Skip to content

Latest commit

 

History

History
107 lines (95 loc) · 7.33 KB

socc-2023.md

File metadata and controls

107 lines (95 loc) · 7.33 KB

SoCC 2023

Meta Info

Homepage: https://acmsocc.org/2023/

Paper list: https://acmsocc.org/2023/accepted-papers.html

Papers

Resource Allocation

  • Lifting the Fog of Uncertainties: Dynamic Resource Orchestration for the Containerized Cloud [Paper]
    • UofT
    • Adaptively configure resource parameters
    • Built on contextual bandit techniques
    • Balance between performance and resource cost
  • Not All Resources are Visible: Exploiting Fragmented Shadow Resources in Shared-State Scheduler Architecture [Paper]
    • SJTU & Huawei
    • Shared-state schedulers: A central state view periodically updates the global cluster status to distributed schedulers
    • Shadow resources: Resources invisible to shared-state schedulers until the next view update
    • Resource Miner (RMiner) includes a shadow resource manager to manage shadow resources, an RM filter to select suitable tasks as RM tasks, an RM scheduler to allocate shadow resources to RM tasks
  • Gödel: Unified large-scale resource management and scheduling at ByteDance [Paper]
    • ByteDance & UVA
    • Industry Paper
    • A unified infrastructure for all business groups to run their diverse workloads
    • Built upon Kubernetes

Machine Learning

  • Anticipatory Resource Allocation for ML Training Clusters [Paper]
    • Microsoft Research & UW
    • Schedule based on predictions of future job arrivals and durations
    • Deal with prediction errors
  • tf.data service: A Case for Disaggregating ML Input Data Processing [Paper]
    • Google & ETH
    • Industry Paper
    • A disaggregated input data processing service built on top of tf.data in TensorFlow
    • Horizontally scale out to right-size host resources (CPU/RAM) for data processing in each job
    • Share ephemeral preprocessed data results across jobs
    • Coordinated reads to avoid stragglers
  • Is Machine Learning Necessary for Cloud Resource Usage Forecasting? [Paper]
    • IMDEA Software Institute
    • Vision Paper
    • Question: Whether complex machine learning models are necessary to use?
    • Proposal: Practical memory management systems need to first identify the extent to which simple solutions can be effective.

Serverless Computing

  • Golgi: Performance-Aware, Resource-Efficient Function Scheduling for Serverless Computing [Paper]
    • HKUST & WeBank
    • Best Paper Award!
    • A scheduling system for serverless functions to minimize resource provisioning costs while meeting the function latency requirements
    • Overcommit functions based on their past resource usage; Identify nine low-level metrics (e.g., request load, resource allocation, contention on shared resources); Use the Mondrian Forest to predict the function performance
    • Employ a conservative exploration-exploitation strategy for request routing; By default, route requests to non-overcommitted instances; Explore to use overcommitted instances
    • Vertical scaling to dynamically adjust the concurrency of overcommitted instances
  • Parrotfish: Parametric Regression for Optimizing Serverless Functions [Paper]
    • UBC & UTokyo & INSAT
    • Find optimal configurations through an online learning process
    • Use parametric regression to choose the right memory configurations for serverless functions
  • AsyFunc: A High-Performance and Resource-Efficient Serverless Inference System via Asymmetric Functions [Paper] [Code]
    • HUST & Huawei & Peng Cheng Laboratory
    • Problem: The time-consuming and resource-hungry model-loading process when scaling out function instances
    • Observation: The sensitivity of each layer to the computing resources is mostly anti-correlated with its memory resource usage
    • Asymmetric Functions
      • The original Body Function loads a complete model to meet stable demands
      • The proposed lightweight Shadow Function only loads a portion of resource-sensitive layers to deal with sudden demands effortlessly
    • AsyFunc — an inference serving system with an auto-scaling and scheduling engine; Built on top of Knative
  • Chitu: Accelerating Serverless Workflows with Asynchronous State Replication Pipeline [Paper] [Code]
    • ISCAS & ICT, CAS
    • Asynchronous State Replication Pipelines (ASRP) to speed up serverless workflows for general applications
    • Three insights
      • Provide differentiable data types (DDT) at the programming model level to support incremental state sharing and computation
      • Continuously deliver changes of DDT objects in real-time
      • Direct communication and change propagation
    • Built atop OpenFaaS
  • How Does It Function? Characterizing Long-term Trends in Production Serverless Workloads [Paper] [Trace]
    • Huawei
    • Industry Paper
    • Two new serverless traces in Huawei Cloud
      • The first trace: Huawei's internal workloads; Per-second statistics for 200 functions
      • The second trace: Huawei's public FaaS platform; Per-minute arrival rates for over 5000 functions
    • Characterize resource consumption, cold-start times, programming languages used, periodicity, per-second versus per-minute burstiness, correlations, and popularity.
    • Findings
      • Requests vary by up to 9 orders of magnitude across functions, with some functions executed over 1 billion times per day
      • Scheduling time, execution time and cold-start distributions vary across 2 to 4 orders of magnitude and have very long tails
      • Function invocation counts demonstrate strong periodicity for many individual functions and on an aggregate level
    • The need for further research in estimating resource reservations and time-series prediction
  • Function as a Function [Paper]
    • ETH
    • Vision Paper
    • Dandelion -- a clean state FaaS system; Treat serverless functions as pure functions; Explicitly separate computation and I/O; Hardware acceleration; Enable dataflow-aware function orchestration
  • The Gap Between Serverless Research and Real-world Systems [Paper]
    • SJTU & Huawei Cloud
    • Vision Paper
    • Five open challenges
      • Optimize cold start latency: Most existing works only consider synchronous starts; Asynchronous start in Industry
      • Declarative approach: Whether Kubernetes is the right system for serverless computing?
      • Scheduling cost
      • Balance different scheduling policies within a serverless system
      • Costs of sidecar

Sustainable Computing

  • Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale [Paper]
    • MIT & NEU
    • Significant decreases in both temperature and power draw, reducing power consumption and potentially improving hardware life-span, with minimal impact on job performance