47 research outputs found

    The Politics of Platformization: Amsterdam Dialogues on Platform Theory

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    What is platformization and why is it a relevant category in the contemporary political landscape? How is it related to cybernetics and the history of computation? This book tries to answer such questions by engaging in multidisciplinary dialogues about the first ten years of the emerging fields of platform studies and platform theory. It deploys a narrative and playful approach that makes use of anecdotes, personal histories, etymologies, and futurable speculations to investigate both the fragmented genealogy that led to platformization and the organizational and economic trends that guide nowadays platform sociotechnical imaginaries

    QoS-aware architectures, technologies, and middleware for the cloud continuum

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    The recent trend of moving Cloud Computing capabilities to the Edge of the network is reshaping how applications and their middleware supports are designed, deployed, and operated. This new model envisions a continuum of virtual resources between the traditional cloud and the network edge, which is potentially more suitable to meet the heterogeneous Quality of Service (QoS) requirements of diverse application domains and next-generation applications. Several classes of advanced Internet of Things (IoT) applications, e.g., in the industrial manufacturing domain, are expected to serve a wide range of applications with heterogeneous QoS requirements and call for QoS management systems to guarantee/control performance indicators, even in the presence of real-world factors such as limited bandwidth and concurrent virtual resource utilization. The present dissertation proposes a comprehensive QoS-aware architecture that addresses the challenges of integrating cloud infrastructure with edge nodes in IoT applications. The architecture provides end-to-end QoS support by incorporating several components for managing physical and virtual resources. The proposed architecture features: i) a multilevel middleware for resolving the convergence between Operational Technology (OT) and Information Technology (IT), ii) an end-to-end QoS management approach compliant with the Time-Sensitive Networking (TSN) standard, iii) new approaches for virtualized network environments, such as running TSN-based applications under Ultra-low Latency (ULL) constraints in virtual and 5G environments, and iv) an accelerated and deterministic container overlay network architecture. Additionally, the QoS-aware architecture includes two novel middlewares: i) a middleware that transparently integrates multiple acceleration technologies in heterogeneous Edge contexts and ii) a QoS-aware middleware for Serverless platforms that leverages coordination of various QoS mechanisms and virtualized Function-as-a-Service (FaaS) invocation stack to manage end-to-end QoS metrics. Finally, all architecture components were tested and evaluated by leveraging realistic testbeds, demonstrating the efficacy of the proposed solutions

    Co-designing reliability and performance for datacenter memory

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    Memory is one of the key components that affects reliability and performance of datacenter servers. Memory in today’s servers is organized and shared in several ways to provide the most performant and efficient access to data. For example, cache hierarchy in multi-core chips to reduce access latency, non-uniform memory access (NUMA) in multi-socket servers to improve scalability, disaggregation to increase memory capacity. In all these organizations, hardware coherence protocols are used to maintain memory consistency of this shared memory and implicitly move data to the requesting cores. This thesis aims to provide fault-tolerance against newer models of failure in the organization of memory in datacenter servers. While designing for improved reliability, this thesis explores solutions that can also enhance performance of applications. The solutions build over modern coherence protocols to achieve these properties. First, we observe that DRAM memory system failure rates have increased, demanding stronger forms of memory reliability. To combat this, the thesis proposes Dvé, a hardware driven replication mechanism where data blocks are replicated across two different memory controllers in a cache-coherent NUMA system. Data blocks are accompanied by a code with strong error detection capabilities so that when an error is detected, correction is performed using the replica. Dvé’s organization offers two independent points of access to data which enables: (a) strong error correction that can recover from a range of faults affecting any of the components in the memory and (b) higher performance by providing another nearer point of memory access. Dvé’s coherent replication keeps the replicas in sync for reliability and also provides coherent access to read replicas during fault-free operation for improved performance. Dvé can flexibly provide these benefits on-demand at runtime. Next, we observe that the coherence protocol itself requires to be hardened against failures. Memory in datacenter servers is being disaggregated from the compute servers into dedicated memory servers, driven by standards like CXL. CXL specifies the coherence protocol semantics for compute servers to access and cache data from a shared region in the disaggregated memory. However, the CXL specification lacks the requisite level of fault-tolerance necessary to operate at an inter-server scale within the datacenter. Compute servers can fail or be unresponsive in the datacenter and therefore, it is important that the coherence protocol remain available in the presence of such failures. The thesis proposes Āpta, a CXL-based, shared disaggregated memory system for keeping the cached data consistent without compromising availability in the face of compute server failures. Āpta architects a high-performance fault-tolerant object-granular memory server that significantly improves performance for stateless function-as-a-service (FaaS) datacenter applications

    Negotiating Software: Redistributing Control at Work and on the Web

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    Since the 1970s, digital technologies increasingly determine who gets what, when, and how; and the workings of informational capitalism have concentrated control over those technologies into the hands of just a few private corporations. The normative stance of this dissertation is that control over software should be distributed and subject to processes of negotiation: consensus-based decision making oriented towards achieving collective benefits. It explores the boundaries of negotiating software by trying to effect a change in two different kinds of software using two different approaches. The first approach targets application software – the paradigmatic model of commodified, turn-key computational media – in the context of knowledge work – labour that involves the creation and distribution of information through non-routine, creative, and abstract thinking. It tries to effect change by developing negotiable software as an alternative to the autocratic application model, which is software that embeds the support for distributed control in and over its design. These systems successfully demonstrate the technological feasibility of this approach, but also the limitations of design as a solution to systemic power asymmetries. The second approach targets consent management platforms – pop-up interfaces on the web that capture visitor’s consent for data processing – in the context of the European Union’s data protection regulation. It tries to effect change by employing negotiation software, which is software that supports existing processes of negotiation in complex systems, i.e., regulatory oversight and the exercise of digital rights. This approach resulted in a considerable increase in data protection compliance on Danish websites, but showed that sustainable enforcement using digital tools also requires design changes to data processing technologies. Both approaches to effecting software change – making software negotiable and using software in negotiations – revealed the drawbacks of individualistic strategies. Ultimately, the capacity of the liberal subject to stand up against corporate power is limited, and more collective approaches to software negotiation need to be developed, whether when making changes to designs or leveraging regulation

    Systems Support for Trusted Execution Environments

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    Cloud computing has become a default choice for data processing by both large corporations and individuals due to its economy of scale and ease of system management. However, the question of trust and trustoworthy computing inside the Cloud environments has been long neglected in practice and further exacerbated by the proliferation of AI and its use for processing of sensitive user data. Attempts to implement the mechanisms for trustworthy computing in the cloud have previously remained theoretical due to lack of hardware primitives in the commodity CPUs, while a combination of Secure Boot, TPMs, and virtualization has seen only limited adoption. The situation has changed in 2016, when Intel introduced the Software Guard Extensions (SGX) and its enclaves to the x86 ISA CPUs: for the first time, it became possible to build trustworthy applications relying on a commonly available technology. However, Intel SGX posed challenges to the practitioners who discovered the limitations of this technology, from the limited support of legacy applications and integration of SGX enclaves into the existing system, to the performance bottlenecks on communication, startup, and memory utilization. In this thesis, our goal is enable trustworthy computing in the cloud by relying on the imperfect SGX promitives. To this end, we develop and evaluate solutions to issues stemming from limited systems support of Intel SGX: we investigate the mechanisms for runtime support of POSIX applications with SCONE, an efficient SGX runtime library developed with performance limitations of SGX in mind. We further develop this topic with FFQ, which is a concurrent queue for SCONE's asynchronous system call interface. ShieldBox is our study of interplay of kernel bypass and trusted execution technologies for NFV, which also tackles the problem of low-latency clocks inside enclave. The two last systems, Clemmys and T-Lease are built on a more recent SGXv2 ISA extension. In Clemmys, SGXv2 allows us to significantly reduce the startup time of SGX-enabled functions inside a Function-as-a-Service platform. Finally, in T-Lease we solve the problem of trusted time by introducing a trusted lease primitive for distributed systems. We perform evaluation of all of these systems and prove that they can be practically utilized in existing systems with minimal overhead, and can be combined with both legacy systems and other SGX-based solutions. In the course of the thesis, we enable trusted computing for individual applications, high-performance network functions, and distributed computing framework, making a <vision of trusted cloud computing a reality

    NFV Platforms: Taxonomy, Design Choices and Future Challenges

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    Due to the intrinsically inefficient service provisioning in traditional networks, Network Function Virtualization (NFV) keeps gaining attention from both industry and academia. By replacing the purpose-built, expensive, proprietary network equipment with software network functions consolidated on commodity hardware, NFV envisions a shift towards a more agile and open service provisioning paradigm. During the last few years, a large number of NFV platforms have been implemented in production environments that typically face critical challenges, including the development, deployment, and management of Virtual Network Functions (VNFs). Nonetheless, just like any complex system, such platforms commonly consist of abounding software and hardware components and usually incorporate disparate design choices based on distinct motivations or use cases. This broad collection of convoluted alternatives makes it extremely arduous for network operators to make proper choices. Although numerous efforts have been devoted to investigating different aspects of NFV, none of them specifically focused on NFV platforms or attempted to explore their design space. In this paper, we present a comprehensive survey on the NFV platform design. Our study solely targets existing NFV platform implementations. We begin with a top-down architectural view of the standard reference NFV platform and present our taxonomy of existing NFV platforms based on what features they provide in terms of a typical network function life cycle. Then we thoroughly explore the design space and elaborate on the implementation choices each platform opts for. We also envision future challenges for NFV platform design in the incoming 5G era. We believe that our study gives a detailed guideline for network operators or service providers to choose the most appropriate NFV platform based on their respective requirements. Our work also provides guidelines for implementing new NFV platforms

    Protecting applications using trusted execution environments

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    While cloud computing has been broadly adopted, companies that deal with sensitive data are still reluctant to do so due to privacy concerns or legal restrictions. Vulnerabilities in complex cloud infrastructures, resource sharing among tenants, and malicious insiders pose a real threat to the confidentiality and integrity of sensitive customer data. In recent years trusted execution environments (TEEs), hardware-enforced isolated regions that can protect code and data from the rest of the system, have become available as part of commodity CPUs. However, designing applications for the execution within TEEs requires careful consideration of the elevated threats that come with running in a fully untrusted environment. Interaction with the environment should be minimised, but some cooperation with the untrusted host is required, e.g. for disk and network I/O, via a host interface. Implementing this interface while maintaining the security of sensitive application code and data is a fundamental challenge. This thesis addresses this challenge and discusses how TEEs can be leveraged to secure existing applications efficiently and effectively in untrusted environments. We explore this in the context of three systems that deal with the protection of TEE applications and their host interfaces: SGX-LKL is a library operating system that can run full unmodified applications within TEEs with a minimal general-purpose host interface. By providing broad system support inside the TEE, the reliance on the untrusted host can be reduced to a minimal set of low-level operations that cannot be performed inside the enclave. SGX-LKL provides transparent protection of the host interface and for both disk and network I/O. Glamdring is a framework for the semi-automated partitioning of TEE applications into an untrusted and a trusted compartment. Based on source-level annotations, it uses either dynamic or static code analysis to identify sensitive parts of an application. Taking into account the objectives of a small TCB size and low host interface complexity, it defines an application-specific host interface and generates partitioned application code. EnclaveDB is a secure database using Intel SGX based on a partitioned in-memory database engine. The core of EnclaveDB is its logging and recovery protocol for transaction durability. For this, it relies on the database log managed and persisted by the untrusted database server. EnclaveDB protects against advanced host interface attacks and ensures the confidentiality, integrity, and freshness of sensitive data.Open Acces

    Cloud-based homomorphic encryption for privacy-preserving machine learning in clinical decision support

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    While privacy and security concerns dominate public cloud services, Homomorphic Encryption (HE) is seen as an emerging solution that ensures secure processing of sensitive data via untrusted networks in the public cloud or by third-party cloud vendors. It relies on the fact that some encryption algorithms display the property of homomorphism, which allows them to manipulate data meaningfully while still in encrypted form; although there are major stumbling blocks to overcome before the technology is considered mature for production cloud environments. Such a framework would find particular relevance in Clinical Decision Support (CDS) applications deployed in the public cloud. CDS applications have an important computational and analytical role over confidential healthcare information with the aim of supporting decision-making in clinical practice. Machine Learning (ML) is employed in CDS applications that typically learn and can personalise actions based on individual behaviour. A relatively simple-to-implement, common and consistent framework is sought that can overcome most limitations of Fully Homomorphic Encryption (FHE) in order to offer an expanded and flexible set of HE capabilities. In the absence of a significant breakthrough in FHE efficiency and practical use, it would appear that a solution relying on client interactions is the best known entity for meeting the requirements of private CDS-based computation, so long as security is not significantly compromised. A hybrid solution is introduced, that intersperses limited two-party interactions amongst the main homomorphic computations, allowing exchange of both numerical and logical cryptographic contexts in addition to resolving other major FHE limitations. Interactions involve the use of client-based ciphertext decryptions blinded by data obfuscation techniques, to maintain privacy. This thesis explores the middle ground whereby HE schemes can provide improved and efficient arbitrary computational functionality over a significantly reduced two-party network interaction model involving data obfuscation techniques. This compromise allows for the powerful capabilities of HE to be leveraged, providing a more uniform, flexible and general approach to privacy-preserving system integration, which is suitable for cloud deployment. The proposed platform is uniquely designed to make HE more practical for mainstream clinical application use, equipped with a rich set of capabilities and potentially very complex depth of HE operations. Such a solution would be suitable for the long-term privacy preserving-processing requirements of a cloud-based CDS system, which would typically require complex combinatorial logic, workflow and ML capabilities

    Internet of Things From Hype to Reality

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    The Internet of Things (IoT) has gained significant mindshare, let alone attention, in academia and the industry especially over the past few years. The reasons behind this interest are the potential capabilities that IoT promises to offer. On the personal level, it paints a picture of a future world where all the things in our ambient environment are connected to the Internet and seamlessly communicate with each other to operate intelligently. The ultimate goal is to enable objects around us to efficiently sense our surroundings, inexpensively communicate, and ultimately create a better environment for us: one where everyday objects act based on what we need and like without explicit instructions
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