Skip to main content

BlazeMeter Load Testing Enables Dynamic Master-Slave Clusters

BlazeMeter, provider of the JMeter based load testing cloud, announced the release of a Dynamic Master-Slave feature.

The new Master-Slave configuration provides an efficient way to run several load tests in parallel, all controlled by a single ‘Master’ test and then aggregates all test runs into a single report.

The dynamic configuration allows users to define a test as a Master before and during the test, in real-time. At any point before and during the load test run, additional tests, coined ‘Slaves’ can be added or removed from the Master configuration.

The new Master-Slave feature also aggregates the results from numerous test sessions simulating different business processes into one single report. This includes the option for aggregating the results from numerous geo locations. Previously, developers were required to define a single test that encompassed all processes. BlazeMeter’s Dynamic Master-Slave configuration alleviates the need for usage of JMeter’s Include Controller in the case of multiple JMX files, further simplifying test set up.

Blazemeter Delayed Load Start Feature

Last month, Blazemeter announced the release of a delayed load start feature, designed to provider greater control over multi-server load tests. Developers can now start their load test engines without pushing the pedal to the metal, and have complete control over which servers are running and exactly when they start to run.

The new feature allows performance engineers absolute control over whether to start a test with the maximum load, or to control the amount of load in real-time.

Furthermore, for scenarios when you need to simulate flash traffic that goes from zero to 100,000 users in seconds, not minutes, you can start up all the load servers and have them ready to go and at the press of a button unleash the full fury of all your load server within a matter of seconds.

The new delayed load start feature spawns all servers at the onset of a load test run at once, while allowing users to add actual load incrementally for a wider scope of ramp up scenarios.

The delayed load start feature also provides developers more leeway at the onset of a test, and allows for adding users via ramp up which not only adds more traffic to the test, but also simulates a more precise real world scenario.

The Latest

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 ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

BlazeMeter Load Testing Enables Dynamic Master-Slave Clusters

BlazeMeter, provider of the JMeter based load testing cloud, announced the release of a Dynamic Master-Slave feature.

The new Master-Slave configuration provides an efficient way to run several load tests in parallel, all controlled by a single ‘Master’ test and then aggregates all test runs into a single report.

The dynamic configuration allows users to define a test as a Master before and during the test, in real-time. At any point before and during the load test run, additional tests, coined ‘Slaves’ can be added or removed from the Master configuration.

The new Master-Slave feature also aggregates the results from numerous test sessions simulating different business processes into one single report. This includes the option for aggregating the results from numerous geo locations. Previously, developers were required to define a single test that encompassed all processes. BlazeMeter’s Dynamic Master-Slave configuration alleviates the need for usage of JMeter’s Include Controller in the case of multiple JMX files, further simplifying test set up.

Blazemeter Delayed Load Start Feature

Last month, Blazemeter announced the release of a delayed load start feature, designed to provider greater control over multi-server load tests. Developers can now start their load test engines without pushing the pedal to the metal, and have complete control over which servers are running and exactly when they start to run.

The new feature allows performance engineers absolute control over whether to start a test with the maximum load, or to control the amount of load in real-time.

Furthermore, for scenarios when you need to simulate flash traffic that goes from zero to 100,000 users in seconds, not minutes, you can start up all the load servers and have them ready to go and at the press of a button unleash the full fury of all your load server within a matter of seconds.

The new delayed load start feature spawns all servers at the onset of a load test run at once, while allowing users to add actual load incrementally for a wider scope of ramp up scenarios.

The delayed load start feature also provides developers more leeway at the onset of a test, and allows for adding users via ramp up which not only adds more traffic to the test, but also simulates a more precise real world scenario.

The Latest

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 ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...