Policy makers in regions such as Europe are increasingly concerned about the trustworthiness and sovereignty of the foundations of their digital economy, because it often depends on systems operated or manufactured elsewhere. To help curb this problem, we propose the novel notion of a responsible Internet, which provides higher degrees of trust and sovereignty for critical service providers (e.g., power grids) and all kinds of other users by improving the transparency, accountability, and controllability of the Internet at the network-level. A responsible Internet accomplishes this through two new distributed and decentralized systems. The first is the Network Inspection Plane (NIP), which enables users to request measurement-based descriptions of the chains of network operators (e.g., ISPs and DNS and cloud providers) that handle their data flows or could potentially handle them, including the relationships between them and the properties of these operators. The second is the Network Control Plane (NCP), which allows users to specify how they expect the Internet infrastructure to handle their data (e.g., in terms of the security attributes that they expect chains of network operators to have) based on the insights they gained from the NIP. We discuss research directions and starting points to realize a responsible Internet by combining three currently largely disjoint research areas: large-scale measurements (for the NIP), open source-based programmable networks (for the NCP), and policy making (POL) based on the NIP and driving the NCP. We believe that a responsible Internet is the next stage in the evolution of the Internet and that the concept is useful for clean slate Internet systems as well.

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Traditional cloud Service Level Agreement (SLA) suffers from lacking a trustworthy platform for automatic enforcement. The emerging blockchain technique brings in an immutable solution for tracking transactions among business partners. However, it is still very challenging to prove the credibility of possible violations in the SLA before recording them onto the blockchain. To tackle this challenge, we propose a witness model using game theory and the smart contract techniques. The proposed model extends the existing service model with a new role called 'witness' for detecting and reporting service violations. Witnesses gain revenue as an incentive for performing these duties, and the payoff function is carefully designed in a way that trustworthiness is guaranteed: in order to get the maximum profit, the witness has to always tell the truth. This is analyzed and proved through game theory using the Nash equilibrium principle. In addition, an unbiased sortition algorithm is proposed to ensure the randomness of the independent witnesses selection from the decentralized witness pool, to avoid possible unfairness or collusion. An auditing mechanism is also introduced in the paper to detect potential irrational or malicious witnesses. We have prototyped the system leveraging the smart contracts of Ethereum blockchain. Experimental results demonstrate the feasibility of the proposed model and indicate good performance in accordance with the design expectations.

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The Dutch Advanced School for Computing and Imaging has built five generations of a 200-node distributed system over nearly two decades while remaining aligned with the shifting computer science research agenda. The system has supported years of award-winning research, underlining the benefits of investing in a smaller-scale, tailored design.@en

Due to increasingly large datasets, graph analytics - traversals, all-pairs shortest path computations, centrality measures, etc. - are becoming the focus of high-performance computing (HPC). Because HPC is currently dominated by many-core architectures (both CPUs and GPUs), new graph processing solutions have to be defined to efficiently use such computing resources. Prior work focuses on platform-specific performance studies and on platform-specific algorithm development, successfully proving that algorithms highly tuned to GPUs or multi-core CPUs can provide high performance graph analytics. However, the portability of such algorithms remains an important concern for many users, especially the many companies without the resources to invest in HPC or concerned about lock-in in single-use parallel techniques. In this work, we investigate the functional portability and performance of graph analytics algorithms. We conduct an empirical study measuring the performance of 3 graph analytics algorithms (a single code implemented in OpenCL and targeted at many-core CPUs and GPUs), on 3 different platforms, using 11 real-world and synthetic datasets. Our results show that the code is functionally portable, that is, applications can run unchanged on both CPUs and GPUs. The large variation in their observed performance indicates that portability is necessary not only for productivity, but, surprisingly, also for performance. We conjecture that the impact of datasets on performance is too high to allow platform-specific algorithms to outperform the portable algorithms by large margins, in all cases. Our conclusion is that portable parallel graph analytics is feasible without significant performance loss, and provides a productive alternative to the expensive trial-and-error selection of one algorithm for each (graph,platform) pair. @en