REX: A Development Platform and Online Learning Approach for Runtime Emergent Software Systems


Introduction:

The idea in this paper is similar to a paper[Auto DBMS Tuning...] we have discussed on class before. In the paper[Auto DBMS Tuning...], the author use ML model to automatically tune database knobs, while in this paper the author use ML model to optimize the combination of components in a complex software system to achieve better performance.

The whole framework could be divided into 3 parts:

1.  Dane: A light weight programming language for implementing small components. These components could be assembled together to build a large scale software system. Some of these components may have the same function but do jobs in different ways. The main contribution of Dane is that it is fast so that different component could be dynamically rewired with very low cost.

2.  A perception, assembly and learning framework (PAL): It contains 2 modules: 1). Assembly Module. It could assemble different components to implement a feature. 2). Perception Module. It could monitor the performance of the software system, as well as the runtime environment.

3.  The online learning part. It uses reinforcement learning model “multi-armed bandit” to optimize the selection of components. Also, like the [Auto DBMS Tuning...] paper, some selection of components are related so they could share information. Thus, the author uses regression model to reduce the search space.

Application of ML method:

It used a continuous way when updating configuration. First, the software will run with a random combination of components. After several seconds, the PAL will collect perception data, and the online learning part will update estimates and update the model. Then the software will run under the new configuration for several seconds, and update the model again. In this way we could better shape the software system to the environment.

 Positive Points:

1.  The idea is interesting. It used an automatic ML model to replace manually written rules in previous work. This idea could also apply to other computer system problems where configurations and performance are linked.

2.  The usage of regression factor model reduces the search space, which achieved balancing exploration and exploitation.
2.  Compared to [Auto DBMS Tuning...] paper, this paper considered how to adapt to the changes in deployment environment. 

Negative Points:

1.  The performance of the model deeply relies on the components implemented by Dane. Firstly, it means that this model could not be applied to present software systems that are written in more common languages, like C++ and java. Secondly, this requires that the whole software could be disassembled into multiple irrelevant, small, dynamic components. In some situations it may not be guaranteed.

2.  For a specific tasks(like web server), the author could have built a configuration dataset, so when first running the model, the application could be have a less optimal, but not bad combination of components. This could be better than choosing randomly at first.



Yuanli Wang


Comments

  1. jon_weissmanApril 4, 2019 at 5:49 AM

    You are correct that the paper is limited to decomposable applications that fit this particular structure. It is certainly interesting. However, the emergent learning didn't discover anything surprising or hard for a human to have encoded using simple if-then clauses within the code.

    ReplyDelete

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