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

{"title":"Let’s trace it: Fine-grained serverless benchmarking for synchronous and asynchronous applications","authors":"Joel Scheuner ,&nbsp;Simon Eismann ,&nbsp;Sacheendra Talluri ,&nbsp;Erwin van Eyk ,&nbsp;Cristina Abad ,&nbsp;Philipp Leitner ,&nbsp;Alexandru Iosup","doi":"10.1016/j.future.2025.108336","DOIUrl":"10.1016/j.future.2025.108336","url":null,"abstract":"<div><div>Making serverless computing widely applicable requires detailed understanding of performance. Although benchmarking approaches exist, their insights are coarse-grained and typically insufficient for (root cause) analysis of realistic serverless applications, which often consist of asynchronously coordinated functions and services. Addressing this gap, we design and implement ServiTrace, an approach for fine-grained distributed trace analysis and an application-level benchmarking suite for diverse serverless-application architectures. ServiTrace (i) analyzes distributed serverless traces using a novel algorithm and heuristics for extracting a detailed <em>latency breakdown</em>, (ii) leverages a suite of serverless applications representative of production usage, including <em>synchronous and asynchronous serverless applications</em> with external service integrations, and (iii) automates comprehensive, <em>end-to-end experiments</em> to capture application-level performance. Using our ServiTrace reference implementation, we conduct a large-scale empirical performance study in the market-leading AWS environment, collecting over 7.5 million execution traces. We make four main observations enabled by our <em>latency breakdown analysis</em> of median latency, cold starts, and tail latency for different application types and invocation patterns. For example, the median end-to-end latency of serverless applications is often dominated not by function computation but by external service calls, orchestration, and trigger-based coordination; all of which could be hidden without ServiTrace-like benchmarking. We release empirical data under FAIR principles and ServiTrace as a tested, extensible, open-source tool at <span><span>https://github.com/ServiTrace/ReplicationPackage</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108336"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845124","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":"Optimizing deep reinforcement learning through vectorized and parallel NeuroEvolution","authors":"Hesham Magdy,&nbsp;Amany M. Sarhan,&nbsp;Mohammad Ali Eita","doi":"10.1016/j.future.2025.108334","DOIUrl":"10.1016/j.future.2025.108334","url":null,"abstract":"<div><div>Deep reinforcement learning (DRL) has achieved remarkable success in solving complex decision-making problems; however, training efficiency, scalability, and ease of implementation remain significant challenges. It is evident that the advances in hardware accelerators such as GPUs and TPUs can reduce the time obstacle that faces the development in the deep learning field. As a novel approach, we propose a framework for NeuroEvolution-based deep reinforcement learning, leveraging JAX, a high-performance numerical computing library optimized for machine learning and automatic differentiation, for efficient parallel execution. Our framework enables seamless vectorized policy optimization, significantly reducing computational overhead while maintaining sample efficiency. We introduce a user-friendly interface designed for accessibility and flexibility, allowing researchers to easily experiment with diverse evolutionary strategies, leading to broader exploration and improved performance in certain environments. Additionally, we extend the application of NeuroEvolution-based DRL to environments that have not been previously explored using such methods, further demonstrating the versatility of our approach. Finally, we incorporate recent evolutionary algorithms into the training process, achieving better results. Through extensive benchmarking, we show that our framework outperforms traditional evolutionary strategies and gradient-based DRL methods in both convergence speed and scalability, achieving a speedup of up to 34 × compared to state-of-the-art approaches.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108334"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813865","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":"Resource-Efficient joint clustering and storage optimization for blockchain-Based IoT systems","authors":"Kai Peng ,&nbsp;Xueyan Hu ,&nbsp;Jiaxing Hu ,&nbsp;Zhiheng Yao ,&nbsp;Tianping Deng ,&nbsp;Menglan Hu ,&nbsp;Chao Cai ,&nbsp;Zehui Xiong","doi":"10.1016/j.future.2025.108354","DOIUrl":"10.1016/j.future.2025.108354","url":null,"abstract":"<div><div>Blockchain technology is leveraged in the Internet of Things (IoT) systems to enhance data reliability and management efficiency, ensuring integrity, security, and auditability through a decentralized ledger architecture. However, resource-constrained IoT devices are unable to store the complete blockchain due to prohibitive resource consumption and performance degradation. While collaborative storage strategies have been proposed to mitigate these constraints, existing approaches often prioritize storage scalability without sufficiently addressing the selection of cooperative nodes for distributed ledger maintenance. This can lead to significant communication delays during block retrieval, undermining the real-time performance and overall efficiency of the blockchain-enabled IoT system. To address this challenge, this paper introduces a clustering-based collaborative storage scheme and proposes a novel joint optimization algorithm that iteratively refines both node clustering and block allocation strategies within the blockchain network. By structuring IoT devices into clustered peers, the algorithm reduces block query latency and facilitates efficient blockchain synchronization and update processes. Experimental evaluations confirm that the proposed method effectively alleviates storage limitations and lowers access costs in static blockchain-based IoT environments.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108354"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885835","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":"Edge-based proactive and stable two-tier routing for IoV","authors":"Asif Mehmood ,&nbsp;Muhammad Afaq ,&nbsp;Faisal Mehmood ,&nbsp;Wang-Cheol Song","doi":"10.1016/j.future.2026.108367","DOIUrl":"10.1016/j.future.2026.108367","url":null,"abstract":"<div><div>The Internet of Vehicles (IoV) is an evolving domain fueled by advancements in vehicular communications and networking. To enhance vehicle coverage, integrating vehicle-to-everything (V2X) networks with cellular networks has become essential, though this integration places increased demand on cellular infrastructure. To address this, we propose a two-tier stable path routing algorithm designed to improve the stability and proactiveness of V2X networks. Our approach divides the coverage area into zones, further segmented into road segments based on road structure. The first tier manages routing within a road segment, while the second tier handles routing between vehicles in adjacent segments. This method improves road awareness, stabilizes topologies, adapts to dynamic changes, and reduces routing overhead. Additionally, the incorporation of Kalman filter-based prediction model further strengthens proactive routing. To validate the proposed approach, we conduct synthetic evaluations across varying vehicular densities with different mobility and traffic scenarios. We compare the traditional centralized routing strategy with the proposed distributed two-tier mechanism to assess execution cost, end-to-end latency, network resource consumption, data rates, packet flow, and packet loss. Quantified results demonstrate that our two-tier approach reduces the average execution cost from 438.61 to 230.48, lowers average latency from 232.34 ms to 129.03 ms, and minimizes average network consumption from 231.26 MB to 129.39 MB. The proposed approach continues to significantly enhance data rates, reduce packet flow processing, decrease packet loss across various routing strategies. Overall, the proposed solution enhances stability, responsiveness, and robustness of V2X communication, making it suitable for future large-scale IoV deployments.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108367"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956907","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":"Probabilistic response time analysis for preemption threshold scheduling in real-time systems","authors":"Jin-Long Zhang,&nbsp;Yi-Wen Zhang","doi":"10.1016/j.future.2025.108363","DOIUrl":"10.1016/j.future.2025.108363","url":null,"abstract":"<div><div>Probabilistic real-time systems employ probabilistic models to characterize the dynamic variations and uncertainties in task attributes. Such systems can tolerate occasional deadline misses, provided that their occurrence probability does not exceed a predefined threshold. However, existing scheduling approaches for probabilistic real-time systems often result in excessive preemption overhead or suffer from limited scheduling flexibility, leading to suboptimal system performance. To address this issue, this paper investigates the application of preemption threshold scheduling in probabilistic real-time systems, derives the corresponding probabilistic response time analysis, and proposes a priority allocation algorithm (MDMP-MTBPA) that aims to minimize the deadline miss probability while maximizing the tolerable blocking time. Experimental evaluations indicate that, compared to existing fixed-priority scheduling strategies, the proposed approach can improve schedulability, with results showing an average improvement of 23.7%.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108363"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893749","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":"Large-scale HPC approaches and applications on highly distributed platforms.","authors":"Alessia Antelmi ,&nbsp;Emanuele Carlini","doi":"10.1016/j.future.2025.108365","DOIUrl":"10.1016/j.future.2025.108365","url":null,"abstract":"<div><div>The ever-increasing complexity of scientific and industrial challenges due to the enormous amount of data available nowadays requires advanced high-performance computing (HPC) solutions capable of processing and analyzing data efficiently on highly distributed platforms. Traditional centralized HPC systems frequently fall short of the demands of contemporary large-scale applications (e.g., large language models), prompting a move towards more flexible and scalable distributed computing environments. Furthermore, the growing emphasis on the environmental impact of large-scale computing has highlighted the need for sustainable computing practices that minimize energy consumption and carbon footprint. This special issue targets contributions that investigate both the challenges and the opportunities arising from this evolution. The accepted articles highlight enhancements in five key areas: <em>(i)</em> HPC in the cloud continuum, <em>(ii)</em> heterogeneous HPC architectures, performance tools, and programming models, <em>(iii)</em> parallel and distributed algorithms and applications, <em>(iv)</em> data management and storage systems, and <em>(v)</em> sustainable and energy-efficient HPC systems. In total, 29 submissions were received, and 20 papers were selected after a rigorous peer-review process. Collectively, these contributions provide a representative snapshot of current research efforts towards resilient, efficient, and sustainable HPC approaches and applications on highly distributed platforms.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108365"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957075","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":"LE OS: A lightweight edge operating system for industrial internet of things under resource constraints","authors":"Xianhui Liu ,&nbsp;Yangyang Yang ,&nbsp;Chenlin Zhu ,&nbsp;Yihan Hu ,&nbsp;Weidong Zhao","doi":"10.1016/j.future.2025.108360","DOIUrl":"10.1016/j.future.2025.108360","url":null,"abstract":"<div><div>With the rise of Industry 4.0 and edge computing, intelligent manufacturing has undergone rapid development. However, existing research on operating systems for resource-constrained edge devices still exhibits significant limitations: mainstream operating systems require large hardware resources and lack adaptability for edge deployment; the industrial Internet lacks a unified and efficient scheduling and management framework for large-scale devices; and traditional monolithic systems suffer from tight component coupling, where a single component failure can cause system-wide crashes, threatening production stability. To address these challenges, this paper proposes LE OS, a lightweight edge operating system tailored for resource-constrained industrial Internet environments. LE OS leverages container technology to encapsulate system-level components into functional system containers and integrates them with the seL4 microkernel, forming a lightweight, containerized microkernel operating system.Experimental evaluation shows that LE OS improves CPU and I/O performance by 10%-40% and reduces system-level memory usage by over 70% compared with mainstream operating systems, while maintaining high resource efficiency and strong isolation. These results demonstrate that LE OS effectively overcomes the limitations of existing systems and provides a practical and scalable foundation for next-generation industrial Internet edge operating systems.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108360"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957077","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":"An in-depth study of GPU frequency-scaling latency and its optimization on modern architectures","authors":"Daniel Velicka,&nbsp;Ondrej Vysocky,&nbsp;Osman Yasal,&nbsp;Lubomir Riha","doi":"10.1016/j.future.2025.108331","DOIUrl":"10.1016/j.future.2025.108331","url":null,"abstract":"<div><div>The move towards the exascale systems in High-Performance Computing and the demand for Artificial Intelligence brought together thousands of CPUs and even more GPU accelerators. This massive hardware consolidation has made energy optimization a critical challenge. The immense amount of energy consumption creates a cascade of secondary issues: it increases the carbon footprint, generates significant heat that demands advanced cooling, and causes dramatic power fluctuations that threaten the stability of the electrical grid. Although energy-saving techniques based on Dynamic Voltage and Frequency Scaling are well understood for CPUs, a critical knowledge gap exists for GPU accelerators, limiting the ability to apply similar optimizations.</div><div>This paper presents a method for measuring how long it takes the CPU to adjust the operating frequency of the GPU (switching latency), and how long the frequency change itself takes to complete (transition latency). The approach employs a minimal iterative workload that allows statistically distinguishing runtime differences between frequency pairs. It first measures execution times for each frequency and then determines the switching and transition latency of the change from an initial to a target frequency by tracking runtime changes and repeating measurements to ensure statistical robustness. Finally, the methodology filters out outliers from external factors such as driver management or system interruptions.</div><div>The methodology is implemented in the open-source LATEST [1] tool with support for NVIDIA GPU accelerators. It is evaluated on three GPUs based on different generations of architecture, GH200, A100–SXM4, and RTX Quadro 6000. These results show that the transition latency takes from hundreds of microseconds up to hundreds of milliseconds, while the absolute majority of the time is spent in the GPU applying the frequency change. From the analysed GPUs, the GH200 exhibited the widest range, with switching latencies spanning from 5.6 ms to 477 ms and transition latencies from 0.2 ms to 471 ms. Additionally, the transition latency measurement can be used to identify manufacturing variability of accelerators, showing differences in frequency scaling reactivity.</div><div>Our analysis identifies specific frequency pairs with high switching latencies, creating a challenge that the slow transitions discourage their use, yet the target frequencies themselves may be highly efficient in terms of energy consumption. To address this, we introduce an indirect switching method that leverages an intermediate frequency. This technique effectively circumvents overhead, allowing the system to access these efficient frequency states without the high latency penalty of a direct transition. The use of the indirect frequency switching technique produced a latency reduction between 250 and 431 ms, for a single frequency change on GH200.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108331"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823159","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":"ProtoFedGAN: A novel federated learning framework for training generative adversarial networks via dynamic dual-prototype alignment","authors":"Zhigang Wang,&nbsp;Yuzi Li,&nbsp;Qinghua Zhang,&nbsp;Junfeng Zhao","doi":"10.1016/j.future.2025.108353","DOIUrl":"10.1016/j.future.2025.108353","url":null,"abstract":"<div><div>Generative Adversarial Networks (GANs) have demonstrated significant potential in data-generation tasks. However, traditional centralized training requires the sharing of raw data, which poses risks of sensitive information leakage. Federated learning offers a solution, leading to the development of Federated GANs. This approach mitigates the risk to some extent by enabling distributed training without exchanging raw data. Nevertheless, existing Federated GAN frameworks face challenges in real-world scenarios characterized by heterogeneous client data and heterogeneous client models, including degraded generation performance, mode collapse, and potential privacy leaks. To address these challenges, this paper proposes ProtoFedGAN, a Federated Generative Adversarial Network based on Dynamic Dual-Prototype Alignment. Specifically, ProtoFedGAN introduces a prototype learning-based federated knowledge-sharing paradigm, which abstracts local client features into lightweight class prototypes and dynamically aggregates them on the server. This approach facilitates knowledge sharing among heterogeneous client models, enhances privacy protection through feature abstraction, and reduces communication overhead. Furthermore, a latent space alignment mechanism is proposed to enforce consistency between client generators’ latent spaces and the global distribution, coupled with a dynamic prototype aggregator that mitigates feature shifts induced by non-independent and identically distributed (Non-IID) data through similarity-weighted parameter adjustment. Finally, a dual-prototype-driven generation enhancement strategy is proposed, where the Main Prototype ensures global distribution stability by anchoring consensus features across clients, while the subprototypes promote multi-modal feature expression, thereby jointly optimizing both realism and diversity in generated data. Experimental results across four benchmark datasets (MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100) demonstrate that ProtoFedGAN consistently achieves the lowest FID, KL, and MMD, and the highest IS under both IID and Non-IID settings, outperforming recent federated GANs such as CAP-GAN, IFL-GAN, PRIVATE FL-GAN, and PerFED-GAN, particularly in heterogeneous environments.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108353"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845126","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":"Semi-asynchronous energy-efficient federated prototype learning for end-edge-cloud architectures","authors":"Wendian Luo ,&nbsp;Tong Yu ,&nbsp;Shengxin Dai ,&nbsp;Bing Guo ,&nbsp;Xuesen Lin ,&nbsp;Yanglin Pu","doi":"10.1016/j.future.2025.108351","DOIUrl":"10.1016/j.future.2025.108351","url":null,"abstract":"<div><div>As Industry 5.0 advances rapidly, the Industrial Internet of Things (IIoT) integrates Artificial Intelligence (AI) technology to significantly enhance the intelligence of production processes. However, this advancement results in faster data generation and a higher demand for data processing in industrial scenarios. This leads to the sustained high-load operation of edge devices and cloud servers, which increases carbon emissions and raises concerns about data security. Implementing Federated Learning (FL) in IIoT frameworks effectively distributes the computational burden between the client and server, resolving privacy issues and enhancing energy efficiency. However, achieving energy efficiency while improving model performance is challenging within an IIoT system marked by heterogeneous AI models and imbalanced data. We present a semi-asynchronous, energy-efficient, federated prototype learning approach tailored to tackle these challenges with end-edge-cloud architectures. This method uploads data distribution instead of the raw data for privacy protection, employing Dynamic Voltage and Frequency Scaling (DVFS) technology to manage power consumption during training, thus achieving optimal energy efficiency. To boost model performance, we confront data imbalance by collecting feature distribution data from clients, generating virtual samples on the cloud server, and training a global classifier to promote local client learning. Our experiments across various datasets, including industrial datasets and large-scale heterogeneous scenarios, demonstrate that the proposed method enhances model accuracy and significantly reduces energy consumption compared to competitive methods, thereby validating its applicability in real-world, diverse environments.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108351"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893753","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}