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Online RL-based cloud autoscaling for scientific workflows: Evaluation of Q-Learning and SARSA Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-15 Yisel Garí, Elina Pacini, Luciano Robino, Cristian Mateos, David A. Monge
Q-Learning and SARSA are two well-known reinforcement learning (RL) algorithms that have shown promising results in several application domains. However, their approach to build solutions is quite different. For example, SARSA tends to be more conservative than Q-Learning while exploring the solution space. Motivated by such differences, in this paper, we conducted an evaluation of both algorithms
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Principled and automated system of systems composition using an ontological architecture Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-10 Abdessalam Elhabbash, Yehia Elkhatib, Vatsala Nundloll, Vicent Sanz Marco, Gordon S. Blair
A distributed system’s functionality must continuously evolve, especially when environmental context changes. Such required evolution imposes unbearable complexity on system development. An alternative is to make systems able to self-adapt by opportunistically composing at runtime to generate (SoSs) that offer value-added functionality. The success of such an approach calls for abstracting the heterogeneity
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Quantum particle swarm optimization algorithm based on diversity migration strategy Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-09 Chen Gong, Nanrun Zhou, Shuhua Xia, Shuiyuan Huang
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Pro-active component image placement in Edge computing environments Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-09 Antonios Makris, Evangelos Psomakelis, Emanuele Carlini, Matteo Mordacchini, Theodoros Theodoropoulos, Patrizio Dazzi, Konstantinos Tserpes
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Multi-objective optimization-based workflow scheduling for applications with data locality and deadline constraints in geo-distributed clouds Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Dongkuo Wu, Xingwei Wang, Xueyi Wang, Min Huang, Rongfei Zeng, Kaiqi Yang
Geo-distributed clouds have emerged as a new generation of cloud computing paradigm, in which each cloud is operated and managed by independent cloud service providers (CSPs). By enhancing cooperation among CSPs, it can offer efficient cross-cloud services. In geo-distributed clouds, the resources offered by CSPs are heterogeneous with different billing mechanisms and the data required by workflow
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An assignment mechanism for workflow scheduling in Function as a Service edge environment Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Samaneh Hajy Mahdizadeh, Saeid Abrishami
Serverless computing has revolutionized cloud-based software development for software developers, addressing many of the associated challenges. With resource management and infrastructure provisioning handled by the provider, developers can focus on deploying services at the application level, which has gained significant popularity. Edge computing, with its proximity to end-users and ability to offer
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A sustainable smart IoT-based solid waste management system Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Amira Henaien, Hadda Ben Elhadj, Lamia Chaari Fourati
In this paper, we present a sustainable Smart City Solid Waste Management System (SCSWMS) that integrates trending technologies such as Internet of Things (IoT), Low Power Wide Area Networks (LPWANs), and Intelligent Traffic Systems (ITS) to improve solid garbage management from its inception through disposal. The Proposed SCSWMS involves three main subsystems: Smart Garbage Bins (SGBs), Smart Garbage
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Enabling DevOps for Fog Applications in the Smart Manufacturing domain: A Model-Driven based Platform Engineering approach Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Julen Cuadra, Ekaitz Hurtado, Isabel Sarachaga, Elisabet Estévez, Oskar Casquero, Aintzane Armentia
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Towards energy and QoS aware dynamic VM consolidation in a multi-resource cloud Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Sounak Banerjee, Sarbani Roy, Sunirmal Khatua
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Optimizing fog device deployment for maximal network connectivity and edge coverage using metaheuristic algorithm Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-07 Satveer Singh, Eht E Sham, Deo Prakash Vidyarthi
Fog computing emerged to address the limitations and challenges of traditional Cloud computing, particularly in handling real-time, heterogeneous, and latency-sensitive applications. However, the spread of Fog computing devices across the network introduces various challenges, especially concerning device connectivity and ensuring sufficient coverage to fulfil users’ requests. To maintain network operability
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Mobility-aware personalized handover function provisioning system in B5G networks Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-07 Haneul Ko, Yeunwoong Kyung, Jaewook Lee, Sangheon Pack, Namseok Ko
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Potential-based reward shaping using state–space segmentation for efficiency in reinforcement learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-07 Melis İlayda Bal, Hüseyin Aydın, Cem İyigün, Faruk Polat
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An automated framework for selectively tolerating SDC errors based on rigorous instruction-level vulnerability assessment Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-06 Hussien Al-haj Ahmad, Yasser Sedaghat
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Faster or Cheaper: A Q-learning based cost-effective mixed cluster scaling method for achieving low tail latencies Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-06 Hao Yang, Li Pan, Shijun Liu
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kubeFlower: A privacy-preserving framework for Kubernetes-based federated learning in cloud–edge environments Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-04 Juan Marcelo Parra-Ullauri, Hari Madhukumar, Adrian-Cristian Nicolaescu, Xunzheng Zhang, Anderson Bravalheri, Rasheed Hussain, Xenofon Vasilakos, Reza Nejabati, Dimitra Simeonidou
Federated Learning (FL) enables collaborative model training across edge devices while preserving data locally. Deploying FL faces challenges due to device heterogeneity. Using cloud technologies like Kubernetes (K8s) can offer computational elasticity, yet may compromise FL privacy principles. K8s can jeopardise FL privacy by potentially allowing malicious FL clients to access other resources given
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A new approach to Mergesort algorithm: Divide smart and conquer Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-03 Sahin Emrah Amrahov, Yilmaz Ar, Bulent Tugrul, Bekir Emirhan Akay, Nermin Kartli
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GenArchBench: A genomics benchmark suite for arm HPC processors Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-02 Lorién López-Villellas, Rubén Langarita-Benítez, Asaf Badouh, Víctor Soria-Pardos, Quim Aguado-Puig, Guillem López-Paradís, Max Doblas, Javier Setoain, Chulho Kim, Makoto Ono, Adrià Armejach, Santiago Marco-Sola, Jesús Alastruey-Benedé, Pablo Ibáñez, Miquel Moretó
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Edge model: An efficient method to identify and reduce the effectiveness of malicious clients in federated learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-02 Mahdi shahraki, Amir Jalaly Bidgoly
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CoTwin: Collaborative improvement of digital twins enabled by blockchain Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-02 Marisol García-Valls, Alejandro M. Chirivella-Ciruelos
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Lightweight block ciphers for resource-constrained environments: A comprehensive survey Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-01 Yue Zhong, Jieming Gu
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Exploiting microservices and serverless for Digital Twins in the cloud-to-edge continuum Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-01 Paolo Bellavista, Nicola Bicocchi, Mattia Fogli, Carlo Giannelli, Marco Mamei, Marco Picone
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Efficient decentralized optimization for edge-enabled smart manufacturing: A federated learning-based framework Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-30 Huan Liu, Shiyong Li, Wenzhe Li, Wei Sun
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A scalable multi-density clustering approach to detect city hotspots in a smart city Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-28 Eugenio Cesario, Paolo Lindia, Andrea Vinci
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QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-27 Nathaniel Hudson, Hana Khamfroush, Matt Baughman, Daniel E. Lucani, Kyle Chard, Ian Foster
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Enabling privacy-aware interoperable and quality IoT data sharing with context Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-27 Tek Raj Chhetri, Chinmaya Kumar Dehury, Blesson Varghese, Anna Fensel, Satish Narayana Srirama, Rance J. DeLong
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A trust and privacy-preserving intelligent big data collection scheme in mobile edge-cloud crowdsourcing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-26 Zihui Sun, Anfeng Liu, Neal N. Xiong, Qian He, Shaobo Zhang
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Accelerating range minimum queries with ray tracing cores Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-26 Enzo Meneses, Cristóbal A. Navarro, Héctor Ferrada, Felipe A. Quezada
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LPP-BPSI: A location privacy-preserving scheme using blockchain and Private Set Intersection in spatial crowdsourcing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-25 Libo Feng, Yifan Liu, Kai Hu, Xue Zeng, Fake Fang, Jiale Xie, Shaowen Yao
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NS+NDT: Smart integration of Network Simulation in Network Digital Twin, application to IoT networks Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-25 Samir Si-Mohammed, Anthony Bardou, Thomas Begin, Isabelle Guérin Lassous, Pascale Vicat-Blanc
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Validity constraints for data analysis workflows Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-25 Florian Schintke, Khalid Belhajjame, Ninon De Mecquenem, David Frantz, Vanessa Emanuela Guarino, Marcus Hilbrich, Fabian Lehmann, Paolo Missier, Rebecca Sattler, Jan Arne Sparka, Daniel T. Speckhard, Hermann Stolte, Anh Duc Vu, Ulf Leser
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Fluidity: Providing flexible deployment and adaptation policy experimentation for serverless and distributed applications spanning cloud–edge–mobile environments Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-21 Foivos Pournaropoulos, Alexandros Patras, Christos D. Antonopoulos, Nikos Bellas, Spyros Lalis
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MiniPFL: Mini federations for hierarchical personalized federated learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-21 Yuwei Fan, Wei Xi, Hengyi Zhu, Jizhong Zhao
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Towards providing a priority-based vital sign offloading in healthcare with serverless computing and a fog-cloud architecture Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-21 Gustavo André Setti Cassel, Rodrigo da Rosa Righi, Cristiano André da Costa, Marta Rosecler Bez, Marcelo Pasin
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BDPM: A secure batch dynamic password management scheme in industrial internet environments Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-20 Jingyu Feng, Rui Yan, Gang Han, Wenbo Zhang
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A Big Data architecture for early identification and categorization of dark web sites Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-20 Javier Pastor-Galindo, Hông-Ân Sandlin, Félix Gómez Mármol, Gérôme Bovet, Gregorio Martínez Pérez
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Grassroots operator search for model edge adaptation using mathematical search space Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-19 Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smail Niar
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Enhancing generalization in Federated Learning with heterogeneous data: A comparative literature review Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-18 Alessio Mora, Armir Bujari, Paolo Bellavista
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LogETA: Time-aware cross-system log-based anomaly detection with inter-class boundary optimization Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-18 Kun Gong, Senlin Luo, Limin Pan, Linghao Zhang, Yifei Zhang, Haomiao Yu
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Coarse-to-Fine: A hierarchical DNN inference framework for edge computing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-16 Zao Zhang, Yuning Zhang, Wei Bao, Changyang Li, Dong Yuan
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A transferred spatio-temporal deep model based on multi-LSTM auto-encoder for air pollution time series missing value imputation Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-15 Xiaoxia Zhang, Pengcheng Zhou
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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
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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