Content Filtering – Optimizing Cache for Performance Tuning

Content Filtering – Optimizing Cache for Performance Tuning.

 (SAMPLE_SIZE).

                        TARGETED_NUMBER_OF_CACHE_OBJECTS
H_FACTOR = ——————————————————-
SAMPLE_SIZE

In this case H_ FACTOR = 100000 / 4464014 = 0.022401. Simply multiply the figures in the Request column in TABLE 1 with H_FACTOR to get the calculations for 100,000 objects. Table 2 below shows the figures thus derived.

Table 1

Sr. No.

Size Distribution Diagram

Object-size (bytes)

Request

%

Hit-%

req/sec

Byte

%

hit-%

kB/sec

1

0-0

234400

5.25

0

0.2

0

0

0

0

2

1 to 9

334934

7.5

0

1.47

1704547

0

0

0.01

3

10 to 99

475956

10.66

0.04

2.11

22964810

0.03

0.06

0.1

4

100-999

810276

18.15

0.06

1.26

329162K

0.39

0.06

0.51

5

1000-9999

1805144

40.44

0.04

0.23

7435051K

8.72

0.05

0.93

6

10000-99999

732465

16.41

0.08

0.38

20523M

24.65

0.05

10.9

7

100000-999999

63649

1.43

0

0.2

12988M

15.6

0.01

42

8

1000000-9999999

6237

0.14

0

0.02

18842M

22.63

0

68.92

9

10000000-99999999

941

0.02

0

0.01

18096M

21.73

0

101.4

10

100000000-999999999

10

0

0

0

1579920K

1.85

0

211.63

11

1000000000-9999999999

2

0

0

0.09

3757234K

4.41

0

171924.3

Sum

4464014

100

0.05

0.34

83267M

100

0.02

6.59

TABLE 2

Sr. No. Object-size (bytes) Request
1 0-0 5,251
2 1 to 9 7,503
3 10 to 99 10,662
4 100-999 18,151
5 1000-9999 40,437
6 10000-99999 16,408
7 100000-999999 1,426
8 1000000-9999999 140
9 10000000-99999999 21
10 100000000-999999999 0.22
11 1000000000-9999999999 0.44
Sum 1,00,000

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