<p>With the recent advancement of software development methodologies and frameworks, service meshes has been to rapidly gain the lime light. In this paper, we provide an introduction to service meshes and discuss their key use cases in modern cloud-native architectures. We also explore the challenges associated with performance analysis of service meshes and present a survey of recent research in this area. To address these challenges, we propose the use of soft computing techniques for performance analysis of service meshes. Our study includes a comparative analysis of different service mesh platforms and their performance under varying workloads. Overall, this paper provides insights into the benefits and limitations of service meshes and highlights the importance of performance analysis in ensuring the reliability and scalability of micro-services based applications.</p>

<p>With the recent advancement of software development methodologies and frameworks, service meshes has been to rapidly gain the lime light. In this paper, we provide an introduction to service meshes and discuss their key use cases in modern cloud-native architectures. We also explore the challenges associated with performance analysis of service meshes and present a survey of recent research in this area. To address these challenges, we propose the use of soft computing techniques for performance analysis of service meshes. Our study includes a comparative analysis of different service mesh platforms and their performance under varying workloads. Overall, this paper provides insights into the benefits and limitations of service meshes and highlights the importance of performance analysis in ensuring the reliability and scalability of micro-services based applications.</p>

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Today’s internet world becomes really introducing a landmark between Peta to Exa byte which is significantly generating an enormous size of data while computing the digital things including the format of each datasets which signifies highly unstructured because which could be generating from different social sites, IOT, Google engine, Twitter, Yahoo, monitoring and controlling through sensors essentially called big data. Because of this fast era, we apply just contemporary techniques with common tools regarding having focused performing, smooth process and to execute computations on huge data. Though such tremendous universal data has some shortcomings for getting effective processing, analyzing the universe immense datasets and scalability techniques. Apache open free source Hadoop does the latest big data weapon which can process Zetta byte dimensions of databases by its most developed and popular components as hdfs and Map Reduce, to make up vast storage facility plus great administration in the sense to process zettabyte of datasets as powerfully, flexible. MR likes more famous software popular structure for handling big-data existing issues with full parallel, highly distributed and most scalable manner. However, public and unrestricted source tools on Hadoop, map reduces become major limitations like poor allocate process on needy resources working regarding stream-oriented processing, Shortage significant viewpoints like latency, dynamic manner execution, optimization, computing as online and diverse logical solutions. We consider significant various complex data computing orientated techniques. This study paper address Apache fastest spark tool, online-oriented tool public and unrestricted source and Flink are in Apache project are efficient frameworks to conquer that limitation.