Typhoon: An SDN Enhanced Real-Time Big Data Streaming...
Transcript of Typhoon: An SDN Enhanced Real-Time Big Data Streaming...
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Typhoon: An SDN Enhanced Real-Time Big Data
Streaming Framework
Junguk Cho, Hyunseok Chang, Sarit Mukherjee, T.V. Lakshman,
and Jacobus Van der Merwe
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Big Data Era
● Big data analysis is increasingly common○ Recommendation systems (e.g., Netflix, Amazon)○ Targeted advertising (e.g., Google, Facebook)○ Mobile network management (e.g., AT&T, Verizon)
● Real time stream framework○ Continuously process unlimited streams of data ○ High throughput and low latency using computation parallelism○ Fault tolerance ○ A lot of open-source stream frameworks (e.g., Storm, Heron,
Flink, Spark etc.)
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Limitations of Current Real-Time Stream Frameworks
● Lack of runtime flexibility○ Scale-in/out, computation logic, and routing policy
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Limitations of Current Real-Time Stream Frameworks
● Lack of runtime flexibility○ Scale-in/out, computation logic, and routing policy ○ Solutions
■ Shutdown -> modification -> restart● Lose data
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Limitations of Current Real-Time Stream Frameworks
● Lack of runtime flexibility○ Scale-in/out, computation logic, and routing policy ○ Solutions
■ Shutdown -> modification -> restart● Lose data
■ Instant swapping● Require stable front-end queue systems (e.g., kafka)● Still resource inefficient
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Limitations of Current Real-Time Stream Frameworks
● Lack of runtime flexibility○ Scale-in/out, computation logic, and routing policy ○ Solutions
■ Shutdown -> modification -> restart● Lose data
■ Instant swapping● Require stable front-end queue systems (e.g., kafka)● Still resource inefficient
● Not optimal for one-to-many communication ○ No broadcast concept○ Multiple same computations in application-layer
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Limitations of Current Real-Time Stream Frameworks
● Lack of runtime flexibility○ Scale-in/out, computation logic, and routing policy ○ Solutions
■ Shutdown -> modification -> restart● Lose data
■ Instant swapping● Require stable front-end queue systems (e.g., kafka)● Still resource inefficient
● Not optimal for one-to-many communication ○ No broadcast concept○ Multiple same computations in application-layer
Problem on inflexibility and performance is
mainly from application-layer data routing 7
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Limitations of Current Real-Time Stream Frameworks
● Lack of runtime flexibility○ Scale-in/out, computation logic, and routing policy ○ Solutions
■ Shutdown -> modification -> restart● Lose data
■ Instant swapping● Require stable front-end queue systems (e.g., kafka)● Still resource inefficient
● Not optimal for one-to-many communication ○ No broadcast concept○ Multiple same computations in application-layer
● Lack of management for deployed stream apps○ Limited to application monitoring 8
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Typhoon : An SDN Enhanced Real-Time Stream Framework
● Offer flexible stream processing pipelines for dynamic reconfigurations
○ Scale-in/out, computation logic, routing policy ● Optimize one-to-many communication
○ Broadcast concept at network layer● Enhance the management capabilities in stream
frameworks○ SDN control plane applications
● Cross-layer design○ Partially offload application data routing layer into network layer○ Manage both layers from an SDN controller
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Why SDN in Stream Framework?
● SDN benefits ○ Centralized views and control○ Programmability
● One-to-many communication○ More efficient packet-level replications at the network layer
● Well-defined protocol and interface○ OpenFlow
● Evolvable framework○ Extensible via control plane applications
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Can We Apply SDN to Stream Framework?
● Conceptually similar to computer networks
Graph-based communication pattern
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Can We Apply SDN to Stream Framework?
● Conceptually similar to computer networks
Graph-based communication pattern
Round-Robin Key-basedComputing & Routing
components
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Can We Apply SDN to Stream Framework?
● All topology information pre-defined in submitted application
Logical Topology
Aggreg
ator (1)Input
(1)
Split
(2)
Count
(2)
Aggreg
ator Input
Split Count
Split Count
Physical Topology
Stream frameworks
App Developer
New Application Or
Reconfiguration Request
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Can We Apply SDN to Stream Framework?
● All topology information pre-defined in submitted application
Stream frameworks
App Developer
New Application Or
Reconfiguration Request
Well fit in proactive SDN model
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Logical Topology
Aggreg
ator (1)Input
(1)
Split
(2)
Count
(2)
Aggreg
ator Input
Split Count
Split Count
Physical Topology
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Current Stream Frameworks
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Streaming ManagerCentral
Coordinator
Worker Agent
Worker …..Topology Builder &
SchedulerWorker
Compute Cluster
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Typhoon Architecture
SDN Controller
Streaming Manager
Central
Coordinator
Worker Agent
Worker …..Topology Builder &
SchedulerWorker
Dynamic Topology
Manager
Software SDN SwitchSDN Control Plane
Application
Compute Cluster
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Typhoon Design Challenges
● How can we integrate SDN into stream frameworks?● How can we support dynamic reconfigurations?
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Typhoon Design Challenges
● How can we integrate SDN into stream framework?● How can we support dynamic reconfigurations?
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Integration of SDN into Stream Framework
Worker Agent
Worker ….. Worker
Software SDN Switch
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Integration of SDN into Stream Framework
Worker Agent
Worker ….. Worker
Software SDN Switch
Worker Agent
Data Routing
Data
Computation
Worker
I/O Layer
Software SDN Switch
Data Routing
Data
Computation
Worker
I/O Layer
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Integration of SDN into Stream Framework
Worker Agent
Worker ….. Worker
Software SDN Switch
Worker Agent
Data Routing
Data
Computation
Worker
I/O Layer
Software SDN Switch
Port Port
Data Routing
Data
Computation
Worker
I/O Layer
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Integration of SDN into Stream Framework
Worker Agent
Worker ….. Worker
Software SDN Switch
Worker Agent
Data Routing
Data
Computation
Worker
I/O Layer
Software SDN Switch
Port Port
Data Routing
Data
Computation
Worker
I/O Layer
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To use SDN as data forwarding functions, Typhoon needs
match fields in flow rules and packet format to be matched
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Design Flow Rules and Packet Format in Typhoon
Source
worker ID
Destination
worker ID
StreamID
Tuple Length
Output from
Data Computation
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Metadata
of data tuple
Data Tuple: stream communication data model for
worker-to-worker communications in existing stream frameworks
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Design Flow Rules and Packet Format in Typhoon
Source
worker ID
Destination
worker ID
StreamID
Tuple Length
Output from
Data Computation
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Unique IDs in a stream application
Metadata
of data tuple
Data Tuple: stream communication data model for
worker-to-worker communications in existing stream frameworks
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Design Flow Rules and Packet Format in Typhoon
Tuple Length
Destination
worker ID
StreamID
Output from
Data Computation
Source
worker ID
Ether
type
Ethernet
header
Payload
(Data tuple)
Custom transport protocol in Typhoon
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Metadata
of data tuple
In Typhoon
Source
worker ID
Destination
worker ID
StreamID
Tuple Length
Output from
Data Computation
Unique IDs in a stream application
Metadata
of data tuple
Data Tuple: stream communication data model for
worker-to-worker communications in existing stream frameworks
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Offloading Data Forwarding from Application Layer
Data Routing
Data Computation
Port
Data Routing
Data Computation
I/O Layer
Port
Software SDN Switch
Source worker Destination worker
Match in_port=[src worker port], dl_dst=[dest worker ID],
dl_src=[src worker ID]
Action output=[dest worker port]
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Packet format created by I/O LayerDestination
worker ID
Set of data tuples
Source
worker ID
Ethernet
header
Payload
Ether
type
I/O Layer
Pre-installed flow rule
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● How can we integrate SDN into stream framework?● How can we support dynamic reconfigurations?
○ What are the requirements for dynamic reconfigurations?
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Typhoon Design Challenges
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Application Layer Data Routing in Typhoon
● Worker data routing ○ Policy-specific routing states○ Policy-independent routing states○ Routing computations
Data Routing
Data Computation
Worker
I/O Layer
Routing Type States
Round-robin Counter
Key-based Index for hash
One-to-many
/* Round robin */Index = (counter++) % numDestWorkers;
dstWorker = nextWorkers[index]; 28
Destination Worker IDs
Destination 1
...
Destination N
Policy-specific routing states
Policy-independent routing states
Routing computations
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Application Layer Data Routing in Typhoon
● Update routing states if needed○ Policy-independent routing states
■ Scale-in/out■ Computation logic update
○ Policy-specific routing states■ Change routing type and states
Data Routing
Data Computation
Worker
I/O Layer
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Routing Type States
Round-robin Counter
Key-based Index for hash
One-to-many
Destination Worker IDs
Destination 1
...
Destination N
Policy-specific routing states
Policy-independent routing states
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● How can we integrate SDN into stream framework?● How can we support dynamic reconfigurations?
○ What are the requirements for dynamic reconfigurations?■ Updating per-worker routing states for reconfigurations
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Typhoon Design Challenges
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● How can we integrate SDN into stream framework?● How can we support dynamic reconfigurations?
○ What are the requirements for dynamic reconfigurations?■ Updating per-worker routing states for reconfigurations■ Framework-level coordination to avoid packet loss during
reconfiguration
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Typhoon Design Challenges
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● How can we integrate SDN into stream framework?● How can we support dynamic reconfigurations?
○ What are the requirements for dynamic reconfigurations?■ Updating per-worker routing states for reconfigurations■ Framework-level coordination to avoid packet loss during
reconfiguration
○ How can Typhoon manage per-worker routing states in centralized manners?
○ What are the protocol and Interface for managing application-layer routing states?
○ How can Typhoon prevent packet loss during reconfigurations?
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Typhoon Design Challenges
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How to manage per-worker routing states in Typhoon
● Offloading routing states management from the application layer to the network layer
● SDN Controller (i.e., network) ○ Manage application-layer routing states with centralized
manners■ Policy-independent routing states (e.g., forwarding tables)■ Policy-specific routing states (e.g., index for key-based routing)
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What is a management layer in Typhoon?
● What is a transport method to control per-worker?○ PacketOut in OpenFlow○ Each worker is coupled to a port in a software SDN switch.
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Worker Agent
Data Routing
Data
Computation
Worker
I/O Layer
Software SDN Switch
SDN Controller
SDN Control Plane
ApplicationPort
(i.e., Protocol)
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What is a management layer in Typhoon?
● What is a transport method to control per-worker?○ PacketOut in OpenFlow○ Each worker is coupled to a port in a software SDN switch.
● What is a message format to carry routing states? ○ Control tuple: Re-use data tuple formatin stream frameworks for framework compatibility
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Control tuple
type
Tuple Length
Routing States
(e.g., New
destination IDs)
Metadata
Worker Agent
Data Routing
Data
Computation
Worker
I/O Layer
Software SDN Switch
SDN Controller
SDN Control Plane
ApplicationPort
Control Tuple
(i.e., Interface)
(i.e., Protocol)
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Stable Dynamic Reconfiguration in Typhoon
● How can Typhoon prevent packet loss during reconfigurations?
○ Well-defined order to update each component under framework coordination
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Stable Dynamic Reconfiguration in Typhoon
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Scale-out Scale-inSource New destination
Destination
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Stable Dynamic Reconfiguration in Typhoon
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Scale-outSource New destination
Destination
Scale-inNo packet loss under framework coordination
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Runtime Flexibility in Typhoon
● Design flexible stream processing pipelines ○ Offload routing managements to an SDN controller
■ Leverage control tuples carried by OpenFlow PacketOut
○ Offload the data forwarding to network with SDN flow rules
● Support reconfigurations under framework coordination○ Prevent packet loss during reconfigurations
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Limitations of Current Real-Time Stream Frameworks
● Lack of runtime flexibility● Not optimal for one-to-many communication ● Lack of management for deployed stream apps
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Data Routing
Data Computation
I/O Layer
Suboptimal One-to-Many Communication
Output from
Data Computation
Destination
Worker 1
Destination
Worker 2
Source Worker
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Data Routing
Data Computation
I/O Layer
Suboptimal One-to-Many Communication
Source
worker ID
Destination
worker ID
StreamID
Tuple Length
Output from
Data Computation
Data Tuple
Destination
Worker 1
Destination
Worker 2
Source Worker
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● Degrade performance due to multiple same computations○ Multiple same computations
■ Heavy multiple serializations■ Data copy to different TCP connections
○ No broadcast concept in application layers43
Data Routing
Data Computation
Source Worker
Destination
Worker 1
I/O Layer
Suboptimal One-to-Many Communication
Source
worker ID
Destination
worker ID
StreamID
Tuple Length
Output from
Data Computation
Data Tuple
Destination
Worker 2
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Data Routing
Data Computation
I/O Layer
Port
Data Routing
Data Computation
I/O Layer
Port
Data Routing
Data Computation
I/O Layer
Port
Software SDN Switch
Typhoon for One-to-Many Communication
Source Worker Destination Worker 1 Destination Worker 2
Match in_port=[source worker port], dl_dst=[BROADCAST],
dl_src=[source worker ID]
Action output=[all destination worker’s ports]
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Data Routing
Data Computation
I/O Layer
Port
Data Routing
Data Computation
I/O Layer
Port
Data Routing
Data Computation
I/O Layer
Port
Software SDN Switch
Typhoon for One-to-Many Communication
BROADCAST
Set of tuples
Source
Worker ID0xffff
Ethernet
header
Payload
Source Worker Destination Worker 1 Destination Worker 2
Match in_port=[source worker port], dl_dst=[BROADCAST],
dl_src=[source worker ID]
Action output=[all destination worker’s ports]
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Data Routing
Data Computation
I/O Layer
Port
Data Routing
Data Computation
I/O Layer
Port
Data Routing
Data Computation
I/O Layer
Port
Match in_port=[source worker port], dl_dst=[BROADCAST],
dl_src=[source worker ID]
Action output=[all destination worker’s ports] Software SDN Switch
Typhoon for One-to-Many Communication
BROADCAST
Set of tuples
Source
Worker ID0xffff
Ethernet
header
Payload
Source Worker Destination Worker 1 Destination Worker 2
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One-to-Many Communication In Typhoon
● Straightforward, but very effective● Use a broadcast concept in network
○ One-to-many communication aware worker I/O and an SDN controller
○ Packet-level replications at the network layer
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Limitations of Current Real-Time Stream Frameworks
● Lack of runtime flexibility● Not optimal for one-to-many communication ● Lack of management for deployed stream apps
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Limitations of Current Real-Time Stream Frameworks
● Lack of runtime flexibility● Not optimal for one-to-many communication ● Lack of management for deployed stream apps
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Only monitoring
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SDN Control Plane Applications
● Unique opportunities by tightly coupling framework and SDN
○ Rich cross-layer information from application and network layers
● SDN control plane applications based on cross-layer information
○ Fault detector○ Auto Scaler○ Live debugger○ Load balancer
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Fault Detector SDN Control Plane Application
● Existing stream frameworks○ Polling approach○ Heartbeat based on timeout
■ Expensive in case of large scale■ Not instantaneous
● Typhoon○ Event-driven approach and network layer information○ OpenFlow event
■ Unexpected port deletion events■ Traffic steering with flow rule changes
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Auto Scaler SDN Control Plane Application
● How to decide scale-in/out operation○ Application-layer information is more accurate than network
information to know worker is overloaded■ Capacity = (# of processed tuples * average process time) /
measurement time
● Auto Scaler SDN control plane application○ Threshold-based scale-in/out operations using capacity
metric (i.e., application layer information)
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Typhoon Prototype: Refactor Apache Storm
● Re-define global states using Thrift○ Update global states to include reconfigurations and detailed topology
information stored in a central coordinator (i.e., zookeeper)
● Stream manager (i.e., Nimbus)○ Extend the topology builder and the scheduler○ Implement dynamic topology manager module
■ For updating global states for initial submissions and reconfigurations
● Worker I/O layer○ Replace netty-based Storm transport with Typhoon custom transport ○ Implement with JAVA and C using JNI to bridge them
■ C using the DPDK framework● (De)Packetizing and Packet batching in C
■ Java implementing Storm transport interface● Queue and batch tuples● (De)Multiplexing tuples
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Typhoon Prototype
● Software SDN switch○ DPDK-based user space OVS
● Floodlight controller○ Use apache curator for interaction between an SDN controller
and the zookeeper○ Implement SDN control plane applications○ Provide rest APIs for control plane applications○ Re-use tuple libraries from Storm for control tuples
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Evaluation
● Baseline performance● One-to-many communication performance● SDN control plane applications
○ Auto Scaler○ Fault detector○ Update compute nodes○ Live debugger○ Load balancer
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Evaluation
● Baseline performance● One-to-many communication performance● SDN control plane applications
○ Auto Scaler○ Fault detector○ Update compute nodes○ Live debugger○ Load balancer
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Destination
One-to-Many Communication Performance
Increase the number of destination workers from two to six
Destination
Source
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Destination
# of destination workers
One-to-Many Communication Performance
Increase the number of destination workers from two to six
Destination
Source
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Destination
# of destination workers
One-to-Many Communication Performance
Increase the number of destination workers from two to six
Destination
Source
Typhoon outperforms Storm due to lightweight
packet replications in the SDN switch
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Input
Split
Count Split
Count
Count
Auto Scaler SDN Control Plane Application
Packet loss due to faulty split worker
Count
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Input
Split
Count
Split
Count
Count
Split
Auto Scaler SDN Control Plane Application
Scale-out split worker
Count
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Input
Split
Count
Split
Count
Count
Split
Auto Scaler SDN Control Plane Application
Count
Typhoon avoids packet loss due to worker faults
using threshold-based auto scaler
Scale-out split worker
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Conclusion
● Propose an SDN-based real time stream framework which tightly integrates SDN functionality into a realtimestream frameworks
● Demonstrate that the proposed system provides highly flexible stream pipeline and high-performance application-level data broadcast using cross-layer design and SDN
● Showcase several SDN control plane applications leveraging cross-layer information
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