A Serverless Architecture for Building Scalable, Reliable, and CostEffective Videoondemand Systems

1
A Server-less Architecture for Building Scalable,
Reliable, and Cost-Effective Video-on-demand
Systems

• Jack Lee Yiu-bun, Raymond Leung Wai Tak
• Department of Information Engineering
• The Chinese University of Hong Kong

2
Contents

• 1. Introduction
• 2. Challenges
• 3. Server-less Architecture
• 4. Performance
• 5. Conclusion

3
1. Introduction

• Traditional Client-server Architecture
• Clients connect to server and request for
  streaming
• Server capacity limits the system capacity
• Cost increases with system scale
• Server-less Architecture
• Motivated by the availability of powerful user
  devices
• Each user node contributes to the system
• Memory
• Network bandwidth
• Storage
• Costs shared by users

4
1. Introduction

• Composed of clusters
• Each node serves as a mini server

5
2. Challenges

• Video Data Storage
• Retrieval and Transmission Scheduling
• Fault Tolerance
• Distributed Directory Service
• Heterogeneous User Nodes
• System Adaptation node joining/leaving

6
3. Server-less Architecture

• Storage Policy
• Video data is divided into fixed-size blocks and
  then distributed among nodes in the cluster (data
  striping)
• Low storage requirement, load balanced
• Capable of fault tolerance using redundant blocks
  (discussed later)

7
3. Server-less Architecture

• Retrieval and Transmission Scheduling
• Round-based scheduler
• Retrieval scheduling in terms of macro rounds
  composed of GSS groups (micro rounds)
• Transmission lasts for one macro round

8
3. Server-less Architecture

• Fault Tolerance
• Recover from not a single node failure, but
  multiple simultaneously node failures as well
• Redundancy by Forward Error Correction (FEC) Code
• e.g. Reed-Solomon Erasure Code (REC)

9
4. Performance Evaluation

• Reliability Analysis
• Find out the system mean time to failure (MTTF)
• Assuming independent node failure/repair rate
• Tolerate up to h failures by redundancy
• Analysis by Markov chain model

10
4. Performance Evaluation

• Redundancy Level
• Defined as the proportion of nodes serving
  redundant data
• Redundancy level versus number of nodes on
  achieving the target system MTTF

11
4. Performance Evaluation

• System Response Time
• Sum of the scheduling delay and the prefetch
  delay
• Prefetch Delay
• Time required to receive the first group of
  blocks from all nodes
• Increases linearly with system scale not
  scalable
• Ultimately limits the cluster size
• What is the Solution?
• Multiple parity groups

12
4. Performance Evaluation

• Multiple Parity Groups
• Instead of single parity group, the redundancy is
  encoded with multiple parity groups
• Playback begins after receiving the data of first
  parity group

13
4. Performance Evaluation

• Multiple Parity Groups
• Performance gain shorten the prefetch delay
• Drawback higher redundancy level to maintain the
  same system MTTF
• Tradeoff between response time and redundancy
  level

14
4. Performance Evaluation

• System Response Time
• Increases with cluster size
• Shortened by using multiple parity groups

15
4. Performance Evaluation

• System Dimensioning
• What are the system configurations if the system
• achieves a MTTF of 10,000 hours, and
• keeps under a response time constraint of 5
  seconds?

16
5. Conclusion

• Server-less Architecture
• Scalable
• Acceptable redundancy level to achieve reasonable
  response time in a cluster
• Further scale up by forming new autonomous
  clusters
• Reliable
• Fault tolerance by redundancy
• Comparable reliability as high-end server by the
  analysis using Markov chain
• Cost-Effective
• Costs shared by all users

17
5. Conclusion

• Future Work
• Distributed Directory Service
• Heterogeneous User Nodes
• Dynamic System Adaptation
• Node joining/leaving
• Data re-distribution

18
End of Presentation

• Thank you.