data they generate, the traditional cloud-based approach is gradually unable to meet the actual needs of many scenarios. Distributed collaborative machine learning (DCML) paradigms such as Federated Learning (FL) and Split Learning (SL) provide possibilities for effective use of decentralized data in edge-based
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Uncertainty-aware autonomous sensing with deep reinforcement learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-11 Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
Constructing an accurate representation model of phenomena with fewer measurements is a fundamental challenge in the Internet of Things. Leveraging sparse sensing policies to select the most informative measurements is a prominent technique for addressing resource constraints. However, designing such sensing policies requires significant domain knowledge and involves manually fine-tuned heuristics
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Smartgrid-based hybrid digital twins framework for demand side recommendation service provision in distributed power systems Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-11 Abiodun E. Onile, Eduard Petlenkov, Yoash Levron, Juri Belikov
Electricity consumers face challenges in selecting an optimal energy-saving plan, and this is a sustainability problem. To set consumers focus on sustainable energy management, developments around ”Industry 4.0” are needed to achieve an optimal balance between cost and energy consumption with a focus on cutting-edge machine-learning models and smart services introduction. Energy modelling is crucial
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Knowledge-guided evolutionary algorithm for multi-satellite resource scheduling optimization Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-06 Xingyi Yao, Xiaogang Pan, Tao Zhang, Wenhua Li, Jianjiang Wang
The Multi-Satellite Resource Scheduling Optimization Problem (MSRSOP) represents a complex optimization challenge, focusing on the allocation of limited ground tracking resources to satellite Tracking, Telemetry, and Command (TT&C) tasks, each with complex requirements. This paper introduces a novel mathematical model and a Knowledge-guided Evolutionary Algorithm (KgEA) tailored for the MSRSOP. Our
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A prefetching indexing scheme for in-memory database systems Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-05 Qian Zhang, Haoyun Song, Kaiyan Zhou, Jianhao Wei, Chuqiao Xiao
In-memory databases (IMDBs) store all working data in the main memory, making memory access the dominant factor in system performance. Moreover, for modern multi-version systems, the extended version chain makes the access pattern more complex, putting extra pressure on indexing. Our micro-architectural profiling results of existing IMDB indexing schemes show that over half of the execution time goes
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DAG-aware harmonizing job scheduling and data caching for disaggregated analytics frameworks Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-05 Yulai Tong, Jiazhen Liu, Hua Wang, Mingjian He, Ke Zhou, Rongfeng He, Qin Zhang, Cheng Wang
Modern data analytics frameworks often integrate with external storage services, which can lead to storage bottlenecks. Existing caching and prefetching solutions utilize high-level information from data analytics frameworks to forecast future data accesses. They employ these predictions to prefetch data into the cache and manage the cache contents. However, this approach overlooks a fundamental opportunity:
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CoPiFL: A collusion-resistant and privacy-preserving federated learning crowdsourcing scheme using blockchain and homomorphic encryption Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-05 Ruoting Xiong, Wei Ren, Shenghui Zhao, Jie He, Yi Ren, Kim-Kwang Raymond Choo, Geyong Min
Federated learning (FL) is one of many tasks facilitated by crowdsourcing. Generally in such a setting, participating workers cooperate to train a comprehensive model by exchanging the trained parameters. While blockchain-based crowdsourcing approaches offer advantages such as data integrity and tamper-proof properties, platform designers must also address potential risks such as data leakage, de-anonymization
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Load-aware task migration algorithm toward adaptive load balancing in Edge Computing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-04 Xikang Zhu, Wenbin Yao, Wenhao Wang
The rapid advancement of the Internet of Things (IoT) is leading to more and more devices joining the network to interact with information, which requires improving the performance of IoT applications to accommodate more data, faster response times, and more complex tasks. Edge computing, as a new computing paradigm, brings resource contention and load imbalance while reducing communication overhead
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EdgeOptimizer: A programmable containerized scheduler of time-critical tasks in Kubernetes-based edge-cloud clusters Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-04 Yufei Qiao, Shihao Shen, Cheng Zhang, Wenyu Wang, Tie Qiu, Xiaofei Wang
Edge computing has garnered significant attention in recent years, leading to the evolution of more delay-sensitive applications towards a three-tier architecture with edge-cloud collaboration. Concurrently, technologies associated with containerization have been maturing. Notably, (Kubernetes) emerges as a prominent solution for the management of extensive, dynamically evolving, and intricate container
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SLA-based task offloading for energy consumption constrained workflows in fog computing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-04 Hongjian Li, Xue Zhang, Hua Li, Xiaolin Duan, Chen Xu
As an emerging computing paradigm, fog computing provides more available computing resources for Internet of Things (IoT) users in an efficient and timely manner. However, the energy consumption generated by fog computing is also further increased, which makes electricity costs and carbon emissions continue to rise. At the same time, the mobile characteristics of computing nodes in fog computing will
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Lightweight verifiable blockchain top-[formula omitted] queries Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-04 Jingxian Cheng, Saiyu Qi, Bochao An, Yong Qi, Jianfeng Wang, Yanan Qiao
Blockchain has been exploited in many applications as a fundamental technology to construct trust and share data among multiple participants. A user with limited resources who runs a light node fetches data records stored on the blockchain by requesting a full node that maintains the complete blockchain data. As a type of broadly used query, top- queries which ask for data records with the highest
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Microservice instances selection and load balancing in fog computing using deep reinforcement learning approach Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-04 Wassim Boudieb, Abdelhamid Malki, Mimoun Malki, Ahmed Badawy, Mahmoud Barhamgi
Fog-native computing is an emerging paradigm that makes it possible to build flexible and scalable Internet of Things (IoT) applications using microservice architecture at the network edge. With this paradigm, IoT applications are decomposed into multiple fine-grained microservices, strategically deployed on various fog nodes to support a wide range of IoT scenarios, such as smart cities and smart
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Predicting ride-hailing passenger demand: A POI-based adaptive clustering federated learning approach Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-03 Zhuhua Liao, Shoubin Li, Yijiang Zhao, Yizhi Liu, Wei Liang, Shaohua Wan
Passenger demand prediction is a key task for online ride-hailing platforms to optimize their resource allocation and service quality. However, centralized data collection and mining of massive passengers’ travel data expose serious privacy and security risks. To address this challenge, we propose a POI-based Adaptive Clustering Federated Learning with Spatio-Temporal Graph Attention Gate Recurrent
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ANNProof: Building a verifiable and efficient outsourced approximate nearest neighbor search system on blockchain Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-02 Lingling Lu, Zhenyu Wen, Ye Yuan, Qinming He, Jianhai Chen, Zhenguang Liu
Data-as-a-service is increasingly prevalent, with outsourced K-approximate nearest neighbors search (K-ANNS) gaining popularity in applications like similar image retrieval and anti-money laundering. However, malicious search service providers and dataset providers in current outsourced query systems cause incorrect user query results. To address this, we propose ANNProof, a novel framework supporting
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End-to-end network slicing for edge computing optimization Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-02 Ahmet Cihat Baktır, Atay Özgövde, Cem Ersoy
User-centric services proliferated by the smart devices is getting more demanding and characteristically diversified. Fall-risk assessment, augmented reality and similar services coexist in a shared heterogeneous setting. To meet the diversified and often conflicting requirements of the services, the physical network is decomposed into virtual slices. In order to address the optimal network slicing
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To store or not: Online cost optimization for running big data jobs on the cloud Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-02 Xiankun Fu, Li Pan, Shijun Liu
As businesses increasingly rely on cloud-based big data analytics services to drive insights, reducing the cost of storing and analyzing large volumes of data in the cloud has become a major concern. During the execution of big data analysis jobs, some of the generated data can be reused by subsequent jobs. By storing such intermediate data, the cost of running big data jobs can be greatly reduced
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Penetralium: Privacy-preserving and memory-efficient neural network inference at the edge Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-02 Mengda Yang, Wenzhe Yi, Juan Wang, Hongxin Hu, Xiaoyang Xu, Ziang Li
The proliferation of artificial intelligence and edge computing has led to an increase in the deployment of proprietary deep learning models on third-party edge servers or devices to power mission-critical applications. However, this trend raises concerns about model privacy, particularly on untrusted edge platforms. Protecting model privacy in such scenarios requires addressing challenges such as
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A fragmentation-aware redundancy elimination scheme for inline backup systems Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-02 Yucheng Zhang, Wenxuan Zhu, Dan Feng, Wei Huang, Nan Jiang, Meng Chen, Renxin Xia
Data deduplication is a widely employed technique in backup systems to enhance storage efficiency by eliminating duplicate chunks. Delta compression is a technique that complements deduplication by removing redundant data between similar chunks. However, when integrated into deduplication-based backup systems, delta compression can considerably decrease backup throughput due to the additional I/Os
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Recognition of best paper, outstanding editors, and outstanding reviewers for Future Generation Computer Systems in 2023 Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-28 M, i, c, h, e, l, a, , T, a, u, f, e, r
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Expanding the cloud-to-edge continuum to the IoT in serverless federated learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-28 Davide Loconte, Saverio Ieva, Agnese Pinto, Giuseppe Loseto, Floriano Scioscia, Michele Ruta
Serverless computing enables greater flexibility and efficiency in the cloud-to-edge continuum. Artificial Intelligence and Machine Learning (AI/ML) applications benefit greatly from this paradigm, as they need to gather, preprocess, aggregate and analyze data at various scales. In such contexts, the increasing hardware/software resource availability of Internet of Things (IoT) devices provides the
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A dynamic weight–assignment load balancing approach for workflow scheduling in edge-cloud computing using ameliorated moth flame and rock hyrax optimization algorithms Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-27 M, u, s, t, a, f, a, , I, b, r, a, h, i, m, , K, h, a, l, e, e, l
As the geographically distributed cloud infrastructure continues to grow in scale and the intricacy of workflow applications increases, there is a growing threat to the operational efficiency of the system. This poses the risk of resource inefficiencies and elevated energy consumption. Load balancing becomes a critical concern for this category of applications, particularly when they encounter overwhelming
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A multi-objective approach for communication reduction in federated learning under devices heterogeneity constraints Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-24 José Ángel Morell, Zakaria Abdelmoiz Dahi, Francisco Chicano, Gabriel Luque, Enrique Alba
Federated learning is a paradigm that proposes protecting data privacy by sharing local models instead of raw data during each iteration of model training. However, these models can be large, with many parameters, provoking a substantial communication cost and having a notable environmental impact. Reducing communication overhead is paramount but conflictual to maintaining the model’s accuracy. Most
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Mitigating bias in artificial intelligence: Fair data generation via causal models for transparent and explainable decision-making Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-24 Rubén González-Sendino, Emilio Serrano, Javier Bajo
In the evolving field of Artificial Intelligence, concerns have arisen about the opacity of certain models and their potential biases. This study aims to improve fairness and explainability in AI decision making. Existing bias mitigation strategies are classified as pre-training, training, and post-training approaches. This paper proposes a novel technique to create a mitigated bias dataset. This is
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Improved binary marine predator algorithm-based digital twin-assisted edge-computing offloading method Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-24 Shaoming Qiu, Jiancheng Zhao, Xuecui Zhang, Fen Chen, Yahui Wang
A vast number of mobile internet-of-things (IoT) devices are connected to the internet, and they constantly generate new computing tasks. Owing to an IoT device’s limited resources and restricted computing power, heavy computing tasks are generally offloaded to edge servers, where a digital twin (DT) of the IoT device is synchronized using the current information from the physical device, followed
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QoS-aware offloading policies for serverless functions in the Cloud-to-Edge continuum Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-23 Gabriele Russo Russo, Daniele Ferrarelli, Diana Pasquali, Valeria Cardellini, Francesco Lo Presti
Function-as-a-Service (FaaS) paradigm is increasingly attractive to bring the benefits of serverless computing to the edge of the network, besides traditional Cloud data centers. However, FaaS adoption in the emerging Cloud-to-Edge Continuum is challenging, mostly due to geographical distribution and heterogeneous resource availability. This emerging landscape calls for effective strategies to trade
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Efficient knowledge management for heterogeneous federated continual learning on resource-constrained edge devices Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-23 Zhao Yang, Shengbing Zhang, Chuxi Li, Miao Wang, Haoyang Wang, Meng Zhang
Federated learning (FL) is a promising and privacy-preserving distributed learning method that is widely deployed on edge devices. However, in practical applications, the data collected by edge devices exhibits temporal variations. This leads to the problem of FL models adapting to new data while forgetting knowledge from old data, resulting in a catastrophic forgetting issue. Continual learning methods
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Digital Twins-enabled Zero Touch Network: A smart contract and explainable AI integrated cybersecurity framework Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-20 Randhir Kumar, Ahamed Aljuhani, Danish Javeed, Prabhat Kumar, Shareeful Islam, A.K.M. Najmul Islam
Data-driven modeling using Artificial Intelligence (AI) is envisioned as a key enabling technology for Zero Touch Network (ZTN) management. Specifically, AI has shown huge potential for automating and modeling the threat detection mechanism of complicated wireless systems. The current data-driven AI systems, however, lack transparency and accountability in their decisions, and assuring the reliability
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Quantum annealing-driven branch and bound for the single machine total weighted number of tardy jobs scheduling problem Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-19 Wojciech Bożejko, Jarosław Pempera, Mariusz Uchroński, Mieczysław Wodecki
In the paper we present a new approach to solving -hard problems of discrete optimization adapted to the architecture of quantum processors (QPU, Quantum Processor Unit) implementing hardware quantum annealing. This approach is based on the use of the quantum annealing metaheuristic in the exact branch and bound algorithm to compute the lower and upper bounds of the objective function. To determine
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VFL-Chain: Bulletproofing Federated Learning in the V2X environments Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-19 Abla Smahi, Hui Li, Wang Han, Ahmed Ameen Fateh, Ching Chuen Chan
Federated Learning (FL) has gained significant traction as a promising approach to enable collaborative machine learning (ML) while safeguarding data privacy across diverse applications, with the Vehicle-to-Everything (V2X) environment being a notable use case. However, conventional FL systems remain susceptible to model poisoning attacks, wherein malicious participants can introduce inaccurate or
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Feature selection based on dataset variance optimization using Hybrid Sine Cosine – Firehawk Algorithm (HSCFHA) Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-19 Syed Kumayl Raza Moosavi, Ahsan Saadat, Zainab Abaid, Wei Ni, Kai Li, Mohsen Guizani
Feature selection plays a pivotal role in preprocessing data for machine learning (ML) models. It entails choosing a subset of pertinent features to enhance the model’s accuracy and minimize overfitting. Wrapper methods based on metaheuristics are one approach to feature selection, leveraging the predictive accuracy of a learning algorithm to form a condensed set of features. Traditionally, this method
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Serverless-like platform for container-based YARN clusters Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-17 Óscar Castellanos-Rodríguez, Roberto R. Expósito, Jonatan Enes, Guillermo L. Taboada, Juan Touriño
Serverless computing is an emerging paradigm that has gained a lot of relevance in recent years, as it allows users to consume computing resources without worrying about the underlying infrastructure and pay only for what they actually use. Most current services that implement this paradigm typically rely on the Function-as-a-Service (FaaS) model, which works perfectly for simple applications based
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A fine-grained robust performance diagnosis framework for run-time cloud applications Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-17 Ruyue Xin, Peng Chen, Paola Grosso, Zhiming Zhao
To maintain the required service quality of time-critical cloud applications, operators must continuously monitor their runtime status, detect potential performance anomalies, and diagnose the root causes of these anomalies effectively. However, existing performance diagnosis methods face challenges such as the need for high-quality labeled data, the low reusability and robustness of performance anomaly
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A highly write-optimized concurrent B+-tree for persistent memory Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-16 Yan Wei, Zhang Xingjun
Writes to persistent memory are considered expensive for the limited naive write performance of the device and the implementation of persist primitives. Write-optimized strategies are widely adopted in designing B-Trees for persistent memory. In this paper, we concluded two major issues, including successive writes and recovery efficiency, on existing write-optimizing strategies. A highly write-optimized
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Development and evaluation of a reference measurement model for assessing the resource and energy efficiency of software products and components—Green Software Measurement Model (GSMM) Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-16 Achim Guldner, Rabea Bender, Coral Calero, Giovanni S. Fernando, Markus Funke, Jens Gröger, Lorenz M. Hilty, Julian Hörnschemeyer, Geerd-Dietger Hoffmann, Dennis Junger, Tom Kennes, Sandro Kreten, Patricia Lago, Franziska Mai, Ivano Malavolta, Julien Murach, Kira Obergöker, Benno Schmidt, Arne Tarara, Joseph P. De Veaugh-Geiss, Sebastian Weber, Max Westing, Volker Wohlgemuth, Stefan Naumann
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An optimization framework for task allocation in the edge/hub/cloud paradigm Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-16 Andreas Kouloumpris, Georgios L. Stavrinides, Maria K. Michael, Theocharis Theocharides
With the advent of the Internet of Things (IoT), novel critical applications have emerged that leverage the edge/hub/cloud paradigm, which diverges from the conventional edge computing perspective. A growing number of such applications require a streamlined architecture for their effective execution, often comprising a single edge device with sensing capabilities, a single hub device (e.g., a laptop
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GridMesa: A NoSQL-based big spatial data management system with an adaptive grid approximation model Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-14 Xiangyang Yang, Xuefeng Guan, Zhaoxing Pang, Xing Kui, Huayi Wu
Due to the urgent demand for managing massive spatial data, various spatial data management systems built on distributed NoSQL databases have emerged. However, current systems usually employ coarse MBR to approximate spatial objects in order to accelerate spatial operations; this leads to numerous mismatched spatial objects entering the time-consuming refinement process and thus limits retrieval efficiency
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PAC: A monitoring framework for performance analysis of compression algorithms in Spark Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-13 Changpeng Zhu, Bo Han, Gang Li
In Spark, a massive amount of immediate data inevitably leads to excessive I/O overhead. To mitigate this issue, Spark incorporates four compression algorithms to reduce the size of the data for better performance. However, compression and decompression only constitute a portion of the overall logical flows of Spark applications. This indicates a potential considerable interaction between compression
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A digital twin-based energy-efficient wireless multimedia sensor network for waterbirds monitoring Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-13 Aya Sakhri, Arsalan Ahmed, Moufida Maimour, Mehdi Kherbache, Eric Rondeau, Noureddine Doghmane
Wetlands play a critical role in maintaining the global climate, regulating the hydrological cycle, and protecting human health. However, they are rapidly disappearing due to human activities. Waterbirds are valuable bio-indicators of wetland health, but it is challenging to monitor them effectively. Wireless Multimedia Sensor Networks (WMSNs) offer a promising technology for monitoring wetlands. Nonetheless
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Microservices and serverless functions—lifecycle, performance, and resource utilisation of edge based real-time IoT analytics Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-12 Francesco Tusa, Stuart Clayman, Alina Buzachis, Maria Fazio
harnesses resources close to the data sources to reduce end-to-end latency and allow process automation for verticals such as Smart City, Healthcare and Industry 4.0. resources are limited when compared to traditional data centres; hence the choice of proper resource management strategies in this context becomes paramount. and architectures support modular and agile patterns, compared to a monolithic
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Connection-density-aware satellite-ground federated learning via asynchronous dynamic aggregation Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-12 Zhuo Xu, Mengqing Jin, Jian Lin, Yuelong Liu, Jianlong Xu, Zhi Xiong, Hao Cai
With the development of space technology, the use of satellites for Earth observation is becoming more and more common. Among them, low-earth orbit (LEO) satellites have received a lot of attention because of their short return period and low cost. However, traditional satellite systems are unable to process data in orbit due to technical limitations and can only transmit massive amounts of data with
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A balanced and reliable data replica placement scheme based on reinforcement learning in edge–cloud environments Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-12 Mengke Zheng, Xin Du, Zhihui Lu, Qiang Duan
With the rapid development of edge–cloud computing, distributing resources to edge nodes and terminal devices to provide high-quality services for latency-sensitive applications and reduce network communication costs has become increasingly important. However, the complexity and heterogeneity of the edge–cloud environment pose significant challenges to the reliability of data storage and device load
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Autonomous proactive data management in support of pervasive edge applications Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-07 Kostas Kolomvatsos, Christos Anagnostopoulos
Recently, context-aware data management becomes the focus of many research efforts placed at the intersection between the Internet of Things (IoT) and Edge Computing (EC). Huge volumes of data can be collected by IoT devices being ‘connected’ with EC environments transferring data towards the Cloud. EC nodes undertake the responsibility of managing the collected data, however, they are characterized
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Robust indoor localization based on multi-modal information fusion and multi-scale sequential feature extraction Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-05 Qinghu Wang, Jie Jia, Jian Chen, Yansha Deng, Xingwei Wang, Abdol Hamid Aghvami
Magnetic-assisted indoor localization has attracted significant attention because of its commercial and social values. However, it is challenging to construct a robust and accurate system due to the severe feature ambiguity caused by different users, mobiles, attitudes, and moving speeds. In order to cope with this issue, we first propose to fuse magnetic with the multi-modal features, including Bluetooth
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Efficient and precise visual location estimation by effective priority matching-based pose verification in edge-cloud collaborative IoT Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-03 Ning Li, Xiaojun Ren, Aniello Castiglione, Mengyun Liu
A robust visual location estimation scheme in large-scale complex scenes is critical for location-relevant Internet of Things (IoT) applications such as autonomous vehicles and intelligent robots. However, it is challenging due to viewpoint changes, weak textures, and large-view scenes. To address the location ambiguities arising and positioning timeliness in complex scenes, we propose an efficient
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HazardNet: A thermal hazard prediction framework for datacenters Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-01 Mohsen Seyedkazemi Ardebili, Andrea Acquaviva, Luca Benini, Andrea Bartolini
Modern scientific discoveries rely on an insatiable demand for computational resources. To meet this ever-growing computing demand, the datacenters have been established, which are complex controlled environments that host thousands of computing nodes, storage, high-performance communication networks, cooling systems, etc. A datacenter consumes a large amount of electrical power (in the range of megawatts)
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Joint optimization of multi-dimensional resource allocation and task offloading for QoE enhancement in Cloud-Edge-End collaboration Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-01-29 Chao Zeng, Xingwei Wang, Rongfei Zeng, Ying Li, Jianzhi Shi, Min Huang
Cloud-Edge-End Collaboration (CEEC) computing architecture inherits many merits from both edge computing and cloud computing and thus is considered as a promising candidate for future network services. In CEEC, user’s QoE is impacted by offload performance which should consider offload strategy, computational resources and network status simultaneously. However, previous offload optimization studies
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Towards a digital twin architecture for the lighting industry Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-02 Victor Guerra, Benoit Hamon, Benoit Bataillou, Adwait Inamdar, Willem D. van Driel
This paper introduces an ontology-based Digital Twin (DT) architecture for the lighting industry, integrating simulation models, data analytics, and visualization to represent luminaires. The ontology standardizes luminaire components, facilitating interoperability with design tools. The calculated ontology-level metrics suggest mid-level complexity with Size Of Vocabulary (SOV) at 37, Edge-to-Node
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Stream clustering guided supervised learning for classifying NIDS alerts Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-02 Risto Vaarandi, Alejandro Guerra-Manzanares
A Network Intrusion Detection System (NIDS) is a network monitoring technology for identifying cyber attacks, botnet command and control traffic, and other unwanted network activity. Unfortunately, organizational NIDS solutions can often generate tens or hundreds of thousands of alerts on a daily basis, with a significant part of them having low importance or being false positives. Therefore, high
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DAI-NET: Toward communication-aware collaborative training for the industrial edge Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-01 Christine Mwase, Yi Jin, Tomi Westerlund, Hannu Tenhunen, Zhuo Zou
The industrial edge generates an abundance of spatially distributed and dynamic data that needs to remain on-site for privacy and security reasons. Collaborative training at the edge can leverage this data to refine pre-trained models locally for specific industrial tasks and environments and have them adapt to local changes for enhanced performance, agility, and resilience. However, communication
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Road to efficiency: Mobility-driven joint task offloading and resource utilization protocol for connected vehicle networks Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-02-01 Oğuzhan Akyıldız, Feyza Yıldırım Okay, İbrahim Kök, Suat Özdemir
Connected Vehicle Networks (CVNs) is an emerging technology that enables vehicles to communicate with each other and with various Internet of Things (IoT) devices of the transportation infrastructure to enhance safety, efficiency, and convenience. In CVN, task offloading is a critical issue due to utilizing high resource computation and dynamic network changes. Specifically, the dynamically changing
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A reference architecture for serverless big data processing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-01-30 Sebastian Werner, Stefan Tai
Despite significant advances in data management systems in recent decades, the processing of big data at scale remains very challenging. While cloud computing has been well-accepted as a solution to address scalability needs, cloud configuration and operation complexity persist and often present themselves as entry barriers, especially for novice data analysts. Serverless computing and Function-as-a-Service
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Prebaking runtime environments to improve the FaaS cold start latency Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-01-29 Daniel Fireman, Paulo Silva, Thiago Emmanuel Pereira, Luis Mafra, Dalton Valadares
Function-as-service (FaaS) platforms promise a simpler programming model for cloud computing, given that providers take care of the overall resource management while the developers can concentrate only on writing their applications in the scope of a function, not having to care about installing or scaling resources. As FaaS users are billed based on the execution of the functions, platform providers
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Replay with Feedback: How does the performance of HPC system impact user submission behavior? Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-01-29 Maël Madon, Georges Da Costa, Jean-Marc Pierson
High Performance Computing (HPC) is a key infrastructure to solve large scale scientific problems, from weather to quantum simulations. Scheduling jobs in HPC infrastructures is complex due to their scale, the different behaviors of their users, and the multiple objectives, from performance to ecological impact. Schedulers are evaluated on data center simulations, due to the complexity and cost of