Fault tolerance in distributed systems using deep learning approaches

Basem Assiri; Abdullah Sheneamer

doi:10.1371/journal.pone.0310657

Peer Review History

Original SubmissionMarch 10, 2024
26 Mar 2024 Decision Letter - Abul Bashar, Editor PONE-D-24-09708Fault Tolerance in Distributed Systems Using Deep Learning ApproachesPLOS ONE Dear Dr. Sheneamer, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by May 10 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0310657.r001
Revision 1
11 May 2024 Author Response Accept warm greetings. Thanks for considering our manuscript No. PONE-D-24-09708R1 for revision. Please find enclosed the revised manuscript titled: " Fault Tolerance in Distributed Systems Using Deep Learning Approaches. " which we are resubmitting for review and possible inclusion as an article in the coming issues of PLOS ONE We addressed every issue raised by esteemed reviewers very carefully. Enclosed herewith responses against the issues raised by the reviewers. This manuscript is the authors' original work and has not been published nor has it been submitted simultaneously elsewhere. Please address all correspondence concerning this manuscript to me. I am ready to provide any necessary supports related to the manuscript. Best Regards, Authors Attachments Attachment Submitted filename: Response to Reviewers.pdf https://doi.org/10.1371/journal.pone.0310657.r002
1 Aug 2024 Decision Letter - Abul Bashar, Editor PONE-D-24-09708R1Fault Tolerance in Distributed Systems Using Deep Learning ApproachesPLOS ONE Dear Dr. Sheneamer, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. The following UPDATED comments from the reviewer(s) are to be addressed as well. *The abstract is to be quantified. The highlights are not properly noted.* The limitations of previous works are to be included. Section 2 and 3 needs improvement. Discussion part is poorly presented. Conclusion may be modified. The limitations and time loss analysis may be included. Bound condition for all equations are to be included. Please submit your revised manuscript by Sep 15 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. 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Kind regards, Abul Bashar Academic Editor PLOS ONE [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author (These comments are to be ignored, as the comments mentioned above take precedence) 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. 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Revision 2
16 Aug 2024 Author Response These comments below are mentioned in orgnaized table in attached repsonse to reviewers file. ------------------------------------------------------------------------------------------------------- Responses to reviewer 1 First of all, we would like to thank you very much for your valuable and fruitful comments and suggestions, that are taken as a guide to improve this work. Comment Response The abstract is to be quantified. The highlights are not properly noted. Updated, the main highlights are noted, including the use of deep learning for fault tolerance and recovery in three major scenarios. The abstract now is quantified. The accuracies of the models are measured and the abstract shows the accuracy results. Thanks for your valuable comments. The limitations of previous works are to be included. Updated, in the related work we first highlight previous works of the distributed system works. Then, the fault tolerance concept. After that, the use of machine learning in fault tolerance and finally the use of deep learning in fault tolerance. At the end of first and second paragraph, in paragraph four as well, we show the limitations of the previous works and how we handle such limitation. For example, the traditional distributed system and fault tolerance do not use intelligent tools such as deep learning. In addition, they do not use deep learning for recovery, and to handle missing, corrupted and unrelated input. Thanks for your valuable comments. Section 2 and 3 needs improvement. Section 2 and 3 have been improved. The updates are highlighted. Thanks for your valuable comments. Discussion part is poorly presented. Updated, see sections 3, 4 and 6. The discussion has been improved. Actually, to avoid the overlength of the paper, there is no specific section of discussion. However, the discussion of the main idea is included in Section 3, while the evaluation of our model and experiment is discussed within Section 4, and the limitations and threat of validity is presented in Section 6. Thanks for your valuable comments. Conclusion may be modified. The conclusion had been modified. Thanks for your valuable comments. The limitations and time loss analysis may be included. Updated, now it is included in the section of Limitation and Threat of Validity Fault Tolerance Scope: Employing intelligent techniques such as deep learning techniques in the distributed system and fault recovery is an innovative task, without deep history or approved benchmark. Actually, deep learning models are effective in managing certain types of faults within distributed systems. However, their approach may not cover all potential fault scenarios, especially those involving complex, interconnected faults that can propagate across various system components. Dataset Dependence: Find an existing suitable dataset to test the proposed idea is another challenge. In fact, the models’ performance is heavily reliant on the quality and nature of the datasets they are trained on. For example, models trained on specific structured or unstructured data types might not perform optimally when applied to different data types. Therefore, train our models on both structured and unstructured datasets. Fault Types: Examine different kinds of faults is another issue, where we investigate different kinds of faults, using three scenarios. Fault Ratio: Faults ratios is another critical issue to investigate. Our work extends the experiment to test different faults ratios. Moreover, in cases involving larger faults, the recovery time could be considerable. Computational Overhead: Implementing deep learning models for real-time fault detection and correction introduces computational overhead, which could be problematic for systems with stringent latency requirements. For example, the time taken for a model to make predictions during the fault recovery process can also be a bottleneck, especially in real-time systems where rapid decision-making is essential. Training Time: Training deep learning models, particularly with large datasets or complex architectures like VGG16, VGG19, or ResNet34, is time-intensive. This significant time investment must be taken into account when deploying these models in practical applications. Additionally, finding proper deep learning techniques and to generalize our findings are vital points to consider. Bound condition for all equations are to be included. Updated, see section 4, all equation results are between 0 and 1, as all variables are positive and the numerator is smaller than the denominator 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Thanks for your valuable comments 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly The technical part has been improved in all parts of the paper. Thanks for your valuable comments 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: N/A - 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Thanks for your valuable comments 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Thanks for your valuable comments Attachments Attachment Submitted filename: Review_Responses_R2.pdf https://doi.org/10.1371/journal.pone.0310657.r004
5 Sep 2024 Decision Letter - Abul Bashar, Editor Fault Tolerance in Distributed Systems Using Deep Learning Approaches PONE-D-24-09708R2 Dear Dr. Sheneamer, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Abul Bashar Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0310657.r005
Formally Accepted
26 Dec 2024 Acceptance Letter - Abul Bashar, Editor PONE-D-24-09708R2 PLOS ONE Dear Dr. Sheneamer, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Abul Bashar Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0310657.r006

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