Paper: Using Gossip Enabled Distributed Circuit Breaking for Improving Resiliency of Distributed Systems
Table of Contents
The circuit breaker resiliency pattern has been popularly used to improve the resiliency of distributed systems. We targetted a scope of improvement in this pattern by bringing in some modifications to the traditional circuit breaker pattern and coupling it with a gossip-based information dissemination protocol. We modelled the relevant systems using UPPAAL and provided data supporting the efficacy of our proposals. This repository holds the three UPPAAL models that were used to claim the efficacy. These proposals have been submitted and accepted for presentation at the 19th IEEE International Conference on Software Architecture (ICSA 2022) as a full technical paper.
Running and experimenting with the models require UPPAAL v4.1.25. The experiments were conducted on a machine using Linux (Ubuntu 20.04) operating system with the 64-bit installation of UPPAAL available on their website. The model .xml files can be launched using the UPPAAL GUI.
To support our claims of superiority of Gossip Enabled Distributed Circuit Breaking (GEDCB) over the traditional circuit breaker pattern, we modelled three systems - a system that uses GEDCB, one that uses the traditional circuit breakers (CB) and a baseline system that uses no resiliency pattern. We can get a good idea about the efficacy of our proposals based on the relative performance of the three models.
The system models are composed from the following components:
- Server
- Clients (using GEDCB, traditional circuit breaker, no resiliency pattern)
- Client Shuffler
- Gossiper
- Gossip Set Revisor
Systems under study:
- Using no resiliency pattern - One Server, Eight Clients (using no resiliency pattern), One Client Shuffler
- Using traditional CB - One Server, Eight Clients (using traditional circuit breaker), One Client Shuffler
- Using GEDCB - One Server, Eight Clients (using GEDCB), eight (corresponding) Gossipers, One Client Shuffler, One Gossip Set Reviser
| Parameter | Meaning | Value Used |
|---|---|---|
| resp_time | Response Time | 4 |
| time_out | Timeout Period | 25 |
| UST | Unavailability Streak Time | 250 |
| window_size | Window Size | 10 |
| softFT | Soft Failure Threshold | 2 |
| (H)FT | (Hard) Failure Threshold | 6 |
| HOFT | Half-Open Failure Threshold | 1 |
| HOST | Half-Open Success Threshold | 2 |
| SST | Suspicion Success Threshold | 2 |
| OD | Open Duration | 100 |
| s_delay | Client Shuffling Period | 500 |
| delay | Client Gossiper Period | 4 |
| revision_time | Gossip Set Revision Period | 40 |
| MRC | Maximum Request Count | 500 |
| gossip_count | Number of clients gossiped to | 2 |
| infection_count | Number of clients receiving set revision message | 2 |
| Availability | ASRC | USC |
|---|---|---|
| 1 | 500 | 0 |
| 0.8 | 167 | 2 |
| 0.6 | 84 | 5 |
| 0.4 | 39 | 12 |
| 0.2 | 16 | 32 |
The variables of interest are TC and exec where the former hold the number of timeouts the system encounters to successfully address MRC number of requests and the latter one holds the total time required to do the same.
We performed the following two example SMC queries on the three models:
E[<= 10500; 500](max : TC)
E[<= 10500; 500](max : exec)
The queries shown above provide the expected maximum value of the variable TC and the clock variable exec respectively within the time 10500 units evaluated over 500 simulations (use 50 simulations for a less accurate but faster result). For every given availability value configuration and the system at hand, we first run a few simulations to find the time bound by which the system halts and use this time period for the above SMC queries For example, we found that 10500 is a time bound by which all our modelled systems halted. Such simulations may be run using a query like:
simulate [<=10500; 1] {TC}
Once the system halts, the two variables of interest do not increase in value with time. Hence the queries to find the expected maximum value of the variables within this experimentally found time-bound provide the correct estimations.
To reproduce the results of the study, use the step by step procedure mentioned here
Distributed under the MIT License. See LICENSE.md for more information.
Aashay Palliwar - avp10@iitbbs.ac.in
Srinivas Pinisetty - spinisetty@iitbbs.ac.in