Category: Mainframe infrastructure
If you are still using a legacy VTL/Tape solution, you could be enjoying better performance by sending backup and archive copies of mainframe data directly to cloud object storage.
The reason for this is when you replace legacy technology with modern object storage, you can eliminate bottlenecks that throttle your performance. In other words, you can build a connection between your mainframe and your backup/archive target that can move data faster. You can think of this as “ingestion throughput.”
3 ways you can increase ingestion throughput for backup and archive copies of mainframe data
Here are the top three ways you can increase ingestion throughput:
#1: Write data in parallel, not serially
The legacy mainframe tapes used to make backup and archive copies required data to be written serially. This is because physical tape lived on reels, and you could only write to one place on the tape at a time. When VTL solutions virtualized tape, they carried over this sequential access limitation.
In contrast, object storage does not have this limitation and does not require data to be written serially. Instead, it is possible to use a new method to send multiple chunks of data simultaneously directly to object storage using TCP/IP.
#2: Use zIIP engines instead of mainframe MIPS
Legacy mainframe backup and archive solutions use MSUs, taking away from the processing available to other tasks on the mainframe. This in effect means that your mainframe backups are tying up valuable mainframe computing power, reducing the overall performance you can achieve across all the tasks you perform there.
You do not need to use MSUs to perform backup and archive tasks. Instead, you can use the mainframe zIIP engines—reducing the CPU overhead and freeing up MSUs to be used for other things.
#3: Compress data before sending it
Legacy mainframe backup and archive solutions do not support compressing data before sending it to Tape/VTL. This means that the amount of data that needs to be sent is much larger than it could be using modern compression techniques.
Rather it is possible to compress your data before sending it to object storage. Not only do you benefit from smaller data transfer sizes, but you can increase the effective capacity of your existing connection between the mainframe and storage target. For example, compressing data at a 3:1 ratio would effectively turn a 1GB line into a 3GB line—allowing you to send the same amount of data faster while still using your existing infrastructure.
Faster than VTL: Increase Mainframe Data Management Performance
Replacing your legacy VTL/Tape solution with a modern solution that can compress and move data to cloud-based object storage can significantly decrease the amount of time it takes to backup and archive your mainframe data, without increasing resource consumption.
Writing in parallel, leveraging zIIP engines, and employing compression is a low-risk, and high-reward option that leverages well-known, well-understood, and well-proven technologies to address a chronic mainframe challenge. This can yield immediate, concrete benefits such as reducing the amount of time it takes for you to backup and archive your mainframe data and cut costs while boosting capabilities.
Mainframe modernization is a broad topic and one that elicits symptoms of anxiety in many IT professionals. Whether the goals are relatively modest, like simply updating part of the technology stack or offloading a minor function to the cloud, or an ambitious goal like a change of platform with some or all functions heading to the cloud, surveys show it is a risky business…
For example, according to the 2020 Mainframe Modernization Business Barometer Report, published by OneAdvanced.com, a UK software company, some 74 percent of surveyed organizations have started a modernization program but failed to complete it. This is in accord with similar studies highlighting the risks associated with ambitious change programs.
Perhaps that’s why mainframe-to-cloud migration is viewed with such caution. And, indeed, there are at least five reasons to be wary (but in each case, the right strategy can help!)
Top 5 reasons why mainframe to cloud migration initiatives fail
A focus on lift and shift of business logic
Lift and shift is easier said than done when it comes to mainframe workloads. Mainframe organizations that have good documentation and models can get some clarity regarding business logic and the actual supporting compute infrastructure. However, in practice, such information is usually inadequate. Even when the documentation and models are top notch, they can miss crucial dependencies or unrecognized processes. As a consequence, efforts to recreate capabilities in the cloud can yield some very unpleasant surprises when the switch is flipped. That’s why many organizations take a phased and planful approach, testing the waters one function at a time and building confidence in the process and certainty in the result. Indeed, some argue that the lift and shift approach is actually obsolete. One of the enablers for the more gradual approach is the ability to get mainframe data to the cloud when needed. This is a requirement for any ultimate switchover but if it can be made easy and routine it also allows for parallel operations, where cloud function can be set up and tested with real data, at scale, to make sure nothing is left to chance and that a function equal to or better than on-premises has been achieved.
Ignoring the need for hybrid cloud infrastructure
Organizations can be forgiven for wanting to believe they can achieve a 100 percent cloud-based enterprise. Certainly, there are some valid examples of organizations that have managed this task. However, for a variety of good, practical reasons, analysts question whether completely eliminating on-premises computing is either achievable or wise. A “Smarter with Gartner” article, Top 10 Cloud Myths, noted “The cloud may not benefit all workloads equally. Don’t be afraid to propose non cloud solutions when appropriate.” Sometimes there’s a resiliency argument in favor of retaining on-prem capabilities. Or, of course, there may be data residency or other requirements tilting the balance. The point is that mainframe cloud migration that isn’t conceived in hybrid terms is nothing less than a rash burning of one’s bridges. And a hybrid future, particularly when enabled by smooth and reliable data movement from mainframe to cloud, can deliver the best of both worlds in terms of performance and cost-effective spending.
Addressing technology infrastructure without accounting for a holistic MDM strategy
Defined by IBM as “a comprehensive process to drive better business insights by providing a single, trusted, 360-degree view into customer and product data across the enterprise,” master data management (MDM) is an important perspective to consider in any migration plan. After taking initial steps to move data or functions to the cloud, it quickly becomes apparent that having a comprehensive grasp of data, no matter where it is located, is vital. Indeed, a recent TDWI webinar dealt with exactly this topic, suggesting that multi-domain MDM can help “deliver information-rich, digitally transformed applications and cloud-based services.” So, without adaptable, cloud-savvy MDM, migrations can run into problems.
Assuming tape is the only way to back up mainframe data
Migration efforts that neglect to account for the mountains of data in legacy tape and VTL storage can be blindsided by how time consuming and difficult it can be to extract that data from the mainframe environment. This can throw a migration project off schedule or lead to business problems if backup patterns are interrupted or key data suddenly becomes less accessible. However, new technology makes extraction and movement much more feasible and the benefits of cloud data storage over tape in terms of automation, access, and simplicity are impressive.
Overlooking the value of historical data accumulated over decades
A cloud migration is, naturally, a very future-focused activity in which old infrastructure and old modes of working are put aside. In the process, organizations are sometimes tempted to leave some of their data archives out of the picture, either through simply shredding tapes no longer retained under a regulatory mandate or simply warehousing them. This is particularly true for older and generally less accessible elements. But for enterprises fighting to secure their future in a highly competitive world, gems of knowledge are waiting regarding every aspect of the business – from the performance and function of business units, the shop floor and workforce demographics to insights into market sectors and even consumer behavior. With cloud storage options, there are better fates for old data than gathering dust or a date with the shredder. Smart organizations recognize this fact and make a data migration strategy, the foundation for their infrastructure modernization efforts. The data hiding in the mainframe world, is truly an untapped resource that can now be exploited by cloud-based services.
Failure is not an option
Reviewing these five potential paths to failure in mainframe-cloud migration should not be misconstrued as an argument against cloud. Rather, it is intended to show the pitfalls to avoid. When considered carefully and planfully – and approached with the right tools and the right expectations – most organizations can find an appropriate path to the cloud.
One of the great revelations for those considering new or expanded cloud adoption is the cost factor – especially with regard to storage. The received wisdom has long been that nothing beats the low cost of tape for long-term and mass storage.
In fact, though tape is still cheap, cloud options are getting very close such as with Amazon S3 Glacier Deep Archive, and offer tremendous advantages that tape can’t match. A case in point is Amazon S3 Intelligent-Tiering.
Tiering (also called hierarchical storage management or HSM) is not new. It’s been part of the mainframe world for a long time, but with limits imposed by the nature of the storage devices involved and the software. According to Amazon, Intelligent Tiering helps to reduce storage costs by up to 95 percent and now supports automatic data archiving. It’s a great way to modernize your mainframe environment by simply moving data to the cloud, even if you are not planning to migrate your mainframe to AWS entirely.
How does Intelligent-Tiering work? The idea is pretty simple. When objects are found to have been rarely accessed over long periods of time, they are automatically targeted for movement to less expensive storage tiers.
Migrate Mainframe to AWS
In the past (both in mainframes and in the cloud) you had to define a specific policy stating what needed to be moved to which tier and when, for example after 30 days or 60 days. The point with the new AWS tiering is that it automatically identifies what needs to be moved, when, and then moves it at the proper time. To migrate mainframe to Amazon S3 is no problem because modern data movement technology now allows you to move both historical and active data directly from tape or virtual tape to Amazon S3. Once there, auto-tiering can transparently move cold and long-term data to less expensive tiers.
This saves the trouble of needing to specifically define the rules. By abstracting the cost issue, AWS simplifies tiering and optimizes the cost without impacting the applications that read and write the data. Those applications can continue to operate under their usual protocols while AWS takes care of selecting the optimal storage tier. According to AWS, this is the first and, at the moment, the only cloud storage that delivers this capability automatically.
When reading from tape, the traditional lower tier for mainframe environments, recall times are the concern as the system has to deal with tape mount and search protocols. In contrast, Amazon S3 Intelligent-Tiering can provide a low millisecond latency as well as high throughput whether you are calling for data in the Frequent or Infrequent access tiers. In fact, Intelligent-Tiering can also automatically migrate the most infrequently used data to Glacier, the durable and extremely low-cost S3 storage class for data archiving and long-term backup. And with new technology allowing efficient and secure data movement over TCP/IP, getting mainframe data to S3 is even easier.
The potential impact on mainframe data practices
For mainframe-based organizations this high-fidelity tiering option could be an appealing choice compared with tape from both a cost and benefits perspective. However, the tape comparison is rarely that simple. For example, depending on the amount of data involved and the specific backup and/or archiving practices, any given petabyte of data needing to be protected may have to be copied and retained two or more times, which immediately makes tape seem a bit less competitive. Add overhead costs, personnel, etc., and the “traditional” economics may begin to seem even less appealing.
Tiering, in a mainframe context, is often as much about speed of access as anything else. So, in the tape world, big decisions have to be made constantly about what can be relegated to lower tiers and whether the often much-longer access times will become a problem after that decision has been made. But getting mainframe data to S3, where such concerns are no longer an issue, is now easy. Modern data movement technology means you can move your mainframe data in mainframe format directly to object storage in the cloud so it is available for restore directly from AWS.
Many mainframe organizations have years, even decades of data on tape. The management of this tape data is retained only in the tape management system. Or perhaps it was just copied forward from a prior tape system upgrade. How much of this data is really needed? Is it even usable anymore? To migrate mainframe to AWS, specifically this older data, allows management of the data in a modern way and can reduce the amount of tape data on-premises.
And what about those tapes that today are shipped off-site for storage and recovery purposes? Why not put that data on cloud storage for recovery anywhere?
For mainframe organizations interested in removing on-premise tape technology, reducing tape storage sizes, or creating remote backup copies, cloud options like Amazon S3 Intelligent Tiering can offer cost optimization that is better “tuned” to an organization’s real needs than anything devised manually or implemented on-premises. Furthermore, with this cloud-based approach, there is no longer any need to know your data patterns or think about tiering, it just gets done.
Best of all, you can now perform a stand-alone restore directly from cloud. This is especially valuable with ransomware attacks on the rise because there is no dependency on a potentially compromised system.
You can even take advantage of AWS immutable copies and versioning capabilities to further protect your mainframe data.
Of course, in order to take advantage of cloud storage like Amazon S3 Intelligent Tiering, you need to find a way to get your mainframe data out of its on-premises environment. Traditionally, that has presented a big challenge. But, as with multiplying storage options, the choices in data movement technology are also improving. For a review of new movement options, take a look at a discussion of techniques and technologies for Mainframe to Cloud Migration.
For mainframe shops that need to move data on or off the mainframe, whether to the cloud or to an alternative on-premises destination, FICON, the IBM mainstay for decades, is generally seen as the standard, and with good reason. When it was first introduced in 1998 it was a big step up from its predecessor ESCON that had been around since the early 1990s. Comparing the two was like comparing a firehose to a kitchen faucet.
FICON is fast, in part, because it runs over Fibre Channel in an IBM proprietary form defined by ANSI FC-SB-3 Single-Byte Command Code Sets-3 Mapping Protocol for Fibre Channel (FC) protocol. In that schema it is a FC layer 4 protocol. As a mainframe protocol it is used on IBM Systems Z to handle both DASD and tape I/O. It is also supported by other vendors of disk and tape storage and switches designed for the IBM environment.
Over time, IBM has increased speeds and added features such as High Performance FICON, without significantly enhancing the disk and tape protocols that traverse over it; meaning these limitations on data movement remain. For this reason, the popularity and a long-history of FICON does not make it the answer for every data movement challenge.
Stuck in the Past
One challenge, of particular concern today, is that mainframe secondary storage is still being written to tape via tape protocols, whether it is real physical tape or virtual tape emulating actual tape. With tape as a central technology, it implies dealing with tape mount protocols and tape management software to maintain where datasets reside on those miles of Mylar. The serial nature of tape and limitations of the original hardware required large datasets to often span multiple tape images.
Though virtual tapes written to DASD improved the speed of writes and recalls, the underlying protocol is still constrained by tape’s serialized protocols. This implies waiting for tape mounts and waiting for I/O cycles to complete before next data can be written. When reading back, the system must traverse through the tape image to find the specific dataset requested. In short, while traditional tape may have its virtues, speed – the 21st century speed of modern storage – is not among them. Even though tape and virtual tape is attached via FICON, the process of writing and recalling data relies on the underlying tape protocol for moving data, thus making FICON attached less-than-ideal for many modern use cases.
Faster and Better
But there is an alternative that doesn’t rely on tape or emulate tape because it does not have to.
Instead, software generates multiple streams of data from a source and pushes data over IBM Open Systems Adapter (OSA) cards using TCP/IP in an efficient and secure manner to an object storage device, either on premise or in the cloud. The Open Systems Adapter functions as a network controller that supports many networking transport protocols, making it a powerful helper for this efficient approach to data movement. Importantly, as an open standard, OSA is developing faster than FICON. For example, with the IBM z15 there is already a 25GbE OSA-Express7S card, while FICON is still at 16Gb with the FICON Express16 card.
While there is a belief common among many mainframe professionals that OSA cards are “not as good as FICON,” that is simply not true when the necessary steps are taken to optimize OSA throughput.
To achieve better overall performance, the data is captured well before tape handling, thus avoiding the overhead of tape management, tape mounts, etc. Rather than relying on serialized data movement, this approach breaks apart large datasets and sends them across the wire in simultaneous chunks, while also pushing multiple datasets at a time. Data can be compressed prior to leaving the mainframe and beginning its journey, reducing the amount of data that would otherwise be written. Dataset recalls and restores are also compressed and use multiple streams to ensure quick recovery of data from the cloud.
Having the ability to write multiple streams further increases throughput and reduces latency issues. In addition, compression on the mainframe side dramatically reduces the amount of data sent over the wire. If software is also designed to run on zIIP engines within the mainframe, data discovery and movement as well backup and recovery workloads will consume less billable MIPS and TCP/IP cycles also benefit.
This approach delivers mainframe data to cloud storage, including all dataset types and historical data, in a quick and efficient manner. And this approach can also transform mainframe data into standard open formats that can be ingested by BI and Analytics off of the mainframe itself, with a key difference. When data transformation occurs on the cloud side, no mainframe MIPS are used to transform the data. This allows for the quick and easy movement of complete datasets, tables, image copies, etc. to the cloud, then makes all data available to open applications by transforming the data on the object store.
A modern, software-based approach to data movement means there is no longer a need to go to your mainframe team to update the complex ETL process on the mainframe side.
To address the problem of hard-to-move mainframe data, this software-based approach provides the ability to readily move mainframe data and, if desired, readily transform it to common open formats. This data transformation is accomplished on the cloud side, after data movement is complete, which means no MF resources are required to transform the data.
- Dedicated software quickly discovers (or rediscovers) all data on the mainframe. Even with no prior documentation or insights, Model9 can rapidly assemble and map the data to be moved, expediting both modernization planning and data movement.
- Policies are defined to move either selected data sets or all data sets automatically, reducing oversight and management requirements dramatically as compared to other data movement methods.
- For the sake of simplicity, a software approach can be designed to invoke actions via a RESTful API, or a management UI, as well as from the Mainframe side via a traditional batch or command line,
- A software approach can also work with targets both on premises or in the cloud.
In summary, a wide-range of useful features can make data movement with a software-based approach intuitive and easy. By avoiding older FICON and tape protocols, a software-based approach can push mainframe data over TCP/IP to object storage in a secure and efficient manner, making it the answer to modern mainframe data movement challenges!
The recently posted Computer Weekly article, “Mainframe storage: Three players in a market that’s here to stay”, did a good job of describing the central players in mainframe disk storage but neglected to mention other types of mainframe storage solutions such as tapes and cloud data management.
In particular, the article omitted mention of one of the biggest opportunities for mainframe storage modernization and cost reduction, namely leveraging the cloud to reduce the footprint and cost of the petabytes of data still locked in various kinds of on-premises tape storage. Model9 currently offers the key to this dilemma by eliminating the dependency on FICON connectivity for mainframe secondary storage. This means, specifically, that mainframe-based organizations can finally gain real access to reliable and cost-effective on-premises and cloud storage from Cohesity, NetApp, Amazon Web Services, Microsoft Azure, Google Cloud Platform, etc. that until now could not be considered due to the proprietary nature of traditional mainframe storage. And, while keeping mainframe as the core system that powers transactions, its data can be accessible for analytics, BI and any other cloud application.
Surely, this is major news for such a key part of the computing market that has hitherto been essentially monopolized by the three players author Antony Adshead discussed at length.
Mainframe professionals know that new technologies can help them achieve even more; they deserve guidance with regard to the wide options opening up for them.
Enterprises are generating huge volumes of data every year with an average annual data growth of 40-50%. This growth has to be handled using IT budgets that are only growing at an annual average of 7%. Such disproportion creates a challenge for mainframe professionals: how can they store all this data cost-effectively?
Particularly challenging is deciding on the right strategy for long-term storage, also known as cold storage, for archived data that is rarely or never accessed. There can be different causes for keeping such data for the long term, which often lasts years or even decades:
- Financial data is stored for compliance and might be required in case of an audit
- Legal information must be kept in case of legal action
- Medical archives are stored in vast quantities and their availability is highly regulated
- Government data has to be stored for legal reasons, sometimes even indefinitely
- Raw data is stored by many enterprises for future data mining and analysis
Desired attributes of a cold storage solution
Cold storage, also referred to as “Tier 3 storage,” has different needs than Tier 0 (high-performance), Tier 1 (primary), and Tier 2 (secondary) storage. These are some of the considerations to keep in mind when designing your cold storage solution:
- Scalability – As the amount of generated data doubles in less than two years on average, your cold storage technology needs to be infinitely scalable accordingly.
- Cost – Cold storage must be as inexpensive as possible especially because you will need a lot of it. Luckily, as it is rarely accessed it allows compromising on accessibility and performance, which can be leveraged to reduce cost.
- Durability and Reliability – Reliability is the ability of a storage media not to fail within its durability time frame. Both are important to check, and you will find that some cold storage options are durable but not necessarily as reliable as others, and vice versa.
- Accessibility – Cold storage is meant only for data that does not need to be accessed very often or very rapidly, yet the ability to access it is still important. As mentioned above, compromising on this aspect enables a lower cost.
- Security – The security of cold data is vital. If it is stored onsite you need to take the same security precautions as with your active data. If it is in the cloud, you must ensure the vendor has proper security mechanisms in place.
Cold storage technology options for mainframe
Mainframe professionals have three general technology options when it comes to cold storage: tape, virtual tape, and cloud. While tapes are still the dominant cold storage media for mainframes, cloud is gaining momentum with its virtually limitless storage and pay-as-you-go model.
Here is a summary of these technologies, and their relative advantages and disadvantages:
Tape drives store data on magnetic tapes and are typically used for offline, archival data. Despite many end-of-life forecasts, the tape market is still growing at a CAGR of 7.6% and is expected to reach $6.5 billion by 2022. Tapes are considered the most reliable low-cost storage medium and if maintained properly can last for years. However, they are also the most difficult to access and it can be quite an ordeal to recover from tapes in case of disaster.
Pros of Tape:
- Often cheaper than other options, depending on the use case
- Full control over where data is stored
- Secure and not susceptible to malware or viruses as it is offline
- Portable and can be carried or sent anywhere
- Easy to add capacity
Cons of Tape:
- Capital investment required for large tape libraries
- Difficult to access (slow and with bottlenecks)
- High recovery time objective (RTO)
- Requires physical access and manual handling (problematic in lockdown, for example)
- Requires careful maintenance
Virtual Tape Libraries (VTL)
A VTL is a storage system made up of hard disk drives (HDDs) that appears to the backup software as traditional tape libraries. While not as cheap as tape, HDDs are relatively inexpensive per gigabyte. They are easier to access than tape and their disks are significantly faster than magnetic tapes (although data is still written sequentially).
Pros of VTL:
- Scalability – HDDs added to a VTL are perceived as tape storage to the mainframe
- Performance – data access is faster than tape or cloud
- Compatibility – works with tape software features like deduplication
- Familiarity – behaves like traditional tape libraries
Cons of VTL:
- Cost varies. Infrastructure, maintenance, and skilled admins should also be considered
- Capital investment required
- Usually less reliable than other options
- Less secure than offline tapes and lacks the latest security features of cloud platforms
Cold storage in the cloud is maintained by third-party service providers in a pay-as-you-go model. Rather than selling products, they charge for usage of storage space, bandwidth, data access, and the like. Cloud is becoming extremely popular for cold storage, mainly because it is considerably cheaper than on-prem storage. Pay-as-you-go means that it can start at affordable prices without needing to stock up on tapes and VTLs anymore. There is also no more need to maintain infrastructure or recruit personnel to manage data archives, as these are all handled by the cloud vendor. The cloud provides superior agility and scalability, and although magnetic tapes are more secure it also provides higher levels of security and compliance than many businesses can on their own. When it comes to durability, the cloud really excels by storing data redundantly across many different storage systems. On the downside, administrators need to consider network bandwidth and the cost of uploads and restores, as using cloud is often more expensive than it appears at first glance. The leading vendors of long-term cloud storage are Amazon (Glacier and Glacier Deep Archive), Google (Cloud Storage Nearline and Cloud Storage Coldline), Microsoft (Azure Archive Blob Storage), and Oracle (Archive Storage). These vendors charge low rates for storage space but extra fees for bringing data back on-premises, which might prove costly if too much data is retrieved.
Pros of Cloud:
- Can be cheaper, especially when being aware of hidden costs
- Can improve cash flow thanks to an OpEx financial model rather than CapEx
- Infinitely scalable
- Accessible from anywhere
- Advanced data management
- High data redundancy and easy replication
- Leading-edge security
- Easy to integrate with mainframes
Cons of Cloud:
- Hidden costs (depends on use)
- Data retrieval, backup, and RTO times depend on network bandwidth
Cloud is Rising as a Mainframe Cold Storage Choice
The cloud storage market is expected to reach $88.91 billion by 2022 growing at a CAGR of 23.7%—much higher than the CAGRs of all the other cold storage options combined. Cold storage in the cloud offers a unique combination of scalability, reliability, durability, security, and cost-effectiveness that on-prem options are challenged to meet.
So, in which cases cloud is preferable for cold storage over tape and VTL?
- When data access frequency changes: The cloud offers different cold storage tiers, based on the data access requirements, that balance between data storage cost and the data access frequency. Cold storage tiers can be cost effective, however with high data access frequency you need to be mindful of choosing a service that addresses those access needs.
- When the data grows quickly or unpredictably: Cloud platforms can scale to infinity with very little effort, unlike on-prem options.
- When improving cash flow is a priority: Predictable OpEx monthly fees can improve cash flow compared to large upfront investment in on-prem storage and infrastructure.
- In case of mainframe skills shortage: Attracting and retaining mainframe experts is a challenge to many enterprises. With cloud cold storage, this problem completely goes away.
New Jersey Governor Phil Murphy’s open call for COBOL programmers because of system failures in supporting unemployment benefits processing and distribution is completely missing the point.
The fact that the mainframe system could not handle the workload and increase in demand is not the fault of the app and it’s certainly not an issue of the app’s programming language. It is a matter of upgrading the mainframe’s infrastructure to sustain the increased workload.
Governments and institutions have allowed their systems to stagnate, neglecting to invest in agile, newer technologies with greater scalability to keep up with increased workload. In fact, to date the system was working well with most believing “if it ain’t broke…,” don’t bother to “fix” it.
The challenges lie with a lack of maintenance and modernization of the mainframe’s infrastructure. If there’s any type of skill shortage, it is that of mainframe system programmers whose mainframe expertise is unique due to the proprietary nature of the system. Any dependency on a unique, proprietary set of skills is a risk for any organization and, therefore, the resolution lies in opening up the system to cloud-native, modern architectures.
To summarize, had organizations invested in integrating cloud technology with their mainframe infrastructure, they would have been benefiting from quick scaling and fast-paced app development on the cloud side to process their mainframe data.
In today’s current crisis, the cloud serves to decrease the dependency of on-prem hardware and infrastructure, and offloads the work from the mainframe infrastructure to increase capacity.