Microsoft Technology Licensing, LLC

Gopal Kakivaya

Mihail Gavril Tarta

Yang Li

Lu Xun

Matthew Snider

Preetha Lakshmi Subbarayalu

Erik B Christensen

Kenneth David Wolf

Hua-Jun Zeng

Mark Eugene Russinovich

David Allen Dion

James Ernest Johnson

David E Langworthy

Jeffrey C Schlimmer

John Raymond Justice

Marcus Felipe Fontoura

Elliot Lee Waingold

Michael Steven Vernal

Martin James Gudgin

Anurag Gupta

Milan Vukosavljevic

Yaniv Pessach

Abhiram Chivukula

In various embodiments, methods and systems for optimizing database transactions based on replicable differential data store data structure are provided. A write operation request, having a key for a write operation on a replicable differential store data structure, is accessed. An intent write lock on a differential state and a write lock on the key are acquired. The differential state comprises a result set of currently committing transactions. A transaction instance, of the write operation, is generated for a write set, the transaction instance comprising a modification to the key. The write-set comprises an uncommitted set of writes for in-flight transactions. A determination is made that the write operation is committed. A result of the transaction instance is persisted when the write operation is committed. It is contemplated that the differential state and a consolidated state can be merged, the consolidated state comprises a result set of previously committed transactions.

Replicable differential store data structure

In various implementations, methods and systems resource balancing in a distributed computing environment are provided. A client defined resource metric is received that represents a resource of nodes of the cloud computing platform. A placement plan for job instances of service applications is generated. The placement plan includes one or more movements that are executable to achieve a target placement of the job instances on the nodes. It is determined that the placement plan complies with placement rules. Each placement rule dictates whether a given job instance of the job instances is suitable for placement on a given node of the nodes. The placement plan is executed based on determining that the target placement of the job instances improves balance of resources across the nodes of the cloud computing platform based on the resource represented by the client defined resource metric.

Balancing resources in distributed computing environments

A resource balancer for a cluster of nodes in a cloud computing system includes a load calculation module that receives load reports for respective load metrics associated with the nodes, wherein the load reports are indicative of respective loads on a plurality of resources provided by the nodes, calculates, based on the received load reports, an expected distribution of loads across the nodes for each load metric, and calculates, based on the expected distribution of loads, probabilities that each of the load metrics will exceed a respective predetermined capacity for each node. A load balancing module selectively balances, based on the calculated probabilities, the loads on the resources provided by the nodes.

Statistical resource balancing of constrained microservices in cloud PAAS environments

Certain embodiments of computing systems, devices, components, modules, routines, and processes for implementing distributed operational control in a computing fabric are described herein. In one embodiment, a method includes receiving, at a control cluster, a tenant request for a cloud-based computing service at the computing system. The method also includes creating an application configured to provide the requested cloud-based computing service based on the tenant request and pushing configuration data of the created application to the execution cluster to be executed at the execution cluster without further intervention from the control cluster.

Distributed operational control in computing systems

Arbitrating node failures. A method includes determining at a first node that communication to a second node has been lost. The method further includes sending an arbitration request to a plurality of arbitrators. The method further includes receiving from the plurality of arbitrators a sufficient number of arbitration reply grant messages to have arbitration reply grant messages from a quorum of the arbitrators. As a result of receiving a sufficient number of arbitration reply grant messages to have arbitration reply grant messages from a quorum of the arbitrators, the method further includes declaring the second node down.

Enhanced arbitration protocol for nodes in a cluster

A sending computer system can identify one or more available network resources at one or more network computers by sending a request for services using Simple Object Access Protocol (SOAP) over User Datagram Protocol (UDP). In particular, the sending computer system prepares a SOAP message that includes a request for available resources. The sending computer system then encapsulates the SOAP message into a user datagram, and sends the user datagram to one or more identifiable computer systems on the network. In one implementation, the sending computer system sends the user datagram to a multi-cast Uniform Resource Identifier (URI). The receiving computer system receives the message through UDP, unpacks the message, and responds to the SOAP message request. Accordingly, the sending computer system can query multiple computer systems in an efficient manner without necessarily incurring the overhead otherwise associated with connection-oriented communication.

Querying for services using soap over UDP

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Sammamish, WA, United States of America

Gopal Kakivaya