Free Network Measurement for Adaptive Virtualized Distributed Computing Ashish Gupta, Marcia Zangrilli, Ananth Sundararaj, Anne Huang, Peter A. Dinda, Bruce B. Lowekamp

2 Overview Benefits of VMs: transparent portability, adaptation, security Contributions: 1.Online passive measurement of physical layer’s available bandwidth (Wren) 2.Integration of Virtuoso’s application monitoring and Wren’s traffic monitoring 3.Adaptation algorithms that use passive monitoring to solve challenging adaptation problems Virtual Machines Virtual Network Physical Network

3 Adaptive Virtualized Distributed Computing How can we efficiently utilize resources in a virtual machine distributed system? –Accurately monitor resource availability –Transparently adapt to changing conditions –Keep application portability simple

4 Claim Virtualization enables the broad application of dream techniques… –Adaptation –Resource reservation … using existing, unmodified applications and operating systems –So everyone can use the techniques

5 Optimization of Virtual System Environment Benefit: Completely independent of application or Operating System

6 Outline Virtuoso –Overview of distributed VM system –VTTIF –VNET Wren –Online Wren overview –Wren performance Integration of Virtuoso and Wren Adaptation –Algorithms –Results

7 Virtuoso 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 Distributed computing environment composed of virtual machines interconnected with virtual networks

8 VM Layer Vnetd Layer Physical Layer Application communication topology and traffic load; application processor load Network bandwidth and latency; sometimes topology Vnetd layer can collect all this information as a side effect of packet transfers and invisibly act VM Migration Topology change Routing change Reservation

9 Virtual Topology and Traffic Inference Framework (VTTIF) Operation 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

10 Virtual Topology and Traffic Inference Framework (VTTIF) Operation Application topology is recovered using normalization and pruning algorithms Ethernet-level traffic monitoring VNET daemons collectively aggregate a global traffic matrix for all VMs

11 VNET Virtual overlay network → creates illusion of LAN over wide area –Network transparency with VM migration –Ideal monitoring point for application monitoring

12 Watching Resources from the Edge of the Network (Wren): A Hybrid Monitoring Approach Wren Design: –Kernel-level instrumentation to collect traces of application traffic. –Analysis and management of traces handled in user-level. Wren capabilities: 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

13 Wren Architecture Linux Kernel WRENPacket Tracer WREN Analysis Thread Grid Application SOAP Interface IP UDPTCP bw measurements Network Linux Kernel WRENPacket Tracer WREN Analysis Thread Grid Application SOAP Interface IP UDPTCP bw measurements Network

14 Wren Online Available Bandwidth Algorithm Applies self-induced congestion principle –If packets are sent at a rate larger than the available bandwidth, the queuing delays will have an increasing trend. –Find the rate just before queuing delays are incurred 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.

15 Wren Performance Key Advantage : WREN accurately reports available bandwidth when application traffic does not saturate the path Controlled load/latency testbed Nistnet → emulate WAN environment with congestion Latency : 20 to 100 ms, bw : 3 to 25 Mbps

16 Wren Network Inference Host OS Kernel TCP / UDP Forwarding Layer 2 Network Interface VTTIF Application Inference VADAPT Adaptation Virtual Machine Monitor Guest OS Kernel Application Virtual Machine LANOther VNET daemon Integrating Virtuoso and Wren

17 Adaptation Process

18 What defines Good Adaptation? Various ways to define good adaptation 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

19 Optimization Problem Given the –network traffic load matrix of the application –computational intensity in each VM –topology of the network –load on its links, routers and hosts What is the –mapping of VMs to hosts –overlay topology connecting the hosts –forwarding rules on that topology –required CPU and network reservations That –maximizes the application performance?

20 Problem formulation Objective function Application demands Measured data Constraints

21 Greedy Heuristic Mapping –Identifies Hosts which have good bandwidth connectivity and maps VMs over them Overlay paths –Uses adapted Dijktra to find “widest” paths depending on bandwidth demands of application process pairs (sorted in decreasing order) → finds path which leaves maximum residual bottleneck bandwidth

22 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

23 Experimental Setup Evaluation conducted in simulation In each scenario the goal is –to generate a configuration consisting of VM to Host mappings –paths between the communicating VMs –Such that the total residual bottleneck bandwidth is maximized We compare –greedy heuristic (GH) –simulated annealing approach (SA) –SA with the GH solution as the starting point (SA+GH). –Additionally we also maintain the best solution found so far with (SA+GH), i.e. (SA+GH+B), where ’B’ indicates the best solution so far.

24 Adaptation Results Scenario 1 : Only a particular VM to Host mapping yields good performance.

25 Scenario 1 Results Both Annealing and Greedy perform well. Annealing advantage : Multi-Constraint optimization easy

26 Results for Multi Constraint Cost Function : Bandwidth and Latency Annealing easy to adapt and finds good mappings compared to heuristic Scenario 2 : Large 256 host topology. 32 potential hosts, 8 Virtual Machines

27 Conclusion Network measurements can be provided for free! These measurements can be used to improve application performance through adaptation Virtuoso and Wren Integrated system –Low overhead –Provides application and resource measurements –Allows transparent optimization of application performance Adaptation Strategies –Greedy heuristic and simulated annealing approaches are able to find good mappings/configurations

28 Please visit –Prescience Lab (Northwestern University) –Wren: Watching Resources fro the Edge of the Network (William and Mary) –Virtuoso: Resource Management and Prediction for Distributed Computing using Virtual Machines VNET is publicly available from above URL For More Information