AS/ System – AS/ iSeries & Mainframe Technology | HCL Technologies
Dive into this discussion about database scalability and elasticity, how they help systems for tasks that previously required larger computers, such as mainframes. That said, depending on your database system's hardware. Linux on IBM Z is the collective term for the Linux operating system compiled to run on IBM . Mainframes typically allow hot-swapping of hardware, such as processors and memory. IBM Z provides fault tolerance and security breaches). Acquisition costs are often more visible, and small, non-scalable servers are " cheap. What scalability means; Differences between scaling in and scaling out; Mainframe's hardware scalability; Software scalability levels; Parallel Sysplex relationship with scalability; Workload management main concepts. Introduction to the new.
Additional savings can be seen from reduced need for floor space, power, cooling, networking hardware, and the other infrastructure needed to support a data center. IBM mainframes allow transparent use of redundant processor execution steps and integrity checkingwhich is important for critical applications in certain industries such as banking.
Mainframes and IBM i (AS/400)
Mainframes typically allow hot-swapping of hardwaresuch as processors and memory. Through internal monitoring, possible problems are detected and problem components are designed to be switched over without even failing a single transaction.
This is transparent to the operating system, allowing routine repairs to be performed without shutting down the system. Many industries continue to rely on mainframes where they are considered to be the best option in terms of reliability, security, or cost.
Most software vendors, including IBM, treat the highly virtualized IFLs just like non-virtualized processors on other platforms for licensing purposes. Test, development, quality assurance, training, and redundant production server instances can all run on one IFL or more IFLs, but only if needed for peak demand performance capacity.
Thus, beyond some minimum threshold, Linux on z can quickly become cost-advantageous when factoring in labor and software costs. The cost equation for Linux on z is not always well understood and is controversial, and many businesses and governments have difficulty measuring, much less basing decisions on, software, labor, and other costs such as the costs of outage and security breaches.
Acquisition costs are often more visible, and small, non-scalable servers are "cheap.
Reliability, availability and serviceability - Wikipedia
Also, individual users and departments within larger businesses and governments sometimes have difficulty sharing computing infrastructure or any other resources, for that matterciting a loss of control.
Server centralization, as Linux on z provides, might reward cooperation with better service and lower costs, but that's not to say that cooperation is always easily accomplished within a corporate bureaucracy. Appropriate workloads[ edit ] Mainframe characteristics are designed for such business workloads as transaction processing especially in conjunction with concurrent, high volume batch processing and large database management.
However, every such machine also has 27 additional main cores: There are also separate processors handling memory and cache control tasks, environmental monitoring, and internal interconnections, as examples. Historically, mainframes in general, and Linux on z in particular, did not execute "CPU-intensive" single task computations with notably high performance compared to certain other platforms with a few notable exceptions such as cryptographic calculations.
Examples included most scientific simulations, weather forecastingand molecular modeling. Supercomputersincluding Linux-based supercomputers, excel at these workloads. The greater the number of changes that can be tolerated, and the ease with which clustering can be managed, the more elastic the DBMS.
Types of Database Scalability First, let's look at the ways that databases can be scaled and examine the benefits and drawbacks of each method. There are two broad categories for scaling database systems: Vertical scaling, also known as scaling up, is the process of adding resources, such as memory or more powerful CPUs to an existing server. Removing memory or changing to a less powerful CPU is known as scaling down. Adding or replacing resources to a system typically results in performance gains, but realizing such gains often requires reconfiguration and downtime.
Furthermore, there are limitations to the amount of additional resources that can be applied to a single system, as well as to the software that uses the system. Vertical scaling has been a standard method of scaling for traditional RDBMSs that are architected on a single-server type model. Nevertheless, every piece of hardware has limitations that, when met, cause further vertical scaling to be impossible.
For example, if your system only supports GB of memory, when you need more memory you must migrate to a bigger box, which is a costly and risky procedure requiring database and application downtime.
Horizontal scaling, sometimes referred to as scaling out, is the process of adding more hardware to a system. This typically means adding nodes new servers to an existing system.
Doing the opposite, that is removing hardware, is known as scaling in. With the cost of hardware declining, it makes more sense to adopt horizontal scaling using low-cost "commodity" systems for tasks that previously required larger computers, such as mainframes. Of course, horizontal scaling can be limited by the capability of software to exploit networked computer resources and other technology constraints.
And keep in mind that traditional database servers cannot run on more than a few machines. In such cases, scaling is limited, in that you are scaling to several machines, not to x or more.
Horizontal and vertical scaling can be combined, with resources added to existing servers to scale vertically and additional servers added to scale horizontally when required. It is wise to consider the tradeoffs between horizontal and vertical scaling as you consider each approach. Horizontal scaling results in more computers networked together and that will cause increased management complexity.
It can also result in latency between nodes and complicate programming efforts if not properly managed by either the database system or the application. That said, depending on your database system's hardware requirements, you can often buy several commodity boxes for the price of a single, expensive, and often custom-built server that vertical scaling requires. On the other hand, depending on your requirements, vertical scaling actually can be less costly if you've already invested in the hardware; it typically costs less to reconfigure existing hardware than to procure and configure new hardware.
Of course, vertical scaling can lead to over-provisioning which can be quite costly.