From Kubernetes to Knactor: A Data-Centric Rethink of Service Composition

• URL: http://arxiv.org/abs/2309.01805v2
• Date: Thu, 23 Nov 2023 21:38:46 GMT
• Title: From Kubernetes to Knactor: A Data-Centric Rethink of Service Composition
• Authors: Silvery Fu, Hong Zhang, Ryan Teoh, Taras Priadka, Sylvia Ratnasamy
• Abstract summary: Microservices are increasingly used in modern applications, leading to a growing need for effective service composition solutions. Traditional API-centric composition mechanisms introduce rigid code-level coupling, scatter logic, and visibility into cross-service data exchanges. We propose a rethinking of service composition and present Knactor, a new data-centric framework to restore the modularity that were intended to offer.
• Score: 5.250111701278031
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Microservices are increasingly used in modern applications, leading to a growing need for effective service composition solutions. However, we argue that traditional API-centric composition mechanisms (e.g., RPC, REST, and Pub/Sub) hamper the modularity of microservices. These mechanisms introduce rigid code-level coupling, scatter composition logic, and hinder visibility into cross-service data exchanges. Ultimately, these limitations complicate the maintenance and evolution of microservice-based applications. In response, we propose a rethinking of service composition and present Knactor, a new data-centric composition framework to restore the modularity that microservices were intended to offer. Knactor decouples service composition from service development, allowing composition to be implemented as explicit data exchanges among multiple services. Our initial case study suggests that Knactor simplifies service composition and creates new opportunities for optimizations.

Related papers

• Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach [49.63614966954833]
  Federated Collaborative Filtering (FedCF) is an emerging field focused on developing a new recommendation framework with preserving privacy. This paper proposes a novel personalized FedCF method by preserving users' personalized information into a latent variable and a neural model simultaneously. To effectively train the proposed framework, we model the problem as a specialized Variational AutoEncoder (VAE) task by integrating user interaction vector reconstruction with missing value prediction.
  arXiv  Detail & Related papers  (2024-08-16T05:49:14Z)
• A Conceptual Framework for API Refactoring in Enterprise Application Architectures [0.0]
  We introduce a conceptual framework for the implementation of APIs. Our framework elicits that there are important trade-offs and choices, which significantly affect the efficiency, maintainability, and isolation properties of the resulting architecture. Our work also elicits, for the first time, how certain programming language features can reduce friction in applying API and open up more architectural choices.
  arXiv  Detail & Related papers  (2024-07-10T07:32:16Z)
• Service Weaver: A Promising Direction for Cloud-native Systems? [0.0]
  Google's Service Weaver aims to simplify the complexities associated with implementing cloud-native systems. Service Weaver presents a promising approach to streamline the development of cloud-native applications. It is important to acknowledge that certain features, including separate code bases, routing mechanisms, resiliency, and security, are lacking in the framework.
  arXiv  Detail & Related papers  (2024-04-14T20:57:32Z)
• A Feature Dataset of Microservices-based Systems [2.3734388579113275]
  Poor practices in the design and development of datasets are called microservice bad smells. There is a lack of an appropriate open-source microservice feature dataset.
  arXiv  Detail & Related papers  (2024-04-02T09:52:18Z)
• CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning [62.090051975043544]
  Attribute and object (A-O) disentanglement is a fundamental and critical problem for Compositional Zero-shot Learning (CZSL) We propose a novel A-O disentangled framework for CZSL, namely Class-specified Cascaded Network (CSCNet)
  arXiv  Detail & Related papers  (2024-03-09T14:18:41Z)
• Blockchain-enabled Trustworthy Federated Unlearning [50.01101423318312]
  Federated unlearning is a promising paradigm for protecting the data ownership of distributed clients. Existing works require central servers to retain the historical model parameters from distributed clients. This paper proposes a new blockchain-enabled trustworthy federated unlearning framework.
  arXiv  Detail & Related papers  (2024-01-29T07:04:48Z)
• The Microservice Dependency Matrix [0.0]
  This paper introduces the Dependency Matrix (EDM) and Data Dependency Matrix (DDM) as tools to address this challenge. We present an automated approach for tracking these dependencies and demonstrate their extraction through a case study.
  arXiv  Detail & Related papers  (2023-09-06T07:41:00Z)
• Handling Communication via APIs for Microservices [6.5499625417846685]
  We discuss the challenges with conventional techniques of communication using and propose an alternative way of ID-passing via APIs. We also devise an algorithm to reduce the number of APIs.
  arXiv  Detail & Related papers  (2023-08-02T17:40:34Z)
• Heterogeneous Continual Learning [88.53038822561197]
  We propose a novel framework to tackle the continual learning (CL) problem with changing network architectures. We build on top of the distillation family of techniques and modify it to a new setting where a weaker model takes the role of a teacher. We also propose Quick Deep Inversion (QDI) to recover prior task visual features to support knowledge transfer.
  arXiv  Detail & Related papers  (2023-06-14T15:54:42Z)
• RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in Multi-Agent Deep Reinforcement Learning [55.55009081609396]
  We propose a novel method, called Relation-Aware Credit Assignment (RACA), which achieves zero-shot generalization in ad-hoc cooperation scenarios. RACA takes advantage of a graph-based encoder relation to encode the topological structure between agents. Our method outperforms baseline methods on the StarCraftII micromanagement benchmark and ad-hoc cooperation scenarios.
  arXiv  Detail & Related papers  (2022-06-02T03:39:27Z)
• Prior Guided Feature Enrichment Network for Few-Shot Segmentation [64.91560451900125]
  State-of-the-art semantic segmentation methods require sufficient labeled data to achieve good results. Few-shot segmentation is proposed to tackle this problem by learning a model that quickly adapts to new classes with a few labeled support samples. Theses frameworks still face the challenge of generalization ability reduction on unseen classes due to inappropriate use of high-level semantic information.
  arXiv  Detail & Related papers  (2020-08-04T10:41:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.