Methods and Apparatus for Data Transfer Optimization



Publication Source: Patent US9858053B2

Methods, apparatus and computer software product for optimization of data transfer between two memories includes determining access to master data stored in one memory and/or to local data stored in another memory such that either or both of the size of total data transferred and the number of data transfers required to transfer the total data can be minimized. The master and/or local accesses are based on, at least in part, respective structures of the master and local data.
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Methods and Apparatus for Automatic Communication Optimizations in a Compiler Based on a Polyhedral Representation



Publication Source: Patent US9830133B1

Methods, apparatus and computer software product for source code optimization are provided. In an exemplary embodiment, a first custom computing apparatus is used to optimize the execution of source code on a second computing apparatus. In this embodiment, the first custom computing apparatus contains a memory, a storage medium and at least one processor with at least one multi-stage execution unit. The second computing apparatus contains at least one local memory unit that allows for data reuse opportunities. The first custom computing apparatus optimizes the code for reduced communication execution on the second computing apparatus. This Abstract is provided for the sole purpose of complying with the Abstract requirement rules. This Abstract is submitted with the explicit understanding that it will not be used to interpret or to limit the scope or the meaning of the claims.
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Efficient Packet Forwarding Using Cyber-Security Aware Policies



Publication Source: Patent US9798588B1

For balancing load, a forwarder can selectively direct data from the forwarder to a processor according to a loading parameter. The selective direction includes forwarding the data to the processor for processing, transforming and/or forwarding the data to another node, and dropping the data. The forwarder can also adjust the loading parameter based on, at least in part, feedback received from the processor. One or more processing elements can store values associated with one or more flows into a structure without locking the structure. The stored values can be used to determine how to direct the flows, e.g., whether to process a flow or to drop it. The structure can be used within an information channel providing feedback to a processor.
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Enhancing Network Visibility and Security Through Tensor Analysis



Publication Source: The 4th International Workshop on Innovating the Network for Data Intensive Science (INDIS) 2017, Denver, CO, USA.

The increasing size, variety, rate of growth and change, and complexity of network data has warranted advanced network analysis and services. Tools that provide automated analysis through traditional or advanced signature-based systems or machine learning classifiers suffer from practical difficulties. These tools fail to provide comprehensive and contextual insights into the network when put to practical use in operational cyber security. In this paper, we present an effective tool for network security and traffic analysis that uses high-performance data analytics based on a class of unsupervised learning algorithms called tensor decompositions. The tool aims to provide a scalable analysis of the network traffic data and also reduce the cognitive load of network analysts and be network-expert-friendly by presenting clear and actionable insights into the network.
In this paper, we demonstrate the successful use of the tool in two completely diverse operational cyber security environments, namely, (1) security operations center (SOC) for the SCinet network at SC16 - The International Conference for High Performance Computing, Networking, Storage and Analysis and (2) Reservoir Labs’ Local Area Network (LAN). In each of these environments, we produce actionable results for cyber security specialists including (but not limited to) (1) finding malicious network traffic involving internal and external attackers using port scans, SSH brute forcing, and NTP amplification attacks, (2) uncovering obfuscated network threats such as data exfiltration using DNS port and using ICMP traffic, and (3) finding network misconfiguration and performance degradation patterns.

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Algorithms and Data Structures to Accelerate Network Analysis



Publication Source: The 4th International Workshop on Innovating the Network for Data Intensive Science (INDIS) 2017, Denver, CO, USA.

As the sheer amount of computer generated data continues to grow exponentially, new bottlenecks are unveiled that require rethinking our traditional software and hardware architectures. In this paper, we present five algorithms and data structures (long queue emulation, lockless bimodal queues, tail early dropping, LFN tables, and multiresolution priority queues) designed to optimize the process of analyzing network traffic. We integrated these optimizations on R-Scope, a high performance network appliance that runs the Bro network analyzer, and present benchmarks showcasing performance speed-ups of 5X at traffic rates of 10 Gbps.
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