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New BMC Control-M 7 Released

Harnessing the Cloud with Dynamic Workload Management

BMC Software has introduced BMC Control-M 7, a new version of the solution that automates the management of dynamic and static workloads across applications as well as physical, virtual and cloud platforms, making sure business needs are met – regardless of how complex or unpredictable they may be.

The BMC Control-M 7 solution introduces Dynamic Workload Management, a capability that automatically maps workloads to IT priorities using policies that react to real-time business needs. This new feature enables enterprises to ensure workloads are aligned with business operational cycles, maximizing IT resources and reducing costs.

In addition, BMC Control-M 7 helps harness cloud environments, providing the elasticity needed to cost-effectively support fluctuating workloads. Its portable product architecture is built for virtual and physical environments, both on- and off-premise, fitting varied IT and business needs. The solution provides the flexibility needed to meet business service levels and priorities by easily enabling a cloud for processing workloads.

Playing a key role in BMC’s Business Service Management strategy, BMC’s Control-M workload automation solution allows customers to automate the execution of processes across the enterprise, which is critical to achieving IT cost reduction. The solution minimizes errors and gives IT a higher level of control and flexibility over their dynamic and scheduled workloads which are increasingly integrated with business services and online processing.

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

New BMC Control-M 7 Released

Harnessing the Cloud with Dynamic Workload Management

BMC Software has introduced BMC Control-M 7, a new version of the solution that automates the management of dynamic and static workloads across applications as well as physical, virtual and cloud platforms, making sure business needs are met – regardless of how complex or unpredictable they may be.

The BMC Control-M 7 solution introduces Dynamic Workload Management, a capability that automatically maps workloads to IT priorities using policies that react to real-time business needs. This new feature enables enterprises to ensure workloads are aligned with business operational cycles, maximizing IT resources and reducing costs.

In addition, BMC Control-M 7 helps harness cloud environments, providing the elasticity needed to cost-effectively support fluctuating workloads. Its portable product architecture is built for virtual and physical environments, both on- and off-premise, fitting varied IT and business needs. The solution provides the flexibility needed to meet business service levels and priorities by easily enabling a cloud for processing workloads.

Playing a key role in BMC’s Business Service Management strategy, BMC’s Control-M workload automation solution allows customers to automate the execution of processes across the enterprise, which is critical to achieving IT cost reduction. The solution minimizes errors and gives IT a higher level of control and flexibility over their dynamic and scheduled workloads which are increasingly integrated with business services and online processing.

The Latest

For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...