136 lines
7.5 KiB
Markdown
136 lines
7.5 KiB
Markdown
---
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title: CCGrid 2024 Digest
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venue: CCGrid
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year: 2024
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date: '2024-05-06'
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tags:
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- cloud-computing
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- distributed-systems
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- hpc
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paper_count: 10
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draft: false
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---
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10 papers selected.
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---
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### Fair, Efficient Multi-Resource Scheduling for Stateless Serverless Functions with Anubis
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*Amit Samanta 0001, Ryan Stutsman*
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**TL;DR** — Anubis introduces a fair, multi-resource scheduler for stateless serverless functions that achieves efficiency without sacrificing isolation between tenants.
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**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.
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[→ Read paper](https://doi.org/10.1109/CCGrid59990.2024.00021)
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### SLO-Power: SLO and Power-aware Elastic Scaling for Web Services
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*Mehmet Savasci, Abel Souza, Li Wu, David Irwin 0001 *et al.**
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**TL;DR** — SLO-Power co-optimizes SLO compliance and power consumption during elastic scaling of web services, reducing energy use without violating latency targets.
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**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.
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[→ Read paper](https://doi.org/10.1109/CCGrid59990.2024.00025)
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### HAPPIES: a History-Aware Efficient Cloud Resource Overcommitment System
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*Ziwei Huang 0003, Shibo Tang, Zihao Chang, Lin Tan *et al.**
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**TL;DR** — HAPPIES uses historical utilization traces to safely overcommit cloud resources, improving cluster efficiency while bounding the risk of SLO violations.
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**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.
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[→ Read paper](https://doi.org/10.1109/CCGrid59990.2024.00064)
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### COTuner: Joint Optimization of Resource Configuration and Software Parameters for Recurring Streaming Jobs on the Cloud
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*Hui Dou, Shanshan Zhu, Yuxuan Zhou 0005, Yiwen Zhang 0001 *et al.**
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**TL;DR** — COTuner simultaneously tunes cloud resource allocation and application-level software knobs for recurring streaming jobs, reducing cost and latency together.
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**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.
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[→ Read paper](https://doi.org/10.1109/CCGrid59990.2024.00019)
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### DeepVM: Integrating Spot and On-Demand VMs for Cost-Efficient Deep Learning Clusters in the Cloud
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*Yoochan Kim, Kihyun Kim, Yonghyeon Cho, Jinwoo Kim *et al.**
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**TL;DR** — DeepVM dynamically mixes spot and on-demand VM instances to build cost-efficient, fault-tolerant deep learning training clusters in the cloud.
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**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.
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[→ Read paper](https://doi.org/10.1109/CCGrid59990.2024.00034)
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### Opportunistic Energy-Aware Scheduling for Container Orchestration Platforms Using Graph Neural Networks
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*Philipp Raith, Gourav Rattihalli, Aditya Dhakal, Sai Rahul Chalamalasetti *et al.**
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**TL;DR** — A GNN-based scheduler for Kubernetes-style container orchestration platforms exploits opportunistic energy signals to reduce power consumption without degrading application performance.
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**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.
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[→ Read paper](https://doi.org/10.1109/CCGrid59990.2024.00042)
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### Jingle: IoT-Informed Autoscaling for Efficient Resource Management in Edge Computing
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*Yixuan Wang, Abhishek Chandra, Jon B. Weissman*
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**TL;DR** — Jingle leverages real-time IoT device signals to drive predictive autoscaling decisions at the edge, reducing both over-provisioning and latency spikes.
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**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.
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[→ Read paper](https://doi.org/10.1109/CCGrid59990.2024.00052)
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### XFBench: A Cross-Cloud Benchmark Suite for Evaluating FaaS Workflow Platforms
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*Varad Kulkarni, Nikhil Reddy, Tuhin Khare, Harini Mohan *et al.**
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**TL;DR** — XFBench is a portable benchmark suite that evaluates FaaS workflow platforms across multiple cloud providers using representative workload patterns.
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**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.
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[→ Read paper](https://doi.org/10.1109/CCGrid59990.2024.00067)
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### Hades: A Context-Aware Active Storage Framework for Accelerating Large-Scale Data Analysis
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*Jaime Cernuda, Luke Logan, Ana Gainaru, Scott Klasky *et al.**
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**TL;DR** — 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.
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**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.
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[→ Read paper](https://doi.org/10.1109/CCGrid59990.2024.00070)
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---
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### Workflow Mini-Apps: Portable, Scalable, Tunable & Faithful Representations of Scientific Workflows
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*Ozgur O. Kilic, Tianle Wang 0001, Matteo Turilli, Mikhail Titov *et al.**
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**TL;DR** — 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.
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**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.
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[→ Read paper](https://doi.org/10.1109/CCGrid59990.2024.00059)
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