Making BFT Protocols Really Adaptive

Many state-machine Byzantine Fault Tolerant (BFT) protocols have been introduced so far. Each protocol addressed a different subset of conditions and use-cases. However, if the underlying conditions of a service span different subsets, choosing a single protocol will likely not be a best fit. This yields robustness and performance issues which may be even worse in services that exhibit fluctuating conditions and workloads. In this paper, we reconcile existing state-machine BFT protocols in a single adaptive BFT system, called ADAPT, aiming at covering a larger set of conditions and use-cases, probably the union of individual subsets of these protocols. At anytime, a launched protocol in ADAPT can be aborted and replaced by another protocol according to a potential change (an event) in the underlying system conditions. The launched protocol is chosen according to an "evaluation process" that takes into consideration both: protocol characteristics and its performance. This is achieved by applying some mathematical formulas that match the profiles of protocols to given user (e.g., service owner) preferences. ADAPT can assess the profiles of protocols (e.g., throughput) at run-time using Machine Learning prediction mechanisms to get accurate evaluations. We compare ADAPT with well known BFT protocols showing that it outperforms others as system conditions change and under dynamic workloads.

[1]  Fred B. Schneider,et al.  Implementing fault-tolerant services using the state machine approach: a tutorial , 1990, CSUR.

[2]  Thomas A. Henzinger,et al.  Quantitative Evaluation of BFT Protocols , 2011, 2011 Eighth International Conference on Quantitative Evaluation of SysTems.

[3]  Arun Venkataramani,et al.  Separating agreement from execution for byzantine fault tolerant services , 2003, SOSP '03.

[4]  Mike Hibler,et al.  An integrated experimental environment for distributed systems and networks , 2002, OPSR.

[5]  Marko Vukolic,et al.  The Next 700 BFT Protocols , 2015, ACM Trans. Comput. Syst..

[6]  Ravishankar K. Iyer,et al.  Chameleon: A Software Infrastructure for Adaptive Fault Tolerance , 1999, IEEE Trans. Parallel Distributed Syst..

[7]  Ali Shoker,et al.  BFT Selection , 2013, NETYS.

[8]  Switzerland rachid. guerraoui.  Stretching BFT , 2010 .

[9]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[10]  Fabrice Huet,et al.  Adaptive Fault Tolerance in Real Time Cloud Computing , 2011, 2011 IEEE World Congress on Services.

[11]  Atul Singh,et al.  BFT Protocols Under Fire , 2008, NSDI.

[12]  Michael Dahlin,et al.  Making Byzantine Fault Tolerant Systems Tolerate Byzantine Faults , 2009, NSDI.

[13]  Ali Shoker,et al.  Towards Byzantine Resilient Directories , 2012, 2012 IEEE 11th International Symposium on Network Computing and Applications.

[14]  Ramakrishna Kotla,et al.  Zyzzyva , 2007, SOSP.

[15]  John Lane,et al.  Prime: Byzantine Replication under Attack , 2011, IEEE Transactions on Dependable and Secure Computing.

[16]  Michael K. Reiter,et al.  Fault-scalable Byzantine fault-tolerant services , 2005, SOSP '05.

[17]  Leslie Lamport,et al.  The Byzantine Generals Problem , 1982, TOPL.

[18]  Miguel Castro,et al.  Practical byzantine fault tolerance and proactive recovery , 2002, TOCS.

[19]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[20]  Ing-Ray Chen,et al.  Adaptive Fault-Tolerant QoS Control Algorithms for Maximizing System Lifetime of Query-Based Wireless Sensor Networks , 2011, IEEE Transactions on Dependable and Secure Computing.

[21]  Sushil Jajodia,et al.  An adaptive data replication algorithm , 1997, TODS.