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Measurement Study on Performance of Serverless Platforms

Ruan Jian Xue Bao/Journal of Software 2025年第5期36卷 1974-2005页

作者： Wen, Jin-Feng Chen, Zhen-Peng Liu, Yi Liu, Xuan-Zhe School of Computer Science Peking University Beijing100871 China Department of Computer Science University College London LondonWC1E 6BT United Kingdom Advanced Institute of Big Data Beijing100091 China

Serverless computing is an emerging paradigm of cloud computing, allowing developers to focus only on application logic development without the need to manage complex underlying tasks. This paradigm allows developers to quickly build smaller-granularity applications, the one at the function level. With the increasing popularity of serverless computing, major cloud computing vendors have introduced their commercial serverless platforms one after another. However, the characteristics of these platforms have yet to be systematically studied and reliably compared. A comprehensive analysis of these characteristics can help developers choose an appropriate serverless platform while developing and executing serverless applications in the right way. To this end, an empirical study is conducted on the characteristics of mainstream commercial serverless platforms. This study involves such mainstream serverless platforms as AWS Lambda, Google Cloud Functions, Microsoft Azure Functions, and Alibaba Function Compute. This study is divided into two major parts: feature summarization and runtime performance analysis. In the feature summarization, the official documents of these serverless platforms are discussed and their key features are summarized and compared in terms of development, deployment, and runtime. In the runtime performance analysis, representative benchmarks are applied to analyze the runtime performance offered by these serverless platforms on a multidimensional basis. Specifically, key factors for the cold-start performance of the applications are first analyzed, such as programming languages and memory sizes. Furthermore, the tasks-executing performance of serverless platforms is discussed. Based on the results of feature summarization and runtime performance analysis, this study sums up a series of findings and provides practical insights and potential research opportunities for developers, cloud computing vendors, and researchers. © 2025 Chinese Academy of Scie

关键词： Application programs

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AI Enabled Convolutional Neural Networks to Detect Brain Tumors 1

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Rao, Ankur School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

In the early stages of a person’s existence, it is common practice to screen for the presence of brain tumors. Nevertheless, in the current day, it has been improved using various AI computations. Along these same lines, in order to diagnose a patient with a brain tumor, we take into consideration the data of patients, such as MRI scans of the patient’s brain. Our main concern at this point is determining whether or not the tumor is present in the patient’s brain. For a patient to have a healthy lifespan, it is essential for the tumor to be identified at the earliest possible stage. During this phase of the project, we use a calculation called a convolutional neural network to determine the level of severity, which provides accurate results. The division of tumors is an essential and challenging task that falls within the purview of clinical image production. This is due to the fact that human-aided manual characterization might result in erroneous anticipations and discoveries. We proposed the use of a convolutional neural network as a means of differentiating cancers from 2D appealing resonance brain imaging (MRI). The exploratory study was carried out on a dataset that included tumors with a wide range of dimensions, localizations, configurations, and picture forces. Convolutional neural networks, which are built using Keras and Tensorflow, were chosen for usage by our team because, in comparison to regular neural networks, they provide a much-improved presentation. During the course of our investigation, CNN demonstrated an accuracy of 98.73%. Because of its great speculation capacity and quickness, the CNN design that was only recently developed has the potential to be employed as an effective decision-aid device for radiologists working in clinical diagnostics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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A n empirical study to examine the student activity analysis components of technology using an extended multi-labeled gradient boosting methodology

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Materials Today: Proceedings 2023年 81卷 620-624页

作者： R. Renuga Devi B. Suresh Department of Computer Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai.Tamilnadu India

The data surveying this study mines methods for extracting informative and interesting knowledge from learning student datasets. The amount of data stored in education big databases is growing at an alarming rate. As such, data processing and knowledge discovery techniques can be used to discover interesting relationships between different characteristics and assessments of students. Initially to propose a novel self-analysis model which performs analysis about the different big data learning services. From the result provided by the user, the service will be ranked and analyze about the knowledge from daily activity gathered by the learner. The proposed model performs analysis to provide efficient Educational Data Mining (EDM) E-Learning system based on machine learning to ranking the users and performance evaluation in their activities. The existing method does not consider the learner activities, ranking and it focus on single feature based classification so the system doesn’t provide a better classification result. These issues to overcome introduce an Extended Multi-Labeled Gradient Boosting (XMGB) improve the classification performance both of imbalanced dataset. The method initial stage apply the Adaptive Expectation-Maximization Mixture (AEMM) method cluster the data various group and extract the E-learning multiple feature set. In this method to compute the classification time and memory provide an efficient result. Then introduce an academic activity based weight score analysis model to evaluate the learner daily activates and exam score its help to the ranking and cluster the data groups. In this proposed method analysis result to evaluate the following performance matrix: Recall, precision, F measure, classification accuracy. In this simulation analysis of the proposed method XMGB are provide an efficient classification with less time complexity compared to other existing methods.

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A Hybrid Methodology for Software Development and IT Team Analysis in Manufacturing 1

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

The IT team working in the manufacturing industry may use this paper to learn about the results of the recently established hybrid software development model with the aid of this paper. The IT team’s needs, difficulties, working practices, past working strategies, and the client’s expectations in the manufacturing firm all play a role in determining how the hybrid model is implemented. The IT team was able to accomplish goals such as on-time delivery with quality improvement, more accuracy, reduced maintenance cycle, the involvement of clients, reduced development time, and improved defect removal efficiency by utilizing a new model and adopting appropriate features from Traditional and Agile methodologies. This article presents a guideline on prior outcomes for the IT team at a manufacturing business and results after using a hybrid approach. The results of deploying a hybrid software development paradigm inside an in-house IT team that works in the manufacturing business are the subject of this research, which explores those results. This study intends to evaluate the effects of combining conventional and agile approaches for software development by thoroughly examining software development practices and determining how successful this combination is. The research investigates the subsequent enhancements in project delivery deadlines, product quality, and team cooperation. The findings provide light on the possible advantages and drawbacks of using a hybrid method. The results give organizations who are looking to optimize their software development processes in a manufacturing setting with useful insights that will eventually contribute to increased efficiency and competitiveness in the sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Efficiency

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Test Case Optimization Technique for Web Applications

Test Case Optimization Technique for Web Applications

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： V Manojkumar R. Mahalakshmi Department of Computer Applications Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Software testing is indeed an important part of the application development. In order to identify the bugs, present in the developed source code, software testing techniques are applied. The testing method considers various test cases provided to meet the software outcome on demand. The emerging growth of artificial intelligence (AI) technology and machine learning (ML) techniques reduces the complexity in test case running sequences, as the AI algorithms reduces the frequently repeated test cases, accelerating the processing depth by understanding the pattern of test logs etc. The comprehensive study is proposed on optimization of test case execution on development of web applications is given. The challenges in existing implementations such as latency, duplicate test cases, redundant test cases are considered. The proposed model is developed with a novel architecture using a case study on machine learning model named Regressive vector analysis (RV A). Since the optimization process increase the quality of the test sequence, the quantitative outcome of the test model is improved. The system achieved with accuracy of 92 % and Mathew's correlation constant (MCC) is 98%.

关键词： Software testing Analytical models Machine learning algorithms Correlation Source coding Software algorithms Machine learning

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Investigating Blockchain Security Mechanisms for Tamper-Proof Data Storage 2

Investigating Blockchain Security Mechanisms for Tamper-Proo...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Thilakavathy, P. Jayachitra, S. Aeron, Anurag Kumar, Narendra Ali, Syed Sumera Malathy, M. Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Psna College of Engineering and Technology Department of Ece Tamilnadu Dindigul India School of Computing Dit University Dehradun Computer Science and Engineering India Amity University Department of Computer Science and Engineering Jharkhand Ranchi India Csmss Chh Shahu College of Engineering Electronics and Computer Engineering Department Maharashtra Aurangabad India Vel Tech High Tech Dr Rangarajan Dr Sakunthala Engineering College Department of Computer Science and Engineering Chennai Avadi India

ISBN: (纸本)9798350369960

Across a wide range of sectors, blockchain technology has emerged as a game-changing option for secure data storage. This research dives into the mechanisms that enable blockchain to protect data integrity and immutability. This research investigates how blockchain technology reduces the chances of data tampering and unauthorized adjustments by examining its underlying concepts and features, such as decentralization, cryptography, consensus procedures, and immutability. Blockchain networks are decentralized, so they don't rely on any one entity to function. This eliminates one potential point of failure or manipulation. Data is protected from tampering and disclosure thanks to the use of hash functions and digital signatures, two examples of strong cryptographic algorithms. By confirming and agreeing on transactions, consensus methods like Proof of Work (PoW) and Proof of Stake (PoS) keep the blockchain ledger up-to-date and accurate. By linking blocks together and recording timestamps, blockchain makes it practically impossible to alter data after it has been recorded. The distributed ledger's availability to the public and ease of use encourage accountability and deter dishonest participants. Smart contracts eliminate the need for third parties by automating operations based on predetermined rules that cannot be altered after they have been created. This study also delves into blockchain fork resistance, incentives for truthful involvement, and other network security measures. It also covers the security implications of both permissioned and permissionless blockchains. Blockchain technology provides strong security procedures, but it has several drawbacks that must be taken into account. These include issues with scalability and the possibility of security flaws in smart contracts. Therefore, securing security and performance requires cautiously considering particular use case needs and picking the right blockchain platform and configuration. For blockchain networ

关键词： Blockchain Consensus Algorithms Cryptography Immutable Ledger

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Anti-Forensic Technique Using Alternate Data Streams for Data Hiding in NTFS 1

Anti-Forensic Technique Using Alternate Data Streams for Dat...

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Hermon, Rahul Singh, Upasna Singh, Bhupendra Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India Indian Institute of Information Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665450461

Data hiding techniques are one of the most commonly used anti-forensic methods by attackers or perpetrators. There are many data hiding techniques in various kinds of file system in different OS. Alternate data streams (ADS) in NTFS are one such means by which data can be hidden in Windows OS. In this work, ADS will be used to demonstrate how data can be hidden in it, to include different kinds of data files. The same will be implemented using command prompt. Lastly, we compared the data hiding technique using ADS in Windows 10 and 11 Operating System. © 2022 IEEE.

关键词： Windows operating system

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Test Case Optimization Through Ant Colony Optimization Enabled Boosted Regression Model

Test Case Optimization Through Ant Colony Optimization Enabl...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Manojkumar V R. Mahalakshmi Department of Computer Applications Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

The avalanche of data creates complex operational issues in real world scenarios. In terms of making the test procedures simple and effective test case optimization techniques are applied. To achieve better test case performance, the proposed system focused on creating a model centric method that comparatively overheads the test case optimization works. The presented system considers the comparative evaluation of test case optimization of MIS dataset. The methods such as Linear regression (LR), Customized Random Forest regression (RFR), the robust Gradient boost regression (GBR), dynamically working Support vector machine (SVM) and proposed Ant colony optimized Boosted regression model (ACO-BRM). The proposed approach exposed to the benefit of nature inspired optimization model named ant colony and optimized the test cases with boosted regression model and achieved the minimum of mean absolute error of 0.34 comparatively with existing state of art approaches. The priority score is enhanced after the optimization process.

关键词： Support vector machines Deep learning Machine learning algorithms Computational modeling Optimization models Linear regression Data models Surges Optimization Random forests

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Exploring Human Gesture Recognition: Bridging Communication Gaps Through Dynamic Signal Analysis 3rd

Exploring Human Gesture Recognition: Bridging Communication ...

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (纸本)9789819751457

The ability of humans to communicate more effectively via the use of gestures is widely recognised as one of the most important aspects of human interaction. Because of the inextricable link that exists between body language and successful communication, there has been a significant amount of study conducted in the domains of computer vision and human–computer interaction (HCI). Signal recognition is one of the most important aspects of this study. Signal recognition attempts to analyse and decode the information that is transmitted by human motions. These movements include gestures such as hand and facial expressions. Signal recognition is a key bridge that enables people with varying skills to successfully communicate and connect with others. This is particularly true for those who have trouble understanding speech. There is a possibility that algorithms developed for signal detection can help bridge the communication gap and make interactions more welcoming to all parties involved. These attempts constitute a big step forward in the direction of making interactions with computers seem more natural and more analogous to communication between humans. Static signals and dynamic signals are the two basic types that may be distinguished within the field of signal recognition. These critical frames act as turning points, summing together the most important aspects of the meaning sent by the signal. The purpose of this study is to investigate the area of dynamic signal identification and key frame extraction in order to shed light on the advantages and disadvantages associated with both of these processes. By investigating these facets, our ultimate goal is to get a more profound comprehension of the complexities involved in interpreting dynamic gestures and the possible uses of these gestures. This understanding will add to the continuing attempts to make interactions between humans and computers more natural, inclusive, and human-like, which will eventually improve th

关键词： Gesture recognition

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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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