Abstract: Technology described herein can be employed to automatically recommend a tiering policy for data storage of data at a data storage system, such as a cloud storage system. An example method can comprise determining, by a system comprising a processor, context information defining a data storage attribute applicable to data at a cloud storage system. The method can comprise, in response to determining the context information, generating, by the system, a tiering policy defining an element of tiering storage of data at the cloud storage system, wherein the tiering policy is based on the data storage attribute defined by the context information. The method also can comprise, in response to generating the tiering policy, outputting, by the system, the tiering policy to a storage device associated with a customer. The analysis can be performed using an artificial intelligence process, machine learning process or a combination thereof.

Abstract: Technology described herein can be employed to automatically recommend a tiering policy for data storage of data at a data storage system, such as a cloud storage system. An example method can comprise determining, by a system comprising a processor, context information defining a data storage attribute applicable to data at a cloud storage system. The method can comprise, in response to determining the context information, generating, by the system, a tiering policy defining an element of tiering storage of data at the cloud storage system, wherein the tiering policy is based on the data storage attribute defined by the context information. The method also can comprise, in response to generating the tiering policy, outputting, by the system, the tiering policy to a storage device associated with a customer. The analysis can be performed using an artificial intelligence process, machine learning process or a combination thereof.

Abstract: Techniques are provided for high availability events in a layered architecture. In an example two computing nodes coordinate to provide a computing service, where each node has a base operating system configured to fence the other base operating system, and an application configured to fence the other application. In some examples, fencing requests by an application are routed through its base operating system, which coordinates application-level fencing requests and operating system-level fencing requests.

Abstract: Techniques are provided for high availability events in a layered architecture. In an example two computing nodes coordinate to provide a computing service, where each node has a base operating system configured to fence the other base operating system, and an application configured to fence the other application. In some examples, fencing requests by an application are routed through its base operating system, which coordinates application-level fencing requests and operating system-level fencing requests.

Abstract: Techniques for chunking data in data storage systems that provide increased data storage security across multiple cloud storage providers. The techniques employ a chunking engine and a policy engine, which evaluates one or more storage policies relating to, for example, cost, security, and/or network conditions in view of services and/or requirements of the multiple cloud storage providers. Having evaluated such storage policies, the policy engine generates and provides operating parameters to the chunking engine, which uses the operating parameters when chunking and/or distributing the data across the multiple cloud storage providers, thereby satisfying the respective storage policies. In this way, users of data storage systems obtain the benefits of cloud storage resources and/or services while reducing their data security concern and optimizing the total cost of data storage.

Abstract: Techniques for chunking data in data storage systems that provide increased data storage security across multiple cloud storage providers. The techniques employ a chunking engine and a policy engine, which evaluates one or more storage policies relating to, for example, cost, security, and/or network conditions in view of services and/or requirements of the multiple cloud storage providers. Having evaluated such storage policies, the policy engine generates and provides operating parameters to the chunking engine, which uses the operating parameters when chunking and/or distributing the data across the multiple cloud storage providers, thereby satisfying the respective storage policies. In this way, users of data storage systems obtain the benefits of cloud storage resources and/or services while reducing their data security concern and optimizing the total cost of data storage.