Rethinking State Management in Actor Systems for Cloud-Native Applications

• URL: http://arxiv.org/abs/2410.15831v1
• Date: Mon, 21 Oct 2024 09:48:34 GMT
• Title: Rethinking State Management in Actor Systems for Cloud-Native Applications
• Authors: Yijian Liu, Rodrigo Laigner, Yongluan Zhou,
• Abstract summary: We develop SmSa, a novel data management layer for actor systems. It allows developers to declare dependencies that cut across actors, including foreign keys, data replications, and other dependencies. We demonstrate SmSa can support core data management tasks where dependencies across components appear frequently jeopardizing application logic and performance.
• Score: 2.2665094235990124
• License:
• Abstract: The actor model has gained increasing popularity. However, it lacks support for complex state management tasks, such as enforcing foreign key constraints and ensuring data replication consistency across actors. These are crucial properties in partitioned application designs, such as microservices. To fill this gap, we start by analyzing the key impediments in state-of-the-art actor systems. We find it difficult for developers to express complex data relationships across actors and reason about the impact of state updates on performance due to opaque state management abstractions. To solve this conundrum, we develop SmSa, a novel data management layer for actor systems, allowing developers to declare data dependencies that cut across actors, including foreign keys, data replications, and other dependencies. SmSa can transparently enforce the declared dependencies, reducing the burden on developers. Furthermore, SmSa employs novel logging and concurrency control algorithms to support transactional maintenance of data dependencies. We demonstrate SmSa can support core data management tasks where dependencies across components appear frequently without jeopardizing application logic expressiveness and performance. Our experiments show SmSa significantly reduces the logging overhead and leads to increased concurrency level, improving by up to 2X the performance of state-of-the-art deterministic scheduling approaches. As a result, SmSa will make it easier to design and implement highly partitioned and distributed applications.

Related papers

• Histrio: a Serverless Actor System [44.99833362998488]
  Histrio is a programming model and execution environment that simplifies the development of stateful applications. It lifts concerns such as state management, database interaction, and programming handling from developers. It guarantees exactly-once-processing consistency, meaning that the application always behaves as if any interaction with external clients was processed once and only once.
  arXiv  Detail & Related papers  (2024-10-29T06:58:56Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RL [57.202733701029594]
  Decision Mamba is a novel multi-grained state space model with a self-evolving policy learning strategy. To mitigate the overfitting issue on noisy trajectories, a self-evolving policy is proposed by using progressive regularization. The policy evolves by using its own past knowledge to refine the suboptimal actions, thus enhancing its robustness on noisy demonstrations.
  arXiv  Detail & Related papers  (2024-06-08T10:12:00Z)
• See to Believe: Using Visualization To Motivate Updating Third-party Dependencies [1.7914660044009358]
  Security vulnerabilities introduced by applications using third-party dependencies are on the increase. Developers are wary of library updates, even to fix vulnerabilities, citing that being unaware, or that the migration effort to update outweighs the decision. In this paper, we hypothesize that the dependency graph visualization (DGV) approach will motivate developers to update.
  arXiv  Detail & Related papers  (2024-05-15T03:57:27Z)
• X-lifecycle Learning for Cloud Incident Management using LLMs [18.076347758182067]
  Incident management for large cloud services is a complex and tedious process. Recent advancements in large language models [LLMs] created opportunities to automatically generate contextual recommendations. In this paper, we demonstrate that augmenting additional contextual data from different stages of SDLC improves the performance.
  arXiv  Detail & Related papers  (2024-02-15T06:19:02Z)
• Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
  We propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
  arXiv  Detail & Related papers  (2024-02-04T05:50:38Z)
• Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning [7.473473066047965]
  This paper introduces a novel approach to ORAN slicing using distributed deep reinforcement learning (DDRL) Simulation results demonstrate significant improvements in network performance, particularly in reducing violations. This emphasizes the importance of using the prediction rApp and distributed actors' information jointly as part of a dynamic xApp.
  arXiv  Detail & Related papers  (2024-01-12T22:43:07Z)
• TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning [73.53576440536682]
  We introduce TACO: Temporal Action-driven Contrastive Learning, a powerful temporal contrastive learning approach. TACO simultaneously learns a state and an action representation by optimizing the mutual information between representations of current states. For online RL, TACO achieves 40% performance boost after one million environment interaction steps.
  arXiv  Detail & Related papers  (2023-06-22T22:21:53Z)
• Object as a Service (OaaS): Enabling Object Abstraction in Serverless Clouds [2.0575037267955305]
  We propose a new abstraction level atop the function abstraction, known as Object as a Service (O) programming. O encapsulates the application data and function into the object abstraction and relieves the developers from resource and data management burdens. It also unlocks opportunities for built-in optimization features, such as software reusability, data locality, and caching.
  arXiv  Detail & Related papers  (2022-06-10T21:31:22Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)

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.