Vmware, Inc.

Senko Advanced Components, Inc.

Jin Feng

Hong Li

Yu Wu

Zhou Huang

Jian Zhao

Zhaohui Guo

Sifan Liu

Haitao Zhou

Chen Jing

Shugang Guo

Zhongping Lu

Wenguang Wang

Chengmao Lu

Jiushi Wan

Yixiang Zhang

Yim Wong

Xinyu Zhang

Christos Karamanolis

Zhao Xia Jin

He Wang

Kenji Iizumi

Xiang Yu

Yizheng Chen

Aditya Kotwal

RunQing Li

Lachezar Petkov Petkov

Georgi Stoyanov Georgiev

Denitsa Borislavova Tsvetkova

Zhiliang Zhang

Kai-chia Chen

An adaptive idle detection method determines whether software defined data centers (SDDCs) in a hyperconverged infrastructure (HCI) environment are idle. Idleness may be quantified via a coefficient of variation (CV) against resource usage, so as to adapt the idle detection method to SDDCs with different hardware specifications and workloads. Management overhead may also be filtered out by the idle detection method, and the idle detection method may use idleness scores to further reduce overhead.

Adaptive idle detection in a software-defined data center in a hyper-converged infrastructure

The disclosure relates to processing application programming interface (API) requests. Embodiments include receiving, at an API wrapper, from a first caller, a first call to an API and sending the first call to the API. Embodiments include receiving, by the API wrapper, from one or more second callers, a second one or more calls to the API prior to receiving a response from the API to the first call. Embodiments include receiving, by the API wrapper, the response from the API to the first call and responding to the first call from the first caller with the response from the API to the first call. Embodiments include responding, by the API wrapper, to the second one or more calls from the one or more second callers with the response from the API to the first call without sending the second one or more calls to the API.

Highly concurrent and responsive application programming interface (API) in eventual consistency environment

The disclosure provides automated update notifications. Embodiments include receiving, by a cloud provider, a request to manage updates for a computing device communicating with the cloud provider via a network. Embodiments include registering an agent at the computing device with the cloud provider as a recipient for an automated update notification based on configuration details of the computing device and detection of an update item related to an aspect of the configuration details. Embodiments include identifying, by the agent, the configuration details of the computing device, the configuration details comprising details of a hardware configuration of the computing device and a software configuration of the computing device. Embodiments include monitoring one or more databases for the update item. Embodiments include transmitting the automated update notification to the agent based on detection of the update item in the one or more databases.

System and method for resource optimized intelligent product notifications

Example methods and systems for request handling with automatic scheduling are described. In one example, a computer system may receive, from multiple client devices, respective multiple requests that are generated and sent according to a first set of control parameters. Based on the multiple requests, request characteristic(s) may be monitored to determine whether an automatic scheduling condition is satisfied. In response to determination that the automatic scheduling condition is satisfied, the computer system may assign a second set of control parameters to the respective client devices and instruct the client devices to generate and send respective multiple subsequent requests according to the second set of control parameters to cause a modification of the request characteristic(s).

Request handling with automatic scheduling

A troubleshooting technique provides faster and more efficient troubleshooting of issues in a distributed system, such as a distributed storage system provided by a virtualized computing environment. The distributed system includes a plurality of hosts arranged in a cluster. The troubleshooting technique uses cluster-wide correlation analysis to identify potential causes of a particular issue in the distributed system, and executes workflows to remedy the particular issue.

Troubleshooting for a distributed storage system by cluster wide correlation analysis

A method to migrate a cluster's hosts and virtual machines from virtual standard switches to a virtual distributed switch includes creating distributed port groups on the virtual distributed switch, where properties of the distributed port groups are automatically replicated to host proxy switches on the hosts. The method further includes configuring the distributed port group with ephemeral binding so port binding of the distributed port group is configurable through a host in the cluster even when an infrastructure virtual machine that manages the cluster is down, determining (or receiving user input indicating) the infrastructure virtual machine is on the host, and issuing a call to the host to migrate (1) the infrastructure virtual machine to the distributed port group and (2) one or more physical network interface cards of the host to the virtual distributed switch.

Seamless virtual standard switch to virtual distributed switch migration for hyper-converged infrastructure

Described herein are systems, methods, and software to generate user interfaces to indicate software-defined storage information in view of available hardware resources. In one example, a user interface service may obtain enclosure information associated with enclosures in a computing environment and may determine a storage overview for the plurality of enclosures based on the enclosure information. The user interface service may further determine a function for each storage device in the storage overview in relation to a software-defined storage configuration for the computing environment and generate a user interface to indicate the storage overview with the function for each storage device in the plurality of enclosures.

Software-defined storage information in view of available hardware resources

A method is provided for a hyper-converged storage-compute system to implement an active-active failover architecture for providing Internet Small Computer System Interface (iSCSI) target service. The method intelligently selects multiple hosts to become storage nodes that process iSCSI input/output (I/O) for a target. The method further enables iSCSI persistent reservation (PR) to handle iSCSI I/Os from multiple initiators.

Active-active architecture for distributed ISCSI target in hyper-converged storage

A failure analysis system identifies a root cause of a failure (or other health issue) in a virtualized computing environment and provides a recommendation for remediation. The failure analysis system uses a model-based reasoning (MBR) approach that involves building a model describing the relationships/dependencies of elements in the various layers of the virtualized computing environment, and the model is used by an inference engine to generate facts and rules for reasoning to identify an element in the virtualized computing environment that is causing the failure. Then, then the failure analysis system uses a decision tree analysis (DTA) approach to perform a deep diagnosis of the element, by traversing a decision tree that was generated by combining the rules for reasoning provided by the MBR approach, in conjunction with examining data collected by health monitors. The result of the DTA approach is then used to generate the recommendation for remediation.

Failure analysis system for a distributed storage system

One example method to perform object-based load balancing in a distributed storage system of a virtualization system supported by a cluster of host machines may include determining, by a first host machine in the cluster, whether any host machine in the cluster has affinity to a site. The method may also include determining, by the first host machine, whether to distribute affinity Internet small computer system interface (iSCSI) targets owned by the first host machine to at least the second machine based on a first balance objective associated with the site, and after having considered the first balance objective, determining, by the first host machine, whether to distribute iSCSI targets without affinity owned by the first host machine to other host machines in the cluster based on a second balance objective associated with the cluster.

Object-based load balancing approaches in distributed storage system

One example method to support Internet small computer system interface (iSCSI) services in a distributed storage system of a virtualization system may include receiving a notification of a change event associated with a global virtual Internet Protocol (VIP) object, wherein the global VIP object is associated with a virtual IP address for the iSCSI services. The method may also include in response to a first determination that the first host machine does not own the global VIP object and in response to a second determination that the first host machine gains ownership of the global VIP object based on the notification, locking the global VIP object and binding the virtual IP address to the first host machine.

Object-based approaches to support internet small computer system interface (ISCSI) services in distributed storage system

Example methods are provided for performing fast building and testing a software suite with multiple software components. In one example, the method may include obtaining a changed code file, identifying a software component of the software suite impacted by the changed code file, and instructing to generate a software component build based on the software component but excluding other software components of the software suite. Before completing generating the software component build, the method may also include selecting a software suite build. The method further includes instructing to prepare a testbed based on the software suite build and instructing to test the software component build on the testbed.

Method and system for fast building and testing software

Certain Embodiments described herein relate to configuring the network-storage stack of two devices (e.g., physical or virtual) communicating together (e.g., an initiator and a target, as defined below) with Internet Small Computer Systems Interface (iSCSI) extension for remote direct memory access (RDMA) iSER, which is a protocol designed to utilize RDMA to accelerate iSCSI data transfer. The iSER protocol is implemented as an iSER datamover layer that acts as an interface between an iSCSI layer and an RDMA layer of the network-storage stacks of the two devices. Using iSER in conjunction with RDMA allows for bypassing the existing traditional network protocol layers (e.g., TCP/IP protocol layers) of the devices and permits data to be transferred directly, between the two devices, using certain memory buffers, thereby avoiding memory copies taking place when the existing network protocol layers are used.

Internet small computer interface systems extension for remote direct memory access (RDMA) for distributed hyper-converged storage systems

The disclosure herein describes enabling use of a logical unit for data storage in a distributed storage system using a plurality of backend data objects. Based on receiving instructions to create a logical unit of a logical unit size, a target backend object size to be used with the logical unit is determined, and a plurality of backend objects for allocation to the logical unit is calculated. The backend objects are allocated to the logical unit and a metadata mapping associated with the logical unit is generated. The metadata mapping associates logical block addresses of the logical unit to the allocated backend objects. The logical unit is linked with the metadata mapping in an input/output (I/O) service and, based on the linked metadata mapping, I/O traffic is routed to and from the logical unit. Using multiple backend objects enhances flexibility and efficiency of data storage on the distributed storage system.

Growing community of inventors

Shanghai, China

Jin Feng