Microsoft Technology Licensing, LLC

Electronic Arts Inc.

Xuezheng Liu

Zheng Zhang

Wei Lin

Zhenyu Guo

Wei Chen

Jian Tang

Michael C Caldarone

Kellen Christopher Smalley

Matthew Curtis

Stephanie K Schultz

Dan Teodosiu

Xi Wang

Lintao Zhang

Lidong Zhou

Ming Wu

Cristian Teodorescu

Weiwei Geng

JunFeng Yang

Mao Yang

Haoxiang Lin

A system and method for non-deterministically providing denominated virtual currency in a virtual space are disclosed. The units of virtual currencies may be classified by different denominations and levels. Non-purchasing utilities may be obtained for the denomination and level of the virtual currencies and provided to users in the virtual space. In one example, the denomination and level of the virtual currencies may be used as socketing elements to provide boosts to base attributes to virtual items. In some implementations, an amount of boost provided by a denomination and level of virtual currency when socketed to a virtual item may be determined based on numerical relationships among the denomination and level of the virtual currencies. The denomination and level of virtual currencies may be harvested non-deterministically by users based on probabilities determined for the users.

System and method for non-deterministically providing denominated virtual currencies in a virtual space

Analyzing and detecting soft hang program errors may lead to suggestions for either curing the programming errors at runtime or refactoring the source code. For instance, responsive function invocation patterns and blocking function invocation patterns may be used to detect soft hang program errors in a source code file. Deductive database rules may be compiled from the responsive and blocking function invocation patterns to find matching function invocations in a call graph.

Analysis and detection of soft hang responsiveness program errors

Predicate checking in conjunction with distributed systems can enable an investigating user to check predicates in the context of instance states and/or distributed states of a distributed system. In an example embodiment, a method entails accepting distributed system simulation data and performing a simulation of a distributed system using the distributed system simulation data to create distributed system simulated states. At least a portion of the distributed system simulated states are exposed. The exposed portion of the distributed system simulated states is retrieved and stored as exposed states for the distributed system. The exposed states for the distributed system are compared to predicate states for the distributed system. If an inconsistency is detected between the exposed states and the predicate states based on the comparing, at least one alarm is issued.

Predicate checking for distributed systems

Collection-based object replication for a system that includes a client computing device (client) connected to a server and multiple data storage nodes. In certain cases, a data storage node generates a replica of multiple replicas of a collection. The collection is a unit of data placement, access, replication, and repair. Other data storage nodes are also configured with a respective replica of the multiple replicas. The data storage node verifies whether an object received directly from the client for storage in the collection has been fully replicated by the other data storage nodes in respective replicas.

Collection-based object replication

A distributed system checker may check a distributed system against events to detect bugs in the distributed system. The events may include machines crashes, network partitions, and packet losses, for example. The distributed system checker may check a distributed system that can have multiple threads and multiple processes running on multiple nodes. To obtain control over a distributed system, a distributed system checker may insert an interposition layer between a process and the operating system on each node.

Distributed system checker

Predicate checking in conjunction with distributed systems can enable an investigating user to check predicates in the context of instance states and/or distributed states of a distributed system. In an example embodiment, a method entails accepting distributed system simulation data and performing a simulation of a distributed system using the distributed system simulation data to create distributed system simulated states. At least a portion of the distributed system simulated states are exposed. The exposed portion of the distributed system simulated states is retrieved and stored as exposed states for the distributed system. The exposed states for the distributed system are compared to predicate states for the distributed system. If an inconsistency is detected between the exposed states and the predicate states based on the comparing at least one alarm is issued.

Replaying distributed systems involves playing a distributed system in a simulator using data from a real-world operation. In an example embodiment, a simulation process is to simulate a distributed system that has a first instance and a second instance. The simulation process includes a first simulation object corresponding to the first instance and a second simulation object corresponding to the second instance. In another example embodiment, communications between the first instance and the second instance are simulated within the simulation process using function calls between the first simulation object and the second simulation object.

Replaying distributed systems

Exemplary methods, computer-readable media, and systems describe detecting a performance bug or a nontrivial correctness in a distributed system. The techniques describe using an online predicate checker by specifying a predicate on distributed properties of the distributed system. The process describes monitoring and checking the predicate when the system is deployed and providing a service. In response to detection of the performance bug, the process describes performing modifications of the predicate or installing new predicate.

Online predicate checking for distributed systems

A structured peer-to-peer overlay performs a key-based routing (KBR) that achieves a strong routing consistency guarantee as well as reasonable scalability. The key space of the structured overlay is partitioned into zones, each separately managed by a group membership service that provides total ordering of membership query and change actions. The strongly consistent KBR has two phases: first, a key value is routed to a contact node in its zone via a weakly consistent KBR protocol; and then performing a lookup of the destination node for the key value by the contact node using the group membership service of the zone. By appropriately tuning the zone size, the strongly consistent KBR balances the trade-off between scalability and routing liveness. The KBR can maintain this balance by merging and splitting zones to account for system chum and scale changes.

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Beijing, China

Xuezheng Liu