Free Network Measurement for Adaptive Virtualized Distributed Computing
Post on 01-Jan-2016
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DESCRIPTIONFree Network Measurement for Adaptive Virtualized Distributed Computing. Ashish Gupta, Marcia Zangrilli , Ananth Sundararaj, Anne Huang, Peter A. Dinda, Bruce B. Lowekamp. Overview. Benefits of VMs: transparent portability, adaptation, security. Virtual Machines. Contributions: - PowerPoint PPT Presentation
Free Network Measurement for Adaptive Virtualized Distributed Computing
Ashish Gupta, Marcia Zangrilli, Ananth Sundararaj, Anne Huang, Peter A. Dinda, Bruce B. Lowekamp
OverviewBenefits of VMs: transparent portability, adaptation, security Contributions:Online passive measurement of physical layers available bandwidth (Wren)Integration of Virtuosos application monitoring and Wrens traffic monitoring Adaptation algorithms that use passive monitoring to solve challenging adaptation problemsVirtual MachinesVirtual NetworkPhysicalNetwork
Adaptive Virtualized Distributed ComputingHow can we efficiently utilize resources in a virtual machine distributed system?Accurately monitor resource availabilityTransparently adapt to changing conditionsKeep application portability simple
ClaimVirtualization enables the broad application of dream techniquesAdaptationResource reservation
using existing, unmodified applications and operating systemsSo everyone can use the techniques
Optimization of Virtual System Environment
Benefit: Completely independent of application or Operating System
OutlineVirtuosoOverview of distributed VM systemVTTIFVNETWrenOnline Wren overviewWren performanceIntegration of Virtuoso and WrenAdaptationAlgorithmsResults
Virtuoso Automatically infer application demands (network/CPU)Monitor resource availability (bw/latency/CPU)Adapt distributed application for better performance/cost effectivenessReserve Resources when possible
Distributed computing environment composed of virtual machines interconnected with virtual networks
VMLayerVnetdLayerPhysicalLayerApplication communicationtopology and traffic load;application processor loadNetwork bandwidth andlatency; sometimes topologyVnetd layer can collect all this information as a side effect of packet transfersand invisibly act VM Migration Topology change Routing change Reservation
Virtual Topology and Traffic Inference Framework (VTTIF) OperationInfers application topology and traffic load at runtimeResistant to rapid fluctuations and provides damped network viewAll local views aggregated to central proxy to give global view of distributed application
Virtual Topology and Traffic Inference Framework (VTTIF) OperationApplication topology is recovered using normalization and pruning algorithmsEthernet-level traffic monitoringVNET daemons collectively aggregate a global traffic matrix for all VMs
VNETVirtual overlay network creates illusion of LAN over wide areaNetwork transparency with VM migrationIdeal monitoring point for application monitoring
Watching Resources from the Edge of the Network (Wren): A Hybrid Monitoring ApproachWren Design:Kernel-level instrumentation to collect traces of application traffic.Analysis and management of traces handled in user-level.
Wren capabilities:Observes incoming/outgoing packetsOnline analysis to derive latency/bandwidth information for all host pair connectionsAnswers network queries for any pair of hosts
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
Identifies outgoing Maximal length trains with similar spaced packets.Calculates ISR ( Initial Sending Rate ) for these trains.Monitors ACK return rate to determine trends in RTTs.Increase trend indicates congestion, non increasing trend indicates lower bound for bw.
Wren PerformanceKey Advantage : WREN accurately reports available bandwidth when application traffic does not saturate the pathControlled load/latency testbedNistnet emulate WAN environment with congestionLatency : 20 to 100 ms , bw : 3 to 25 Mbps
WrenNetworkInferenceHost OS KernelTCP / UDP ForwardingLayer 2 Network InterfaceVTTIF Application InferenceVADAPT AdaptationVirtual Machine MonitorGuest OS KernelApplicationVirtual MachineLANOther VNET daemonIntegrating Virtuoso and Wren
What defines Good Adaptation?Various ways to define good adaptation
Current Metric : Maximum residual bottleneck bandwidthHow can we map the processes and paths such that (available bandwidth demanded bandwidth) is maximized ? Maximum room for performance improvement
Optimization ProblemGiven thenetwork traffic load matrix of the application computational intensity in each VMtopology of the networkload on its links, routers and hosts What is the mapping of VMs to hostsoverlay topology connecting the hostsforwarding rules on that topologyrequired CPU and network reservationsThat maximizes the application performance?
Problem formulationObjective functionApplication demandsMeasured dataConstraints
Greedy HeuristicMappingIdentifies Hosts which have good bandwidth connectivity and maps VMs over themOverlay pathsUses 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
Simulated AnnealingMotivation : Search Space is very large Huge number of possibilities for mapping and overlay pathsApproachStart with an initial solutionPerturb current configuration and evaluate with a cost functionContinue Controlled Perturbation until a good cost function is achievedPerturbation function and algorithm details in paper
Experimental SetupEvaluation conducted in simulationIn each scenario the goal isto generate a configuration consisting of VM to Host mappingspaths between the communicating VMs Such that the total residual bottleneck bandwidth is maximizedWe 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.
Adaptation ResultsScenario 1 : Only a particular VM to Host mapping yields good performance.
Scenario 1 Results
Both Annealing and Greedy perform well.Annealing advantage : Multi-Constraint optimization easy
Results for Multi Constraint Cost Function : Bandwidth and LatencyAnnealing easy to adapt and finds good mappings compared to heuristicScenario 2 : Large 256 host topology. 32 potential hosts, 8 Virtual Machines
ConclusionNetwork measurements can be provided for free!These measurements can be used to improve application performance through adaptationVirtuoso and Wren Integrated systemLow overhead Provides application and resource measurementsAllows transparent optimization of application performanceAdaptation StrategiesGreedy heuristic and simulated annealing approaches are able to find good mappings/configurations
Please visitPrescience Lab (Northwestern University)http://plab.cs.northwestern.eduWren: Watching Resources fro the Edge of the Network (William and Mary)http://www.cs.wm.edu/~lowekamp/wren.htmlVirtuoso: Resource Management and Prediction for Distributed Computing using Virtual Machineshttp://virtuoso.cs.northwestern.eduVNET is publicly available from above URL
For More Information
Marcia- the following sequence of slides might get across the idea of what we mean by inference and adaptation with a bit more depth.
Its probably a good idea to remind them several times that the adaptation we are doing in this paper is topology, forwarding, and migration.
You might want to say here that VTTIF and VNET are not the focuses of the paper, and maybe provide a cite for those who are interested
Explain the visualization generated.Finally a one step process:Doitall [parallel program name] and it went through all the steps and displayed the infered topologyAdd: pvmpov runningImportant to note that arbitrary topology and forwarding rules are permitted, despite its layer 2 model.