Adapting Enterprise Distributed Real-time and Embedded (DRE) Pub/Sub Middleware for Cloud Computing Environments
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1 Adapting Enterprise Distributed Real-time and Embedded (DRE) Pub/Sub Middleware for Cloud Computing Environments Joe Hoffert, Douglas Schmidt, and Aniruddha Gokhale Vanderbilt University Nashville, TN, USA
2 Context: Cloud Computing Resources provided as service Resources on demand Pay-as-you-go usage fee Computing resources CPUs, RAM Networking resources Bandwidth, network latency Popular implementations Amazon Elastic Compute Cloud (EC2), Google App Engine, GoGrid, AppNexus, Emulab OS, Database, RAM, CPU, Disk space, cores, load balancing, applications (e.g., Apache, Facebook servers), bandwidth, link latency 12/2/2010 Joe Hoffert, Vanderbilt University 2
3 Context: Enterprise DRE (E-DRE) Systems Wide range of DRE application domains testing & training of experimental aircraft across large geographic area air traffic management systems disaster recovery operations Manage data, resources critical to the system operations Correct configuration for given environment crucial to proper operation Typically developed for specific computing/networking environment Simplifies development by focusing on single operating environment Decreases system flexibility (e.g., porting to new computing hardware) 3 GHz CPUs, 4 GB RAM 1 Gb/s LAN Problem: Deploying E-DRE Systems In Cloud Infrastructures 12/2/2010 Joe Hoffert, Vanderbilt University 3
4 Motivating Example: Search & Rescue Missions (1/2) Scenario Regional disasters (e.g., hurricane, flooding) Survivors trapped Search & rescue mission initiated Search application fuses multiple sensor streams Thermal scans from unmanned aerial vehicles (UAVs) Video from existing camera infrastructure Data streams sent to ad-hoc datacenter for fusion & dissemination 4
5 Motivating Example: Search & Rescue Missions (2/2) Datacenter Requirements Operate in flexible environments, e.g., Support multiple missions & applications Varying # of senders, receivers Leverage available resources local resources unavailable Flexible resources, e.g., network bandwidth, CPU speed, RAM UAV providing infrared scan stream Cloud computing infrastructure Infrastructure camera providing video stream Ad-hoc datacenter Support Multiple QoS Reliability & latency e.g., video & streamed thermal scans Multimedia data Rescue helicopter Disaster victims 5
6 Challenges for Datacenter in Flexible Environments (1/3) Challenge 1: Reduction of Development Complexity Developing adaptive behavior is challenging: Inherent complexity designing appropriate responses for environment Accidental complexity transforming & managing appropriate responses from design to implementation 850 MHz CPUs, 2 GB RAM X 100 Mb/s LAN network loss data sending rate Increased development complexity reduces availability, assurance, and portability 6
7 Challenges for Datacenter in Flexible Environments (2/3) Challenge 2: Accurate Configuration in Flexible Environments Environment resources unknown a priori make static configuration inadequate Cloud computing infrastructure Ad-hoc datacenter Multicast TCP/IP UDP/IP??? Custom? protocol QoS mechanisms Inaccurate adaptation can result in loss of life & property 7
8 Challenges for Datacenter in Flexible Environments (3/3) Challenge 3: Timely Adaptation in Flexible Environments DRE systems require timely configuration Cloud computing infrastructure Ad-hoc datacenter TCP/IP UDP/IP Multicast Custom protocol QoS mechanisms Untimely adaptation can result in loss of life & property 8
9 Solution Approach: Autonomically Configuring Middleware and Transport Protocols via Machine Learning Techniques ADAptive Middleware And Network Transports (ADAMANT): Data Distribution Service (DDS) OMG pub/sub standard, rich QoS support OpenDDS, OpenSplice implementations Pluggable transport protocol frameworks Open source Adaptive Network Transports (ANT) framework Transport protocol framework Composable modules Fine-grained protocol control NAK-based Reed-Solomon encoding Artificial Neural Network (ANN) Trained on protocol properties Interpolates/Extrapolates for new environments 0 FEC-group Determines optimal protocol/parameters Constant time performance ACK-based Tornado encoding FEC-receiver FEC-sender XOR encoding Interpolation between training data ACK-based FEC-sender XOR encoding Custom protocol 1 NAK-based FEC-receiver Reed-Solomon encoding Custom protocol 2 Protocol Optimization 9
10 ADAMANT Architecture & Control Flow SAR Topic(s) 1. ADAMANT queries environment for resources. DDS Data Writer App Publisher Domain Data Reader App Subscriber Data Reader ADAMANT Adaptive Network Transport (ANT) Protocols ADAMANT Protocol Optimizer (ANN) 2. Resource information passed to ADAMANT. 3. ANN selects appropriate protocol in a timely manner & notifies ANT 4. ANT configures the protocol for the middleware Cloud Computing Environment Key: Control interaction between subsystems Assoc. between reader/writer &topic
11 Addressing Challenges for Datacenter in Flexible Envs. (1/3) ADAMANT addresses challenge 1 (development) via ANNs to manage protocol selection & implementation transformation ANNs manage the development complexity of protocol management: Automatically manage inherent complexity of relationships between environment and protocols Used directly in implementations (i.e., avoids accidental complexity of developing implementation) 3 GHz CPUs, 4 GB RAM X network loss 1 Gb/s LAN CPU speed RAM Network speed Network % loss Sending rate ADAMANT DDS ANN Autonomic Configuration Controller protocol parameters data sending rate Adaptive Network Transport (ANT) Framework 11
12 Challenges for Datacenter in Flexible Environments (2/3) ADAMANT addresses challenge 2 (accuracy) by overfitting ANN to the data Overfitting data increases ANN s accuracy for selecting appropriate protocol Cloud computing infrastructure Ad-hoc datacenter X X X TCP/IP Multicast UDP/IP Custom protocol QoS mechanisms ADAMANT accurately selects correct protocol 12
13 Challenges for Datacenter in Flexible Environments (3/3) ADAMANT addresses challenge 3 (timeliness) via ANN w/ bounded constanttime response ANNs are equation based: Equations based on nodes and connections Fixed number of inputs, hidden nodes, outputs (determined at off-line training time) Constant # of connections (determined at training time) CPU speed, RAM Network speed Network % loss protocol parameters ANN 13
14 Empirical Results Different Hardware Different Protocols Experimental environment: Using protocols that balance reliability and low latency IP Multicast w/ NAKs (NAKcast) Modified FEC (Ricochet) Varied CPU speed, network bandwidth Conducted several training runs ReLate2 Values GHz CPU, 1Gb LAN, 3 rcvrs, 5% loss NAKcast Hz Ricochet R4C3-25Hz Experiment Experiment Difference in hardware triggers a difference in appropriate transport protocol ReLate2 Values 850 MHz CPU, 100Mb LAN, 3 rcvrs, 5% loss NAKCast Hz Ricochet R4 C3-25Hz 14
15 Empirical Results - Accuracy Experimental environment: 394 operating environments Varied CPU speed, network bandwidth, # of data receivers, sending rate Conducted several training runs ANN outputs tested against known correct responses & cross-validation Accuracy (%) ANN Accuracy (known environments) hidden nodes hidden nodes hidden nodes 86 6 hidden nodes Accuracy for excluded data (%) ANN Accuracy (10-fold cross-validation) hidden nodes, error 24 hidden nodes, error 12 hidden nodes, error 6 hidden nodes, error Training Run Training Run ANNs w/ 24 nodes provide most instances of 100% accuracy, highest accuracy with cross-validation 15
16 Empirical Results - Timeliness Experimental environment: 394 operating environments Emulab: 3 GHz CPU, 2GB of RAM, Fedora Core 6 w/ real-time patches Sub 10 µs average response times for all ANN configurations Sub µs jitter for all ANN configurations 8 7 Average ANN Response Times Std Deviation ANN Response Times Time (µs) hidden nodes 12 hidden nodes 24 hidden nodes 36 hidden nodes Time (µs) hidden nodes 12 hidden nodes 24 hidden nodes 36 hidden nodes Classification Run Classification Run ANNs provide the predictable timeliness needed for DRE systems 16
17 Concluding Remarks & Future Work DRE systems in flexible environments need Accurate guidance Bounded, constant-time responses Overfitted ANNs provide Perfect accuracy for known inputs Low latency, constant time performance 0 Current/Future Work Combining multiple supervised machine learning techniques Autonomically adapting while environment dynamically changes Thank you for your time and attention Accuracy (%) ANN Accuracy Training Run Time (µs) 10 5 Average ANN Response Times Classification Run More information at ANN + Support Vector Machines Soli Deo Gloria 17
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