12.2.2.2 Cache effectiveness Overall Summary

Figure 73 on page 153 shows an extract of these reports for both RVAs that contains information similar to RMF, but with more details on caching algorithms, and also explains the origin of the observed disconnect times. The I/O demand the host submits to the RVAs and the staging effectiveness are analyzed:

Analysis of host I/Os. Both RVAs receive a similar I/O activity demand which is read-only. That explains the high value of the READ RATIO field, which is the read-to-write ratio. Moreover, the sum (read-plus-write) is larger than number of I/Os. There are probably more than one read per I/O.

Hit ratios are more than 99%. That is an indication that practically all reads find their addressed tracks into the cache. This situation is current when a sequential pre-staging algorithm anticipates the I/O read demand.

Staging activity (measured in number of tracks per second) is 2.4 times the I/O activity:

(113.2 + 103 ) / ( 45.7 + 44.5 ) = 2.4

There are 2.4 tracks staged per I/O: This intensive staging activity explains the observed disconnect time.

XSA/REPORTER

 

 

 

CACHE EFFECTIVENESS OVERALL SUMMARY

 

18FEB1999

17:32:04

SUBSYSTEM NAME: 20395

(CACHE SIZE:

1024 MB

NVS SIZE:

8 MB)

 

 

 

 

 

 

 

SUBSYSTEM

READ

WRITE

I/O

READ

 

READ

WRITE

I/O

DFW

STAGE

HITS/

 

LOW

TRACK

SUMMARY

PER SEC

PER SEC

PER SEC

RATIO

HIT %

HIT %

HIT %

CONSTR

PER SEC

STGE

REF CT

OCCUP

 

-------

-------

-------

-----

-----

-----

-----

------

-------

-----

------

------

PROD PARTITION

53.8

0.0

45.7

61329

 

99.3

100.0

99.3

0.0

113.2

0.5

 

73.7

 

OVERALL TOTALS

53.8

0.0

45.7

61329

 

99.3

100.0

99.3

0.0

113.2

0.5

 

73.7

25050

SUBSYSTEM NAME: 22897

(CACHE SIZE:

1280 MB

NVS SIZE:

8 MB)

 

 

 

 

 

 

 

SUBSYSTEM

READ

WRITE

I/O

READ

READ

WRITE

I/O

DFW

STAGE

HITS/

 

LOW

TRACK

SUMMARY

PER SEC

PER SEC

PER SEC

RATIO

HIT %

HIT %

HIT %

CONSTR

PER SEC

STGE

REF CT

OCCUP

 

-------

-------

-------

-----

-----

-----

-----

------

-------

-----

------

------

PROD PARTITION

50.1

0.0

44.5

57064

 

99.9

100.0

99.9

0.0

103.0

0.5

 

73.5

 

OVERALL TOTALS

50.1

0.0

44.5

57064

 

99.9

100.0

99.9

0.0

103.0

0.5

 

73.5

22086

Figure 73. Case Study IXFP Cache Effectiveness Overall Extract

12.2.2.3 Space Utilization Summary

Figure 74 on page 154 shows the extracts of space utilization reports for both RVAs. Table 27 on page 153 summarizes different space utilization by both RVAs. It also shows how, in this balanced configuration with an evenly distributed workload and with consistent service times, the NCLs are not homogenous.

Table 27. RVA Space Utilization Comparison

Subsystem

20395

22897

 

 

 

Net Capacity Load

56.4%

25.2%

 

 

 

% Functional Capacity Stored

28.1

5.4

 

 

 

Compression Ratio

3.1

1.9

 

 

 

The partitions of the table spaces into RVA 22897 contain data already compressed by CPU. From performance and transfer bandwidth points of view (KB per second), both RVAs have similar appeareances.

Case Study 153

Page 175
Image 175
IBM 5695-DF1, 5655-DB2 manual Cache effectiveness Overall Summary, Space Utilization Summary

5695-DF1, 5655-DB2 specifications

IBM 5655-DB2 and 5695-DF1 are significant components within the IBM software ecosystem, predominantly focusing on data management and integration solutions. These offerings cater primarily to enterprise environments that require robust database management systems and associated frameworks to maintain and manipulate data efficiently.

IBM 5655-DB2 is a well-known relational database management system (RDBMS) that excels in managing large volumes of structured data. Its architecture is designed to support high availability, scalability, and performance, crucial for businesses operating in today’s data-driven world. Some of its main features include advanced indexing capabilities, support for complex queries, and dynamic workload management. Additionally, it provides strong concurrency controls, which enable multiple users to access and manipulate data simultaneously without compromising data integrity.

One of the key characteristics of DB2 is its support for various data types, including JSON and XML, making it versatile for modern applications that generate data in diverse formats. It also features robust security mechanisms to protect sensitive data, aligning with compliance standards across industries. Integration with analytics tools further allows businesses to derive insights from their data, enhancing decision-making processes.

On the other hand, IBM 5695-DF1, also known as the InfoSphere DataStage, is a powerful data integration tool that facilitates the extraction, transformation, and loading (ETL) of data from various sources to target systems. It empowers organizations to streamline their data flows, ensuring that clean, consistent information is available for analysis and operational use. Key features of 5695-DF1 include a user-friendly graphical interface that enhances developer productivity and a rich set of connectors for numerous data sources, enabling seamless data integration.

DataStage also supports real-time data integration, allowing businesses to keep their data synchronized across multiple platforms. Its parallel processing capabilities dedicatedly optimize performance, enabling organizations to handle vast datasets efficiently. It incorporates data quality tools that help in validating and cleansing data before it is used for decision-making processes.

Both IBM 5655-DB2 and 5695-DF1 are part of a broader strategy to accommodate the evolving landscape of data management. Businesses leverage these technologies to enhance their data architectures, fostering agility and competitive advantage in their respective markets. Their integration capabilities, along with a focus on security and scalability, position them as vital assets in modern enterprise environments. Whether managing critical data within a database or ensuring seamless data flow across systems, these IBM offerings provide a comprehensive approach to handling complex data challenges.