The PowerEdge C6620 with PERC 12 delivered lower latency and higher throughput than an HPE ProLiant XL170r Gen9 server with an HPE Smart Array P440ar controller

Overview

Today’s businesses both generate and take in enormous quantities of data as part of their daily operations. Smartphones, computers, servers, and the activities of people online are the sources of some of this data, but more and more of the data also come from a wide variety of other places, such as weather sensors, streaming video cameras, wearable devices, and onboard computers in vehicles, to name just a few examples. One estimate suggests that the number of connected Internet of Things (IoT) devices will reach over 29 billion by 2030.1 With an ever-increasing mountain of data, much of it from non-traditional sources, organizations need a way to extract value from the noise. NoSQL database systems such as Apache Cassandra can help organizations store, process, and analyze this data to glean useful insights. To be most effective, however, the database system should run on a high-performing computing platform that can complete big data workloads quickly and get insights into decision makers’ hands fast. We assessed the ability of two platforms to handle Cassandra workloads. The first was the new Dell™ PowerEdge™ C6620 with Broadcom®-based Dell PowerEdge RAID Controller (PERC) 12, which companies might choose if they’re upgrading to new servers to better handle their big data needs. The second was the older HPE ProLiant XL170r Gen9 server with an HPE Smart Array P440ar controller, which represents a server that organizations might already have in their data centers. The Dell and Broadcom solution provided higher throughput and lower latencies in our testing, meaning that it completed more big data work in the same amount of time as the older HPE solution. With a strong big data solution, businesses can put their data to work and use it to optimize processes, cut costs, improve customer experience, and grow their offerings. This report explores how and why running Apache Cassandra as a big data system on the Dell PowerEdge C6620 server with PERC 12 might be that solution for you.

Assessing Cassandra performance on the Dell PowerEdge C6620 with Broadcom-based PERC 12

Upgrading to new servers is a big decision. You know that newer, more modern technology is likely to offer performance improvements, but exactly what will those benefits look like, and how much more will the new systems be able to handle? Our testing quantifies the performance boost you might see on your Cassandra workloads by moving from HPE ProLiant XL170r Gen9 servers with Smart Array P440ar controllers to new Dell PowerEdge C6620 servers with Broadcom-based PERC 12.

Our configurations

For our test environment, we installed VMware® vSphere® 8 on both servers before configuring a separate infrastructure server with VMware ESXi™ and VMware vCenter®. We used this infrastructure server to manage the servers and to host client VMs that ran our test workload against our databases. The Dell PowerEdge C6620 server with PERC 12 used two Dell U2 Gen4 NVMe® 3.84TB drives. The HPE ProLiant XL170r Gen9 server with HPE Smart Array P440ar controller used six 960GB mixed-use SAS 12Gbps drives. Table 1 highlights more details of our configuration.

On each server, we created a Cassandra gold VM and cloned it five times to create a total of six VMs, which we joined in a cluster configuration. We then used the Yahoo Cloud Serving Benchmark (YCSB) to create a 100GB database across the six VMs to take advantage of the distributed database functionality of Cassandra, ran YCSB workload B for 30 minutes, and recorded the results. In the results we highlight below, we provide two perspectives on the performance of each setup: the total throughput and the average read and write latency. Both results reflect the performance across all six VMs. 

Why YCSB?

YCSB is an industry-standard benchmark for NoSQL databases. In 2010, a group from Yahoo! Research created it with “the goal of facilitating performance comparisons of the new generation of cloud data serving systems.”3 It is open source, meaning that anyone can access and modify the source code. In a recent interview, contributors to the YCSB open-source community note that it “is rather largely accepted by users” and “represents a series of scenarios that can be abstracted from the real world.”4 Apache Cassandra was one of the first four databases that the YCSB creators tested with the benchmark in 2010, and YCSB remains a good fit for testing Cassandra performance today.5

YCSB functions by letting users create a database populated with synthetic data on their database system of choice. Users can then run a pre-defined or customized workload against the database to gauge system performance. YCSB offers six core workloads, each of which represents a different type of database work. Our testing used the read-intensive workload B. This workload is 95 percent reads (pulling data from a database) and 5 percent writes (adding to or changing data in a database). YCSB gives one application example as photo tagging, where a user might occasionally add a tag to a photo (write) but will mostly search a library of tagged photos (read).6 A solution that offers higher performance on YCSB workload B is likely to improve performance on other read-intensive workloads, such as data analysis. We chose this workload to focus on reading and analyzing a database.

Upgrade to the Dell PowerEdge C6620 with Broadcom-based PERC 12 for lower latencies and better throughput

In our testing with YCSB, the Dell PowerEdge C6620 with PERC 12 offered better performance on all three metrics we measured: read latency, update (or write) latency, and throughput (measured in operations per second). The improvements were significant, meaning that trading out your HPE ProLiant XL170r Gen9 servers for new PowerEdge C6620 servers could enable your organization to handle substantially more Cassandra work. The first metrics we examined were read latency, which measures the delay between the application requesting a piece of data and the database system delivering it, and update latency, which measures the delay between the application changing or adding a piece of data and the database system completing the action. The Dell PowerEdge C6620 with PERC 12 was much faster on both types of latency, with the largest advantage on update latency. There, it offered 60.2 percent lower—or 1.97 milliseconds less—latency than the HPE ProLiant XL170r Gen9 server with Smart Array P440ar controller. It may seem like a sub-two-millisecond delay is inconsequential; if you were loading a webpage or pulling up a video, you wouldn’t notice a two-millisecond difference. The significance of this advantage, however, is due to the enormous number of operations that the database system must perform before it can deliver usable results. For this testing in YCSB, we set the max execution time variable (or how long the benchmark should run) to 30 minutes. At a rate of 249,210 operations per second (see Figure 3), the Dell PowerEdge C6620 with Broadcom-based PERC 12 executed over 400 million operations during the 30-minute test. So, while a difference of one or two milliseconds might not mean much on a single operation, on 400 million operations, the benefit of the faster solution becomes clear.

With these lower latencies, a solution will be quicker to handle interactions with the Cassandra database, which might include anything from pulling up X-ray images in a hospital to analyzing a large set of data on an ecommerce business’s customer preferences.

The Dell PowerEdge C6620 also offered an enormous advantage on throughput, delivering over twice the operations per second of the HPE ProLiant XL170r Gen9. Given the lower latencies we saw, this is unsurprising—because the PowerEdge C6620 could process operations faster (with lower latency), we would expect it to also be able to handle more operations in a given time. Depending on the read- intensive workloads you’re running, this increase in throughput could translate to quicker load times for your customers or faster data analysis, among other possibilities.

NoSQL databases and Cassandra in today’s business landscape

For this study, we tested with Apache Cassandra, a widely used NoSQL database system. NoSQL, or non-relational, databases are a category of database system that store and query data that do not have a traditional data structure. Traditional SQL databases organize data in a column-row format for finding or creating relationships across the data. To store data in a SQL database, all data in each table must have the same structure and fit a pre-defined schema, with every row in each table including the same columns and formats every time. NoSQL databases, however, can organize data more dynamically. They can deal with data from documents, graphs, key-values, and more. This flexibility lets people use them to analyze documents or data that don’t follow identical structuring formats. For organizations that need to store and analyze unstructured data—which may include data from Internet of Things (IoT) applications, audio, video, text files, social media posts, and more—a NoSQL database is a great option.

There are many types of NoSQL database systems; Apache Cassandra is a type of key-value and wide- column store. These databases have essentially two fields: One is the key, and the other is the value. The value can be any type of data (text, numbers, etc.). Taking our previous example, a key-value database could have some keys that correspond to a date, others that are numbers, and so on. A wide-column database, which Cassandra uses, is a two-dimensional key-value database, where instead of mapping to just one value, the keys can map to several columns of values.

Apache Cassandra is a distributed database, meaning that it can run on multiple nodes while acting as a single entity. This makes it resilient and highly scalable. Its scalability, combined with the flexibility afforded by its hybrid key-value/tabular model, allows it to handle many types of big data work very well. Cassandra is also open-source and free, a compelling benefit for organizations seeking to save on licensing fees.

The flexibility of Cassandra makes it suitable for a very large range of use cases. For example, Instagram uses Cassandra to support its content feed, Spotify uses it to store playlist metadata, and Intuit uses it as part of their largest production clusters supporting TurboTax.10,11,12 Common uses of Cassandra include:

• Analysis of customer data for personalization and recommendation, such as in ecommerce environments and content sharing or streaming websites
• Storage and analysis of IoT data, such as data gathered from mobile and wearable devices, environmental sensors, and edge devices
• Fraud detection, especially for financial organizations
• Messaging, such as for organizations’ internal messaging platforms

We chose to test with Cassandra in part because so many organizations rely on it for everyday operations. Approximately 90 percent of Fortune 100 companies use Apache Cassandra in some capacity.13 If your organization uses Cassandra or is considering doing so, to get the most value from it, you will want to ensure that the solution backing your implementation offers high performance. As our testing highlights, the Dell PowerEdge C6620 with Broadcom-based PERC 12 can deliver just that.

Conclusion

Data proliferation today is rapid, and its growth shows no signs of stopping. For businesses that can take advantage of that data, there is tremendous potential value. One recent McKinsey study notes that “companies that are using data-driven B2B sales-growth engines report above-market growth and EBITDA increases in the range of 15 to 25 percent.”16 With data flooding in so quickly and in so many different forms, however, companies need high-performing big data solutions to have a chance at utilizing that data effectively.

We tested the performance of two platforms with a read-intensive Apache Cassandra database system big- data workload to assess which might be better suited to speedily deliver the insights decision makers need. Compared to an older HPE ProLiant XL170r Gen9 server with an HPE Smart Array P440ar controller, the new Dell PowerEdge C6620 with Broadcom-based PERC 12 RAID controller delivered faster read and update latencies and more than twice the throughput. This improvement in performance can help you glean more value from your unstructured data more quickly. If you’re watching your stores of unstructured data grow but are still leaning on older servers for your critical Cassandra workloads, it may be time for an upgrade.

1. Lionel Sujay Vailshery, “Number of Internet of Things (IoT connected devices worldwide from 2019 to 2021, in forecasts from 2022 to 2030,” accessed July 13, 2023, https://www.statista.com/statistics/1183457/iot-connect- ed-devices-worldwide/.
2. “PowerEdge C6620,” accessed June 23, 2023, https://www.delltechnologies.com/asset/en-us/products/servers/ technical-support/poweredge-c6620-spec-sheet.pdf.
3. Brian F. Cooper, Adam Silberstein, Erwin Tam, Raghu Ramakrishnan, Russell Sears, “Benchmarking Cloud Serving Systems with YCSB,” accessed June 23, 2023, https://courses.cs.duke.edu/fall13/compsci590.4/838-CloudPa- pers/ycsb.pdf.
4. “The Ultimate YCSB Benchmark Guide (2021),” accessed June 23, 2023, https://benchant.com/blog/ycsb.
5. Brian F. Cooper, Adam Silberstein, Erwin Tam, Raghu Ramakrishnan, Russell Sears, “Benchmarking Cloud Serving Systems with YCSB,” accessed June 23, 2023, https://courses.cs.duke.edu/fall13/compsci590.4/838-CloudPa- pers/ycsb.pdf.
6. “brianfrankcooper/YCSB,” accessed June 23, 2023, https://github.com/brianfrankcooper/YCSB/blob/master/doc/coreworkloads.html.
7. “Dell PowerEdge RAID Controller 12 User’s Guide PERC H965i Adapter, PERC H965i Front, and PERC H965i MX,” accessed June 27, 2023, https://www.dell.com/support/manuals/en-us/perc-h965i-front/perc12/dell-tech- nologies-poweredge-raid-controller-12?guid=guid-5889415d-b297-43a0-9197-113a56c33c79&lang=en-us.
8. “SAS4116W 24G SAS Tri-Mode RAID-on-Chip (ROC),” accessed June 27, 2023, https://www.broadcom.com/products/storage/raid-on-chip/sas-4116w.
9. “SAS4116W 24G SAS Tri-Mode RAID-on-Chip (ROC).”
10. Instagram Engineering, “Open-sourcing a 10x reduction in Apache Cassandra tail latency,” accessed June 27, 2023, https://instagram-engineering.com/open-sourcing-a-10x-reduction-in-apache-cassandra-tail-latencyd- 64f86b43589.
11. Kinshuk Mishra and Matt Brown, “Personalization at Spotify using Cassandra,” accessed June 27, 2023, https://engineering.atspotify.com/2015/01/personalization-at-spotify-using-cassandra/.
12. Denson Pokta, “Pronto! Intuit Releases First Open Source Cassandra Cluster Manager,” accessed June 27, 2023, https://thenewstack.io/pronto-intuit-releases-first-open-source-cassandra-cluster-manager/.
13. Jeff Carpenter, “How the world caught up with Apache Cassandra,” accessed June 27, 2023, https://techcrunch.com/sponsor/datastax/how-the-world-caught-up-with-apache-cassandra/.
14. “Move your private cloud to Dell EMC PowerEdge C6420 server nodes and boost Apache Cassandra database analysis,” accessed June 23, 2023, https://www.principledtechnologies.com/Dell/Power-Edge-C6420-Apache- Cassandra-1019-v2.pdf.
15. “Update your private cloud with 14th generation Dell EMC PowerEdge FC640 servers and do more work in less space,” accessed June 23, 2023, https://www.principledtechnologies.com/Dell/PowerEdge_FX2s_FC640_ Apache_Cassandra_1117.pdf.
16. Jochen Böringer, Alexander Dierks, Isabel Huber, and Dennis Spillecke, “Insights to impact: Creating and sustain- ing data-driven commercial growth,” accessed July 13, 2023, https://www.mckinsey.com/capabilities/growthmar- keting-and-sales/our-insights/insights-to-impact-creating-and-sustaining-data-driven-commercial-growth.

The new PowerEdge C6620 delivered better performance—both higher throughput and lower latency—than a previous-generation PowerEdge C6520 with PERC 11

Overview

Every day, individuals and organizations generate massive quantities of data, from text messages to location data to information from sensors on factory floors and beyond. This rapid proliferation of data offers enormous opportunities: If businesses can extract insights from that data, they can use it to improve their operations, grow their customer base, and provide a better experience to those customers. That task is not simple, however. Much of this data is unstructured, meaning that it comes in many formats that traditional data models, such as SQL databases, cannot process. Processing and analyzing unstructured data may require different methods, such as utilizing a NoSQL database like Apache® Cassandra®. Organizations can use NoSQL databases to store, mine, and analyze unstructured data in its many forms and gain actionable information. To efficiently analyze such large quantities of data, however, they need a powerful computing solution running the database system. Investing in newer server solutions with updated processing, storage, and networking components can offer greater performance and enable companies to get to those vital insights faster. To highlight the advantages of moving from an older server solution to a new one for big data workloads, we tested Apache Cassandra performance on a new Dell™ PowerEdge™ C6620 with a Broadcom®-based Dell PowerEdge RAID Controller (PERC) 12 and an older Dell PowerEdge C6520 with Dell PERC 11. On multiple performance metrics, the newer Dell PowerEdge C6620 with PERC 12 delivered stronger performance than its predecessor, offering businesses the chance to increase the value of their data and realize its benefits more quickly.

About the Dell PowerEdge C6620 server

Part of the Dell modular infrastructure PowerEdge C-Series, Dell says the PowerEdge C6620 is “designed for compute-intensive workloads” but also “ideal for IOPS-heavy workloads.”1 It features up to two 4th Generation Intel® Xeon® Scalable processors, with up to 56 cores per processor; offers memory speeds of up to 4,800 MT/s; and supports up to 16 NVMe® drives for workload acceleration. Optional liquid cooling is also available. visit https://www.dell.com/en-us/shop/ enterprise-products/c6620-two-socket-server-node-intel/spd/poweredge-c6620.

Testing the Dell PowerEdge C6620 with Broadcom-based PERC 12

If you’re still relying on servers you purchased several years ago, it can be helpful to understand exactly how much you could gain by upgrading to a newer solution. We designed our testing to quantify the benefits of upgrading from older to latest-generation servers for organizations relying on Cassandra workloads for critical operations.

Our configurations

To set up our test environment, we installed VMware® vSphere® 8 on both servers. We then configured a separate infrastructure server with VMware ESXi™ and VMware vCenter® to manage the servers and to host client VMs that ran our test workload against our databases. The Dell PowerEdge C6620 server with Broadcom-based PERC 12 used two Dell U2 Gen4 NVMe® 3.84TB drives, while the Dell PowerEdge C6520 server with PERC 11 used six 960GB mixed-use SAS 12Gbps SFF drives. (See Table 1 for more details of our configuration.)

Table 1: System configurations we used in our testing. Source: Principled Technologies.

 On each server, we created a Cassandra gold VM and cloned it five times to create a total of six VMs, which we joined in a cluster configuration. We then used the Yahoo Cloud Serving Benchmark (YCSB) to create a 100GB database across the six VMs to take advantage of the distributed database functionality of Cassandra, ran YCSB workload B for 30 minutes, and recorded the results. In the results we highlight below, we provide two perspectives on the performance of each setup: the total throughput and the average read and write latency. Both results reflect the performance across all six VMs.

Why YCSB?

YCSB is an industry-standard benchmark for NoSQL databases. In 2010, a group from Yahoo! Research created it with “the goal of facilitating performance comparisons of the new generation of cloud data serving systems.”2 It is open source, meaning that anyone can access and modify the source code. In a recent interview, contributors to the YCSB open-source community note that it “is rather largely accepted by users” and “represents a series of scenarios that can be abstracted from the real world.”3 Apache Cassandra was one of the first four databases that the YCSB creators tested with the benchmark in 2010, and YCSB remains a good fit for testing Cassandra performance today.4

YCSB functions by letting users create a database populated with synthetic data on their database system of choice. Users can then run a pre-defined or customized workload against the database to gauge system performance. YCSB offers six core workloads, each of which represents a different type of database work. Our testing used the read-intensive workload B. This workload is 95 percent reads (pulling data from a database) and 5 percent writes (adding to or changing data in a database). YCSB gives one application example as photo tagging, where a user might occasionally add a tag to a photo, (write) but will mostly search a library of tagged photos (read).5 A solution that offers higher performance on YCSB workload B is likely to improve performance on other read-intensive workloads, such as data analysis. We chose this workload to focus on reading and analyzing a database.

See higher throughput and lower latency with the Dell PowerEdge C6620 with Broadcom-based PERC 12

Our testing with YCSB yielded three metrics: read latency, update (or write) latency, and throughput (measured in operations per second). The Dell PowerEdge C6620 with Broadcom-based PERC 12 offered stronger performance than the PowerEdge C6520 with PERC 11 on all three metrics, indicating that an upgrade can help speed your Cassandra workloads.

On the first and second metrics, read latency and update latency, the Dell PowerEdge C6620 was significantly faster than its previous-generation counterpart. Read latency measures the delay between the application requesting a piece of data and the database system delivering it; update latency measures the delay between the application changing or adding a piece of data and the database system completing the action. The shorter these delays, the faster a solution will be at completing user-facing requests, such as retrieving a customer’s buying history when a store manager searches for it, and larger workloads, such as running analysis on a set of tens of thousands of data points.

On the surface, the differences in latency between the two solutions are very small: 0.49 milliseconds for read latency and 0.57 milliseconds for update latency. On a single operation, a delay of less than a millisecond would be impossible for a human to notice. But the database system isn’t handling just one operation—it’s handling thousands or millions of operations all at once. Our YCSB testing, for example, set the maxexecutiontime variable (or how long the benchmark should run) to 30 minutes. This means that at the Dell PowerEdge C6620 server’s rate of 249,210 operations per second (which we show in Figure 3), it executed over 400 million operations during the 30-minute test. As tiny differences in latency scales up, they become very significant indeed. And the shorter these delays, the faster a solution will be at completing both user-facing requests, such as retrieving a customer’s buying history when a store manager searches for it, and larger workloads, such as running analysis on a set of tens of thousands of data points.

On the third metric, throughput, the Dell PowerEdge C6620 delivered 1.25 times as many operations per second as the previous-generation PowerEdge C6520. This increase in throughput is what we would expect to see based on the lower latencies: If a system is able to process operations faster (i.e., with lower latency), it will also boost how many operations the system can handle in a given time (i.e., better throughput). With greater throughput, depending on what read-intensive workloads your organization is running, you might see faster video streaming, quicker recommendations for customers, or an increase in the speed of users pulling up data.

NoSQL databases and Cassandra in today’s business landscape

For this study, we tested with Apache Cassandra, a widely used NoSQL database system. NoSQL, or non-relational, databases are a category of database system that store and query data that do not have a traditional data structure. Traditional SQL databases organize data in a column-row format for finding or creating relationships across the data. To store data in a SQL database, all data in each table must have the same structure and fit a pre-defined schema, with every row in each table including the same columns and formats every time. NoSQL databases, however, can organize data more dynamically. They can deal with data from documents, graphs, key-values, and more. This flexibility lets people use them to analyze documents or data that don’t follow identical structuring formats. For organizations that need to store and analyze unstructured data—which may include data from Internet of Things (IoT) applications, audio, video, text files, social media posts, and more—a NoSQL database is a great option.

There are many types of NoSQL database systems; Apache Cassandra is a type of key-value and wide- column store. These databases have essentially two fields: One is the key, and the other is the value. The value can be any type of data (text, numbers, etc.). Taking our previous example, a key-value database could have some keys that correspond to a date, others that are numbers, and so on. A wide-column database, which Cassandra uses, is a two-dimensional key-value database, where instead of mapping to just one value, the keys can map to several columns of values.

Apache Cassandra is a distributed database, meaning that it can run on multiple nodes while acting as a single entity. This makes it resilient and highly scalable. Its scalability, combined with the flexibility afforded by its hybrid key-value/tabular model, allows it to handle many types of big data work very well. Cassandra is also open-source and free, a compelling benefit for organizations seeking to save on licensing fees.

The flexibility of Cassandra makes it suitable for a very large range of use cases. For example, Instagram uses Cassandra to support its content feed, Spotify uses it to store playlist metadata, and Intuit uses it as part of their largest production clusters supporting TurboTax.9,10,11 Common uses of Cassandra include:

• Analysis of customer data for personalization and recommendation, such as in ecommerce environments and content sharing or streaming websites
• Storage and analysis of IoT data, such as data gathered from mobile and wearable devices, environmental sensors, and edge devices
• Fraud detection, especially for financial organizations
• Messaging, such as for organizations’ internal messaging platforms

We chose to test with Cassandra in part because so many organizations rely on it for everyday operations. Approximately 90 percent of Fortune 100 companies use Apache Cassandra in some capacity.12 If your organization uses Cassandra or is considering doing so, to get the most value from it, you will want to ensure that the solution backing your implementation offers high performance. As our testing highlights, the Dell PowerEdge C6620 with Broadcom-based PERC 12 can deliver just that.

Conclusion

The vast amounts of unstructured data that people and organizations generate daily have the potential to bring incredible value to companies that can utilize it quickly and correctly. Buried in the data are insights about consumer preferences, product performance, environmental trends, and more—but to access those insights at the speed of business, you need high-performing NoSQL databases. Aging servers may be holding you back from the full value of your data.

We found that the new Dell PowerEdge C6620 with Broadcom-based PERC 12 RAID controller can speed read-intensive Apache Cassandra database workloads compared to an older server solution. Faster read and update latencies and higher throughput, as we saw the PowerEdge C6620 deliver, can speed the retrieval, processing, and analysis of your unstructured data, enabling you to more effectively extract its value. To more fully utilize your data to inform your everyday business operations, consider the Dell PowerEdge C6620 with Broadcom-based PERC 12 RAID controller.

1. “PowerEdge C6620,” accessed June 23, 2023, https://www.delltechnologies.com/asset/en-us/products/servers/ technical-support/poweredge-c6620-spec-sheet.pdf.
2. Brian F. Cooper, Adam Silberstein, Erwin Tam, Raghu Ramakrishnan, Russell Sears, “Benchmarking Cloud Serving Systems with YCSB,” accessed June 23, 2023, https://courses.cs.duke.edu/fall13/compsci590.4/838-CloudPa- pers/ycsb.pdf.

1. “The Ultimate YCSB Benchmark Guide (2021),” accessed June 23, 2023, https://benchant.com/blog/ycsb.
2. Brian F. Cooper, Adam Silberstein, Erwin Tam, Raghu Ramakrishnan, Russell Sears, “Benchmarking Cloud Serving Systems with YCSB,” accessed June 23, 2023, https://courses.cs.duke.edu/fall13/compsci590.4/838-CloudPa- pers/ycsb.pdf.
3. “brianfrankcooper/YCSB,” accessed June 23, 2023, https://github.com/brianfrankcooper/YCSB/blob/master/doc/coreworkloads.html.
4. “Dell PowerEdge RAID Controller 12 User’s Guide PERC H965i Adapter, PERC H965i Front, and PERC H965i MX,” accessed June 27, 2023, https://www.dell.com/support/manuals/en-us/perc-h965i-front/perc12/dell-tech- nologies-poweredge-raid-controller-12?guid=guid-5889415d-b297-43a0-9197-113a56c33c79&lang=en-us.
5. “SAS4116W 24G SAS Tri-Mode RAID-on-Chip (ROC),” accessed June 27, 2023, https://www.broadcom.com/products/storage/raid-on-chip/sas-4116w.
6. “SAS4116W 24G SAS Tri-Mode RAID-on-Chip (ROC).”
7. Instagram Engineering, “Open-sourcing a 10x reduction in Apache Cassandra tail latency,” accessed June 27, 2023, https://instagram-engineering.com/open-sourcing-a-10x-reduction-in-apache-cassandra-tail-latencyd- 64f86b43589.
8. Kinshuk Mishra and Matt Brown, “Personalization at Spotify using Cassandra,” accessed June 27, 2023, https://engineering.atspotify.com/2015/01/personalization-at-spotify-using-cassandra/.
9. Denson Pokta, “Pronto! Intuit Releases First Open Source Cassandra Cluster Manager,” accessed June 27, 2023, https://thenewstack.io/pronto-intuit-releases-first-open-source-cassandra-cluster-manager/.
10. Jeff Carpenter, “How the world caught up with Apache Cassandra,” accessed June 27, 2023, https://techcrunch.com/sponsor/datastax/how-the-world-caught-up-with-apache-cassandra/.
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Principled Technologies tested a VMware cluster of new 16^th generation Dell PowerEdge MX servers. This accomplished more OLTP orders per minute and increased VM density compared to a similarly configured 15^th generation MX750c.

Figure 1: Performance of the previous-generation cluster with 24 VMs and the new-generation cluster with 30 VMs. Higher OPM and lower latency are better.

A sample OLTP database workload was used in the test as OLTP plays a crucial role in many digital business processes, such as retail transactions, inventory tracking, customer relationship management, and other business operations. Based on Microsoft SQL Server 2019 DVD using Store 3 hosted in multiple virtual machines, the workload testing targeted the maximum orders per minute (OPM) each cluster could achieve. This was done by increasing the thread count and decreasing the think time until performance degraded. The testing found customers can accelerate existing virtual machines by a simple lift and shift vMotion-type migration to the newer compute sled. However, due to the extra performance available from the new MX760c platform, thanks in part to its 4th Generation Intel Xeon Scalable processors, it was also found additional workloads could be added while still maintaining the required performance.

Figure 2: Total orders per minute the two clusters achieved with 24VMs. 

Figure 3: Total orders per minute the two clusters achieved with 30 VMs.

Regarding workloads, new-generation Dell PowerEdge MX760c servers can offer compelling performance gains. 16th Generation Dell PowerEdge MX760c servers can simultaneously provide increased workload performance and VM density.

 Read the report:

Principled Technologies testing showed customers can save significant administrator time and effort by migrating legacy UCS workloads to Dell PowerEdge MX rather than Cisco UCS X.

By requiring 246 fewer administrator steps from initial configuration through server migration for a three-node cluster, choosing the Dell PowerEdge MX platform could help reduce human error and possible troubleshooting time as you move your new hardware into production. With PowerEdge MX, administrators save time as well—2 hours and 21 minutes for a three-node cluster—compared to moving to new Cisco UCS hardware. That is time that administrators can spend working on new initiatives to further their business goals.

Read the report here! 

Trust the numbers: Dell PowerEdge MX760c using 4th generation Intel® Xeon® Scalable Processors performs better machine learning. 

Grid Dynamics set out to review whether taking a CPU-only (non-GPU) approach is effective in training and inference tasks for small and medium-sized machine learning models (up to ten million parameters), and whether it is a good solution for running larger models (with hundreds of millions of parameters) effectively in inference mode. They also wanted to review improvement in server performance, generation to generation.

To understand how the MX760c model series handles significant artificial intelligence/machine learning workloads as compared to the MX750c model series, Grid Dynamics developed four use cases that simulate the computational needs of retail, industrial, and IT infrastructure:

1. A recommendation system for analyzing user preferences and creating personalized recommendations.

2. A sales forecasting and inventory decision support tool for store managers for keeping the inventory optimized against actual and forecasted demand, and for planning for stock replenishment.

3. Anomaly detection for industrial timeseries for analyzing anomalies in telemetry data and detecting failure probability in industrial hardware.

4. Popular machine learning for evaluating server performance through a series of standardized tests.

(2) Based on testing performed by Grid Dynamics, February 2023.

The tests team concluded that Dell PowerEdge MX760c – leveraging Intel® Xeon® Gold 6430 (2.10 / 32 core) CPUs – performs better in machine learning than MX750c leveraging Intel® Xeon® Platinum 8368 (2.40 GHz /38 core) CPUs. The MX760C used Intel® Xeon® Gold 6430 that supports the Advanced Matrix Extensions accelerator that improves the performance of deep-learning training and inference. The Gold 6430 also supports faster DDR5 memory. 

Figure 1.  Inference speed (neural ML models – Inferences per Second (higher is better) 

Advanced Matrix Extensions (AMX) technology implemented in “Sapphire Rapids” 4th generation of Intel® Xeon® Scalable Processors CPUs allows these servers to perform matrix operations for quantized (Int8 and Bf16) ML models much faster.

(8) Based on testing performed by Grid Dynamics, February 2023.

To summarize, Dell PowerEdge MX760c has proven itself to be a faster and a more capable solution than the previous generation MX750c server for the studied use cases. For more details, see the benchmark and testing results in these documents:

Executive Summary - Performance Comparison of  Dell PowerEdge MX760c and MX750c Server Models

Technical Paper - Performance Comparison of  Dell PowerEdge MX760c and MX750c Server Models