dataesr/scientific-paragraphs-categorization huggingface dataset:
The strategy based on PCA consists of extracting x principal components from all provenance database, pca D , and for each pt ∈ partitions, pca i pt , which are pt partition principal components. Then, the norms are calculated ||pca Dpca i pt ||, and from that n partitioning rules are selected that generated pca i pt such that ||pca Dpca i pt || resulted in the lowest calculated values. Note that both x and n are defined parameters when executing the algorithm. In summary, the PCA strategy will select the partitions where the main components extracted are the closest to the principal components of the original provenance dataset. ANOVA strategy seeks the n partitioning rules that best represent D, selecting those that generate partitions where the data variance is closest to D data variance.
Montage  (Jacob et al. , 2009 ) is a well-known astronomy workflow that assembles astronomical images into mosaics by using FITS (Flexible Image Transport System) files. Those files include a coordinate system and the image size, rotation, and WCS (World Coordinate System) map projection. Figure  9B  shows the montage activities: (i) ListFITS, is gathered from real executions of scientific workflows on the grid and in the cloud from the Pegasus' team in ISI at the University of Southern California. The SciPhy executions consumed from 200 up to 500 fasta files downloaded from RefSeq database. The Montage executions consumed from 50 up to 100 FIT files obtained from the "Two Micron All-Sky Survey" 1. In the case of SciPhy, the executions were performed by 3 different users (one expert and 2 undergraduate students).
In the case of Montage (the executions in SciCumulus were performed by an undergraduate student and the ones downloaded from the Workflow Generator site were performed by experts). Table  1  summarizes the main characteristics of the datasets. The Total Records column shows the number of past executions of each workflow. Each dataset record can be used as an example for generating Machine Learning models during the algorithm's execution. As seen, the SciPhy dataset is relatively small compared to Montage. The column Total Attributes shows how many activity parameters are considered in each workflow execution. Both workflows have the same number of categorical domain parameters, as presented in the column Categorical Attributes. Montage has more numeric domain parameters than SciPhy, as shown in the Numerical Attributes column.
Discrete domain recommendation evaluation This experiment was modeled to evaluate FReeP's algorithm key concepts using the naive version presented in Algorithm 1, that was developed to recommend one discrete domain  parameter at a time. This experiment aims at evaluating and comparing the performance of FReeP when its hypothesis_generation step instantiates either a single classifier or a ranker. The ranker tested as a model was implemented using the Pairwise Label Ranking technique. K Nearest Neighbors  (Keller, Gray & Givens, 1985)  classifier is used as classifier of this ranker implementation. The k parameter of K Nearest Neighbors classifier was set as 3, 5, 7 for both the ranker and classifier. The choice of k ∈ {3,5,7} is because small datasets are used, and thus k values greater than 7 do not return any neighbors in the experiments.
Experiment 1. Algorithm 1 evaluation script 1. The algorithm is instantiated with the classifier or ranker and a recommendation target workflow parameter. 2. The provenance database is divided into k parts to follow a K-Fold Cross Validation procedure  (Kohavi, 1995). At each step, the procedure takes k -1 parts to train the model and the 1 remaining part to make the predictions. In this experiment, k = 5. 3. Each workflow parameter is used as recommendation target parameter. 4. Each provenance record in test data is used to retrieve target parameter real value. 5. Parameters that are not the recommendation target are used as preferences, with values from current test record. 6. Then, algorithm performs recommendation and both the result and the value present in the test record for the recommendation target parameter are stored.
Results Experiment 1 results are presented and analyzed based on the values of precision and recall, in addition to the execution time. Figure  11  shows that Algorithm 1 execution with Sciphy provenance database, using both the classifier and the ranker. Only KNN classifier with k = 3 gives a precision greater than 50%. Also, a high standard deviation is noticed. Even with unsatisfactory performance, Fig. 12  shows that KNN classifier presented better recall results than those for precision, both in absolute values terms and standard deviation, which had a slight decrease. In contrast, the ranker recall was even worse with the precision results and still present a very high standard deviation. Figure  13  shows the execution time, in seconds, to obtain the experiment's recommendations for SciPhy.
The execution time of ranker is much more significant when compared to the time spent by the classifier. This behavior can be explained by the fact that the technique used to generate the ranker creates multiple binary classifiers. Another point to note is that the execution time standard deviation from ranker is also very high. It is important to note that when FReeP uses KNN, it is memory-based, since each recommendation needs to be loaded into main memory. Analyzing Fig. 14  (Montage) one can conclude that with the use of k = 3 for the classifier and for the ranker produces relevant results. The precision for this case reached 80%, and the standard deviation was considerably smaller compared to the precision results with Sciphy dataset in Fig. 11. For k ∈ {5,7}, the same results behavior was observed, considerably below those expected.
In general, it was possible to notice that the use of ranker did not bring encouraging results. In all cases, ranker precision and recall were lower than those presented by the classifier. Besides, the standard deviation of ranker in the execution time spent results was also very high. Another point to be noted is that the best precision and recall results were obtained with the data from Montage workflow. These results may be linked to the fact that the Montage dataset has more records than the Sciphy dataset.
Discrete and continuous domain recommendation evaluation Experiment 1 was modified to evaluate the Algorithm 2 performance, yielding Experiment 2. Algorithm 2 was executed with variations in the choice of classifiers and regressors, partitions strategies, and records percentage from provenance database. All values per algorithm parameter are presented in Table  4. Experiment 2. Algorithm 2 evaluation script 1. Algorithm 2 is instantiated with a classifier or regressor, a partitioning strategy, percentage data to be returned by partitioning strategy, and a target workflow parameter. 2. Provenance database is divided using K-Fold Cross Validation, k = 5 3. Each provenance record on test data is used to retrieve the target parameter's real value. 4. A random number x between 2 and parameters number present in provenance database is chosen to simulated preference number used in recommending target parameter.
The Multi Layer Perceptron classifier presented a behavior similar to the precision results, with a degradation in the setup that includes ANOVA partitioning with 70% of the elements in the partitioning. execution time using different classifiers for each attribute is also much smaller and stable for ANOVA strategy than for PCA, regardless of element partition percentage. Analyzing precision, recall, and execution time spent data jointly, ANOVA partitioning strategy showed the best recommendation performance for the categorical domain parameters of the Sciphy provenance database. Going further, the element partition percentage generated by the strategy has no significant impact on the results. Another interesting point is that a simpler classifier like KNN presented results very similar to those obtained by a more complex classifier like SVM.
From all results obtained in the Experiment 2 using Sciphy provenance database, it can be noticed that the ANOVA partitioning strategy had the best performance. Further precision, recall, and MSE results, for the Algorithm 2 setup with ANOVA partitioning strategy also proved to be the one that performed the recommendations in the shortest time, generally in half the time that the PCA partitioning strategy. Note that the recommendation time can be treated as training time since the proposed algorithm has a memory-based approach. Finally, the choice of the generated partition size and the classifier or regressor used have no significant impact on the final result unless the classifier or regressor is based on Multi Layer Perceptron with the same parametrization used in this article. Analyzing Fig.
21 , precision results obtained with categorical domain parameters from Montage workflow provenance database is observed that in almost all the experiment setup variations evaluated, maximum performance is reached. As seen in Table  1 , the Montage workflow provenance database used in the experiments has only two categorical domain parameters. The small variation in possible values in the database is an explanation for the precision results. The recall results in Fig. 22  are similar to the precision ones. Concerning the results about the experiment time with categorical domain parameters from the Montage provenance database, presented in Fig. 23 , one can see that the KNN classifier, k = 3, with PCA partitioning strategy was the most time-consuming. On the other hand, with the same PCA partitioning strategy, the Multi Layer Perceptron classifier used less time, but with a wide variation in recommendation times for different parameters.
The ANOVA partitioning strategy continued to be a partitioning strategy that delivers the fattest recommendations. Still analyzing ANOVA partitioning strategy results,   24A  show that, in general, the MSE was very close to zero for all cases, except in algorithm setup using PCA partitioning strategy with 30% elements in the generated partition and the regressor implemented by Multi Layer Perceptron. The MSE and its variation were very close to zero. Regarding the execution time of Experiment 2 for numerical domain parameters recommendations for Montage data, Fig. 24B  indicates the same behavior shown by results with SciPhy provenance database. Using ANOVA partitioning strategy and KNR regressors with k ∈ {5,7} as setup for Algorithm 2 produced the fastest recommendations. The experiment execution time of Montage provenance database was much greater than the time used with the data from the workflow Sciphy.
Generic FReeP recommendation evaluation A third experiment was modeled to evaluate Algorithm 3 performance. As in Experiment 2, different variations, following Table  4  values, were used in algorithm execution. Precision, recall, and MSE are also the metrics used to evaluate the recommendations made by each algorithm instance. Experiment 3. Algorithm 3 evaluation script 1. n Records from the provenance database were chosen as random examples. 2. m ≥ 2 random parameters were chosen for each example record as preferences, and their values are the same as those present in the example record. 3. Algorithm 3 was instantiated with a classifier or a regressor, a partitioning strategy, the partitions percentage to be returned by the partitioning strategy, and the selected m preferences. 4. Each returned recommendation is separated into numeric and categorical and is stored.
Results Results showed here were obtained by fixing parameter n = 10 in Experiment 3, and using only SciPhy provenance database. Based on Experiment 2 results, it was decided to use the ANOVA partitioning strategy with 50% recovering elements from the provenance database. This choice is because the ANOVA partitioning strategy was the one that obtained the best results in previous experiment. As the percentage of data recovered by the strategy was not an impacting factor in the results, an intermediate percentage used in the previous experiment is selected. In addition, only KNN, with k ∈ {5,7}, and SVM were kept as classifiers, whereas only KNR, with k ∈ {5,7}, and SVR was chosen as regressors. These choices are supported by, in general, are the ones that present the best precision, recall, and MSE results in Experiment 2.
However, considering only the recommendations made, it can be seen that the algorithm had satisfactory results for the precision and recall metrics. The values presented for the MSE metric were mostly satisfactory, differing only in the configurations of lines 4 and 7, both using the regressor KNR with k = 5. Another point to note is that the algorithm had more problems to make recommendations when the SVM classifier was used. Furthermore, it is possible to note that algorithm setups with more sophisticated Machine Learning models such as SVM and SVR do not add performance to the algorithm, specifically for Sciphy provenance dataset used.
In addition, in many research groups there is already a database containing the provenance  (Freire et al. , 2008)  that can be used to recommend parameter values for non-expert users, i. e. , the scientists will not need to effectively execute the workflow to train the model since provenance data is already available. Public provenance repositories such as ProvStore (https://openprovenance. org/ store/)  (Huynh & Moreau, 2015)  can be used as input for FReeP. For example, ProvStore contains 1,136 documents (each one associated with a workflow execution) of several different real workflows uploaded by research groups around the world. From the perspective of runtime, when using the ANOVA partitioning strategy, in the experimental evaluation with the provenance data from the Sciphy workflow, the average time spent on the recommendations is about only 4 minutes.
FReeP has three versions, all of them relying on Machine Learning techniques. Two approaches focused on the value recommendation for one parameter at a time. The third instance addresses recommending values for all the other parameters of a workflow for which a user preference was not defined. The proposed algorithm proved to be useful for recommending one parameter, indicating a path for the recommendation of n parameters. Nevertheless, there are some limitations. FReeP, as a memory-based algorithm, faces scalability issues as its implementation can consume a lot of computational resources. Yet, the recommendations of FReeP are limited to the existence of examples on the provenance dataset. This means that the algorithm cannot make any "default" recommendations if there are no examples for the algorithm's execution or recommend values that are not present in the provenance dataset.
: recommendation target parameter D : fðparam; valÞjparam is a workflow parameter; valis the preference value for paramg D : ffðparam 1 1 ; val 1 1 Þ; …; ðparam l 1 ; val l 1 Þ; …ðparam m l ; val m l Þgjl is the workflow parameters number; m is the provenance dataset lengthg N :number of random sequences orders to be generated 1: procedure Generic FReeP(P, D, N) 2: target_parameters ← parameters_extractor(P, D) 3: votes ← ø 4: for each param ∈ target_parameters do 5: votes ← votes ∪ {(param, []) } 6: ordered_sequences ← sequence_generator(target_parameters, N) 7: for each sequence ∈ ordered_sequences do 8: preferences_tmp ← P 9: for each param ∈ sequence do 10: recommendation ← FReeP(param, preferences_tmp, D) 11: votes[param] ← votes[param] ∪ recommendation 12: new_preference ← generate_preference(param, recommendation) 13: preferences_tmp ← preferences_tmp ∪ new_preference 14: response ← ø 15: for each (param, values) ∈ votes do 16: response[param] most_voted(values) 17: return response Silva Junior et al.
largest standard deviation. The dec (an optional float value that represents Dec for region statistics) and crval2 (a float value that represents Axis 2 sky reference value in Montage workflow) attributes have close statistics and are the attributes with the smallest data range and the smallest Montage data standard deviation. In Fig. 10, it is possible to check the correlation between the different attributes in the datasets. It is notable in both Figs. 10A and 10B that the attributes (i. e. , workflow parameters) present a weak correlation. All those statistics are relevant to understand the results obtained by the experiments performed from each version of FReeP algorithm.
P : recommendation target parameter D : fðparam; valÞjparam is a workflow parameter; valis the preference value for paramg D : ffðparam 1 1 ; val 1 1 Þ; …; ðparam l 1 ; val l 1 Þ; …ðparam m l ; val m l Þgjl is the workflow parameters number; m is the provenance dataset lengthg N :number of random sequences orders to be generated 1: procedure Generic FReeP(P, D, N) 2: target_parameters ← parameters_extractor(P, D) 3: votes ← ø 4: for each param ∈ target_parameters do 5: votes ← votes ∪ {(param, []) } 6: ordered_sequences ← sequence_generator(target_parameters, N) 7: for each sequence ∈ ordered_sequences do 8: preferences_tmp ← P 9: for each param ∈ sequence do 10: recommendation ← FReeP(param, preferences_tmp, D) 11: votes[param] ← votes[param] ∪ recommendation 12: new_preference ← generate_preference(param, recommendation) 13: preferences_tmp ← preferences_tmp ∪ new_preference 14: response ← ø 15: for each (param, values) ∈ votes do 16: response[param] most_voted(values) 17: return response Silva Junior et al.
Example of FReeP's horizontal filter using one partitioning rule for the Sciphy provenance dataset. Full-size  DOI: 10. 7717/peerj-cs. 606/fig-6 Provenance dataset num_aligns length model1 prob1 model2 prob2 10. 0 854. 0 WAG+I+F 4634. 242459 WAG+I+F 4634. 242459 11. 0 339. 0 WAG+I+F 2012. 681247 WAG+I 2052. 864650 10. 0 854. 0 WAG+I+F 4634. 242459 WAG+I+F 4634. 242459 partitions num_aligns length model1 prob1 model2 prob2 [ [num_aligns], [model1], Iteration 1 Current Partition rule [num_aligns] Preferences Selection num_aligns == 10. 0 Horizontal Filter 10. 0 854. 0 WAG+I+F 4634. 242459 WAG+I+F 4634. 242459 [num_aligns, 10. 0 854. 0 WAG+I+F 4634. 242459 WAG+I+F 4634. 242459 model1] ] Preferences num_aligns == 10. 0 model1 == 'WAG' Figure 6 Silva Parameters in Preferences [num_aligns, model1] Workflow Parameters [num_aligns, length, model1, prob1, model2, prob2] Target Parameter y = model2 - [num_aligns] [length,prob1,model2,prob2] ∪ ∪ length model1 854.
0 WAG+I+F 4634. 242459 WAG+I+F 4634. 242459 prob1 model2 prob2 854. 0 WAG+I+F 4634. 242459 WAG+I+F 4634. 242459 [num_aligns, length, 10. 0 10. 0 prob1, model2, prob2] Vertical Filter num_aligns length 10. 0 854. 0 4634. 242459 WAG+I+F 4634. 242459 prob1 model2 prob2 10. 0 854. 0 4634. 242459 WAG+I+F 4634. 242459 Provenance dataset after Horizontal Filter ⊂ Figure 7 FReeP's vertical filter step. Full-size  DOI: 10. 7717/peerj-cs. 606/fig-7 Junior et al. (2021), PeerJ Comput. Sci. , DOI 10. 7717/peerj-cs. 606 13/46.
Introduction In recent years, cloud computing has seen rapid growth and to provide Virtual Machines (VMs) multiple users, a significant number of servers are deployed in Data Centers (DCs). Networks have grown larger and have become more powerful in recent years, bringing convenience to government, business, educational institutes and individuals. A vital problem that is related to this advancement is that it has become increasingly complex to manage networks. Existing standards such as TMN, CMPI and SNMP have been applied to deal with network management issues  [1]. These standards cover protocols, services and management information bases. Among these standards, Simple Network Management Protocol (SNMP) has been considered as an important and most widely used protocol for Transmission Control Protocol/Internet Protocol (TCP/IP) based networks.
Metsch et al. [59]  presented a REST-based approach to provide remote management for API for IaaS model based services. Both these approaches come under the umbrella of monitoring and controlling domain, but these approaches do not use SNMP. Automation: Existing patents suggested automated monitoring virtual machines and using some benchmark to reallocate resources from one virtual machine to other virtual machines. There was also a focus on the reallocation of resources for an application that is running database applications or programmable logic controllers. The concept from patents to automatically monitor virtual machines and CPU utilization as a benchmark to balance load by either changing state of a VM or by creating an entirely new guest operating system has been used. This research concept is aligned to Patents in this regard, but it differs in a sense that it uses a custom-developed C program to do automation and controlling of virtual machines.
reduces power consumption and reduces QoS violations. None of them talked about the use of SNMP in their system. Both of them did not provide anything on the management of virtual machines such as creation, suspension or resumption. Adding SNMP with VM management can facilitate power consumption reduction and load balancing. Minhas et al. [64]  research followed some basic concepts of load balancing presented by Yang et al. in their research. In their research, they proposed a system to provide recommended VM configurations based on the workload of database management systems. Their research also does not provide anything on controlling parameters of virtual machines. In other words, creation or resuming of virtual machines is not discussed in their research. Hyser et al. [65]  presented an algorithm which follows the basic concept as discussed in this paper, i.
Experimental setup The testbed for SNMP version 3 monitoring and controlling VMs was created on a hypervisor and is implemented on two physical machines with several virtual machines, where Processor, Network, Storage and Memory performance was monitored. Step by step installation and configurations was also described: 1. To conduct research experiments on bare-metal hypervisor, VMware ESXi 5. 5 was installed on HP ProLiant DL380 Gen8 server. 2. Bare metal hypervisors run directly and do not need an Operating System installed on the machine. Bare metal hypervisor configuration: the laptop is directly connected to an HP server running VMware hypervisor. The methodology adopted to monitor running domains and controlling them was with using Libvirt API. In the experiments, the authors extended the capability of SNMP by automating the control and management of virtual machines through the design and implementation of an algorithm in C language code.
Results were calculated and plotted using Zabbix, which showed that the average CPU utilization of host machine increased whenever a heavy load was applied, which created new VMs. Hence, it could be concluded that with more VMs, load on the host machine increases, which affects more consumption of power. This section discusses the total power consumption of CPU and underlines as the CPU utilization plays an important role in energy efficiency improvement. Lower CPU utilization means lower power consumption or improved energy efficiency. For the experimental set up to manager, the energy efficiency in a cloud computing environment has used the SNMPv3. The work focuses on monitoring various CPU parameters to determine the machine's power utilization due to computer processing. HP ProLiant DL380p Gen8 Server with Vmware ESXibare-metal hypervisorwas used for experimentations and data was collected using SNMPv3 commands.