Cluster Computing最新文献

{"title":"Empowering bonobo optimizer for global optimization and cloud scheduling problem","authors":"Reham R. Mostafa, Fatma A. Hashim, Amit Chhabra, Ghaith Manita, Yaning Xiao","doi":"10.1007/s10586-024-04671-5","DOIUrl":"https://doi.org/10.1007/s10586-024-04671-5","url":null,"abstract":"<p>Task scheduling in cloud computing systems is an important and challenging NP-Hard problem that involves the decision to allocate resources to tasks in a way that optimizes a performance metric. The complexity of this problem rises due to the size and scale of cloud systems, the heterogeneity of cloud resources and tasks, and the dynamic nature of cloud resources. Metaheuristics are a class of algorithms that have been used effectively to solve NP-Hard cloud scheduling problems (CSP). Bonobo optimizer (BO) is a recent metaheuristic-based optimization algorithm, which mimics several interesting reproductive strategies and social behaviour of Bonobos and has shown competitive performance against several state-of-the-art metaheuristics for many optimization problems. Besides its good performance, it still suffers from inherent deficiencies such as imbalanced exploration-exploitation and trapping in local optima. This paper proposes a modified version of the BO algorithm called mBO to solve the cloud scheduling problem to minimize two important scheduling objectives; makespan and energy consumption. We have incorporated four modifications namely Dimension Learning-Based Hunting (DLH) search strategy, (2) Transition Factor (TF), (3) Control Randomization (DR), and 4) Control Randomization Direction in the traditional BO to improve the performance, which helps it to escape local optima and balance exploration-exploitation. The efficacy of mBO is initially tested on the popular standard CEC’20 benchmarks followed by its application on the CSP problem using real-world supercomputing workloads namely CEA-Curie and HPC2N. Results and observations reveal the supremacy of the proposed mBO algorithm over many contemporary metaheuristics by a competitive margin for both CEC’20 benchmarks and the CSP problem. Quantitatively for the CSP problem, mBO was able to reduce makespan and energy consumption by 8.20–23.73% and 2.57–11.87%, respectively against tested algorithms. For HPC2N workloads, mBO achieved a makespan reduction of 10.99–29.48% and an energy consumption reduction of 3.55–30.65% over the compared metaheuristics.</p>","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vehicle edge server deployment based on reinforcement learning in cloud-edge collaborative environment","authors":"Feiyan Guo, Bing Tang, Ying Wang, Xiaoqing Luo","doi":"10.1007/s10586-024-04659-1","DOIUrl":"https://doi.org/10.1007/s10586-024-04659-1","url":null,"abstract":"<p>The rapid development of Internet of Vehicles (IoV) technology has led to a sharp increase in vehicle data. Traditional cloud computing is no longer sufficient to meet the high bandwidth and low latency requirements of IoV tasks. Ensuring the service quality of applications on in-vehicle devices has become challenging. Edge computing technology moves computing tasks from the cloud to edge servers with sufficient computing resources, effectively reducing network congestion and data propagation latency. The integration of edge computing and IoV technology is an effective approach to realizing intelligent applications in IoV.This paper investigates the deployment of vehicle edge servers in cloud-edge collaborative environment. Taking into consideration the vehicular mobility and the computational demands of IoV applications, the vehicular edge server deployment within the cloud-edge collaborative framework is formulated as a multi-objective optimization problem. This problem aims to achieve two primary objectives: minimizing service access latency and balancing server workload. To address this problem, a model is established for optimizing the deployment of vehicle edge servers and a deployment approach named VSPR is proposed. This method integrates hierarchical clustering and reinforcement learning techniques to effectively achieve the desired multi-objective optimization. Experiments are conducted using a real datasets from Shanghai Telecom to comprehensively evaluate the performance of workload balance and service access latency of vehicle edge servers under different deploy methods. Experimental results demonstrate that VSPR achieves an optimized balance between low latency and workload balancing while ensuring service quality, and outperforms SRL, CQP, K-means and Random algorithm by 4.76%, 44.59%, 40.78% and 69.33%, respectively.</p>","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid butterfly and Newton–Raphson swarm intelligence algorithm based on opposition-based learning","authors":"Chuan Li, Yanjie Zhu","doi":"10.1007/s10586-024-04678-y","DOIUrl":"https://doi.org/10.1007/s10586-024-04678-y","url":null,"abstract":"<p>In response to the issues of local optima entrapment, slow convergence, and low optimization accuracy in Butterfly optimization algorithm (BOA), this paper proposes a hybrid Butterfly and Newton–Raphson swarm intelligence algorithm based on Opposition-based learning (BOANRBO). Firstly, by Opposition-based learning, the initialization strategy of the butterfly algorithm is improved to accelerate convergence. Secondly, adaptive perception modal factors are introduced into the original butterfly algorithm, controlling the adjustment rate through the adjustment factor α to enhance the algorithm's global search capability. Then, the exploration probability <span>(p)</span> is dynamically adjusted based on the algorithm's runtime, increasing or decreasing exploration probability by examining changes in fitness to achieve a balance between exploration and exploitation. Finally, the exploration capability of BOA is enhanced by incorporating the Newton–Raphson-based optimizer (NRBO) to help BOA avoid local optima traps. The optimization performance of BOANRBO is evaluated on 65 standard benchmark functions from CEC-2005, CEC-2017, and CEC-2022, and the obtained optimization results are compared with the performance of 17 other well-known algorithms. Simulation results indicate that in the 12 test functions of CEC-2022, the BOANRBO algorithm achieved 8 optimal results (66.7%). In CEC-2017, out of 30 test functions, it obtained 27 optimal results (90%). In CEC-2005, among 23 test functions, it secured 22 optimal results (95.6%). Additionally, experiments have validated the algorithm’s practicality and superior performance in 5 engineering design optimization problems and 2 real-world problems.</p>","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":"94 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BOC-PDO: an intrusion detection model using binary opposition cellular prairie dog optimization algorithm","authors":"Bilal H. Abed-alguni, Basil M. Alzboun, Noor Aldeen Alawad","doi":"10.1007/s10586-024-04674-2","DOIUrl":"https://doi.org/10.1007/s10586-024-04674-2","url":null,"abstract":"<p>Intrusion detection datasets are highly likely to contain numerous redundant, irrelevant, and noisy features that slow the performance of the machine learning techniques and classifiers that may be applied to them. The feature selection approach is used for reducing the number of features in intrusion detection datasets and eliminating those that are not important. One of the most powerful structured population approaches is the Cellular Automata approach, which is used to enhance the diversity and convergence of population-based optimization algorithms. In this work, the Cellular Automata approach, Mixed opposition-based learning, and the K-Nearest Neighbor classifier are incorporated with the Prairie dog optimization algorithm (PDO) in a new intrusion detection framework called Binary Opposition Cellular Prairie dog optimization algorithm (BOC-PDO). The proposed framework contains four key features. First, the Cellular Automata model is utilized to enhance the population of feasible solutions in the PDO. Second, four S-shaped and four V-shaped Binary Transfer Functions are used to convert the continuous solutions in BOC-PDO to binary ones. Third, the Mixed opposition-based learning approach is used at the end of the optimization loop of BOC-PDO to improve capacity for exploration. Fourth, the K-Nearest Neighbor classifier is used as the main learning model in BOC-PDO. Eleven famous intrusion detection datasets were employed in the evaluation of the effectiveness of BOC-PDO compared to eight popular binary optimization algorithms and four machine learning approaches. According to the overall simulation results, BOC-PDO scored the highest accuracy, best objective value, and fewest selected features for each of the eleven intrusion detection datasets. Besides, the reliability and consistency of the simulation results of BOC-PDO compared to the other tested algorithms were established using Friedman and Wilcoxon statistical tests.</p>","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain enabled secured, smart healthcare system for smart cities: a systematic review on architecture, technology, and service management","authors":"Bhabani Sankar Samantray, K Hemant Kumar Reddy","doi":"10.1007/s10586-024-04661-7","DOIUrl":"https://doi.org/10.1007/s10586-024-04661-7","url":null,"abstract":"<p>Recently, the spotlight has been cast on smart healthcare by many researchers to provide better facilities to patients. Improved services, such as reducing health hazards, monitoring patient health, tracking disease trends, and enhancing service quality, can be offered by smart healthcare. Despite its numerous potential benefits, smart healthcare is associated with some security challenges. These challenges can be mitigated by utilizing blockchain technology, which is characterized by decentralization, cryptography, consensus mechanisms, transparency and accountability, smart contracts, ownership of data, immutability, and distributed ledger. Therefore, the latest blockchain technology is focused in this article to address the security challenges of smart healthcare. In this article, attention is given to smart healthcare, smart cities for smart healthcare, smart and secure healthcare, and cutting-edge technologies for smart cities and smart healthcare.Please provide author biography and photo.</p>","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TFCNN-BiGRU with self-attention mechanism for automatic human emotion recognition using multi-channel EEG data","authors":"Essam H. Houssein, Asmaa Hammad, Nagwan Abdel Samee, Manal Abdullah Alohali, Abdelmgeid A. Ali","doi":"10.1007/s10586-024-04590-5","DOIUrl":"https://doi.org/10.1007/s10586-024-04590-5","url":null,"abstract":"<p>Electroencephalograms (EEG)-based technology for recognizing emotions has attracted a lot of interest lately. However, there is still work to be done on the efficient fusion of different temporal and spatial features of EEG signals to improve performance in emotion recognition. Therefore, this study suggests a new deep learning architecture that combines a time–frequency convolutional neural network (TFCNN), a bidirectional gated recurrent unit (BiGRU), and a self-attention mechanism (SAM) to categorize emotions based on EEG signals and automatically extract features. The first step is to use the continuous wavelet transform (CWT), which responds more readily to temporal frequency variations within EEG recordings, as a layer inside the convolutional layers, to create 2D scalogram images from EEG signals for time series and spatial representation learning. Second, to encode more discriminative features representing emotions, two-dimensional (2D)-CNN, BiGRU, and SAM are trained on these scalograms simultaneously to capture the appropriate information from spatial, local, temporal, and global aspects. Finally, EEG signals are categorized into several emotional states. This network can learn the temporal dependencies of EEG emotion signals with BiGRU, extract local spatial features with TFCNN, and improve recognition accuracy with SAM, which is applied to explore global signal correlations by reassigning weights to emotion features. Using the SEED and GAMEEMO datasets, the suggested strategy was evaluated on three different classification tasks: one with two target classes (positive and negative), one with three target classes (positive, neutral, and negative), and one with four target classes (boring, calm, horror, and funny). Based on the comprehensive results of the experiments, the suggested approach achieved a 93.1%, 96.2%, and 92.9% emotion detection accuracy in two, three, and four classes, respectively, which are 0.281%, 1.98%, and 2.57% higher than the existing approaches working on the same datasets for different subjects, respectively. The open source codes are available at https://www.mathworks.com/matlabcentral/fileexchange/165126-tfcnn-bigru</p>","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive detection and mitigation mechanism to protect SD-IoV systems against controller-targeted DDoS attacks","authors":"Behaylu Tadele Alemu, Alemu Jorgi Muhammed, Habtamu Molla Belachew, Mulatu Yirga Beyene","doi":"10.1007/s10586-024-04660-8","DOIUrl":"https://doi.org/10.1007/s10586-024-04660-8","url":null,"abstract":"<p>Software-defined networking (SDN) has emerged as a transformative technology that separates the control plane from the data plane, providing advantages such as flexibility, centralized control, and programmability. This innovation proves particularly beneficial for Internet of Vehicles (IoV) networks, which amalgamate the Internet of Things (IoT) and Vehicular Ad Hoc Network (VANET) to implement Intelligent Transportation Systems (ITS). IoV provides a safe and secured vehicular environment by supporting V2V, V2I, V2S, and V2P. By employing an SDN controller, IoV networks can leverage centralized control and enhanced manageability, leading to the emergence of Software-Defined Internet of Vehicles (SD-IoV) as a promising solution for future communications. However, the SD-IoV networks introduces a potential vulnerability in the form of a single point of failure, particularly susceptible to Distributed Denial of Service (DDoS) attacks. This is because of the centralized nature of SDN and the dynamic nature of IoV. In this context, the SDN controller becomes a prime target for attackers who flood it with massive packet-in messages. To address this security concern, we propose an efficient and lightweight attack detection and mitigation scheme within the SDN controller. The scheme includes a detection module that utilizes entropy and flow rate to identify patterns indicative of attack traffic behavior. Additionally, a mitigation module is designed to minimize the effect of attack traffic on the normal operation, this is performed through analysis of payload lengths.The mitigation flow rule is set for specific traffic type if its payload is less than the threshold value to decrease the false positive rate. An adaptive threshold computation for all parameter values enhances the scheme’s effectiveness. We conducted simulations using SUMO, Mininet-WiFi, and Scapy. We evaluated the system performance by using Mininet-wifi SDN simulation tool and Ryu controller for control plane. The system detects DDoS attack traffic within a single window by checking both entropy and flow rate simultaneously. The simulation results demonstrate the efficacy of our proposed scheme in terms of detection time, accuracy, mitigation efficiency, controller load, and link bandwidth consumption, showcasing its superiority compared to existing works in the field.</p>","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Video security in logistics monitoring systems: a blockchain based secure storage and access control scheme","authors":"Zigang Chen, Fan Liu, Danlong Li, Yuhong Liu, Xingchun Yang, Haihua Zhu","doi":"10.1007/s10586-024-04667-1","DOIUrl":"https://doi.org/10.1007/s10586-024-04667-1","url":null,"abstract":"<p>With the rapid development of the logistics industry and the continuous growth of e-commerce, effectively monitoring logistics warehouses has become increasingly important to ensure the security of goods and oversee activities within storage facilities. Although current surveillance systems provide a certain level of security for logistics warehouses, they still face issues such as data tampering, storage, and access management. These challenges can compromise the integrity of surveillance video data, making the system vulnerable to unauthorized access. To address these challenges, this paper proposes the implementation of blockchain-based security management and access control of video data in logistics warehouses. Specifically, the solution employs the Hyperledger Fabric consortium blockchain to execute smart contracts and store the hash values of video data, thereby detecting any tampering and enhancing the security and integrity of the data. Additionally, hybrid encryption technology is utilized to ensure the confidentiality of video data during transmission and storage. Furthermore, the solution leverages the InterPlanetary File System (IPFS) for distributed video storage. This not only increases the redundancy and accessibility of data storage but also reduces the risk of single-point failures. A Role-Based Access Control (RBAC) mechanism is also introduced to strictly manage access permissions to video data, ensuring that only authorized users can access the data, thereby effectively preventing unauthorized access and data breaches. Through a comprehensive analysis of computational and communication costs and the evaluation of blockchain performance at 100 transactions per second for different transaction volumes using Hyperledger Caliper, the results demonstrate the effectiveness and efficiency of the proposed method. Compared to current research, this solution exhibits higher security, providing a new approach for the secure management and access control of video data in logistics warehouses.</p>","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An energy-aware ant colony optimization strategy for virtual machine placement in cloud computing","authors":"Lin-Tao Duan, Jin Wang, Hai-Ying Wang","doi":"10.1007/s10586-024-04670-6","DOIUrl":"https://doi.org/10.1007/s10586-024-04670-6","url":null,"abstract":"<p>Virtual machine placement (VMP) directly impacts the energy consumption, resource utilization, and service quality of cloud data centers (CDCs), and it has become an active research topic in cloud computing. Inspired by the ant colony system (ACS) which has been proven effective metaheuristic approach for solving NP-hard problems, this paper proposes an improved ACS-based energy efficiency strategy (EEACS) for VMP problems. Our approach considers each virtual machine (VM) as an energy-consuming block, taking into account its individual energy requirements. EEACS ranks the physical machines (PMs) in a CDC in descending order based on their energy efficiency and optimizes both server selection and pheromone updating rules within the ACS. By guiding artificial ants towards promising solutions that balance energy consumption and resource utilization, EEACS ensures that VMs are placed efficiently based on pheromone and heuristic information. Extensive simulations in both homogeneous and heterogeneous computing environments demonstrate the effectiveness of our proposed strategy. The experimental results show that the EEACS enhances the resource utilization and achieves a notable reduction in energy consumption in comparison to conventional heuristic and evolutionary-based algorithms.</p>","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance analysis of ML models on 5G sub-6 GHz bands: An experimental study","authors":"Avuthu Avinash Reddy, Ramesh babu Battula, Dinesh Gopalani","doi":"10.1007/s10586-024-04677-z","DOIUrl":"https://doi.org/10.1007/s10586-024-04677-z","url":null,"abstract":"<p>Massive Machine Type Communication (mMTC) uses sub-6 GHz bands frequency bands for their applications in the 5G network. The exponential growth of mMTC wireless networks has made these bands overcrowded. Due to the increase in wireless traffic, spectrum scarcity is a significant constraint at sub-6 GHz bands for the 5G and beyond networks. Cognitive radio technology uses spectrum sensing (SS) techniques to access the spectrum opportunistically to resolve this issue where signal processing techniques (SPTs) are considered to design SS. However, the adaptiveness of SPTs is not feasible in the real-time environment due to the random spectrum access behaviour of the primary user and the fading environment. To minimize this issue, machine learning (ML) models are adapted. Different ML models are examined, and their performance is analyzed to find a better accuracy model in spectrum hole identification at 5G sub-6 GHz bands. The dataset of large-scale frequency samples is built from a universal software radio peripheral (USRP—2953R) at sub-6 GHz bands over <span>(eta - mu)</span> fading environmental conditions. A highly imbalanced dataset issue is reduced and compared with varying resampling techniques, and random oversampling is best to resolve the anomalies in the dataset. Random forest, naive Bayes, logistic regression, K-nearest neighbor, and decision trees are primary classifiers to train and detect the spectrum holes at 5G sub-6 GHz bands. Random forest outperforms the remaining ML models in spectrum hole identification in terms of probability of detection and accuracy.</p>","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}