Oracle International Corporation

Force10 Networks, Inc.

Fujitsu Corporation

Yao-Min Chen

Heeloo Chung

Zhenyun Zhuang

Karthik Ganesan

Tomohiko Taniguchi

Eugene W Lee

Ching-Fong Su

Jee Ho Ryoo

Zhijun Tong

Luyang Wang

Ahmed Khawaja

Shrinivas B Joshi

An offload engine may attempt to offload, on behalf of applications, data operations to be performed on a datastream. The offload engine may intercept one or more data operations, such as may be part of a collections API, performed by an application. The data operations and the datastream may be specified and/or provided by the application and, in response, the offload engine may be configured to execute (or attempt to execute) the data operations on the datastream using an analytics accelerating co-processor rather than using a general purpose CPU core. The offload engine may determine whether or not to offload the data operations to the analytics accelerating co-processor. If the offload is unsuccessful or if the offload engine determines that the data operations are not suitable for offloading, the offload engine may then cause the data operations to be performed using general purpose CPU cores on the system.

Resource efficient acceleration of datastream analytics processing using an analytics accelerator

Intelligent adjustment of compression levels of messages being brokered or otherwise handled by message service providers in a cloud-computing environment to strike a balance between the reduction in network usage and storage requirement resources owing to message compression on one hand and the increase in CPU resource usage owing to message compression on the other hand for reducing usage-based charges from the cloud customer's point of view and reducing the equipment and operational cost from the cloud provider's point of view (resulting in the ability of the provider to take on and/or retain customers). Determination of an 'optimal' compression level that strikes an appropriate balance among resource usage may include consideration of a current messaging load (e.g., bytes of data per unit time), a current compression level, and current usage levels of one or more of the various resources.

Cloud messaging services optimization through adaptive message compression

A memory subsystem incorporating a die-stacked DRAM (DSDRAM) is disclosed. In one embodiment, a system include a processor implemented on a silicon interposer of an integrated circuit (IC) package, a DSDRAM coupled to the processor, the DSDRAM implemented on the silicon interposer of the IC package, and a DRAM implemented separately from the IC package. The DSDRAM and the DRAM form a main memory having a contiguous address space comprising a range of physical addresses. The physical addresses of the DSDRAM occupy a first contiguous portion of the address space, while the DRAM occupies a second contiguous portion of the address space. Each physical address of the contiguous address space is augmented with a first bit that, when set, indicates that a page is stored in the DRAM and the DSDRAM.

Low latency, high bandwidth memory subsystem incorporating die-stacked DRAM

Methods and apparatus for interleaved weighted fair data packet queue sequencing are disclosed. An interleaving table specifies a queue sequence. A queue sequencer follows the table order on an epoch-by-epoch basis, selecting a queue for each epoch based on the table order. If the selected queue does not have enough data to fill its epoch, the sequencer can step to the next queue in the table order. Because the table is interleaved, higher-priority queues can be visited frequently, improving jitter and latency for packets associated with these queues. The table structure allows all queues at least some portion of the available output bandwidth, and can be organized to afford some queues a much larger portion without having those queues monopolize the output stream for inordinate amounts of time. In some embodiments, each table entry has a programmable epoch value associated with it. The epoch value can be used to weight each table entry respective to the other entries.

Interleaved weighted fair queuing mechanism and system

Methods and apparatus for an improvement on Random Early Detection (RED) router congestion avoidance are disclosed. A traffic conditioner stores a drop probability profile as a collection of configurable profile segments. A multi-stage comparator compares an average queue size (AQS) for a packet queue to the segments, and determines which segment the AQS lies within. This segment is keyed to a corresponding drop probability, which is used to make a packet discard/admit decision for a packet. In a preferred implementation, this computational core is surrounded by a set of registers, allowing it to serve multiple packet queues and packets with different discard priorities. Each queue and discard priority can be keyed to a drop probability profile selected from a pool of such profiles. This provides a highly-configurable, inexpensive, and fast RED solution for a high-performance router.

Congestion avoidance profiles in a packet switching system

A method and system of combining a plurality of parallel communication channels to emulate a single high-bandwidth communication channel. A continuous stream of packets are grouped as traffic aggregates and assigned to queues associated with the plurality of parallel communication channels. The assignment and reassignment of traffic aggregates to the queues is performed dynamically based on measuring the queue load ratios associated with the lengths of the queues for each of the parallel communication channels. Grouping of existing and future packets as traffic aggregates is based on common attributes shared by the packets such as common source and destination IP addresses.

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San Jose, CA, United States of America

Yao-Min Chen