Recent advances in persistent memory (PM) enable fast, byte-addressable main memory that maintains its state across power cycling events. To survive power outages and prevent inconsistent application state, current approaches introduce persistent logs and require expensive cache flushes. Thus, these solutions cause a performance penalty of up to 10x for write operations on PM. With respect to wear-out effects, and a significantly lower write performance compared to read operations, we identify this as a major flaw that impacts performance and lifetime of PM. In addition, most PM technologies are susceptible to soft-errors that cause corrupted data, which implies a high risk of a permanently inconsistent system state.

The Routing Protocol for Low power and Lossy Networks (RPL) is a wide-spread, flexible routing protocol specifically designed for energy-constrained networks such as Wireless Sensor Networks (WSNs). The resilience of RPL has been verified in several existing evaluations, but how a simple node reset can impact the energy efficiency has not been investigated so far. Thus, in this paper we examine the energy costs of transient node failures in a RPL network. We show that such temporary node failures can have a significant impact on the network's total energy usage. As transient node failures are common in WSNs this work reveals an important weakness of RPL which can even be exploited for attacks.

Power failures in data centers and Cloud Computing infrastructures can cause loss of data and impact revenue. Existing best practice such as persistent logging and checkpointing add overhead during operation and increase recovery time. Other solutions like the use of an uninterruptable power supply incur additional costs and are maintenance-intensive. Novel persistent main memory, i.e. memory that retains stored data without an external source of power, firstly prevents data loss in case of a power outage, secondly reduces the time for a system reboot and thirdly enables to continue operation at full-speed after a recovery. Yet new architectures and programming models are required to utilize persistent main memory. We present Temporality a virtualization layer that runs virtual machines in persistent memory and offers virtual persistent memory. It can be used as a basis for future Cloud platforms to allow applications the utilization of persistent memory without any changes. It provides safety of volatile data, significantly decreases overall recovery time and prevents subsequent performance degradation.

State-machine replication has received widespread attention for the provisioning of highly available services in data centers. However, current production systems focus on tolerating crash faults only and prominent service outages caused by state corruptions have indicated that this is a risky strategy. In the future, state corruptions due to transient faults (such as bit flips) become even more likely, caused by ongoing hardware trends regarding the shrinking of structure sizes and reduction of operating voltages. In this paper we present Crosscheck, an approach to tolerate arbitrary state corruption (ASC) in the context of fault-tolerant replication of multithreaded services. Crosscheck is able to detect silent data corruptions ahead of execution, and by crosschecking state changes with co-executing replicas, even ASCs can be detected. Finally, fault tolerance is achieved by a fine-grained recovery using fault-free replicas. Our implementation is transparent to the application by utilizing fine-grained software-hardening mechanisms using aspect-oriented programming. To validate Crosscheck we present a replicated multithreaded key-value store that is resilient to state corruptions.

This paper presents an algorithm for the global localization of mobile robots. The general idea is to build a local map incrementally which is matched to a global map in each localization step. The matching algorithm is a very time and memory efficient enhancement of the common Random Sample Consensus (RANSAC). It is executed a fixed number of iterations computing a set of hypotheses of the current robot pose. This paper describes how to handle the resulting hypotheses, i.e. a method is introduced deciding when the robot is localized reliably enough. The algorithm has been implemented and its characteristics are evaluated and discussed in an experimental section.