10,534 research outputs found

    TANDEM: taming failures in next-generation datacenters with emerging memory

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    The explosive growth of online services, leading to unforeseen scales, has made modern datacenters highly prone to failures. Taming these failures hinges on fast and correct recovery, minimizing service interruptions. Applications, owing to recovery, entail additional measures to maintain a recoverable state of data and computation logic during their failure-free execution. However, these precautionary measures have severe implications on performance, correctness, and programmability, making recovery incredibly challenging to realize in practice. Emerging memory, particularly non-volatile memory (NVM) and disaggregated memory (DM), offers a promising opportunity to achieve fast recovery with maximum performance. However, incorporating these technologies into datacenter architecture presents significant challenges; Their distinct architectural attributes, differing significantly from traditional memory devices, introduce new semantic challenges for implementing recovery, complicating correctness and programmability. Can emerging memory enable fast, performant, and correct recovery in the datacenter? This thesis aims to answer this question while addressing the associated challenges. When architecting datacenters with emerging memory, system architects face four key challenges: (1) how to guarantee correct semantics; (2) how to efficiently enforce correctness with optimal performance; (3) how to validate end-to-end correctness including recovery; and (4) how to preserve programmer productivity (Programmability). This thesis aims to address these challenges through the following approaches: (a) defining precise consistency models that formally specify correct end-to-end semantics in the presence of failures (consistency models also play a crucial role in programmability); (b) developing new low-level mechanisms to efficiently enforce the prescribed models given the capabilities of emerging memory; and (c) creating robust testing frameworks to validate end-to-end correctness and recovery. We start our exploration with non-volatile memory (NVM), which offers fast persistence capabilities directly accessible through the processor’s load-store (memory) interface. Notably, these capabilities can be leveraged to enable fast recovery for Log-Free Data Structures (LFDs) while maximizing performance. However, due to the complexity of modern cache hierarchies, data hardly persist in any specific order, jeop- ardizing recovery and correctness. Therefore, recovery needs primitives that explicitly control the order of updates to NVM (known as persistency models). We outline the precise specification of a novel persistency model – Release Persistency (RP) – that provides a consistency guarantee for LFDs on what remains in non-volatile memory upon failure. To efficiently enforce RP, we propose a novel microarchitecture mechanism, lazy release persistence (LRP). Using standard LFDs benchmarks, we show that LRP achieves fast recovery while incurring minimal overhead on performance. We continue our discussion with memory disaggregation which decouples memory from traditional monolithic servers, offering a promising pathway for achieving very high availability in replicated in-memory data stores. Achieving such availability hinges on transaction protocols that can efficiently handle recovery in this setting, where compute and memory are independent. However, there is a challenge: disaggregated memory (DM) fails to work with RPC-style protocols, mandating one-sided transaction protocols. Exacerbating the problem, one-sided transactions expose critical low-level ordering to architects, posing a threat to correctness. We present a highly available transaction protocol, Pandora, that is specifically designed to achieve fast recovery in disaggregated key-value stores (DKVSes). Pandora is the first one-sided transactional protocol that ensures correct, non-blocking, and fast recovery in DKVS. Our experimental implementation artifacts demonstrate that Pandora achieves fast recovery and high availability while causing minimal disruption to services. Finally, we introduce a novel target litmus-testing framework – DART – to validate the end-to-end correctness of transactional protocols with recovery. Using DART’s target testing capabilities, we have found several critical bugs in Pandora, highlighting the need for robust end-to-end testing methods in the design loop to iteratively fix correctness bugs. Crucially, DART is lightweight and black-box, thereby eliminating any intervention from the programmers

    Deep generative models for network data synthesis and monitoring

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    Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network. Although networks inherently have abundant amounts of monitoring data, its access and effective measurement is another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset without leaking commercial sensitive information. Second, it could be very expensive to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources in the network element that can be applied to support the measurement function are too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex structure. Various emerging optimization-based solutions (e.g., compressive sensing) or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet meet the current network requirements. The contributions made in this thesis significantly advance the state of the art in the domain of network measurement and monitoring techniques. Overall, we leverage cutting-edge machine learning technology, deep generative modeling, throughout the entire thesis. First, we design and realize APPSHOT , an efficient city-scale network traffic sharing with a conditional generative model, which only requires open-source contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time network telemetry system with latent GANs and spectral-temporal networks. Finally, we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through this research are summarized, and interesting topics are discussed for future work in this domain. All proposed solutions have been evaluated with real-world datasets and applied to support different applications in real systems

    Faster inference from state space models via GPU computing

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    Funding: C.F.-J. is funded via a doctoral scholarship from the University of St Andrews, School of Mathematics and Statistics.Inexpensive Graphics Processing Units (GPUs) offer the potential to greatly speed up computation by employing their massively parallel architecture to perform arithmetic operations more efficiently. Population dynamics models are important tools in ecology and conservation. Modern Bayesian approaches allow biologically realistic models to be constructed and fitted to multiple data sources in an integrated modelling framework based on a class of statistical models called state space models. However, model fitting is often slow, requiring hours to weeks of computation. We demonstrate the benefits of GPU computing using a model for the population dynamics of British grey seals, fitted with a particle Markov chain Monte Carlo algorithm. Speed-ups of two orders of magnitude were obtained for estimations of the log-likelihood, compared to a traditional ‘CPU-only’ implementation, allowing for an accurate method of inference to be used where this was previously too computationally expensive to be viable. GPU computing has enormous potential, but one barrier to further adoption is a steep learning curve, due to GPUs' unique hardware architecture. We provide a detailed description of hardware and software setup, and our case study provides a template for other similar applications. We also provide a detailed tutorial-style description of GPU hardware architectures, and examples of important GPU-specific programming practices.Publisher PDFPeer reviewe

    Exploring Hardware Fault Impacts on Different Real Number Representations of the Structural Resilience of TCUs in GPUs

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    The most recent generations of graphics processing units (GPUs) boost the execution of convolutional operations required by machine learning applications by resorting to specialized and efficient in-chip accelerators (Tensor Core Units or TCUs) that operate on matrix multiplication tiles. Unfortunately, modern cutting-edge semiconductor technologies are increasingly prone to hardware defects, and the trend to highly stress TCUs during the execution of safety-critical and high-performance computing (HPC) applications increases the likelihood of TCUs producing different kinds of failures. In fact, the intrinsic resiliency to hardware faults of arithmetic units plays a crucial role in safety-critical applications using GPUs (e.g., in automotive, space, and autonomous robotics). Recently, new arithmetic formats have been proposed, particularly those suited to neural network execution. However, the reliability characterization of TCUs supporting different arithmetic formats was still lacking. In this work, we quantitatively assessed the impact of hardware faults in TCU structures while employing two distinct formats (floating-point and posit) and using two different configurations (16 and 32 bits) to represent real numbers. For the experimental evaluation, we resorted to an architectural description of a TCU core (PyOpenTCU) and performed 120 fault simulation campaigns, injecting around 200,000 faults per campaign and requiring around 32 days of computation. Our results demonstrate that the posit format of TCUs is less affected by faults than the floating-point one (by up to three orders of magnitude for 16 bits and up to twenty orders for 32 bits). We also identified the most sensible fault locations (i.e., those that produce the largest errors), thus paving the way to adopting smart hardening solutions

    Configuration Management of Distributed Systems over Unreliable and Hostile Networks

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    Economic incentives of large criminal profits and the threat of legal consequences have pushed criminals to continuously improve their malware, especially command and control channels. This thesis applied concepts from successful malware command and control to explore the survivability and resilience of benign configuration management systems. This work expands on existing stage models of malware life cycle to contribute a new model for identifying malware concepts applicable to benign configuration management. The Hidden Master architecture is a contribution to master-agent network communication. In the Hidden Master architecture, communication between master and agent is asynchronous and can operate trough intermediate nodes. This protects the master secret key, which gives full control of all computers participating in configuration management. Multiple improvements to idempotent configuration were proposed, including the definition of the minimal base resource dependency model, simplified resource revalidation and the use of imperative general purpose language for defining idempotent configuration. Following the constructive research approach, the improvements to configuration management were designed into two prototypes. This allowed validation in laboratory testing, in two case studies and in expert interviews. In laboratory testing, the Hidden Master prototype was more resilient than leading configuration management tools in high load and low memory conditions, and against packet loss and corruption. Only the research prototype was adaptable to a network without stable topology due to the asynchronous nature of the Hidden Master architecture. The main case study used the research prototype in a complex environment to deploy a multi-room, authenticated audiovisual system for a client of an organization deploying the configuration. The case studies indicated that imperative general purpose language can be used for idempotent configuration in real life, for defining new configurations in unexpected situations using the base resources, and abstracting those using standard language features; and that such a system seems easy to learn. Potential business benefits were identified and evaluated using individual semistructured expert interviews. Respondents agreed that the models and the Hidden Master architecture could reduce costs and risks, improve developer productivity and allow faster time-to-market. Protection of master secret keys and the reduced need for incident response were seen as key drivers for improved security. Low-cost geographic scaling and leveraging file serving capabilities of commodity servers were seen to improve scaling and resiliency. Respondents identified jurisdictional legal limitations to encryption and requirements for cloud operator auditing as factors potentially limiting the full use of some concepts

    Edge Computing, IoT and Future Internet

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    In this paper, we discuss researches in Edge Computing, Internet of Things (IoT), and Future Internet Technologies (ICN and NDN). We chose these areas because they are hot topics in today research

    Security Aspects in Web of Data Based on Trust Principles. A brief of Literature Review

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    Within scientific community, there is a certain consensus to define "Big Data" as a global set, through a complex integration that embraces several dimensions from using of research data, Open Data, Linked Data, Social Network Data, etc. These data are scattered in different sources, which suppose a mix that respond to diverse philosophies, great diversity of structures, different denominations, etc. Its management faces great technological and methodological challenges: The discovery and selection of data, its extraction and final processing, preservation, visualization, access possibility, greater or lesser structuring, between other aspects, which allow showing a huge domain of study at the level of analysis and implementation in different knowledge domains. However, given the data availability and its possible opening: What problems do the data opening face? This paper shows a literature review about these security aspects

    "Always Contribute Back": A Qualitative Study on Security Challenges of the Open Source Supply Chain

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    Open source components are ubiquitous in companies’ setups, processes, and software. Utilizing these external components as building blocks enables companies to leverage the benefits of open source software, allowing them to focus their efforts on features and faster delivery instead of writing their own components. But by introducing these components into their software stack, companies inherit unique security challenges and attack surfaces: including code from potentially unvetted contributors, as well as the obligation to assess and mitigate the impact of vulnerabilities in external components. In 25 in-depth, semi-structured interviews with software developers, architects, and engineers from industry projects, we investigate their projects’ processes, decisions, and considerations in the context of external open source code. We find that open source components play an important role in many of our participants’ projects, that most projects have some form of company policy or at least best practice for including external code, and that many developers wish for more developer-hours, dedicated teams, or tools to better audit included components. Based on our findings, we discuss implications for company stakeholders and the open source software ecosystem. Overall, we appeal to companies to not treat the open source ecosystem as a free (software) supply chain and instead to contribute towards the health and security of the overall software ecosystem they benefit from and are part of

    Auditable and performant Byzantine consensus for permissioned ledgers

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    Permissioned ledgers allow users to execute transactions against a data store, and retain proof of their execution in a replicated ledger. Each replica verifies the transactions’ execution and ensures that, in perpetuity, a committed transaction cannot be removed from the ledger. Unfortunately, this is not guaranteed by today’s permissioned ledgers, which can be re-written if an arbitrary number of replicas collude. In addition, the transaction throughput of permissioned ledgers is low, hampering real-world deployments, by not taking advantage of multi-core CPUs and hardware accelerators. This thesis explores how permissioned ledgers and their consensus protocols can be made auditable in perpetuity; even when all replicas collude and re-write the ledger. It also addresses how Byzantine consensus protocols can be changed to increase the execution throughput of complex transactions. This thesis makes the following contributions: 1. Always auditable Byzantine consensus protocols. We present a permissioned ledger system that can assign blame to individual replicas regardless of how many of them misbehave. This is achieved by signing and storing consensus protocol messages in the ledger and providing clients with signed, universally-verifiable receipts. 2. Performant transaction execution with hardware accelerators. Next, we describe a cloud-based ML inference service that provides strong integrity guarantees, while staying compatible with current inference APIs. We change the Byzantine consensus protocol to execute machine learning (ML) inference computation on GPUs to optimize throughput and latency of ML inference computation. 3. Parallel transactions execution on multi-core CPUs. Finally, we introduce a permissioned ledger that executes transactions, in parallel, on multi-core CPUs. We separate the execution of transactions between the primary and secondary replicas. The primary replica executes transactions on multiple CPU cores and creates a dependency graph of the transactions that the backup replicas utilize to execute transactions in parallel.Open Acces

    Secure storage systems for untrusted cloud environments

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    The cloud has become established for applications that need to be scalable and highly available. However, moving data to data centers owned and operated by a third party, i.e., the cloud provider, raises security concerns because a cloud provider could easily access and manipulate the data or program flow, preventing the cloud from being used for certain applications, like medical or financial. Hardware vendors are addressing these concerns by developing Trusted Execution Environments (TEEs) that make the CPU state and parts of memory inaccessible from the host software. While TEEs protect the current execution state, they do not provide security guarantees for data which does not fit nor reside in the protected memory area, like network and persistent storage. In this work, we aim to address TEEs’ limitations in three different ways, first we provide the trust of TEEs to persistent storage, second we extend the trust to multiple nodes in a network, and third we propose a compiler-based solution for accessing heterogeneous memory regions. More specifically, • SPEICHER extends the trust provided by TEEs to persistent storage. SPEICHER implements a key-value interface. Its design is based on LSM data structures, but extends them to provide confidentiality, integrity, and freshness for the stored data. Thus, SPEICHER can prove to the client that the data has not been tampered with by an attacker. • AVOCADO is a distributed in-memory key-value store (KVS) that extends the trust that TEEs provide across the network to multiple nodes, allowing KVSs to scale beyond the boundaries of a single node. On each node, AVOCADO carefully divides data between trusted memory and untrusted host memory, to maximize the amount of data that can be stored on each node. AVOCADO leverages the fact that we can model network attacks as crash-faults to trust other nodes with a hardened ABD replication protocol. • TOAST is based on the observation that modern high-performance systems often use several different heterogeneous memory regions that are not easily distinguishable by the programmer. The number of regions is increased by the fact that TEEs divide memory into trusted and untrusted regions. TOAST is a compiler-based approach to unify access to different heterogeneous memory regions and provides programmability and portability. TOAST uses a load/store interface to abstract most library interfaces for different memory regions
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