What's new for Java in clouds?
summary
Java and the JVM shift from long‑running, heap‑heavy servers to fast‑starting, small‑memory cloud units: examples include OpenJ9 vs HotSpot showing measurable startup/throughput differences, Open Liberty and MicroProfile tooling for microservices, and IBM Cloud Private for local clouds. VMs add GPU and FPGA hooks and value‑type work to flatten object layout for data pipelines. Neuromorphic chips and quantum devices are real (research systems at ~50–60 qubits); expect new data‑driven designs and toolchains for ML, not just faster garbage collectors.
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chapters (13)
- 0:00Introduction and speaker background
- 3:37Economics driving Java innovation
- 8:19Cloud economics and Java's challenge
- 12:15New Java workload profiles in cloud
- 16:49Microservices and continuous delivery
- 19:03Faster Java releases and open source
- 23:16Data analytics and machine learning in Java
- 30:21Java's role in AI and cloud services
- 34:37Data-driven programming models
- 38:50GPU and hardware acceleration
- 45:57Neuromorphic and quantum computing
- 52:04Future of Java and JVMs
- 53:12Summary and call to action
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