Pure Storage (United States)

In recent years, the escalating complexity and frequency of cyber threats have presented a formidable challenge to traditional cybersecurity measures. The emergence of artificial intelligence (AI) technologies has revolutionized the landscape, offering a promising solution to fortify defenses against evolving threats. This paper introduces AI Sentry, an innovative approach to cybersecurity that leverages the power of AI for intelligent threat detection and prevention. AI Sentry embodies a paradigm shift in cybersecurity, integrating machine learning, neural networks, and advanced algorithms to proactively identify, analyze, and mitigate potential threats in real time. By continuously learning from vast datasets and adapting to new attack vectors, AI Sentry enhances its ability to recognize anomalous patterns and behaviors, thereby thwarting sophisticated cyber assaults. The core strength of AI Sentry lies in its capability to detect anomalies and predict threats with a high degree of accuracy, surpassing the limitations of traditional signature-based systems. Through anomaly detection, behavioral analysis, and contextual understanding, AI Sentry not only identifies known threats but also anticipates zero-day attacks and previously unseen malicious activities. This paper delves into the technical underpinnings of AI Sentry, elucidating its architecture, data processing techniques, machine learning models, and the orchestration of various AI components. Furthermore, it explores the ethical considerations and challenges associated with AI-powered cybersecurity, including issues of privacy, bias mitigation, and transparency in decision-making.

Main memory dominates data center server cost, and hence data center operators are exploring alternative technologies such as CXL-attached and persistent memory to improve cost without jeopardizing performance. Introducing multiple * Work was performed as a graduate student at UC Santa Cruz This work is licensed under a Creative Commons Attribution International 4.0 License.

With the abundance of database systems implementing various data models, such as the relational, graph, and document-oriented models, learners often find it challenging to understand the trade-offs between different data models and to decide which database system they should learn and why. Additionally, most introductory database courses focus on the predominant relational model for teaching database design and programming, and do not discuss other emerging databases. While the relational database systems still play a vital role in modern data systems, especially with the emergence of NewSQL, it is crucial to introduce students to databases implementing other data models. In this paper, we introduce TriQL, a system for helping novices learn the schema and query languages of three major database systems, including MySQL (a relational database), Neo4J (a graph database), and MongoDB (a document-oriented database). TriQL offers learners a graphical user interface to design and execute a query against a generic database schema without requiring them to have any database programming experience. TriQL follows an interactive approach to learning new database models, supporting a dynamic and agile learning environment that can be easily integrated into database labs and homework assignments.

Abstract To ensure the safe transportation of energy, Canadian pipeline operators spend roughly $1.5 billion every year on pipeline integrity. The most practiced form of pipeline integrity is the use of inline inspection (ILI) tools. To ensure that an ILI inspection occurs with minimal to no complications, operators often utilize tracking techniques for the runs. These techniques can be costly and have large safety risks and environmental impacts due to the nature of using manpower to perform the operation. Using advanced Intemet of Things (IOT) telemetry devices, the tracking of ILI tools can be completed from remote locations by installing IOT devices semi-permanently along a pipeline right -of-way. This advancement has ensured the efficient, safe and reliable tracking of ILI tools while eliminating risks involved with conventional tracking. Furthermore, the current generation of IOT telemetry devices offers a tailored suite of ILI tracking sensors such as magnetics, ultrasonic frequency, extremely low frequency (22 Hz), and geophone. This multi sensor tracking solution increases an operator’s confidence in pig passages and flow rate estimations which allows the operator to optimize pump station bypassing. Finally, the IOT telemetry devices are supported by Global System for Mobile Communications (GSM) and satellite link which has ensured global coverage to remotely track tools. The communication module for the semi-permanent tracking solution is decided based on network availability and endpoints. This paper will present a comprehensive analysis that compares conventional ILI tracking to cutting-edge IOT telemetry ILI tracking and illustrates improvements in operational efficiency, operational risk, overall safety, environmental impact, and cost-effectiveness. In addition, case studies from recent tracking runs will be shared to demonstrate advancements in IOT telemetry, tracking sensor technology, dynamic user interface capabilities, advanced data dissemination methods, and high precision benchmarking.

Byte-addressable non-volatile memory (NVM) placed alongside DRAM promises a fundamental shift in software abstractions, yet many approaches to using NVM promise merely incremental improvement by relying on old interfaces and archaic abstractions. We assert that redesigning the core programming model presented by the operating system is vital to best exploiting this technology. We are developing Twizzler, an OS that presents an effective programming model for NVM sufficient to construct persistent data structures that can be easily and globally shared without serialization costs. We consider and evolve a key-value store that runs on Twizzler, and demonstrate how our programming model improves programmability with early experiments indicating performance need not be lost and may be improved.

The need for reliable and cost-effective data storage grows as digital information becomes increasingly ubiquitous. Archival systems must store valuable data for years while adapting to changing user needs, capacity, and performance requirements. Storage devices differ in terms of performance, capacity, reliability, acquisition cost, power consumption, and the rates at which their features change over time. As a result, choosing the best storage technology to use for an archive has become increasingly challenging with the proliferation of new technologies alongside existing ones. We have designed a simulator that models the capacity, performance, acquisition cost, and power cost of an archival system using the characteristics of the drives and media that comprise it. We simulate and compare four storage technologies that exhibit different cost and performance characteristics: tape, optical disc, hard disk, and NAND flash SSD. We evaluate the total cost of ownership for each storage technology within an archival system, and we explore the effect that prospective technological advancements and growth rates over time may have on the relative cost and viability of each storage technology for archival systems. We show that the lifecycle and upgrade cost of drives are significant cost factors for removable media archives. We observe that increasing performance requires adding more drives to an archival system, and the cost of each drive dominates the cost to increase performance. We compare trends in storage technologies to suggest developments that could minimize the long-term total cost of ownership for archival systems. We show that hard disks and flash could become cost-competitive with tape-based archives by adopting new designs to minimize infrastructure and electricity costs.

Static and dynamic power constraints are steering chip manufacturers to build single-ISA Asymmetric Multicore Processors (AMPs) with big and small cores. To deliver on their energy efficiency potential, schedulers must consider core sensitivity, load balance, and the critical path. Applying these criteria effectively is challenging especially for complex and non-scalable multithreaded applications. We demonstrate that runtimes for managed languages, which are now ubiquitous, provide a unique opportunity to abstract over AMP complexity and inform scheduling with rich semantics such as thread priorities, locks, and parallelism — information not directly available to the hardware, OS, or application. We present the WASH AMP scheduler, which (1) automatically identifies and accelerates critical threads in concurrent, but non-scalable applications; (2) respects thread priorities; (3) considers core availability and thread sensitivity; and (4) proportionally schedules threads on big and small cores to optimize performance and energy. We introduce new dynamic analyses that identify critical threads and classify applications as sequential, scalable, or non-scalable. Compared to prior work, WASH improves performance by 20% and energy by 9% or more on frequency-scaled AMP hardware (not simulation). Performance advantages grow to 27% when asymmetry increases. Performance advantages are robust to a complex multithreaded adversary independently scheduled by the OS. WASH effectively identifies and optimizes a wider class of workloads than prior work.

Electric Agents is a multi-player game that uses augmented reality on mobile phones to facilitate in-room collaboration and promote joint media engagement around interactive educational television content in the home. Joint media engagement describes 'collaborative' consumption of media and has potential learning benefits. Our research examines whether technology can support joint media engagement, through the use of a series of prototypes that combine handheld augmented reality with web-based video. In pilot testing, sibling play patterns showed evidence of the kind of in-room collaboration and joint media engagement that previous research has shown to facilitate children in making sense of media content.

<div class="htmlview paragraph">Study of the physical properties of oil contaminants and oil filtration show that present methods of filtration have little effect on a lifter wear problem. Experiments prove that oil additives can influence the effectiveness of conventional oil filters, which are capable of removing particles considerably smaller than their pore size. Lifter wear, therefore, seems to be a function of extended oil drain periods during which contaminants accumulate in the oil and progressive smooth wear occurs. Although present filters are performing adequately, they could be improved by advances in design and in oil formulation so as to minimize contaminant accumulation.</div>

When 20 or more measurements and (or) counts are made on 100 or more samples (thin sections, bottom samples, pollen or foram concentrates, heavy minerals, etc.), the resulting table of data is so large that interpretation by eye becomes difficult. In some geologic studies it is desirable to group together similar samples and to measure the degree of similarity between different groups of samples. Several measures of similarity are available: the product-moment correlation coefficient, cosine-theta (Imbrie, 1962), the matching coefficient, and the distance function (Sokal, 1961). The resulting matrix of intercorrelations is still too large for direct interpretation. Cluster analysis, a technique developed by psychologists, is a method of searching for structure, or relationships, in a matrix of correlation coefficients. Although not so sophisticated as factor analysis, cluster analysis is a useful tool. The results of a cluster analysis can be presented in an hierarchical diagram in two dimensions that will show where the natural breaks occur between groups. A computer program has been written for the IBM 704 that will handle up to 150 measurements on as many as 200 samples. Non-overlapping clusters are used; that is, a sample can appear in only one cluster. A 12 variable 40-sample problem based on constituent particle composition of Bahamian sediment samples (Imbrie and Purdy, 1962) is used to demonstrate the options of the program. The clear-cut groups in the cluster analysis solution are similar to the facies described by Imbrie and Purdy (1962) based on factor analysis. The cluster can be used as a basis for facies maps.