More Like this

1. Diversity vs. Parallelism in Distributed Computing with Redundancy

   https://doi.org/10.1109/ISIT44484.2020.9174030  

   Peng, Pei ; Soljanin, Emina ; Whiting, Philip ( June 2020 , 2020 IEEE International Symposium on Information Theory (ISIT'20))

   null (Ed.)
2. Consistency vs. Availability in Distributed Cyber-Physical Systems

   https://doi.org/10.1145/3609119  

   Lee, Edward A. ; Akella, Ravi ; Bateni, Soroush ; Lin, Shaokai ; Lohstroh, Marten ; Menard, Christian ( October 2023 , ACM Transactions on Embedded Computing Systems)

   In distributed applications, Brewer’s CAP theorem tells us that when networks become partitioned (P), one must give up either consistency (C) or availability (A). Consistency is agreement on the values of shared variables; availability is the ability to respond to reads and writes accessing those shared variables. Availability is a real-time property whereas consistency is a logical property. We extend consistency and availability to refer to cyber-physical properties such as the state of the physical system and delays in actuation. We have further extended the CAP theorem to relate quantitative measures of these two properties to quantitative measures of communication and computation latency (L), obtaining a relation called the CAL theorem that is linear in a max-plus algebra. This paper shows how to use the CAL theorem in various ways to help design cyber-physical systems. We develop a methodology for systematically trading off availability and consistency in application-specific ways and to guide the system designer when putting functionality in end devices, in edge computers, or in the cloud. We build on theLingua Francacoordination language to provide system designers with concrete analysis and design tools to make the required tradeoffs in deployable embedded software.

    

   more » « less
3. RDMA vs. RPC for Implementing Distributed Data Structures

   https://doi.org/10.1109/IA349570.2019.00009  

   Brock, Benjamin A. ; Chen, Yuxin ; Yan, Jiakun ; Owens, John D. ; Buluc, Aydin ; Yelick, Katherine ( November 2019 , Proceedings of the IEEE/ACM 9th Workshop on Irregular Applications: Architectures and Algorithms)

   Distributed data structures are key to implementing scalable applications for scientific simulations and data analysis. In this paper we look at two implementation styles for distributed data structures: remote direct memory access (RDMA) and remote procedure call (RPC). We focus on operations that require individual accesses to remote portions of a distributed data structure, e.g., accessing a hash table bucket or distributed queue, rather than global operations in which all processors collectively exchange information. We look at the trade-offs between the two styles through microbenchmarks and a performance model that approximates the cost of each. The RDMA operations have direct hardware support in the network and therefore lower latency and overhead, while the RPC operations are more expressive but higher cost and can suffer from lack of attentiveness from the remote side. We also run experiments to compare the real-world performance of RDMA- and RPC-based data structure operations with the predicted performance to evaluate the accuracy of our model, and show that while the model does not always precisely predict running time, it allows us to choose the best implementation in the examples shown. We believe this analysis will assist developers in designing data structures that will perform well on current network architectures, as well as network architects in providing better support for this class of distributed data structures. 

   more » « less
4. Single vs Distributed Edge Caching for Dynamic Content

   https://doi.org/10.1109/TNET.2021.3121098  

   Abolhassani, Bahman ; Tadrous, John ; Eryilmaz, Atilla ( April 2022 , IEEE/ACM Transactions on Networking)
5. Minibatch vs Local SGD for Heterogeneous Distributed Learning

   Woodworth, Blake ; Patel, Kumar Kshitij ; Srebro, Nathan ( June 2020 , Advances in Neural Information Processing Systems 33 (NeurIPS 2020))

   null (Ed.)