Tag: USAPatent

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ProphetStor Is Granted a Patent on Modeling Application Workloads to Automate the Management and to Predict the Anomalies of Storage Resources in MultiCloud Environments

MILPITAS, CA, August 31, 2020 — ProphetStor Data Services, Inc. was assigned the patent “METHOD AND SYSTEM FOR STORAGE TRAFFIC MODELING“ (Patent number US 9,906,424) by the United States Patent and Trademark Office. The patent is a foundation for software-defined data centers where the requirement of flexible adjustment of resources in meeting application demands can only

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ProphetStor Is Granted a Foundation Patent on Adapting Infrastructure Resource Deployments According to the Lifecycle of Application Workloads

ProphetStor Data Services, Inc. was assigned the patent “METHOD FOR DEPLOYING STORAGE SYSTEM RESOURCES WITH LEARNING OF WORKLOADS APPLIED THERETO“ (Patent number US 10,013,286) by the United States Patent and Trademark Office.

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ProphetStor’s Patent on Meeting Applications Future Demands Is the Foundation for Viable AIOps and Machine Learning for System Operation

ProphetStor Data Services, Inc. was assigned the patent “METHOD FOR OPTIMIZING STORAGE CONFIGURATION FOR FUTURE DEMAND AND SYSTEM THEREOF“ (Patent number US 10,067,704) by the United States Patent and Trademark Office.

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ProphetStor’s Patent on AI-Powered Workload-Aware Framework Becomes the Foundation of AIOps for Optimization in MultiCloud and 5G

ProphetStor Data Services, Inc. was assigned the patent “WORKLOAD-AWARE I/O SCHEDULER IN SOFTWARE-DEFINED HYBRID STORAGE SYSTEM“ (Patent number US 9,575,664) by the United States Patent and Trademark Office.

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ProphetStor’s Patent on AI-based Methods in Predicting the Life Span of Storage Systems Facilitates Proactive Management to Support SLAs and Reduce Operation Costs in Data and Cloud Centers

ProphetStor Data Services, Inc. was assigned the patent “METHOD AND SYSTEM FOR DIAGNOSING REMAINING LIFETIME OF STORAGES IN DATA CENTER“ (Patent number US 10,606,722) by the United States Patent and Trademark Office.

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Federator.ai – AI Solution for Auto-Scaling on Kubernetes with Datadog

With Datadog, our enterprise customers are now able to monitor their application workloads and get visibility into Kubernetes clusters of any scale.  The new integration between Federator.ai and Datadog has made it easier to deploy our service, collect and monitor any metrics (e.g. CPU and memory utilization, application latency etc) for applications running on Kubernetes.

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