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IBM Simplifies Data Center Management with New zEnterprise Mainframe Server

In a move that will simplify data center management and reduce costs, IBM is offering the ability to integrate and manage workload on select IBM System x servers running Linux as part of the zEnterprise System.

This capability is delivered through the IBM zEnterprise Unified Resource Manager and the IBM zEnterprise BladeCenter Extension (zBX), which allows customers to integrate the management of zEnterprise System resources as a single system and extend mainframe qualities, such as governance and manageability, to workloads running on other select servers.

The zEnterprise System can now integrate and manage workloads running on tens of thousands of off-the-shelf applications on select general purpose IBM POWER7-based and System x blades as well as the IBM Smart Analytics Optimizer to analyze data faster at a lower cost per transaction and the IBM WebSphere DataPower XI50 for integrating web based workloads.

Up to 112 blades can be integrated and managed as part of zBX. Different types of blades and optimizers can be mixed and matched with in the same BladeCenter chassis.

Support for Microsoft Windows on select System x servers will follow.

IBM Introduces New zEnterprise 114 Mainframe Server

IBM introduced the new IBM zEnterprise 114 mainframe server, which allows mid-sized organizations to enjoy the benefits of a mainframe as the foundation for their data centers, costs 25% less and offers significantly more performance than its predecessor, the System z10 BC server.

It is projected that clients can consolidate workloads from 40 x-86 processors running Oracle software on to a new z114 with just three processors running Linux, and over a three year period, total costs for hardware, software and support on the new z114 as compared to consolidated servers can be up to 80% less with similar dramatic savings on floor space and energy.

At a starting price of under $75,000 -- IBM's lowest ever price for a mainframe server -- the zEnterprise 114 is an especially attractive option for emerging markets experiencing rapid growth in new services for banking, retail, mobile devices, government services and other areas. These organizations are faced with ever-increasing torrents of data and want smarter computing systems that help them operate efficiently, better understand customer behavior and needs, optimize decisions in real time and reduce risk.

IBM also introduced new features that allow the zEnterprise System to integrate and manage workloads on additional platforms. New today is support for select System x blades within the zEnterprise System. These select System x blades can run Linux x86 applications unchanged, and in the future will be able to run Windows applications. With these capabilities, the zEnterprise System including the new z114 can help simplify data centers with its ability to manage workloads across mainframe, POWER7 and System x servers as a single system. Using the zEnterprise Blade Center Extension (zBX), customers can also extend mainframe qualities, such as governance and manageability, to workloads running across multiple platforms.

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AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

IBM Simplifies Data Center Management with New zEnterprise Mainframe Server

In a move that will simplify data center management and reduce costs, IBM is offering the ability to integrate and manage workload on select IBM System x servers running Linux as part of the zEnterprise System.

This capability is delivered through the IBM zEnterprise Unified Resource Manager and the IBM zEnterprise BladeCenter Extension (zBX), which allows customers to integrate the management of zEnterprise System resources as a single system and extend mainframe qualities, such as governance and manageability, to workloads running on other select servers.

The zEnterprise System can now integrate and manage workloads running on tens of thousands of off-the-shelf applications on select general purpose IBM POWER7-based and System x blades as well as the IBM Smart Analytics Optimizer to analyze data faster at a lower cost per transaction and the IBM WebSphere DataPower XI50 for integrating web based workloads.

Up to 112 blades can be integrated and managed as part of zBX. Different types of blades and optimizers can be mixed and matched with in the same BladeCenter chassis.

Support for Microsoft Windows on select System x servers will follow.

IBM Introduces New zEnterprise 114 Mainframe Server

IBM introduced the new IBM zEnterprise 114 mainframe server, which allows mid-sized organizations to enjoy the benefits of a mainframe as the foundation for their data centers, costs 25% less and offers significantly more performance than its predecessor, the System z10 BC server.

It is projected that clients can consolidate workloads from 40 x-86 processors running Oracle software on to a new z114 with just three processors running Linux, and over a three year period, total costs for hardware, software and support on the new z114 as compared to consolidated servers can be up to 80% less with similar dramatic savings on floor space and energy.

At a starting price of under $75,000 -- IBM's lowest ever price for a mainframe server -- the zEnterprise 114 is an especially attractive option for emerging markets experiencing rapid growth in new services for banking, retail, mobile devices, government services and other areas. These organizations are faced with ever-increasing torrents of data and want smarter computing systems that help them operate efficiently, better understand customer behavior and needs, optimize decisions in real time and reduce risk.

IBM also introduced new features that allow the zEnterprise System to integrate and manage workloads on additional platforms. New today is support for select System x blades within the zEnterprise System. These select System x blades can run Linux x86 applications unchanged, and in the future will be able to run Windows applications. With these capabilities, the zEnterprise System including the new z114 can help simplify data centers with its ability to manage workloads across mainframe, POWER7 and System x servers as a single system. Using the zEnterprise Blade Center Extension (zBX), customers can also extend mainframe qualities, such as governance and manageability, to workloads running across multiple platforms.

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...