Migrating to In-Memory Computing for Modern, Cloud-Native Workloads
Migrating to in-memory computing for modern, cloud-native workloads WebSphere eXtreme Scale and Hazelcast In-Memory Computing Platform for IBM Cloud® Paks Migrating to in-memory computing for modern, cloud-native workloads Introduction Historically, single instance, multi-user applications Cloud-based application installations, using were sufficient to meet most commercial workloads. virtualization technology, offer the ability to scale With data access latency being a small fraction environments dynamically to meet demand of the overall execution time, it was not a primary peaks and valleys, optimizing computation concern. Over time, new requirements from costs to workload requirements. These dynamic applications like e-commerce, global supply chains environments further drive the need for and automated business processes required a in-memory data storage with extremely much higher level of computation and drove the fast, distributed, scalable data sharing. development of distributed processing using Cloud instances come and go as load dictates, clustered groups of applications. so immediate access to data upon instance creation and no data loss upon instance A distributed application architecture scales removal are paramount. Independently scaled, horizontally, taking advantage of ever-faster in-memory data storage meets all of these processors and networking technology, but with needs and enables additional processing it, data synchronization and coordinated access capabilities as well. become a separate, complex system. Centralized storage area network (SAN) systems became Data replication and synchronization between common, but as computational speeds continued cloud locations enable globally distributed to advance, the latencies of disk-based storage cloud environments to function in active/ and retrieval quickly became a significant active configurations for load balancing and bottleneck.
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