Abstract
Despite years of research and the long-lasting promise of pervasiveness of an "Internet of Things", it is only recently that a truly convincing number of connected things have been deployed in the wild. New services are now being built on top of these things and allow to realize the IoT vision. However, integration of things in complex and interconnected systems is still only in the hands of their manufacturers and of Cloud providers supporting IoT integration platforms. Several issues associated with data privacy arise from this situation. Not only do users need to trust manufacturers and IoT platforms for handling their data, but integration between heterogeneous platforms is still only incipient. In this position paper, we chart a new IoT architecture, SAFETHINGS, that aims at enabling data privacy by design, and that we believe can serve as the foundation for a more comprehensive IoT integration. The SAFETHINGS architecture is based on two simple but powerful conceptual component families, the cleansers and blenders, that allow data owners to get back the control of IoT data and its processing.

Abstract
The increasing demand for real-time analytics requires the fusion of Transactional (OLTP) and Analytical (OLAP) systems, eschewing ETL processes and introducing a plethora of proposals for the so-called Hybrid Analytical and Trans-actional Processing (HTAP) systems. Unfortunately, current benchmarking approaches are not able to comprehensively produce a unified metric from the assessment of an HTAP system. The evaluation of both engine types is done separately, leading to the use of disjoint sets of benchmarks such as TPC-C or TPC-H. In this paper we propose a new benchmark, HTAPBench, providing a unified metric for HTAP systems geared toward the execution of constantly increasing OLAP requests limited by an admissible impact on OLTP performance. To achieve this, a load balancer within HTAPBench regulates the coexistence of OLTP and OLAP workloads, proposing a method for the generation of both new data and requests, so that OLAP requests over freshly modified data are comparable across runs. We demonstrate the merit of our approach by validating it with different types of systems: OLTP, OLAP and HTAP; showing that the benchmark is able to highlight the differences between them, while producing queries with comparable complexity across experiments with negligible variability.

Abstract
Several methods have been proposed for performing schedulability analysis for both uni-processor and multi-processor real-time systems. Very few of these works use the power of formal logic to write unambiguous specifications and to allow the usage of theorem provers for building the proofs of interest with greater correctness guarantees. In this paper we address this challenge by: 1) defining a formal language that allows to specify periodic resource models; 2) describe a transformational approach to reasoning about timing properties of resource models by transforming the latter specifications into a satisfiability modulo theories problem.

Abstract
Conflict-free Replicated Data Types (CRDTs) are data abstractions (registers, counters, sets, maps, among others) that provide a relaxed consistency model called Eventual Consistency. Current designs for CRDT counters do not scale, having a size linear with the number of both active and retired nodes (i.e., nodes that leave the system permanently after previously manipulating the value of the counter). In this paper we present a new counter design called Borrow-Counter, that provides a mechanism for the retirement of transient nodes, keeping the size of the counter linear with the number of active nodes.

Abstract
Conflict-free Data Types (CRDTs) were designed to automatically resolve conflicts in eventually consistent systems. Different CRDTs were designed in both operation-based and state-based flavors such as Counters, Sets, Registers, Maps, etc. In a previous paper [2], Baquero et al. presented the problem with embedded CRDT counters and a solution, covering state-based counters that can be embedded in maps, but needing an ad-hoc extension to the standard counter API. Here, we present a resettable operation-based counter design, with the standard simple API and small state, through a causal-stability-based state compaction.

Abstract
Eventual consistency (EC) is a relaxed data consistency model that, driven by the CAP theorem, trades prompt consistency for high availability. Although, this model has shown to be promising and greatly adopted by industry, the state of the art only assumes that replicas can crash and recover. However, a Byzantine replica (i.e., arbitrary or malicious) can hamper the eventual convergence of replicas to a global consistent state, thus compromising the entire service. Classical BFT state machine replication protocols cannot solve this problem due to the blocking nature of consensus, something at odd with the availability via replica divergence in the EC model. In this work in progress paper, we introduce a new secure highly available protocol for the EC model that assumes a fraction of replicas and any client can be Byzantine. To respect the essence of EC, the protocol gives priority to high availability, and thus Byzantine detection is performed off the critical path on a consistent data offset. The paper concisely explains the protocol and discusses its feasibility. We aim at presenting a more comprehensive and empirical study in the future.