(pfc307) auto scaling: a machine learning approach | aws re:invent 2014

November 12, 2014, Las Vegas NV

Sumit Amar, Electronic Arts

var client = new Amazon.CloudWatch.AmazonCloudWatchClient();

var response = client.GetMetricStatistics(

new GetMetricStatisticsRequest

{

Dimensions = new List<Dimension> {

new Dimension { Name = "InstanceId", Value = instanceId } },

StartTime = startDate, //2014-11-05

EndTime = endDate.Date.AddDays(1).Date.AddMilliseconds(-1), //2014-11-06,

Namespace = "AWS/EC2",

Statistics = new List<string>{ "Average", "Maximum", "Minimum","Sum","SampleCount" },

MetricName = metricName, //CPUUtilization, DiskReadBytes, NetworkIn, more etc..

Period = interval, //seconds – pass 60 * 60 for an hourly range

});

//### CloudWatch GetMetricsStatistics returns unordered data points, ergo..

response.Datapoints.Sort((a,b) => a.Timestamp.CompareTo(b.Timestamp));

Y = a + b X

Time Actual(Y) Deviation X(from mid) XY X2 Yd

8am 83 -3 -249 9 72.22

9am 60 -2 -120 4 61.29

10am 54 -1 -54 1 50.36

11am 21 0 0 0 39.43

12p 22 1 22 1 28.50

1p 13 2 26 4 17.57

2p 23 3 69 9 6.64

N=7 ∑𝑌 = 276 ∑X=0 ∑XY=-306 ∑X2=28

Here: Y = a + b X

a = ∑𝑌/𝑁 = 276/7 = 39.43

b = ∑𝑋𝑌

∑𝑋2 = -306/28 = -10.93

Y = 39.43 – 10.93 X

For X = -3 (8am): Y8am = 39.43 – (10.93 * -3) = 72.22 and so on for other times.

∑Y = Na + b ∑X

∑XY = a ∑X + b ∑X2

∑Y = Na + b ∑X + c ∑X2

∑XY = a ∑X + b ∑X2 + c ∑X3

∑X2Y = a ∑X2 + b ∑X3 + c ∑X4

Yd = a + b X + c X2

∑Y = Na + c ∑X2

∑XY = b ∑X2

∑X2Y = a ∑X2 + c ∑X4

EWMA Article

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