venue: CCGrid
year: 2024
date: "2024-05-06"
tags: [cloud-computing, distributed-systems, hpc]

selected:
  - dblp_key: "conf/ccgrid/0001S24"
    title: "Fair, Efficient Multi-Resource Scheduling for Stateless Serverless Functions with Anubis"
    tldr: "Anubis introduces a fair, multi-resource scheduler for stateless serverless functions that achieves efficiency without sacrificing isolation between tenants."
    why_notable: "Fairness in serverless resource allocation is an open problem as functions compete for heterogeneous resources (CPU, memory, I/O); Anubis provides a concrete, deployable answer. The work directly addresses a gap in production FaaS platforms where existing schedulers optimize for throughput but ignore per-tenant equity."

  - dblp_key: "conf/ccgrid/SavasciSWIAS24"
    title: "SLO-Power: SLO and Power-aware Elastic Scaling for Web Services"
    tldr: "SLO-Power co-optimizes SLO compliance and power consumption during elastic scaling of web services, reducing energy use without violating latency targets."
    why_notable: "Jointly chasing SLOs and power budgets is a critical concern for sustainable cloud operations; this work shows the two objectives can be reconciled in a single scaling controller. The approach is directly applicable to cloud autoscaling stacks where energy cost and QoS guarantees are both first-class concerns."

  - dblp_key: "conf/ccgrid/HuangTCTLOLYBW24"
    title: "HAPPIES: a History-Aware Efficient Cloud Resource Overcommitment System"
    tldr: "HAPPIES uses historical utilization traces to safely overcommit cloud resources, improving cluster efficiency while bounding the risk of SLO violations."
    why_notable: "Resource overcommitment is a primary lever for improving datacenter utilization, and HAPPIES advances the state of the art by making overcommitment decisions history-aware rather than reactive. Its deployment-oriented design makes it immediately relevant to hyperscale cloud operators seeking higher bin-packing ratios."

  - dblp_key: "conf/ccgrid/DouZ00MWD24"
    title: "COTuner: Joint Optimization of Resource Configuration and Software Parameters for Recurring Streaming Jobs on the Cloud"
    tldr: "COTuner simultaneously tunes cloud resource allocation and application-level software knobs for recurring streaming jobs, reducing cost and latency together."
    why_notable: "Streaming workloads on the cloud are poorly served by tools that tune resources and software parameters in isolation; COTuner's joint search closes this gap. The focus on recurring jobs makes the approach practical, amortizing tuning cost over repeated executions in production pipelines."

  - dblp_key: "conf/ccgrid/KimKCK0KACK024"
    title: "DeepVM: Integrating Spot and On-Demand VMs for Cost-Efficient Deep Learning Clusters in the Cloud"
    tldr: "DeepVM dynamically mixes spot and on-demand VM instances to build cost-efficient, fault-tolerant deep learning training clusters in the cloud."
    why_notable: "Training large models on cloud infrastructure is expensive, and spot instance preemptions are a major obstacle to reliability; DeepVM provides a principled integration strategy that achieves both cost savings and resilience. The approach is practically significant given the rapid growth of cloud-hosted AI training workloads."

  - dblp_key: "conf/ccgrid/RaithRDCMFND24"
    title: "Opportunistic Energy-Aware Scheduling for Container Orchestration Platforms Using Graph Neural Networks"
    tldr: "A GNN-based scheduler for Kubernetes-style container orchestration platforms exploits opportunistic energy signals to reduce power consumption without degrading application performance."
    why_notable: "Applying graph neural networks to container scheduling captures the complex topology of cluster resources in a way that heuristic schedulers cannot, while the energy-awareness angle addresses the sustainability imperative facing cloud providers. The work bridges recent ML advances with production container orchestration."

  - dblp_key: "conf/ccgrid/WangCW24"
    title: "Jingle: IoT-Informed Autoscaling for Efficient Resource Management in Edge Computing"
    tldr: "Jingle leverages real-time IoT device signals to drive predictive autoscaling decisions at the edge, reducing both over-provisioning and latency spikes."
    why_notable: "Edge autoscaling is hampered by the lack of load predictors tuned to IoT event patterns; Jingle fills this gap by treating IoT telemetry as a first-class input to the scaling loop. The result is a tighter edge-cloud integration model with direct relevance to smart-city and industrial IoT deployments."

  - dblp_key: "conf/ccgrid/KulkarniRKMMABB24"
    title: "XFBench: A Cross-Cloud Benchmark Suite for Evaluating FaaS Workflow Platforms"
    tldr: "XFBench is a portable benchmark suite that evaluates FaaS workflow platforms across multiple cloud providers using representative workload patterns."
    why_notable: "The absence of standardized, cross-cloud benchmarks for serverless workflow platforms hinders fair comparison and informed vendor selection; XFBench addresses this directly with a reusable, community-shareable artifact. It covers diverse workflow shapes and exposes platform-specific performance cliffs that single-provider benchmarks miss."

  - dblp_key: "conf/ccgrid/CernudaLGKLKS24"
    title: "Hades: A Context-Aware Active Storage Framework for Accelerating Large-Scale Data Analysis"
    tldr: "Hades pushes data transformations into the storage layer using context-aware active storage, dramatically reducing I/O traffic and accelerating large-scale scientific data analysis."
    why_notable: "As HPC datasets grow to petabyte scale, moving data to compute becomes the dominant bottleneck; Hades revives and modernizes the active-storage model with context awareness to match modern workflow patterns. The work is highly relevant to HPC-cloud convergence efforts where storage and compute are increasingly disaggregated."

  - dblp_key: "conf/ccgrid/Kilic0TTMPJ24"
    title: "Workflow Mini-Apps: Portable, Scalable, Tunable & Faithful Representations of Scientific Workflows"
    tldr: "Workflow Mini-Apps are compact, parameterizable proxies that faithfully capture the performance behavior of full scientific workflows, enabling portable scheduling research without the overhead of running complete pipelines."
    why_notable: "Scientific workflow scheduling research is bottlenecked by the cost and complexity of running real applications at scale; mini-apps lower this barrier while preserving the key performance characteristics needed for valid scheduler evaluation. The methodology is immediately reusable by the broader distributed workflow community."