Autonomic Runtime System: Design and Evaluationfor SAMR Applications*Salim HaririHigh Performance Distributed Computing LaboratoryThe University of Arizonahttp://www.ece.arizona.edu/~hpdc

Supported by: NSF, DOE, DARPA, Intel, Raytheon and AOL grants

OutlineMotivation and objectivesAutonomia: An Autonomic Control and Management EnvironmentSelf-OptimizationSelf-ProtectionConclusion Remarks

Information Technology and Biology ConvergenceOur system design methods and management tools seem to be inadequate for handling the complexity, size, and heterogeneity of today and future Information systemsBiological systems have evolved strategies to cope with dynamic, complex, highly uncertain constraints

Current Design and Development of Computing SystemsDifferent fields evolved separately and Targeted few domains/applications

New System Construction: Part to The Whole ApproachAdds ComplexityHigh-CostInteroperabilityIssues

Autonomic Computing System: Wholestic ApproachSelf-Healing ComponentSelf-Optimizing ComponentSelf-Configuring ComponentSelf-Protecting ComponentAutonomic Building BlockSecure , Fault-Tolerant SystemHigh-Performance, Fault-Tolerant SystemAutonomic Computing Systems

Autonomia: An Autonomic Control and ManagementProvide dynamically programmable control and management services to support the development and deployment of autonomic applicationsProvide Autonomic Runtime Services (self-healing, self-configuring, self-protecting, self-optimizing) Provide automated deployment, registration, discovery of autonomic componentsProvide automated configuration of autonomic applications and system resources

Application Management EditorUsers ApplicationEvent ServerCoordinatorMonitoring &Analysis EngineSchedulingEnginePlanningEngineAIK Repository ACA Specifications Policy Component State Resource StatePolicy EngineSelf ProtectingSelf OptimizingSelf HealingSelf ConfiguringAutonomic Runtime ServicesCRM: Component Runtime ManagerVEE: Virtual Execution EnvironmentVEE(App1)VEE(Appn) VEE(App2)Application Runtime Manager (ARM)Know-ledgeHigh Performance Computing Environment (HPCE)Autonomic Runtime SystemACAmACA2ACA3ACA3ACA1ACA2ACA3ACA1ACA2ACAjCRMACA1Computational Component

Application Management EditorUsers ApplicationEvent ServerCoordinatorMonitoring &Analysis EngineSchedulingEnginePlanningEngineAIK Repository ACA Specifications Policy Component State Resource StatePolicy EngineSelf ProtectingSelf OptimizingSelf HealingSelf ConfiguringAutonomic Runtime ServicesCRM: Component Runtime ManagerVEE: Virtual Execution EnvironmentVEE(App1)VEE(Appn) VEE(App2)Application Runtime Manager (ARM)Know-ledgeHigh Performance Computing Environment (HPCE)Autonomic Runtime SystemACAmACA2ACA3ACA3ACA1ACA2ACA3ACA1ACA2ACAjCRMACA1Computational Component1223344Autonomia Process Flow324

Application Execution

Self-Optimizing: Design and Evaluation

Current Implementations Intractable for Large Problems

Wildfire Autonomic Runtime Manager (WARM)Dynamic Data Driven Wildfire Model Analysis ObjectivesNatural Region CharacterizationActive Performance ModelKnowledge RepositoryAutonomic SchedulingHeterogeneous, Dynamic Computational EnvironmentNR1 BurnedNR2 BurningNR3 UnburnedActualPredictedSensorsSurvey FlightsGPSSatelliteWild Fire Model Development EnvironmentRegional WeatherTerrain CharacteristicLocal Weather Temp Humidity Wind Speed Wind Direction Clouds Precipitation LightningFire Behavior Location Intensity Geometry PropagationFuel ConditionsSmoke Locations and concentrationFirefighting ActivitiesExecutionNR2CPU

Forest Fire Cell Space: Dynamic Repartitioning Initial partitioningNR2 Burning zonefiner griddingBurned zonecoarser griddingNR2 NR3 NR3 NR5

Wild Fire Simulation PhysicsThe entire area is represented as a 2-D cell-space.The weather and vegetation conditions are assumed to be uniform within a cell, but may vary in the entire cell spaceWhen a cell is ignited, its state will change from unburned to burning. During its burning phase, the fire will propagate to its eight neighbors along the eight directions as shown below.

As the simulation time advances, the fire will propagate from the first ignition cell to other cells.

Parallel Wild Fire Simulation AnalysisThe composition of execution time at time step t for 4 processors.

To decrease T(t), make the computation time on each processor as even as possible, which minimizing the synchronization time. Imbalance Ratio (IR) characterizes the imbalance situation

Tcomp(1,t)

Tcomp(4,t)

Tcomp(3,t)

Tcomp(2,t)

T(t)

Tbcast(t)

Fire Simulation ExampleThe example above describes the imbalance ratio at different time steps. As the simulation advances, imbalance situation will get worse. t = 1t = Nt = 2N

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Self-Optimization Monitors the state of fire simulation to obtain the computation load at any time stepMonitors the states of the underlying system to obtain the computation capacityMonitor the imbalance ratio at any time step. If the imbalance ratio is larger than a given threshold, dynamically adjust the workload among processors at run time.

Self-Optimization AlgorithmObtain the total workload at time t

Estimate the computation time of one burning cell on processor p with the consideration of system load

Where L(p,t) is the length of CPU queue on processor p at time tCalculate the average execution time of one burning cell

Self-Optimization Algorithm(contd)To balance the load on each processor, processor allocation factor (PAF) is defined as inversely proportional to the processor execution time with respect to the average execution time.

Calculate the Processor Load Ratio (PLR) that characterize the capacities of processors

Note that:

Calculate the workload assigned to processor p at time step t, workload(p,t)

Fire Simulation Example with Self-Optimization AlgorithmWith the self-optimization algorithm, the imbalance situation will be dramatically decreased. t = 1t = 2Nt = N

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Wildfire Autonomic Runtime Manager

Experimental resultsProblem size is 64K and number processors is 8With self-optimization, the imbalance ratio will be controlled as close to the threshold. But without self-optimization, the imbalance ration will get larger as the simulation advances

Chart2

3.17799824036.6191302716

28.469524040824.6476018212

40.817131244535.3582164329

50.236167853849.5441343212

62.025393261869.3019698436

82.134066668762.5759227535

113.1929215421.7320261438

141.57359711918.9119874074

169.725898494425.4154514007

193.566168096635.0316527094

221.66879523242.5348488667

245.356833811559.2554181625

266.477359006321.288881293

284.445323554919.6008070849

305.712504561519.4798517278

322.233720381730.6573881436

357.645520345539.2493631068

353.359204462844.9904443383

365.323992994751.9426914956

373.003415559860.3515110909

387.556736242919.4364400021

Static Partition without Self-Optimization

Dynamic Partition with Self-Optimization

Time Step

Imbalance Ratio (%)

Imbalance Ratio Comparison with threshold = 50%

Sheet1

3.17799824036.6191302716106.61913027163.17799824031

17.362092814710.57934508822024.647601821228.469524040850

28.469524040824.64760182123035.358216432940.8171312445100

35.538522371335.35821643294049.544134321250.2361678538150

40.817131244543.56118591655069.301969843662.0253932618200

46.104250735849.54413432126062.575922753582.1340666687250

50.236167853869.30196984367021.7320261438113.19292154300

54.663850221958.87789088528018.9119874074141.573597119350

58.445032065962.57592275359025.4154514007169.7258984944400

62.025393261869.455837690610035.0316527094193.5661680966450

71.726420967921.732026143811042.5348488667221.668795232500

82.134066668718.911987407412059.2554181625245.3568338115550

90.299869225425.415451400713021.288881293266.4773590063600

95.251375479326.695793052214019.6008070849284.4453235549650

102.847706310335.031652709415019.4798517278305.7125045615700

113.1929215442.534848866716030.6573881436322.2337203817750

120.427232258251.342539825617039.2493631068357.6455203455800

126.415294581459.255418162518044.9904443383353.3592044628850

141.57359711966.399575484219051.9426914956365.3239929947900

148.726100117121.28888129320060.3515110909373.0034155598950

153.144725834719.600807084921019.4364400021387.55673624291000

165.081515534419.4798517278220

169.725898494424.1647269472230

176.596456543230.6573881436240

179.596586501234.5887171135250

184.061072409739.2493631068260

191.514001669644.9904443383270

193.566168096649.3865067323280

200.432228411451.9426914956290

203.347515575155.4908943167300

207.77705177160.3515110909310

218.622957808742.69378886320

221.66879523219.4364400021330

226.740773406725.6367498672340

227.831012437212.1391715192350

234.228575773717.8302755616360

240.125306041823.0517496712370

245.356833811529.7104589731380

247.94501822635.2249091981390

252.89629280640.3009307135400

257.766923814343.3210606875410

262.090936739748.875503255420

266.477359006351.3531127801430

264.010817873856.2759491836440

270.188742325759.2874898147450

275.77630297139.3689851681460

277.697131664114.644166172470

284.445323554911.3483073098480

287.874644913316.5413882702490

290.705492209122.2725837312500

295.355378183227.8175279899510

299.173880631131.3308550186520

305.712504561537.1662576231530

309.561474225441.6592328278540

314.159992691645.8944527178550

317.373970997748.973201011560

319.987850254454.4166132642570

322.233720381757.7777365078580

330.815944477462.4847690004590

329.814533292865.0969323331600

335.30638971811.6217923546610

357.645520345515.2062844909620

342.865191637112.5200155068630

343.977761237117.4136435232640

347.964784770520.4010139033650

350.816013602824.7858640816660

353.359204462829.2851055931670

353.48134883331.6746968636680

358.212692342734.6677011039690

358.802233073237.9837505379700

361.95855442239.8202435951710

365.323992994743.6260025327720

365.29426047945.605010818730

369.523664586848.9214032721740

368.859064469251.8747678698750

370.852338381154.2784486098760

373.003415559857.2803111294770

373.865566617.5852556702780

376.768645357711.3559269528790

376.66408492418.9142892164800

379.023516373412.4123663947810

387.556736242915.2569285172820

385.251388149419.2035262553830

390.83745786921.7646112477840

392.7026453125.4557436341850

395.891223958728.3583004013860

397.025401069530.4545941889870

397.265785118933.4216183019880

402.915190316236.1458620795890

402.122003827838.3397665656900

406.127544262940.7245854638910

406.993123757243.8072920256920

411.302760666245.8973868462930

411.847807904648.5486528954940

413.469263542351.2544747917950

417.162909400353.9671582952960

419.6759224047.931138544970

421.72474266119.0461728812980

424.525833878416.997322042990

430.711359517512.31713468981000

Sheet1

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Static Partition without Self-Optimization

Dynamic Partition with Self-Optimization

Time Step

Time Difference Percentage (%)

Maximum and Minimum Time Difference Percentage Comparison

Sheet2

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Static Partition without Self-Optimization

Dynamic Partition with Self-Optimization

Time Step

Imbalance Ratio (%)

Imbalance Ratios Comparison with threshold = 50%

Sheet3

0.0663300 timestep0.118836600timestep0.155819

0.06620.1399910.239581

0.0662210.0659560.103234

0.0660320.0660330.065889

0.0660890.065930.065992

0.0659220.0656640.065817

0.0661760.0659590.066266

0.0660940.0660230.065907

0.0663840.0776620.088018

0.066380.0886550.086416

0.0663890.0799810.099355

0.066310.0731660.088213

0.0663990.0730560.082163

0.0661870.0728280.081916

0.0639510.0731960.08226

0.0668380.0729940.082007

Sheet3

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Time Step 1

Time Step 300

Time Step 600

Processor Number

Execution Time(s)

Executition Time Distribution of Processors without Self-Optimization

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Time Step 1

Time Setp 300

Time Step 600

Processor Number

Execution Time(s)

Executition Time Distribution of Processors with Self-Optimization

Experimental results (contd)Problem size is 64K and number processors is 8.Without self-optimization, the execution times of processors for one time step will be heterogeneous as the simulation advances.With self-optimization, the execution times of processors for one time step will be almost evenly distributed as the simulation advances.

Chart5

0.0663840.0776620.088018

0.066380.0886550.086416

0.0663890.0799810.099355

0.066310.0731660.088213

0.0663990.0730560.082163

0.0661870.0728280.081916

0.0639510.0731960.08226

0.0668380.0729940.082007

Time Step 1

Time Setp 300

Time Step 600

Processor Number

Execution Time(s)

Executition Time Distribution of Processors with Self-Optimization

Sheet1

3.17799824036.6191302716106.61913027163.17799824031

17.362092814710.57934508822024.647601821228.469524040850

28.469524040824.64760182123035.358216432940.8171312445100

35.538522371335.35821643294049.544134321250.2361678538150

40.817131244543.56118591655069.301969843662.0253932618200

46.104250735849.54413432126062.575922753582.1340666687250

50.236167853869.30196984367021.7320261438113.19292154300

54.663850221958.87789088528018.9119874074141.573597119350

58.445032065962.57592275359025.4154514007169.7258984944400

62.025393261869.455837690610035.0316527094193.5661680966450

71.726420967921.732026143811042.5348488667221.668795232500

82.134066668718.911987407412059.2554181625245.3568338115550

90.299869225425.415451400713021.288881293266.4773590063600

95.251375479326.695793052214019.6008070849284.4453235549650

102.847706310335.031652709415019.4798517278305.7125045615700

113.1929215442.534848866716030.6573881436322.2337203817750

120.427232258251.342539825617039.2493631068357.6455203455800

126.415294581459.255418162518044.9904443383353.3592044628850

141.57359711966.399575484219051.9426914956365.3239929947900

148.726100117121.28888129320060.3515110909373.0034155598950

153.144725834719.600807084921019.4364400021387.55673624291000

165.081515534419.4798517278220

169.725898494424.1647269472230

176.596456543230.6573881436240

179.596586501234.5887171135250

184.061072409739.2493631068260

191.514001669644.9904443383270

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200.432228411451.9426914956290

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218.622957808742.69378886320

221.66879523219.4364400021330

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430.711359517512.31713468981000

Sheet1

Static Partition without Self-Optimization

Dynamic Partition with Self-Optimization

Time Step

Time Difference Percentage (%)

Maximum and Minimum Time Difference Percentage Comparison

Sheet2

Static Partition without Self-Optimization

Dynamic Partition with Self-Optimization

Time Step

Time Difference Percentage (%)

Maximum and Minimum Time Difference Percentage Comparison

Sheet3

0.0663300 timestep0.118836600timestep0.155819

0.06620.1399910.239581

0.0662210.0659560.103234

0.0660320.0660330.065889

0.0660890.065930.065992

0.0659220.0656640.065817

0.0661760.0659590.066266

0.0660940.0660230.065907

0.0663840.0776620.088018

0.066380.0886550.086416

0.0663890.0799810.099355

0.066310.0731660.088213

0.0663990.0730560.082163

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0.0639510.0731960.08226

0.0668380.0729940.082007

Sheet3

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0

0

0

0

0

0

0

Time Step 1

Time Step 300

Time Step 600

Processor Number

Execution Time(s)

Executition Time Distribution of Processors without Self-Optimization

Time Step 1

Time Setp 300

Time Step 600

Processor Number

Execution Time(s)

Executition Time Distribution of Processors with Self-Optimization

Chart5

0.06630.1188360.155819

0.06620.1399910.239581

0.0662210.0659560.103234

0.0660320.0660330.065889

0.0660890.065930.065992

0.0659220.0656640.065817

0.0661760.0659590.066266

0.0660940.0660230.065907

Time Step 1

Time Step 300

Time Step 600

Processor Number

Execution Time(s)

Executition Time Distribution of Processors without Self-Optimization

Sheet1

3.17799824036.6191302716106.61913027163.17799824031

17.362092814710.57934508822024.647601821228.469524040850

28.469524040824.64760182123035.358216432940.8171312445100

35.538522371335.35821643294049.544134321250.2361678538150

40.817131244543.56118591655069.301969843662.0253932618200

46.104250735849.54413432126062.575922753582.1340666687250

50.236167853869.30196984367021.7320261438113.19292154300

54.663850221958.87789088528018.9119874074141.573597119350

58.445032065962.57592275359025.4154514007169.7258984944400

62.025393261869.455837690610035.0316527094193.5661680966450

71.726420967921.732026143811042.5348488667221.668795232500

82.134066668718.911987407412059.2554181625245.3568338115550

90.299869225425.415451400713021.288881293266.4773590063600

95.251375479326.695793052214019.6008070849284.4453235549650

102.847706310335.031652709415019.4798517278305.7125045615700

113.1929215442.534848866716030.6573881436322.2337203817750

120.427232258251.342539825617039.2493631068357.6455203455800

126.415294581459.255418162518044.9904443383353.3592044628850

141.57359711966.399575484219051.9426914956365.3239929947900

148.726100117121.28888129320060.3515110909373.0034155598950

153.144725834719.600807084921019.4364400021387.55673624291000

165.081515534419.4798517278220

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176.596456543230.6573881436240

179.596586501234.5887171135250

184.061072409739.2493631068260

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200.432228411451.9426914956290

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218.622957808742.69378886320

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275.77630297139.3689851681460

277.697131664114.644166172470

284.445323554911.3483073098480

287.874644913316.5413882702490

290.705492209122.2725837312500

295.355378183227.8175279899510

299.173880631131.3308550186520

305.712504561537.1662576231530

309.561474225441.6592328278540

314.159992691645.8944527178550

317.373970997748.973201011560

319.987850254454.4166132642570

322.233720381757.7777365078580

330.815944477462.4847690004590

329.814533292865.0969323331600

335.30638971811.6217923546610

357.645520345515.2062844909620

342.865191637112.5200155068630

343.977761237117.4136435232640

347.964784770520.4010139033650

350.816013602824.7858640816660

353.359204462829.2851055931670

353.48134883331.6746968636680

358.212692342734.6677011039690

358.802233073237.9837505379700

361.95855442239.8202435951710

365.323992994743.6260025327720

365.29426047945.605010818730

369.523664586848.9214032721740

368.859064469251.8747678698750

370.852338381154.2784486098760

373.003415559857.2803111294770

373.865566617.5852556702780

376.768645357711.3559269528790

376.66408492418.9142892164800

379.023516373412.4123663947810

387.556736242915.2569285172820

385.251388149419.2035262553830

390.83745786921.7646112477840

392.7026453125.4557436341850

395.891223958728.3583004013860

397.025401069530.4545941889870

397.265785118933.4216183019880

402.915190316236.1458620795890

402.122003827838.3397665656900

406.127544262940.7245854638910

406.993123757243.8072920256920

411.302760666245.8973868462930

411.847807904648.5486528954940

413.469263542351.2544747917950

417.162909400353.9671582952960

419.6759224047.931138544970

421.72474266119.0461728812980

424.525833878416.997322042990

430.711359517512.31713468981000

Sheet1

Static Partition without Self-Optimization

Dynamic Partition with Self-Optimization

Time Step

Time Difference Percentage (%)

Maximum and Minimum Time Difference Percentage Comparison

Sheet2

Static Partition without Self-Optimization

Dynamic Partition with Self-Optimization

Time Step

Imbalance Ratio (%)

Imbalance Ratio Comparison with threshold = 50%

Sheet3

0.0663300 timestep0.118836600timestep0.155819

0.06620.1399910.239581

0.0662210.0659560.103234

0.0660320.0660330.065889

0.0660890.065930.065992

0.0659220.0656640.065817

0.0661760.0659590.066266

0.0660940.0660230.065907

0.0663840.0776620.088018

0.066380.0886550.086416

0.0663890.0799810.099355

0.066310.0731660.088213

0.0663990.0730560.082163

0.0661870.0728280.081916

0.0639510.0731960.08226

0.0668380.0729940.082007

Sheet3

Time Step 1

Time Step 300

Time Step 600

Processor Number

Execution Time(s)

Executition Time Distribution of Processors without Self-Optimization

Time Step 1

Time Setp 300

Time Step 600

Processor Number

Execution Time(s)

Executition Time Distribution of Processors with Self-Optimization

Experimental results (contd)Problem size (256*256 = 64K)

Problem size (512*512 = 256K)

Memory-based Proactive Runtime PartitioningOptimize performance using memory-based approachminimize number of page faults and balance work among processorsMemory function model for RM3D

W is application workload, ai are PF-based heuristicsMemory-based processor grouping and workload partitioningLightly (X -), moderately (X), or heavily (X +) loaded groups based on 2-level threshold with N -, N, and N + processors respectivelyWork in group X - transferred to X + with unit of work being Sort processors in X + in ascending order of available memoryChecks are made for processors with corresponding least available memoryThreshold conditions for work transfers must be metAfter work transfers, new memory-based work partitioning ratios are computed as

Memory-based Proactive Runtime PartitioningBetter performance moderately, heavily loaded scenariosMost processors have less available memoryFrequent page faults resulting in long application delaysMemory-based algorithm yields better performance

Memory-based proactive adaptation performance gain for RM3D application with base grid size 128*32*32 on 8 processors

CPU-based Proactive Runtime PartitioningAdaptive system sensitive partitioner uses system capacities and obtained performance function to compute the relative computational capacities of each processorSystem Capacity CalculationN processors, the total work to be assigned is L

Runtime monitors application and system stateApplication state: level of refinement, number, shape and aspect ratio of refined patchesSystem state: computational load, memory availability, link bandwidthPerformance engine selects the appropriate performance function to predict the execution time of the application for next time step is the execution time on processor k

The PF of RM3D on processor k for a given load X1 and AMR level X2 is empirically defined as:

CPU Based Proactive System Sensitive Runtime PartitioningCPU-based proactive partitioning performance gain on 16 processors. (Base grid size: 641616)

ScenariosExecution time w/o CPU adaptation (seconds)Execution time with CPU adaptation (seconds)Percentage ImprovementLightly loaded2126.06727.1765.8%Moderately loaded2301.151641.7328.66%Heavily loaded2378.251624.1531.71%

Autonomia Self-HealingAPPLICATION RUNTIME MANAGER

Autonomic Middleware ServicesSELF-HEALING SERVICE

AUTONOMIC RUNTIME SYSTEMComponent FAult Manager

Heterogeneous EnvironmentAIKUser applicationApplication Management Editor

Self-Healing Engine

Self-Protection Methodology

Chart1

0.0260.02631579

0.0394736830.039473683

0.0394736830.039473683

0.0789473650.078947365

0.026315790.02631579

0.052631580.05263158

0.0131578950.013157895

0.0657894760.065789476

0.0131578950.013157895

0.44736840.013157895

0.934210540.02631579

0.98684210.065789476

0.98684210.039473683

0.98684210.02631579

0.98684210

0.98684210.05263158

0.98684210.039473683

0.98684210.039473683

0.98684210.039473683

0.98684210.02631579

0.98684210.065789476

0.98684210.039473683

0.98684210.05263158

0.98684210.078947365

0.98684210.02631579

0.98684210.02631579

0.98684210.05263158

0.98684210.013157895

0.98684210.09210526

0.98684210.013157895

0.98684210.0013386881

0.98684210.065789476

0.98684210.05263158

0.921052630.039473683

0.552631560.02631579

0.131578950.02631579

0.0657894760.039473683

0.0394736830.013157895

0.026315790.05263158

0.052631580.05263158

0.0131578950.0010131713

0.052631580.02631579

0.0394736830.05263158

0.052631580.039473683

0.0394736830.0009157509

0.026315790.02631579

Node VI (Under Attack)

Node VI (No Attackl)

Sheet2

Simulation TimeNode VI (Under Attack)Node VI (No Attackl)SYN Received ( Under Attack)SYNs Received (No Attackl)Connection Queue ( Under Attack)Connection Queue (No Attack)Requests Processed (Under Attack)Requests Processed (No Attack)Flow VI ( Under Attack)Flow VI (No Attack)Attack SYNTrust SYNLegitimate Flows (Under Attack)Legtimate Flows (No Attack)Total Flows (Attack)Total Flows (Normal)

120.0260.0263157954542251510.042553190.04255319113136060

150.0394736830.03947368379793375750.0416666680.0416666680212126060

180.0394736830.03947368310210233102102000014146060

210.0789473650.078947365130130661231230.1041666640.1041666643212126060

240.026315790.02631579149149221471470.06250.06251212126060

270.052631580.05263158168168441631630.0833333360.0833333362212126060

300.0131578950.013157895187187111851850.042553190.042553191113136060

330.0657894760.06578947621021055210210000014146060

360.0131578950.013157895227227112252250.081632650.081632651311116060

390.44736840.0131578952892563412552550.430379750.021739131014149360

420.934210540.026315793482797122772770.614035070.0638297921121312660

450.98684210.0657894764123137552913070.73154360.1041666642381015758

480.98684210.0394736834813407532923370.802139040.021739131051419260

510.98684210.026315795393657522923620.841628970.063829792141322560

540.986842106033837502923830.8661417400041425860

570.98684210.052631586704137542924090.88771930.086956522241228958

600.98684210.0394736837394347532924320.89968650.0227272731031432258

630.98684210.0394736838044597532924570.909090940.0222222230131335558

660.98684210.0394736838604797532924780.90615830.066666672131334458

690.98684210.026315798904917522924890.89225590.0227272731031430058

720.98684210.0657894769335177552925110.87351780.108695653231225658

750.98684210.0394736839745387532925360.87351780.063829790331125658

780.98684210.0526315810125597542925550.846890.10204082053921258

810.98684210.07894736510505857562925790.80606060.0222222230131316858

840.98684210.0263157910766067522926040.735537200031412458

870.98684210.0263157911106247522926210.73553720.066666672131312458

900.98684210.0526315811506517542926460.584415560.045454547203148058

930.98684210.01315789511916797512926770.41818180.022727273103145858

960.98684210.0921052612227057572926980.41818180.022222223013135858

990.98684210.01315789512657287512927260.41818180.08695652223125858

1020.98684210.001338688113037477502927460.41818180.022727273103145858

1050.98684210.06578947613407727552927700.41818180.06521739123125858

1080.98684210.0526315813777927542927910.41818180003145858

1110.921052630.03947368314128187032928140.40740740.045454547204145858

1140.552631560.0263157914408364223178330.145833330.085106381310115858

1170.131578950.0263157914668631023458610.0222222230.0227272731013145858

1200.0657894760.03947368314888905337088600.088888893114135858

1230.0394736830.0131578951512913313929110.066666670.0833333360413115859

1260.026315790.05263158154894424428942000014146060

1290.052631580.052631581574970444519690.102040820.021739131011146060

1320.0131578950.00101317131591987104719860.061224490.021739131011146060

1350.052631580.02631579161710154249410120.102040820.0212765950111136060

1380.0394736830.05263158164010423451810370.063829790.0416666680213126060

1410.052631580.039473683166110664354010630.043478260.06250314106058

1440.0394736839.16E-041679109230560109100.043478261114126058

1470.026315790.026315791698111522578111200.021739130114126058

Sheet2

Node VI (Under Attack)

Node VI (No Attackl)

SYN Received ( Under Attack)

SYNs Received (No Attackl)

Connection Queue ( Under Attack)

Connection Queue (No Attack)

Requests Processed (Under Attack)

Requests Processed (No Attack)

Flow VI ( Under Attack)

Flow VI (No Attack)

Total Flows (Attack)

Total Flows (Normal)

Measurement Attributes for Different ProtocolsInside a network element, the measurement attributes can be monitored at different protocol layers.

During the attack (DoS attack, SQL slammer worm, email worm, etc.), significant behaviors will be observed.

Illustrative Network Example100 Mbps, router to router links. Router to client node links are 30 Mbps and 10 Mbps

Abnormality Distance (AD)Abnormality Distance of measurement attributes is used as an abnormality metric for profile modeling of the component behavior.

where and are the mean and variance under the normal operation condition corresponding to the online measurement of attribute k.

Right figure shows the ADtcp_out based on the single measurement attribute measure wherethe larger magnitude of the ADtcp_out indicatesthe abnormal behavior that might be due toan attack.

Multivariate Analysis Techniques on Network Attack DetectionMeasurement AttributestcpOut: legitimate outgoing TCP segments ratetcpTotal: legitimate outgoing and spoofed outgoing TCP segments rateNRC: Normal Region Center, which is the baseline profile for the normal stateAD: Abnormality DistanceUCLtcpoutLCLtcpoutUCLtcptotaltcpOutAtcpTotalLCLtcptotalNRCADNormal Region

Validation on Attacker Side Spoofed TCP SYN AttackAttack intensity and duration are adjustableTCP SYN attack traffic is spoofedNumber of incoming/outgoing packets only wont detect the attack existenceJointly with the total TCP network activity analysis can reveal the attack.

Chart1

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time (1.0s)

number of packets

TCP traffic legitimate in direction

tcpin

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tcpin

time (1.0s)

number of packets

TCP traffic legitimate in direction

Chart1

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number of packets

TCP traffic legitimate out direction

tcpout

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tcpout

time (1.0s)

number of packets

TCP traffic legitimate out direction

Chart2

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6598

6995

7492

6827

5239

6761

5931

5921

6207

3774

0

0

5

0

0

0

0

0

0

0

0

0

0

0

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8

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11

21

29

4

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15

11

0

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8

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time (1.0s)

number of packets

TCP traffic total activity

tcpin

0

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tcpin

time (1.0s)

number of packets

TCP traffic legitimate in direction

Autonomia Self-Protection ArchitectureRaw Traffic w.r.t. metric 1

Information TheoryAutonomic RuntimeEngineOnlineMonitoringPolicy TranslatorChange Network Topology Abnormality functionw.r.t metrics 1 .. mRaw Traffic w.r.t. metric 2Raw Traffic w.r.t. metric nNormal/AbnormalCharacterizationChange Network Configuration Parameters Analysis Engine

Working Flow of the Analysis EngineInformation theory is used to identify the most important features that can be extracted from network data.

Genetic algorithm is used to train data and obtain the threshold and coefficients used by the linear rule for detection.

Threshold and coefficients are used to detect a wide range of attacks in the period of testing.

Network Attack Feature ExtractionTotal DatasetDoS + NormalU2R+NormalR2L + NormalProbe + NormalDiscrete Features Base dataset has a larger sample size Discrete feature provides little semantics information

Feature(X)I(X;Y)Is_hot_login0Land0Root_shell0Su_attempt0Is_guest_login0.006Flag0.062Protocol_type0.304Logged_in0.381service0.571

Feature(X)I(X;Y)Is_guest_login0Is_hot_login0Su_attempt0Land0Logged_in5.2e-5Protocol_type7.3e-5Flag0.0001Root_shell0.003service0.003

Feature(X)I(X;Y)Is_hot_login0Land0Su_attempt2.8e-5Root_shell0.0002Logged_in0.0021Flag0.0033Protocol_type0.0039Is_guest_login0.0144service0.0505

Feature(X)I(X;Y)Is_hot_login0Land0Su_attempt7e-06Root_shell1.4e-5Is_guest_login0.0022Protocol_type0.0386Logged_in0.0701Flag0.0807service0.1243

Network Attack Feature Extraction (Cont.)Discrete Features on Total DatasetContinuous Features on Total DatasetContinuous Features

Compared with the discrete features, some continuous features will provide more information to the final detection

Information provided by the continuous features is much more meaningful

Partition strategy is deployed in the discretization of the continuous features

Heuristic algorithms (e.g. Genetic Algorithm) is used to determine the optimal partition

Combining both discrete and continuous features will provide better detection rate

Experimental ResultsWe compare our approach that is based on discrete features with fuzzy classifier evolved using Ctree and those of the winner group in the KDDCup99 contest.

ClassOur ApproachCtreeWinner EntryNormal98.34%92.78%99.5%Dos99.33%98.91%97.1%U2R63.64%88.13%13.2%R2L5.86%7.41%8.4%PROBE93.95%50.35%83.3%

Results Discrete vs. Cont. & CombinedWe compare the results of using discrete and continuous features respectively

ClassResults using Discrete FeaturesResults using Continuous FeaturesNormal98.34%98.45% 99.98%Dos99.33%99.93% 99.98%U2R63.64%75.34% 98%R2L5.86%41.34% 80%PROBE93.95%99.91%

Summary and Concluding RemarksIncreased complexity, heterogeneity, uncertainty, and scale require new paradigms to design, control and manage systems and applicationsSystems and Applications need to operate reliably, securely, efficiently and cost-effectivelyNeed Wholestic Approach that can dynamically integrate and address all these issues simultaneously at the layers of the system and application hierarchyAutonomic Computing Provides an interesting, pragmatic approach to address these issuesMany challenges are ahead including composing and analyzing in real-time the operations and states of systems and applicationsneed new bio-inspired metrics that accurately characterize and quantify the system and application normal and abnormal states

Rutgeres - 03/26/97 Manish Parashar Rutgeres - 03/26/97The main modules include Application Management Editor (AME), Autonomic Middleware Services (AMS) and Application Delegated Manager. AME enables users to develop network centric applications with the ability to specify the control and management policies associated with each application task. AMS provides common middleware services and tools needed by applications and systems to operate autonomically. The main focus of ADM is on setting up the application execution environment and then maintaining its requirements at runtime. AME - a graphical user interface for developing an application with pre-developed tasks and specifying management requirements to configure and manage the execution of the application- Main functions offered by the editor are controlling the application editor workplace- storing the application management requirements in the Application Service Template in the Application Information and Knowledge (AIK) repository.- provides menu-driven task libraries that are grouped in terms of their functionality.- A user can develop an application with selected tasks and decide the attributes of the component and relationship between the tasks.- Each task creates a management specification window, which includes management requirement fields such as dependencies among tasks, monitoring parameters, and fault tolerance strategy.- We will see the design and implementation of AME later.

AMS

component repository- automatically register tasks/components that can be used to build complex applications

resource repository- automatically register the resources that can be used to run network centric applications

event server- automatically register and process any monitored events- receives status of any monitored attributes and then notifies the corresponding engines that subscribe to these events once they become true

application information and knowledge repository (AIK)- store application and task service templates

monitoring service- provide the workload information and the component state running on each resource

In Autonomia environment, application development and composition utilizes the AME services. Once that is done, the remaining activities include setting up the application execution environment and running the application.These two activities utilize the services offered by the autonomic middleware service (e.g., self-configuration, self-optimization, self-healing; etc.) and the Application Delegated Manager

ADM- The main function is setting up the application execution environment and then maintaining its requirements at runtime. - AMS assigns one ADM to manage one or several application attributes (performance, fault, security, etc.).- Then, For each application task, the ADM launches an appropriate Task Agent (TA) to monitor and manage the application task execution- I am using task and component interchangeably here- TA monitors the task execution using appropriate task sensors and intervenes using the task actuator whenever the task execution on the assigned machine can not meet its requirements- Another main activities of ADM include allocating the appropriate resources to run the application and maintaining the application requirements at runtime.- The next step is to build the application execution environment, it uses the AMS self-configuration engine and then run the application.- ADM uses the AMS autonomic runtime engines (self healing, self optimization, and self protection) to maintain the requirements at runtime.Manish Parashar