Ashish Gupta, Marcia Zangrilli, Ananth I. Sundararaj, Peter A. Dinda, Bruce B. Lowekamp EECS, Northwestern University Computer Science, College of William.

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Presentation transcript:

Ashish Gupta, Marcia Zangrilli, Ananth I. Sundararaj, Peter A. Dinda, Bruce B. Lowekamp EECS, Northwestern University Computer Science, College of William and Mary Please visit Free Network Measurement for Adaptive Virtualized Distributed Computing Virtuoso A Distributed Computing Platform composed of Virtual Machines interconnected with Virtual Networks Major benefit : Automated Runtime Adaptation to improve performance/cost effectiveness ADAPTATION : A FOUR STEP PROCESS 1.Automatically infer application demands (network/CPU) 2.Monitor resource availability (bw/latency/CPU) 3.Adapt distributed application for better performance/cost effectiveness 4.Reserve Resources when possible CURRENT WORK : Provides automatic adaptation leveraging network measurements Approach Three Main Components VNET VTTIF WREN Layer 2 virtual overlay networking Runtime application topology inference Online passive bw monitoring and network characterization Major benefit : Completely independent of unmodified application or operating system VNET Virtual overlay network  creates illusion of LAN over wide area Benefits: Network transparency with VM migration Ideal monitoring point for application monitoring VTTIF Infers application topology and traffic load at runtime Resistant to rapid fluctuations and provides damped network view All local views aggregated to central proxy to give global view of distributed application WREN How does it work ? 1. Identifies outgoing Maximal length trains with similar spaced packets. 2.Calculates ISR ( Initial Sending Rate ) for these trains. 3. Monitors ACK return rate to determine trends in RTTs. 4. Increase trend indicates congestion, non increasing trend indicates lower bound for bw. Linux Kernel WREN Packet Tracer WREN Analysis Thread Grid Application SOAP Interface WREN Performance Controlled load/latency testbed Nisten  emulate WAN environment with congestion Latency : 20 to 100 ms, bw : 3 to 25 Mbps 1. Observes incoming/outgoing packets 2. Online analysis to derive latency/bandwidth information for all host pair connections 3. Answers network queries for any pair of hosts What does it do ? Key Advantage : WREN accurately reports available bandwidth when application traffic does not saturate the path Adaptation Process Network Availability Application Demand VM to HOST mapping Provide Overlay Topology Provide forwarding rules What defines good adaptation ?  various metrics possible Current Metric : Maximum residual bottleneck bandwidth How can we map the processes and paths such that (available bandwidth – demanded bandwidth) is maximized ?  Maximum room for performance improvement Two approaches Greedy Heuristic Mapping Identifies Hosts which have good bandwidth connectivity and maps VMs over them Overlay paths Uses adapted Dijkstra to find “widest” paths depending on bandwidth demands of application process pairs (sorted in decreasing order)  finds path which leaves maximum residual bottleneck bandwidth Simulated Annealing Motivation : Search Space is very large  Huge number of possibilities for mapping and overlay paths Approach 1.Start with an initial solution 2.Perturb current configuration and evaluate with a cost function 3. Continue Controlled Perturbation until a good cost function is achieved Perturbation function and algorithm details in paper Adaptation Results Scenario 1 : Only a particular mapping yields good performance Scenario 2 : Large 256 host topology. 32 potential hosts, 8 Virtual Machines Both Annealing and Greedy perform well. Annealing advantage : Multi-Constraint optimization easy Results for Multi Constraint Cost Function : Bandwidth and Latency Annealing easy to adapt and finds good mappings compared to heuristic User User’s LAN VM IP UDPTCP bw measurements Network