The document discusses the integration of Alluxio with Kubernetes through Fluid, which enhances data orchestration and application management in containerized environments. It highlights the challenges of data access speed in Kubernetes and how Fluid automates data management and scheduling to improve performance and scalability. The document also provides technical details on using Fluid, including API specifications and cache management.

1. Rong Gu (Nanjing University)
Yang Che (Alibaba Cloud Kubernetes Team)
Fluid: When Alluxio meets
Kubernetes
https://github.com/fluid-cloudnative/fluid

2. What IS THIS TALK ABOUT?
Data and Application Orchestration In Kubernetes

3. an open-source system for automating deployment,
scaling, and management of containerized applications.
Kubernetes (k8s) is...

4. Ecosystem and large Open
Source Community
Kubernetes Advantages
01
03
04 02
Portability inherit from Docker,
better isolation for performance
and security
Out of box management:
Self-healing, Autoscaling,
Security, Logging, Monitoring,
etc…
Heterogeneous Deployment
in different cloud

5. Computing
ECS, EBM, GPU, FPGA, ECI
Network
VPC, ENI, RDMA, SLB, DNS
Storage
EBS, NAS, CPFS, OSS
Kubernetes
Registry
Service
Marketplace
DevOps Micro Service/Service Mesh Enterprise Applications Innovation
Gitlab Jenkins GitOps EDAS Istio .net JEE AI Blockchain IoTC++Spring Cloud
Public Cloud Edge Computing Private Cloud
Serverless Application
Gene Encoding
Big Data AI
Spark Apache Flink TensorFlow PyTorch
Operating System (Linux, Windows)
Presto MxNet Caffe TF-Serving
More and More Data Driven Applications
run on Kubernetes

6. Cloud SSD
But the data accessing in K8s is slow
01
GPUs requires High I/O
Throughput and Low
Latency
02
Separation of computing
and storage causes I/O
latency
03
Single-machine cache can
hardly meet massive data
acceleration

7. Alluxio is...
A data orchestration platform fundamentally enables separation of
storage and compute.

8. Motivations towards Alluxio With Kubernetes
● Poor performance
● Poor scalability
● Good performance
● Limited scalability
● Expensive
● Good Performance
● Good scalability
● Low Cost!
Lustre
Alibaba OSS Alibaba OSS Alibaba OSS

9. Kubernetes Scheduler is no
aware of cache locality
Cache capabilities should be
also scalable
Cache is important, but the challenges are
K8S node K8S node
fuse
K8S node
fuse
IMAGENET
fuse
Distributed Data Cache
Po
d
Po
d
MXNet MXNet
Po
d
Po
d
Caffe Caffe
OSS/NAS/HDFS
CLEVR
TensorFlow TensorFlow
CIFAR-10
V100 P100 M40
0
1
Resource Pooling causes Cache invalidation
• Requires Portability
02
03

10. Fluid
Provide best Alluxio data orchestration
experience in Kubernetes

11. Advantages：The Benefit of Alluxio + Fluid
Advantages 1
Alluxio automates the data orchestration and management
Advantages 2
Fluid orchestrates and auto-scales Alluxio in Kubernetes
Fluid is responsible for scheduling data-driven job with data locality knowledge
Advantages 3
Advantages 4
Fluid automates the user-facing operations
Advantages 5
Fluid makes cache access from Kubernetes transparent

12. Architecture

13. How to Use Fluid
apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
name: imagenet
spec:
mounts:
- mountPoint: oss://imagenet/train
name: train
options:
fs.oss.accessKeyId: xxx
fs.oss.accessKeySecret: yyy
fs.oss.endpoint: oss-cn-huhehaote.aliyuncs.com
- mountPoint: pvc://ceph-pvc
name: validation
1.Create Dataset
2.provision
3. Create Pod
apiVersion: batch/v1
kind: Pod
metadata:
name: resnet50
spec:
containers:
- name: train
image: resnet50
volumeMounts:
- mountPath: /data
name: imagenet
volumes:
- name: imagenet
persistentVolumeClaim:
claimName: imagenet
ImageNet
PV/PVC
Pod
/data
train (OSS)
Validation(PVC)

14. How to check the dataset
Data requires
to cache
Dataset Runtime
Status:
Cache States:
Cache Capacity: 200.00GiB
Cached: 0B
Cached Percentage: 0%
Conditions:
Message: The ddc runtime is ready.
Reason: DatasetReady
Status: True
Type: Ready
Phase: Bound
Runtimes:
Category: Accelerate
Name: imagenet
Namespace: default
Type: alluxio
Ufs Total: 84.29GiB
Status:
Cache States:
Cache Capacity: 600GiB
Cached: 76.08GiB
Cached Percentage: 90.3%
Conditions:
# more conditions
Current Fuse Number Scheduled: 4
Current Master Number Scheduled: 1
Current Worker Number Scheduled: 4
Desired Fuse Number Scheduled: 4
Desired Master Number Scheduled: 1
Desired Worker Number Scheduled: 4
Fuse Number Available: 4
Fuse Status: True
Type: Ready
Phase: Bound
Runtimes:
Category: Accelerate
Name: imagenet
Namespace: default
Type: alluxio
Events: <none>
er Ready: 4
Fuse Phase: Ready
Master Number Ready: 1
Master Phase: Ready
Value File: imagenet-alluxio-values
Worker Number Available: 4
Worker Number Ready: 4
Worker Phase: Ready
Current Cache
Capabilities
Current Number
of workers
• Understand the current cache capabilities
• Determine if need to scale out

15. Schedule job with the dataset locality
client
apiVersion: batch/v1
kind: Pod
metadata:
name: resnet50
spec:
containers:
- name: train
image: resnet50
volumeMounts:
- mountPath: /data
name: imagenet
volumes:
- name: cifar10
persistentVolumeClaim:
claimName: imagenet
Kubernetes
Scheduler
Fluid Scheduler
Fluid Runtime
Service
Alluxio Alluxio
N1 N2 N3
10G Cached 5G Cached
Pod
1. Create Pod
2.Find the cacheable Node
3.Query the cache capabilities
of then odes
4.Start pod in N1

16. Demo 2
Accelerate Remote File
Accessing with Fluid
Machine Learning
with Fluid
Accelerate PVC
with Fluid
Demo 1 Demo 2 Demo 3
Demo

17. End-to-End Optimization
Workload: ResNet50 && ImageNet

18. Github Repo:
https://github.com/fluid-cloudnative/fluid
Project Homepage:
http://pasa-bigdata.nju.edu.cn/fluid/index.html
DingDing Talk Group:
Learn More about Fluid

19. THANKS!
Rong Gu, Nanjing University
Yang Che, Alibaba Cloud Kubernetes Team