International Business Machines Corporation

Green Market Square Limited

Yohei Ueda

Tatsushi Inagaki

Hiroaki Nakamura

Kohichi Kamijoh

Sachiko Yoshihama

Michiaki Tatsubori

Venkatraman Ramakrishna

Chun Hui Suen

Lei Li

Qi Zhang

Takuya Nakaike

Da Li Liu

Angelo De Caro

Emanuele Ragnoli

Moriyoshi Ohara

Qi Feng Huo

Tatsuhiro Chiba

Senthilnathan Natarajan

Sunyanan Choochotkaew

Yu Chin Fabian Lim

Marcelo Carneiro Do Amaral

Described are techniques for modifying existing driver plugin behavior using a plugin wrapper to enable driver compatibility with an unsupported container deployment model. The techniques include intercepting, by a plugin wrapper operating as part of a container orchestration system, an allocation request intended for a driver plugin, where the allocation request is for allocating a computing resource to a containerized application deployed using a container deployment model not supported by the driver plugin. The techniques further include modifying, by the plugin wrapper, the allocation request to correspond to specifications of the container deployment model, thereby forming a modified request to allocate the computing resource to the containerized application. The techniques further include sending, by the plugin wrapper, the modified request to the driver plugin to allow the driver plugin to allocate the computing resource to the containerized application using the specifications of the container deployment model.

Driver plugin wrapper for container orchestration systems

An example operation may include one or more of receiving a blockchain request comprising a timestamp added by one or more endorsing nodes included within a blockchain network, identifying that the timestamp added by an endorsing node from among the one or more endorsing nodes is a modification to a previously added timestamp provided by the computing node, determining a reputation value for the endorsing node based on a difference between the timestamp added by the endorsing node and the previously added timestamp provided by the computing node, and transmitting the determined reputation value of the endorsing node to an ordering node within the blockchain network.

Blockchain timestamp agreement

A computer-implemented method for detecting bottlenecks in microservice cloud systems is provided including identifying a plurality of nodes within one or more clusters associated with a plurality of containers, collecting thread profiles and network connectivity data by periodically dumping stacks of threads and identifying network connectivity status of one or more containers of the plurality of containers, classifying the stacks of threads based on a plurality of thread states, constructing a microservice dependency graph from the network connectivity data, aligning the plurality of nodes to bar graphs to depict an average number of working threads in a corresponding microservice, and generating, on a display, an illustration outlining the plurality of thread states, each of the plurality of thread states having a different representation.

Detecting layered bottlenecks in microservices

An example operation may include one or more of receiving a blockchain request from a client application, determining a network latency between the client application and the endorsing node based on a network path between the client application and the endorsing node, extracting a timestamp from the blockchain request, determining whether the extracted timestamp is invalid based on the network latency between the client application and the endorsing node, and in response to determining that the timestamp is valid, generating an endorsement for the blockchain request and transmitting the endorsement to the client application.

A method is provided for building calling context trees of an application to identify a thread or a device affecting a throughput of a transaction relating to the application. The method obtains profiles of call stacks of multiple threads. The method obtains wake-up profiles which are the call stacks of a notifier thread and a waiter thread, from among the multiple threads, in a case that the waiter thread leaves from a sleep state. The method builds the calling context trees to indicate transitions of methods tracing callee relations and wake-up relations based on the profiles of the call stacks and the wake-up profiles, wherein each method is represented by a node having an average thread number. The method extracts and displays tree portions which are transitively reachable from a calling context tree for the transaction and which include the thread or device affecting throughput.

Identifying software and hardware bottlenecks

An example operation may include one or more of identifying endorsement requests to perform blockchain transaction endorsements, transmitting the endorsement requests to an ordering node, monitoring performance metrics of endorsement blockchain peers, assigning the endorsement requests to the endorsement blockchain peers based on the performance metrics, and receiving endorsed blockchain transactions.

Secure consensus-based endorsement for self-monitoring blockchain

An example operation may include one or more of receiving a blockchain request from a client application in a blockchain network, receiving validity information of a timestamp included in the blockchain request from one or more endorsing nodes in the blockchain network, modifying the timestamp included in the blockchain request based on the validity information received from the one or more endorsing nodes, ordering the blockchain request among a group of blockchain requests based on the modified timestamp with respect to timestamps of other blockchain requests in the group, and storing the ordered group of blockchain requests within a data block among a hash-linked chain of data blocks.

A method for compact data marshaller generation includes determining a plurality of data types having a same memory layout from data to be marshalled using a processor, each of the plurality of data types being associated with one or more separate data marshallers. The one or more separate data marshallers are unified to provide a single data marshaller for the plurality of data types for compact data marshaller generation.

Compact data marshaller generation

A dynamic resource allocation method and system. The method includes the steps of preparing a plurality of instances in different preparation states; receiving a request on a dynamic scheduling condition from the client computer; and launching some of the plurality of instances in the different preparation states in such a combination that the dynamic scheduling condition is satisfied. The method includes computer apparatus for accomplishing the above method. A tangible storage medium includes program steps which, when executed by computer apparatus, causes the computer apparatus to perform the above method.

Dynamic resource allocations method, systems, and program

A computer-implemented method, selection program, device and article of manufacture for selecting images of one or more operating systems (OS) that are cached in a target data processing system. The method can be implemented in a provisioning system where the system includes a first pool having the images of a plurality of different OS and a second pool having a plurality of data processing systems. The method includes: calculating the probability that the respective OS images in the first pool will be used in the next provisioning; and determining a combination of one or more OS images as one or more OS images to be cached.

Device, method, and program for selecting OS image

Starting and ending of a task is detected, where thread pooling is employed. Threads perform a wait operation on a given object are monitored, and threads performing a notify/notify-all operation on the given object are monitored. A labeled directed graph is constructed. Each node of the graph corresponds to one of the threads. Each edge of the graph has a label and corresponds to performance of the wait or notify/notify-all operation. An identifier of the given object is a label of a number of the edges. A set of nodes is selected that each has an edge having the same label. The threads of these nodes are worker threads of a thread pool. The threads of the nodes that are connected to the set of nodes are master threads. An object having an identifier serving as the label of the edges to the set of nodes is a monitoring mechanism.

Growing community of inventors

Tokyo, Japan

Yohei Ueda