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1. Strong and Efficient Consistency with Consistency-aware Durability

   Ganesan, Aishwarya; Alagappan, Ram; Arpaci-Dusseau, Andrea; Arpaci-Dusseau, Remzi (February 2021, ACM transactions on storage)

   null (Ed.)

   We introduce consistency-aware durability or Cad, a new approach to durability in distributed storage that enables strong consistency while delivering high performance. We demonstrate the efficacy of this approach by designing cross-client monotonic reads, a novel and strong consistency property that provides monotonic reads across failures and sessions in leader-based systems; such a property can be particularly beneficial in geo-distributed and edge-computing scenarios. We build Orca, a modified version of ZooKeeper that implements Cad and cross-client monotonic reads. We experimentally show that Orca provides strong consistency while closely matching the performance of weakly consistent ZooKeeper. Compared to strongly consistent ZooKeeper, Orca provides significantly higher throughput (1.8--3.3×) and notably reduces latency, sometimes by an order of magnitude in geo-distributed settings. We also implement Cad in Redis and show that the performance benefits are similar to that of Cad’s implementation in ZooKeeper. 

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2. Strong and Efficient Consistency with Consistency-Aware Durability

   Ganesan, A; Alagappan, R.; Arpaci-Dusseau, A.; Arpaci-Dusseau, R. (April 2020, USENIX FAST)

   We introduce consistency-aware durability or CAD, a new approach to durability in distributed storage that enables strong consistency while delivering high performance. We demonstrate the efficacy of this approach by designing cross-client monotonic reads, a novel and strong consistency property that provides monotonic reads across failures and sessions in leader-based systems. We build ORCA, a modified version of ZooKeeper that implements CAD and cross-client mono- tonic reads. We experimentally show that ORCA provides strong consistency while closely matching the performance of weakly consistent ZooKeeper. Compared to strongly consistent ZooKeeper, ORCA provides significantly higher through- put (1.8 – 3.3x), and notably reduces latency, sometimes by an order of magnitude in geo-distributed settings. 

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3. Strong and Efficient Consistency with Consistency-Aware Durability

   Ganesan, Aishwarya; Alagappan, Ramnatthan; Arpaci-Dusseau, Andrea; Arpaci-Dusseau, Remzi (January 2020, 18th USENIX Conference on File and Storage Technologies (FAST '20))

   We introduce consistency-aware durability or CAD, a new approach to durability in distributed storage that enables strong consistency while delivering high performance. We demonstrate the efficacy of this approach by designing cross-client monotonic reads, a novel and strong consistency property that provides monotonic reads across failures and sessions in leader-based systems. We build ORCA, a modified version of ZooKeeper that implements CAD and cross-client monotonic reads. We experimentally show that ORCA provides strong consistency while closely matching the performance of weakly consistent ZooKeeper. Compared to strongly consistent ZooKeeper, ORCA provides significantly higher throughput (1.8 – 3.3×), and notably reduces latency, sometimes by an order of magnitude in geo-distributed settings. 

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4. A note on the consistency operator

   https://doi.org/10.1090/proc/14948  

   Walsh, James (June 2020, Proceedings of the American Mathematical Society)
5. The Core Consistency of a Compressed Tensor

   https://doi.org/10.1109/DSW.2019.8755593  

   Tsitsikas, Georgios; Papalexakis, Evangelos E. (June 2019, 2019 IEEE Data Science Workshop (DSW))

   Tensor decomposition on big data has attracted significant attention recently. Among the most popular methods is a class of algorithms that leverages compression in order to reduce the size of the tensor and potentially parallelize computations. A fundamental requirement for such methods to work properly is that the low-rank tensor structure is retained upon compression. In lieu of efficient and realistic means of computing and studying the effects of compression on the low rank of a tensor, we study the effects of compression on the core consistency; a widely used heuristic that has been used as a proxy for estimating that low rank. We provide theoretical analysis, where we identify sufficient conditions for the compression such that the core consistency is preserved, and we conduct extensive experiments that validate our analysis. Further, we explore popular compression schemes and how they affect the core consistency. 

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