Building Efficient Query Engines in a High-Level Language

Abstraction without regret refers to the vision of using high-level programming languages for systems development without experiencing a negative impact on performance. A database system designed according to this vision offers both increased productivity and high performance, instead of sacrificing the former for the latter as is the case with existing, monolithic implementations that are hard to maintain and extend. In this article, we realize this vision in the domain of analytical query processing. We present LegoBase, a query engine written in the high-level language Scala. The key technique to regain efficiency is to apply generative programming: LegoBase performs source-to-source compilation and optimizes the entire query engine by converting the high-level Scala code to specialized, low-level C code. We show how generative programming allows to easily implement a wide spectrum of optimizations, such as introducing data partitioning or switching from a row to a column data layout, which are difficult to achieve with existing low-level query compilers that handle only queries. We demonstrate that sufficiently powerful abstractions are essential for dealing with the complexity of the optimization effort, shielding developers from compiler internals and decoupling individual optimizations from each other. We evaluate our approach with the TPC-H benchmark and show that: (a) With all optimizations enabled, LegoBase significantly outperforms a commercial database and an existing query compiler. (b) Programmers need to provide just a few hundred lines of high-level code for implementing the optimizations, instead of complicated low-level code that is required by existing query compilation approaches. (c) The compilation overhead is low compared to the overall execution time, thus making our approach usable in practice for compiling query engines

A coordination protocol for user-customisable cloud policy monitoring

Cloud computing will see a increasing demand for end-user customisation and personalisation of multi-tenant cloud service offerings. Combined with an identified need to address QoS and governance aspects in cloud computing, a need to provide user-customised QoS and governance policy management and monitoring as part of an SLA management infrastructure for clouds arises. We propose a user-customisable policy definition solution that can be enforced in multi-tenant cloud offerings through an automated instrumentation and monitoring technique. We in particular allow service processes that are run by cloud and SaaS providers to be made policy-aware in a transparent way

Why People Search for Images using Web Search Engines

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1)Why do people search for images in text-based Web image search systems? (2)How does image search behavior change with user intent? (3)Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling

National Security Space Launch

The United States Space Force’s National Security Space Launch (NSSL) program, formerly known as the Evolved Expendable Launch Vehicle (EELV) program, was first established in 1994 by President William J. Clinton’s National Space Transportation Policy. The policy assigned the responsibility for expendable launch vehicles to the Department of Defense (DoD), with the goals of lowering launch costs and ensuring national security access to space. As such, the United States Air Force Space and Missile Systems Center (SMC) started the EELV program to acquire more affordable and reliable launch capability for valuable U.S. military satellites, such as national reconnaissance satellites that cost billions per satellite. In March 2019, the program name was changed from EELV to NSSL, which reflected several important features: 1.) The emphasis on “assured access to space,” 2.) transition from the Russian-made RD-180 rocket engine used on the Atlas V to a US-sourced engine (now scheduled to be complete by 2022), 3.) adaptation to manifest changes (such as enabling satellite swaps and return of manifest to normal operations both within 12 months of a need or an anomaly), and 4.) potential use of reusable launch vehicles. As of August 2019, Blue Origin, Northrop Grumman Innovation Systems, SpaceX, and United Launch Alliance (ULA) have all submitted proposals. From these, the U.S. Air Force will be selecting two companies to fulfill approximately 34 launches over a period of five years, beginning in 2022. This paper will therefore first examine the objectives for the NSSL as presented in the 2017 National Security Strategy, Fiscal Year 2019, Fiscal Year 2020, and Fiscal Year 2021 National Defense Authorization Acts (NDAA), and National Presidential Directive No. 40. The paper will then identify areas of potential weakness and gaps that exist in space launch programs as a whole and explore the security implications that impact the NSSL specifically. Finally, the paper will examine how the trajectory of the NSSL program could be adjusted in order to facilitate a smooth transition into new launch vehicles, while maintaining mission success, minimizing national security vulnerabilities, and clarifying the defense acquisition process.No embargoAcademic Major: EnglishAcademic Major: International Studie

CumuloNimbo: parallel-distributed transactional processing

CumuloNimbo aims at solving the lack of scalability of transactional applications that represent a large fraction of existing applications. CumuloNimbo aims at conceiving, architecting and developing a transactional, coherent, elastic and ultra scalable Platform as a Service. Its goals are: Ultra scalable and dependable, able to scale from a few users to many millions of users while at the same time providing continuous availability; Support transparent migration of multi-tier applications (e.g. Java EE applications, relational DB applications, etc.) to the cloud with automatic scalability and elasticity. Avoid reprogramming of applications and non-transparent scalability techniques such as sharding. Support transactions for new data stores such as cloud data stores, graph databases, etc.The main challenges are: Update ultrascalability (million update transactions per second and as many read-only transactions as needed). Strong transactional consistency. Non-intrusive elasticity. Inexpensive high availability. Low latency. CumuloNimbo goes beyond the state of the art by scaling transparently transactional applications to very large rates without sharding, the current practice in Today?s cloud. In this paper we describe CumuloNimbo architecture and its performance