Users of online services such as messaging, code hosting and collaborative document editing expect the services to uphold the integrity of their data. Despite providers' best efforts, data corruption still occurs, but at present service integrity violations are excluded from SLAs. For providers to include such violations as part of SLAs, the competing requirements of clients and providers must be satisfied. Clients need the ability to independently identify and prove service integrity violations to claim compensation. At the same time, providers must be able to refute spurious claims.We describe LibSEAL, a SEcure Audit Library for Internet services that creates a non-repudiable audit log of service operations and checks invariants to discover violations of service integrity. LibSEAL is a drop-in replacement for TLS libraries used by services, and thus observes and logs all service requests and responses. It runs inside a trusted execution environment, such as Intel SGX, to protect the integrity of the audit log. Logs are stored using an embedded relational database, permitting service invariant violations to be discovered using simple SQL queries. We evaluate LibSEAL with three popular online services (Git, ownCloud and Dropbox) and demonstrate that it is effective in discovering integrity violations, while reducing throughput by at most 14%.
Complex event processing for pervasive computing must deal with various sources of error. In this paper, we focus on improving complex event detector handling of several types of communication error, in addition to timing errors caused by the lack of a global clock in distributed systems. We propose extensions to a complex event language that allow programmers specify a variety of detection policies. Although not a panacea, these policies help detectors tolerate a variety of errors such that the output they produce is sensible with respect to the semantics required by individual applications. Of particular interest is a detection policy that ensures no false positives are received. We discuss in detail the implementation of such a policy and the factors that influence its effectiveness. Finally, we evaluate an implementation of our policy, and show how performance is unaffected during normal operation, but that overhead increases with the number of errors.
Abstract-Many real-time decision support and sensing applications can be expressed as continuous stream queries over time-varying data streams, following a data stream management model. We consider the problem of the efficient and resilient execution of continuous stream queries in tactical edge networks formed from mobile ad-hoc networks (MANETs) with limited backend connectivity. Previous approaches for distributed stream query execution target data center environments in which networks are static, and centralized control is feasible. The distributed, bandwidth-constrained and highly dynamic nature of MANETs render such approaches insufficient-while a stream query executes in a MANET, changes in the network topology mean that any fixed query plan eventually becomes outdated.We introduce an adaptive, network-aware approach for stream query planning in MANETs, which supports both single-and multi-input windowed stream query operators. The basic idea is to increase the path diversity available when executing stream queries by replicating query operators across many nodes in the MANET. During execution, it becomes possible to dynamically switch between different operator replicas based on connectivity and other network path conditions. We evaluate our approach in emulated MANETs, showing that it can increase substantially the robustness of distributed stream query processing under mobility.
In an edge deployment model, Internet-of-Things (IoT) applications, e.g. for building automation or video surveillance, must process data locally on IoT devices without relying on permanent connectivity to a cloud backend. The ability to harness the combined resources of multiple IoT devices for computation is influenced by the quality of wireless network connectivity. An open challenge is how practical edge-based IoT applications can be realised that are robust to changes in network bandwidth between IoT devices, due to interference and intermittent connectivity.We present Frontier, a distributed and resilient edge processing platform for IoT devices. The key idea is to express data-intensive IoT applications as continuous data-parallel streaming queries and to improve query throughput in an unreliable wireless network by exploiting network path diversity: a query includes operator replicas at different IoT nodes, which increases possible network paths for data. Frontier dynamically routes stream data to operator replicas based on network path conditions. Nodes probe path throughput and use backpressure stream routing to decide on transmission rates, while exploiting multiple operator replicas for data-parallelism. If a node loses network connectivity, a transient disconnection recovery mechanism reprocesses the lost data. Our experimental evaluation of Frontier shows that network path diversity improves throughput by 1.3×-2.8× for different IoT applications, while being resilient to intermittent network connectivity. Existing IoT processing solutionsNext we survey existing solutions relevant to edge-based IoT data processing, proposed across a number of domains. Table 1 summarises how they meet the requirements outlined in §2.2. Cloud-based IoT data processing. Today many computationallyintensive IoT application leverage remote cloud resources for data processing [22, 43] because a remote cloud can offer effectively unlimited resources. Such solutions, however, require a permanent network link to a remote cloud backend: as a result, they suffer from the constrained bandwidth available to a remote cloud, introduce additional network latency (violating R1 above), and cannot offer any service when the remote connectivity is interrupted (violating R4). Offloading private or security-sensitive data to third-party cloud providers may also introduce privacy issues [19]. Centralised edge-based processing. A trend for cloud-based IoT service providers is to add support for edge-based IoT services that can operate without connectivity to a cloud backend [9,23,56]. A hub device at the edge can support control-plane operations, such
When tenants deploy applications under the control of third-party cloud providers, they must trust the provider's security mechanisms for inter-tenant isolation, resource sharing and access control. Despite a provider's best efforts, accidental data leakage may occur due to misconfigurations or bugs in the cloud platform. Especially in Platform-as-a-Service (PaaS) clouds, which rely on weaker forms of isolation, the potential for unnoticed data leakage is high. Prior work to raise tenants' trust in clouds relies on attestation, which limits the management flexibility of providers, or fine-grained data tracking, which has high overheads.We describe CloudSafetyNet (CSN), a lightweight monitoring framework that gives tenants visibility into the propagation of their application data in a cloud environment with low performance overhead. It exploits the incentive of tenants to co-operate with each other to detect accidental data leakage. CSN transparently adds opaque security tags to a subset of form fields in HTTP requests, using a client-side JavaScript library. Socket-level monitors maintain a log of observed tags flowing between application components. Tenants retrieve their logs and identify foreign tags that indicate data leakage. To check the correct operation of CSN, tenants send probe requests with known tags and verify that monitors are logging correctly. Using an implementation of CSN deployed on the OpenShift and AppScale PaaS platforms, we show that it can discover misconfigurations and bugs with a negligible performance impact.
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