We present an extensive study focused on partial network partitioning. Partial network partitions disrupt the communication between some but not all nodes in a cluster. First, we conduct a comprehensive study of system failures caused by this fault in 13 popular systems. Our study reveals that the studied failures are catastrophic (e.g., lead to data loss), easily manifest, and are mainly due to design flaws. Our analysis identifies vulnerabilities in core systems mechanisms including scheduling, membership management, and ZooKeeper-based configuration management. Second, we dissect the design of nine popular systems and identify four principled approaches for tolerating partial partitions. Unfortunately, our analysis shows that implemented fault tolerance techniques are inadequate for modern systems; they either patch a particular mechanism or lead to a complete cluster shutdown, even when alternative network paths exist. Finally, our findings motivate us to build Nifty, a transparent communication layer that masks partial network partitions. Nifty builds an overlay between nodes to detour packets around partial partitions. Nifty provides an approach for applications to optimize their operation during a partial partition. We demonstrate the benefit of this approach through integrating Nifty with VoltDB, HDFS, and Kafka.

We present COOL, a simple and generic structure for MPI collective operations. COOL enables highly efficient designs for all collective operations in the cloud. We then present a system design based on COOL that implements frequently used collective operations. Our design efficiently uses the intra-rack network while minimizing cross-rack communication, thus improving the application performance and scalability. We use recent software-defined networking capabilities to build optimal network paths for I/O intensive collective operations. Our analytical evaluation shows that our design imposes the least possible network overhead across racks. Furthermore, when compared with OpenMPI and MPICH, our design reduces the number of steps to only three, decreases the number of exchanged messages by a factor of N, the total number of processes, and reduces the network load by up to an order of magnitude. These significant improvements come at the cost of a modest increase in the computation load on a few processes.

Defending against cache pollution attacks, highly detrimental attacks that are easy to implement in Named-Data Networking (NDN), currently suffers from the lack of coordination. Solving cache pollution attacks is a prerequisite for the deployment of NDN, which is widely considered to be the basis for the future Internet. We present CoMon++ to this end, a framework for lightweight coordination that protects from cache pollution and further attacks in NDN. Our simulation studies demonstrate that CoMon++ efficiently and effectively prevents cache pollution, remarkably outperforming a very notable state-of-the-art solution.