hpe 3par storeserv management console 3 · hpe 3par is a leader in storage technology with the...

HPE 3PAR StoreServ Management Console 3.5 Providing workload saturation insights along with application tagging

Contents Executive summary ..................................................................................................................................................................................................................................................................................................................... 2

What’s new in HPE StoreServ Management Console 3.5? ..................................................................................................................................................................................................................... 2

Basics with HPE 3PAR SSMC analytics .................................................................................................................................................................................................................................................................... 2

Studying HPE 3PAR workload dynamics ......................................................................................................................................................................................................................................................... 2

Workload Profiles .................................................................................................................................................................................................................................................................................................................... 3

Outliers ............................................................................................................................................................................................................................................................................................................................................. 4

Takeaway from discussion on workload dynamics .................................................................................................................................................................................................................................. 5

Improvements in performance reporting ................................................................................................................................................................................................................................................................ 5

Improved analytic reporting ......................................................................................................................................................................................................................................................................................... 5

Saturation reporting correction ................................................................................................................................................................................................................................................................................. 5

Performance insights .......................................................................................................................................................................................................................................................................................................... 6

Saturation forecasting ...........................................................................................................................................................................................................................................................................................................11

Workload Insights ......................................................................................................................................................................................................................................................................................................................12

Creating an App Volume Set ....................................................................................................................................................................................................................................................................................12

Summary ............................................................................................................................................................................................................................................................................................................................................15

Technical white paper


Executive summary Storage management requires a complex balance of capacity planning, storage distribution, and active monitoring every day every hour to maintain a high standard of storage operations. Management of assets is paramount in any organization. Nevertheless, data center management is the bedrock of a successful IT organization and allows users nonstop access to stored data.

Within the data center, storage management is a quintessential element of data availability and data management. HPE 3PAR is a leader in storage technology with the industry-leading all-flash array (AFA). With HPE 3PAR being a leader in the storage industry, data center managers rely on effective storage management. The HPE 3PAR StoreServ Management Console (SSMC) using artificial intelligence is the next step forward to meeting the demands of efficient storage management in today’s ever-changing environment.

HPE SSMC 3.4 introduced a new kind of storage management with adaptive analytic software that learned your storage operations and adapted to reporting anomalies accordingly. This new approach to storage management is continually learning and monitoring daily storage operations. Outliers, saturations levels, and advanced analytics are all part of the new approach to managing HPE 3PAR StoreServ arrays. Initially only arrays that are configured with all solid-state drives (SSDs) will be supported with hybrid arrays following in subsequent releases.

HPE 3PAR is building on advanced analytics introduced in HPE SSMC 3.4 and is introducing new capabilities in HPE SSMC 3.5.

What’s new in HPE StoreServ Management Console 3.5? • HPE SSMC 3.5 provides greater insights and offers new features to storage

management further enhancing the ground breaking storage analytics introduced in HPE SSMC 3.4.

• Building on years of HPE 3PAR performance data collection allows an advanced analytical approach to report generation.

• Appliance analytics now becomes the new norm for HPE 3PAR storage reporting utilizing the following advancement in features in HPE SSMC 3.5:

– New panels added to dashboard directly reflective of storage workload insights

– Refine the newly added panels in HPE SSMC 3.4 with greater performance insights

– Introduction of saturation forecasting to assist customers with workload assessments

Basics with HPE 3PAR SSMC analytics HPE SSMC 3.4 introduced a new paradigm in storage reporting. The paradigm is a result of years of data collection and observation into storage patterns. These patterns where then converted into storage models and those models became the base for numerous different reporting characteristics by which we base the HPE 3PAR storage forecasting and performance modeling. No one data characteristic can be used in data modeling and forecasting but numerous collection points can assist in the structures we use for reporting.

Studying HPE 3PAR workload dynamics To begin to characterize workload dynamics, HPE 3PAR engineering first identified over 1000 cases, which were reported by users to HPE 3PAR support. Each case had identified characteristics, which users reported as suboptimal performance.

Each case was examined and broken down as which performance factor lead to the customer reporting the issue. Performance data collected was broken down by workload dynamics and compared. Workload dynamics of each patterns was identified by the block size of the payload and the performance characteristics associated with the report. Block sizes identified where 512B, 1 Kb, 4 Kb, 8 Kb, 16 Kb and so on all the way to 16 Mb.

Once the block sizes were identified, patterns started to form as illustrated in Figure 1.

http://www.hpe.com/info/3PAR

http://www.hpe.com/storage/flash


Figure 1. Workload fingerprint

A workload fingerprint is created for all data, which was collected during the time of examination. In Figure 1, the fingerprint that is displayed was a point-in-time collection as reported by HPE System Reporter and is just used as an illustration to help you visualize the concept. A broader collection data was accumulated and from there a pattern developed as illustrated on the right side of Figure 1. The pattern consists of weighting each of the observed collective collections from the sample.

Thousands of patterns were collected and analyzed by engineering, and from our analysis, eight distinct patterns were identified. HPE 3PAR storage engineering continues to observe and collect these patterns so that other definitions can be developed. Once the patterns were identified, Workload Profiles were created as illustrated in Figure 2.

Workload Profiles Workload Profiles are created once a workload fingerprint has been identified. Since there are a variety of HPE 3PAR storage arrays deployed throughout the world, no one fingerprint can fit every workload. Profiles were developed to reflect different types of HPE 3PAR storage arrays and different configurations. Some of the factors that figure into identifying a profile are as follows:

• Model of array

• Number of nodes in configuration

• Number of drives in configuration

• Replication

• Dedupe

• Compression

There may be other factors that are figured into the profile which are not listed here. The listed items are the most common factors.

Weighted by I/O size

1k 16k 64k

Workload fingerprint I/OsRead/WriteSequential/Random

4k

Pattern

512B

1m

4k1k 2k

32k16k8k

512k256k128k

64k

Workload fingerprint


Figure 2. Workload Profile

Outliers The concept of an outlier was introduced in HPE SSMC 3.4 and until now not completely understood. During our collection of data for the various models, profiles were developed as identified previously. Data measurements identified behaviors that resulted in performance measurements for various models. Illustrated in Figure 2, the profile for HPE 3PAR workload A has calculated the average measured latency to equate to 2 ms. Other latency measurements resulted in the displayed values.

A performance outlier is a value that falls outside the range values considered acceptable. An outlier does not represent a deficiency in the storage array and should not be construed as a fault. It is just an indication that during the collection period, performance metrics exceeded the values, which would be deemed as normal. Outliers are identified to help the user investigate abnormal results of data transmission. Figure 3 is an example of Workload Profiles and outliers.

Figure 3. Workload Profiles defined by system types

System Type: 8450Nodes: 4Drives: 96Replication: SyncDedupe: 20%Compression: 10%

Avg. Latency. 2 ms75% Latency 4 ms98% Latency. 8 ms

Outlier Avg. Latency. >15 ms

System Type: 9450Nodes: 4Drives: 96Replication: SyncDedupe: 40%Compression: 15%



System Type: 20850Nodes: 8Drives: 192Replication: NoneDedupe: 10%Compression: 25%

Avg. Latency. <1 ms75% Latency 2 ms98% Latency. 3 ms


System Type: 8400Nodes: 2Drives: 24Replication: NoneDedupe: 0%Compression: 0%




The illustration in Figure 3 is purely hypothetical and does not reference current Workload Profiles that exist today. It is meant to identify that profiles will vary from system to system, and there are factors that govern these profiles. Profiles shown in Figure 3 are unique between each and the coloring scheme is used to illustrate this. The profiles with red within the icon identify that during the collection period, outliers were detected.

Takeaway from discussion on workload dynamics Outliners help us proactively detect performance issues in real time.

Improvements in performance reporting Improved analytic reporting HPE SSMC 3.4 provided the user industry-leading insights into the array performance metrics. The additional dashlets and array analytics provided users with insights to array performance. As good as the additional reporting was at the inception of HPE SSMC 3.4, there were a few tweaks that were identified to be included in the next version of HPE SSMC 3.5.

The first of the modifications is a simple identification of throughput metrics when examining saturation analytics of the system.

Figure 4. Workload metrics

Figure 4 illustrates the identification of payload characteristics identified in the data collection. Prior to the improvement, the user could see the saturation screen and hover over a point in time. The result was that the saturation level was shown, but no workload metrics where displayed. In HPE SSMC 3.5, the user is assisted in identifying the dynamics of the observed workload.

Saturation reporting correction Saturation reporting in HPE SSMC 3.4 was based on a formula for front-end I/Os. Front-end I/O is defined as the I/O throughput seen at the host level. This metric would be the same workload metric that a customer would observe when monitoring the HPE 3PAR array throughput. While the formula and data displayed is correct for workload throughput, it does not account for back-end workflow on the array.

Back-end workload on an array is the movement of data that is characteristic of the workload request. A simple example of back-end I/O workload is a new write request to RAID 5 VV with RAID 5 snapshot.

New write to RAID 5 VV with RAID 5 snapshot

1. Read (read old data) (always increment of 16k)

2. 2 x read and 2 x write (rewrite old data) (always increment of 16k)

3. 2 x read and 2 x write (new data write) (size varies)

Total IOPS amplification: 9 IOPS

This is an example of a simple back-end operation using RAID 5 and a snapshot. Data is consistently moving on the back end of the array and is not reflective in the workload I/O, which the end user monitors. However, when looking at performance saturation on the array, back-end data movement must be taken into consideration. Figure illustrates the same performance capture from HPE SSMC 3.4 without calculating back-end performance and HPE SSMC 3.5, which factors in back-end performance.


Figure 5. Workload saturation modification

The difference here is the delta in saturation level measurements; it should also be noted that the characteristics of back-end I/O affecting the workload saturation is seen at the higher workloads. There are minor variations in reporting at the lower levels of saturation.

Performance insights HPE SSMC 3.4 provided excellent performance insights by adding graphed illustrations. HPE SSMC 3.5 expands those insights to include performance guidance with certain conditions. The additional performance guidance can assist users by providing information regarding observed performance behaviors.

Two performance insights have been added to HPE SSMC, cache hits, and port imbalance. The following sections along with illustration will assist you with gaining a higher understanding of new insights.

Port imbalance A port imbalance is a scenario where there is an impact to the system performance, but the saturation level on the array is reporting as normal. In this scenario, there is a significant chance the system may not be optimally configured, as it pertains to balanced ports. A port imbalance is when one port on the array is reporting higher throughput than any other ports on the array.

Figure 6. Port configurations

Using Figure 6 as an example, the array from all appearances looks to be balanced between ports when examining the port balance (shown on the right) with the exception of system A, which has an extra port allocated on Node 3 Port 4. In this example, it is an obvious oversight and should be corrected as soon as possible. In real life, rarely will this oversight be caught within system configurations as typically there may be upward to a hundred systems attached in larger data centers.


When examining the workloads attached on the left side of the example, there are several systems, which are running Oracle and the distribution of the workload on the array ports may counterbalance the error created with the attached ports. The error may not appear until the Oracle workloads slowdown during routine maintenance times. During those periods of time the demand on the array ports decrease with a result shown in Figure 7.

Figure 7. Port imbalance high saturation

The illustration shows that saturation of the array has dropped down to just 11%, but due to poor performance observed on a port, the performance figure is very high (10 being the worst score). As a further indication of the previous statement, the saturation level was measured to be very high between the hours of 16:00 to 19:00, but the performance metrics are low.

At closer inspection of the line, which is Performance, the observer can see that port imbalance was identified as an issue. To get a better view of the reported imbalance, the user can left-click on the time frame and drag the mouse to the right. This will provide the user with the following highlight as shown in the illustration of Figure 8.


Figure 8. Highlighted port performance

In Figure 8, we have expanded the view to help highlight the times in which performance on the array was critical. As a review, the higher the performance counts, the worst the impact on the array and subsequently higher latencies. When examining the latency values for the reported period, it is clear the volumes attached to ESX_184_Datastore were affected.

If the user will mouse over one of the vertical bars and right-click, a pop-up display will identify the host port, which leads to the poor performance. Using the illustration in Figure 9, a port imbalance was observed on port 0:2:1 with a throughput of 70,228 KiB/s, which was 81.04% higher than the average throughput of all the other ports.

The user can now identify the port and the time in which the imbalance was observed and modify the port balance or the workload on the array to help eliminate any future issues.


Figure 9. Port imbalance

Cache performance With port imbalance, we observed where saturation levels were low but performance can be affected due to poor distribution or workloads on the ports.

Servicing data out of cache memory on the array signifies a heavy workload with substantial I/Os was submitted to the array and the array was able to satisfy the workload through the cache memory. Workload characteristics in these scenarios are sudden workload patterns, which are not sustained for long periods of time. The workload suggests that the repetitive I/O workload can cached with small data queries from the storage arrays.


Figure 10. Cache hits

The example illustrated in Figure 10 is a perfect example of a cache-hit workload. At the point in time the capture was taken, the cache-hit ratio was at 75%, which highlights that only 25% of data transfers needed to access the disk media. It can also be observed at this point that the array saturation level was at 142%, but response times to host I/O were low. This is a perfect example of HPE 3PAR advanced caching algorithm working at its best.

Dashlet comparisons The dashlets found on the main dashboard clearly show side by side the differences between port imbalance and cache hits. Examine Figure 11 for the comparison.


Figure 11. Dashlet comparison—Port performance versus cache hit performance

The poor port performance is captured as jagged dash lines on the HPE 3PAR 7400 array. The jagged lines in the middle of the dashlet are followed by a gap, which identifies a period of normal performance that is most likely higher throughput on the other ports on the array. The dark red lines in the middle of the graph indicate higher saturation level along with poor port performance.

Performance on the array evens out after a period of time, which suggests that the poor port performance can be due to an imbalance of a workload on the array. The easiest way to identify the load factors is isolate the port as discussed previously and compare port assignments from the port drop-down in the main menu. In this instance port 0:2:2 was assigned to 16 hosts versus all other ports were assigned 10 to 12 hosts.

The right-side dashlet identifies an HPE 3PAR array with a higher saturation score but little to no impact on performance. As previously stated, this is a result of the advanced caching algorithm the HPE 3PAR deploys using the AFA technology.

Saturation forecasting New in HPE SSMC 3.5 is the added drop-down object selection “Saturation Forecasting.” Saturated Forecasting is located under the Section heading of System in the Main Menu. The illustration captured in Figure 12 is modeled from two periods of time, two weeks and two months. A forecast is derived from all the performance collection points from the selected period and projected for a week ahead.

Figure 12. Two-week saturation forecast model

Saturation forecasting is dependent upon a semi-normal workload gathered over the selected collection period. If the workload is unstable, then the forecasting cannot accurately identify a workload pattern. Workload patterns must be consistent throughout the pattern collection. The data that is collected during the collection period is interpolated, smoothened, and forecasted. The algorithm tolerates gap unto the granularity in which the data is considered for forecast (two hours for weekly and eight hours for monthly).


Warnings in forecasting In the forecast, as shown in Figure 12, there are no warning messages, and the forecast is accurate based on the collected information. However, there are two different warnings, which can be displayed in a banner above the collected period. They are as follows.

• Discontinuity in the data for the assessment period may impact forecast: This warning is displayed if the maximum gap between any two points is more than 10 minutes and less than 2 hours during a weekly collection.

• Saturation data available in the selected assessment period is inadequate for forecast: This warning will display when there is inadequate amount of collection points for the selected period.

Workload Insights New with HPE SSMC 3.5 is Workload Insights, Workload Insights bring an application awareness and applications intent to the storage infrastructure. Application awareness provides an insight to monitor and optimize a storage infrastructure for an application awareness. Workload Insights work through tagging a set of volumes to an application instance running on a host of host set.

To help with monitoring application workloads, two new dashlets were included as part of HPE SSMC 3.5. Figure 13 illustrates the additional dashlets added.

Figure 13. Workload Insights dashlets

The two dashlets represent the applications that can be included in an application volume set. An application volume set is a virtual volume set, which is tagged to an application workload. Application workloads are workloads, which have specific workload patterns and similar to Workload Profiles that were covered earlier in the paper. The following are workload patterns that have been identified.

• Oracle Database

• Microsoft® SQL Server

• Microsoft Exchange

• VMware ESXi™ datastore

• Microsoft Hyper-V Cluster Shared Volume

• IBM Db2

• Microsoft SharePoint Server

• SAP HANA®

Creating an App Volume Set To create an App Volume Set, the user navigates to the main menu and selects App Volume Sets from under the Block Persona heading. Clicking the selection will open a new window for the user. The new window allows the user to identify the application volume set they desire to create along with comments for the business unit the application set will be attached to and comments about the volume. There will be a drop-down for the user to select, which application the volume set should be tagged against. The applications available for tagging are the ones listed previously.


When creating an App Volume Set, the user can select from previously created volumes and attach them to the volume set or they can create new volumes. If the user chooses to create new volumes, HPE SSMC will query the number of volumes to be created and size; for our example, in Figure 14, we will create four volumes of 100 GiB each.

Figure 14. Create App Volume Set

The user can modify the volume names to accommodate their individual naming standard, or if no identification is included, the volume names will be that of the volume name filled in on the top of the screen plus a unique delimiter at the end of the name.

The last item to be selected is the system in which the application volume set will be attached to. When depressing Select System, a new screen pop-up will be displayed as illustrated in Figure 15.


Figure 15. App Volume Set array selection

The listing of the arrays is divided between All Flash Systems and systems that are either a hybrid array or consists of all spinning media. The systems are ranked by saturation level with the array with the least amount of saturation ranked first. As noted at the top of the graph, the systems are ranked on saturation levels for the last seven days. The user can override the selection by clicking any of the listed arrays and selecting that array.

When creating an App Volume Set, an entry will also be included under the heading of Virtual Volume Sets on the array. Application volume sets are virtual volume sets, which are tagged against an application. Application volume sets are not confined to just all-flash arrays as pointed out and can be applied to any HPE 3PAR array using HPE 3PAR OS 3.2.2 MU6 or later.


Summary HPE SSMC 3.5 continues on the long tradition of bringing added value reporting to an already solid application. With the addition of App Volume Sets, saturation forecasting, and modifications to existing analytics from HPE SSMC 3.4, this version of HPE SSMC defines the new reporting standard going forward with advancement of storage analytics reporting. Recap of all the features included with HPE SSMC 3.5 include the following:

• Workload application tagging

• Provide user various views (maps, performance, capacity, export) of the App Volume Set

• Create HPE 3PAR volumes by associating the application properties with software by providing user a view of ranked list of HPE 3PAR arrays based on capacity headroom, system saturation levels, and performance values

• Provide user a view of application outliers by latency and IOPS

• Forecast saturation of the system based on workload

• Performance insights with cache hits/port imbalance

In addition to these benefits, two other additions to HPE SSMC 3.5 that were not covered in this paper include:

• Support for audit user role

• Support for Windows® 2019

Learn more at h20392.www2.hpe.com/portal/swdepot/displayProductInfo.do?productNumber=SSMC_CONSOLE

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© Copyright 2019 Hewlett Packard Enterprise Development LP. The information contained herein is subject to change without notice. The only warranties for Hewlett Packard Enterprise products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional warranty. Hewlett Packard Enterprise shall not be liable for technical or editorial errors or omissions contained herein.

Microsoft and Windows are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries. Oracle is a registered trademark of Oracle and/or its affiliates. SAP HANA is a trademark or registered trademark of SAP SE in Germany and in several other countries. VMware ESXi is a registered trademark or trademark of VMware, Inc. in the United States and/or other jurisdictions. All other third-party marks are property of their respective owners.

a00072204ENW, May 2019

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