Revisiting the high-performance reconfigurable computing for future datacenters

Modern datacenters are reinforcing the computational power and energy efficiency by assimilating field programmable gate arrays (FPGAs). The sustainability of this large-scale integration depends on enabling multi-tenant FPGAs. This requisite amplifies the importance of communication architecture and virtualization method with the required features in order to meet the high-end objective. Consequently, in the last decade, academia and industry proposed several virtualization techniques and hardware architectures for addressing resource management, scheduling, adoptability, segregation, scalability, performance-overhead, availability, programmability, time-to-market, security, and mainly, multitenancy. This paper provides an extensive survey covering three important aspects-discussion on non-standard terms used in existing literature, network-on-chip evaluation choices as a mean to explore the communication architecture, and virtualization methods under latest classification. The purpose is to emphasize the importance of choosing appropriate communication architecture, virtualization technique and standard language to evolve the multi-tenant FPGAs in datacenters. None of the previous surveys encapsulated these aspects in one writing. Open problems are indicated for scientific community as well

Virtualizing Reconfigurable Architectures: From Fpgas To Beyond

With field-programmable gate arrays (FPGAs) being widely deployed in data centers to enhance the computing performance, an efficient virtualization support is required to fully unleash the potential of cloud FPGAs. However, the system support for FPGAs in the context of the cloud environment is still in its infancy, which leads to a low resource utilization due to the tight coupling between compilation and resource allocation. Moreover, the system support proposed in existing works is limited to a homogeneous FPGA cluster comprising identical FPGA devices, which is hard to be extended to a heterogeneous FPGA cluster that comprises multiple types of FPGAs. As the FPGA cloud is expected to become increasingly heterogeneous due to the hardware rolling upgrade strategy, it is necessary to provide efficient virtualization support for the heterogeneous FPGA cluster. In this dissertation, we first identify three pairs of conflicting requirements from runtime management and offline compilation, which are related to the tradeoff between flexibility and efficiency. These conflicting requirements are the fundamental reason why the single-level abstraction proposed in prior works for the homogeneous FPGA cluster cannot be trivially extended to the heterogeneous cluster. To decouple these conflicting requirements, we provide a two-level system abstraction. Specifically, the high-level abstraction is FPGA-agnostic and provides a simple and homogeneous view of the FPGA resources to simplify the runtime management and maximize the flexibility. On the contrary, the low-level abstraction is FPGA-specific and exposes sufficient low-level hardware details to the compilation framework to ensure the mapping quality and maximize the efficiency. This generic two-level system abstraction can also be specialized to the homogeneous FPGA cluster and/or be extended to leverage application-specific information to further improve the efficiency. We also develop a compilation framework and a modular runtime system with a heuristic-based runtime management policy to support this two-level system abstraction. By enabling a dynamic FPGA sharing at the sub-FPGA granularity, the proposed virtualization solution can deploy 1.62x more applications using the same amount of FPGA resources and reduce the compilation time by 22.6% (perform as many compilation tasks in parallel as possible) with an acceptable virtualization overhead, i.e., Finally, we use Liquid Silicon as a case study to show that the proposed virtualization solution can be extended to other spatial reconfigurable architectures. Liquid Silicon is a homogeneous reconfigurable architecture enabled by the non-volatile memory technology (i.e., RRAM). It extends the configuration capability of existing FPGAs from computation to the whole spectrum ranging from computation to data storage. It allows users to better customize hardware by flexibly partitioning hardware resources between computation and memory based on the actual usage. Instead of naively applying the proposed virtualization solution onto Liquid Silicon, we co-optimize the system abstraction and Liquid Silicon architecture to improve the performance

MASCARA (ModulAr Semantic CAching fRAmework) towards FPGA Acceleration for IoT Security Monitoring

With the explosive growth of the Internet Of Things (IOTs), emergency security monitoring becomes essential to efficiently manage an enormous amount of information from heterogeneous systems. In concern of increasing the performance for the sequence of online queries on long-term historical data, query caching with semantic organization, called Semantic Query Caching or Semantic Caching (SC), can play a vital role. SC is implemented mostly in software perspective without providing a generic description of modules or cache services in the given context. Hardware acceleration with FPGA opens new research directions to achieve better performance for SC. Hence, our work aims to propose a flexible, adaptable, and tunable ModulAr Semantic CAching fRAmework (MASCARA) towards FPGA acceleration for fast and accurate massive logs processing applications

Automated Testing and Debugging for Big Data Analytics

The prevalence of big data analytics in almost every large-scale software system has generated a substantial push to build data-intensive scalable computing (DISC) frameworks such as Google MapReduce and Apache Spark that can fully harness the power of existing data centers. However, frameworks once used by domain experts are now being leveraged by data scientists, business analysts, and researchers. This shift in user demographics calls for immediate advancements in the development, debugging, and testing practices of big data applications, which are falling behind compared to the DISC framework design and implementation. In practice, big data applications often fail as users are unable to test all behaviors emerging from interleaving dataflow operators, user-defined functions, and framework's code. "Testing based on a random sample" rarely guarantees the reliability and "trial and error" and "print" debugging methods are expensive and time-consuming. Thus, the current practice of developing a big data application must be improved and the tools built to enhance the developer's productivity must adapt to the distinct characteristics of data-intensive scalable computing. By synthesizing ideas from software engineering and database systems, our hypothesis is that we can design effective and scalable testing and debugging algorithms for big data analytics without compromising the performance and efficiency of the underlying DISC framework. To design such techniques, we investigate how we can build interactive and responsive debugging primitives that significantly reduce the debugging time, yet do not pose much performance overhead on big data applications. Furthermore, we investigate how we can leverage data provenance techniques from databases and fault-isolation algorithms from software engineering to pinpoint the minimal subset of failure-inducing inputs efficiently. To improve the reliability of big data analytics, we investigate how we can abstract the semantics of dataflow operators and use them in tandem with the semantics of user-defined functions to generate a minimum set of synthetic test inputs capable of revealing more defects than the entire input dataset.To examine the first hypothesis, we introduce interactive, real-time debugging primitives for big data analytics through innovative and scalable debugging features such as simulated breakpoint, dynamic watchpoint, and crash culprit identification. Second, we design a new automated fault localization approach that combines insights from both the software engineering and database literature to bring delta debugging closer to a reality in the big data applications by leveraging data provenance and by constructing systems optimizations for debugging provenance queries. Lastly, we devise a new symbolic-execution based white-box testing algorithm for big data applications that abstracts the implementation of dataflow operators using logical specifications instead of modeling their implementations and combines them with the semantics of any arbitrary user-defined function. We instantiate the idea of an interactive debugging algorithm as BigDebug, the idea of an automated debugging algorithm as BigSift, and the idea of symbolic execution-based testing as BigTest. Our investigation shows that the interactive debugging primitives can scale to terabytes---our record-level tracing incurs less than 25% overhead on average and provides up to 100% time saving compared to the baseline replay debugger. Second, we observe that by combining data provenance with delta debugging, we can identify the minimum faulty input in just under 30% of the original job execution time. Lastly, we verify that by abstracting dataflow operators using logical specifications, we can efficiently generate the most concise test data suitable for local testing while revealing twice as many faults as prior approaches. Our investigations collectively demonstrate that developer productivity can be significantly improved through effective and scalable testing and debugging techniques for big data analytics, without impacting the DISC framework's performance. This dissertation affirms the feasibility of automated debugging and testing techniques for big data analytics---techniques that were previously considered infeasible for large-scale data processing

Challenges in Cybersecurity and Privacy - the European Research Landscape

Cybersecurity and Privacy issues are becoming an important barrier for a trusted and dependable global digital society development. Cyber-criminals are continuously shifting their cyber-attacks specially against cyber-physical systems and IoT, since they present additional vulnerabilities due to their constrained capabilities, their unattended nature and the usage of potential untrustworthiness components. Likewise, identity-theft, fraud, personal data leakages, and other related cyber-crimes are continuously evolving, causing important damages and privacy problems for European citizens in both virtual and physical scenarios. In this context, new holistic approaches, methodologies, techniques and tools are needed to cope with those issues, and mitigate cyberattacks, by employing novel cyber-situational awareness frameworks, risk analysis and modeling, threat intelligent systems, cyber-threat information sharing methods, advanced big-data analysis techniques as well as exploiting the benefits from latest technologies such as SDN/NFV and Cloud systems. In addition, novel privacy-preserving techniques, and crypto-privacy mechanisms, identity and eID management systems, trust services, and recommendations are needed to protect citizens’ privacy while keeping usability levels. The European Commission is addressing the challenge through different means, including the Horizon 2020 Research and Innovation program, thereby financing innovative projects that can cope with the increasing cyberthreat landscape. This book introduces several cybersecurity and privacy research challenges and how they are being addressed in the scope of 15 European research projects. Each chapter is dedicated to a different funded European Research project, which aims to cope with digital security and privacy aspects, risks, threats and cybersecurity issues from a different perspective. Each chapter includes the project’s overviews and objectives, the particular challenges they are covering, research achievements on security and privacy, as well as the techniques, outcomes, and evaluations accomplished in the scope of the EU project. The book is the result of a collaborative effort among relative ongoing European Research projects in the field of privacy and security as well as related cybersecurity fields, and it is intended to explain how these projects meet the main cybersecurity and privacy challenges faced in Europe. Namely, the EU projects analyzed in the book are: ANASTACIA, SAINT, YAKSHA, FORTIKA, CYBECO, SISSDEN, CIPSEC, CS-AWARE. RED-Alert, Truessec.eu. ARIES, LIGHTest, CREDENTIAL, FutureTrust, LEPS. Challenges in Cybersecurity and Privacy - the European Research Landscape is ideal for personnel in computer/communication industries as well as academic staff and master/research students in computer science and communications networks interested in learning about cyber-security and privacy aspects

Challenges in Cybersecurity and Privacy - the European Research Landscape

Cybersecurity and Privacy issues are becoming an important barrier for a trusted and dependable global digital society development. Cyber-criminals are continuously shifting their cyber-attacks specially against cyber-physical systems and IoT, since they present additional vulnerabilities due to their constrained capabilities, their unattended nature and the usage of potential untrustworthiness components. Likewise, identity-theft, fraud, personal data leakages, and other related cyber-crimes are continuously evolving, causing important damages and privacy problems for European citizens in both virtual and physical scenarios. In this context, new holistic approaches, methodologies, techniques and tools are needed to cope with those issues, and mitigate cyberattacks, by employing novel cyber-situational awareness frameworks, risk analysis and modeling, threat intelligent systems, cyber-threat information sharing methods, advanced big-data analysis techniques as well as exploiting the benefits from latest technologies such as SDN/NFV and Cloud systems. In addition, novel privacy-preserving techniques, and crypto-privacy mechanisms, identity and eID management systems, trust services, and recommendations are needed to protect citizens’ privacy while keeping usability levels. The European Commission is addressing the challenge through different means, including the Horizon 2020 Research and Innovation program, thereby financing innovative projects that can cope with the increasing cyberthreat landscape. This book introduces several cybersecurity and privacy research challenges and how they are being addressed in the scope of 15 European research projects. Each chapter is dedicated to a different funded European Research project, which aims to cope with digital security and privacy aspects, risks, threats and cybersecurity issues from a different perspective. Each chapter includes the project’s overviews and objectives, the particular challenges they are covering, research achievements on security and privacy, as well as the techniques, outcomes, and evaluations accomplished in the scope of the EU project. The book is the result of a collaborative effort among relative ongoing European Research projects in the field of privacy and security as well as related cybersecurity fields, and it is intended to explain how these projects meet the main cybersecurity and privacy challenges faced in Europe. Namely, the EU projects analyzed in the book are: ANASTACIA, SAINT, YAKSHA, FORTIKA, CYBECO, SISSDEN, CIPSEC, CS-AWARE. RED-Alert, Truessec.eu. ARIES, LIGHTest, CREDENTIAL, FutureTrust, LEPS. Challenges in Cybersecurity and Privacy - the European Research Landscape is ideal for personnel in computer/communication industries as well as academic staff and master/research students in computer science and communications networks interested in learning about cyber-security and privacy aspects