To Adapt or Not to Adapt: A Quantification Technique for Measuring an Expected Degree of Self-Adaptation †
Abstract
:1. Introduction
- How can we quantify the observed adaptation effort within a large-scale SASO system consisting of a possibly large set of autonomous entities or subsystems?
- How can we derive statements about the stability of configurations if self-adaptation of the individual entities/subsystems are continuously happening and desired?
- How can we determine an expected degree of self-adaptation of this overall SASO system according to a given environmental context (or a ’situation’)?
2. System Model
3. State of the Art
4. Quantification of Expected Self-Adaptation
4.1. Challenges for Defining a Measurement Framework
4.2. Measuring a Degree of Adaptation Based on Generative Probabilistic Models
- In comparison to approaches following the concept of measuring autonomy as outlined, e.g., in [26], our method does not rely on a static encoding of possible configurations pre-defined in number of bits.
- In comparison to approaches using the discrete entropy (e.g., outlined for measuring emergence in [34]), it is continuous and does not rely on binning (which introduces more parameters and a certain bias).
- It is independent of the notion of other concepts such as self-organization or emergence.
- Although it makes use of measurement period (windows), it is continuously applied. It does not need a trigger (e.g., the detection of a disturbance as used [27], for instance).
- It does not require a model of the internal decision processes of the autonomous subsystems , since it just considers the externally visible configuration settings.
- It can easily be applied to, e.g., hierarchical structures of SASO systems in terms of considering only those subsystems that belong to a certain authority.
- It is zero if both distributions (i.e., derived from the reference and the current observation period) are identical. This means that the same configurations are observed. Theoretically, there may be switches between different agents running the configuration of the other one and vice versa, but in general this is a reliable approximation for constant behavior.
- In turn, high values of KL indicate strong changes in the configurations of the contained subsystems. This indicates a high degree of self-adaptation.
- A major issue in this context is that the values of KL highly depend on the considered feature vector (i.e., the number, the type, and the resolution of the configuration parameters) as well as the frequency in which adaptations are done. These values are highly application-dependent. However, the comparison is always made within the domain—meaning that the ordering is correct, but the individual actual value does not much about the severity of the change. This can be defined in relation to a set of observations, i.e., based on experiences with the application under investigation. This leaves the question open how to determine a real ’level’ out of the KL values.
5. Use Cases and Evaluation
5.1. Application 1: Traffic Control
5.1.1. Parameters
5.1.2. Experimental Setup
5.1.3. Evaluation
5.2. Application 2: Swidden Farming Model
5.2.1. Parameters
5.2.2. Experimental Setup
5.2.3. Evaluation
5.3. Application 3: Organic Network Control
5.3.1. Parameters
5.3.2. Experimental Setup
5.3.3. Evaluation
5.4. Discussion and Threats to the Approach
- Considering the absolute values given in Table 4 and Table 1 we can see that the values themselves come without meaning. We cannot make any statement about the relation between the changes. In particular, the differences just show that there is a change or a stronger/smaller change, but statements such as “X is twice as strong as Y” are not possible.
- A better approach than should come up with values that can be normalized to the standard interval , since this allows for better comparability.
- In turn, simply counting configurations and providing frequencies as done in Figure 10 describes just a small part of the truth, since it completely neglects the actual changes including the differences between two subsequent configurations.
- We can further see that the measurement is independent of changes in the number of participating subsystems, since it just collects samples of configuration vectors no matter how many in a certain time period. This is especially important in the context of open systems, where subsystems are free to join and leave at any time (e.g., in the context of Interwoven System, see [5,60]). However, each new subsystem has a severe impact on the measure.
- The values of KL highly depend on the considered feature vector (i.e., the number, the type, and the resolution of the configuration parameters) as well as the frequency in which adaptations are done. These values are highly application-dependent. For a generalized applicability, more studies and a systematic analysis of the search space are required.
- The absolute value of KL cannot be mapped directly to a ’degree of self-adaptation’—it just gives an ordering. It remains subject to future work how this can be done (e.g., by sampling more values of KL and estimating the range of values or synthetically generating strongly deviating distributions based on the specifications of the feature space).
- The changes in the adaptation behavior are quantified as difference between distributions of configurations within measurement cycles. Considering this as novel process or abnormal behavior suggests to augment the approach with mechanisms for anomaly detection (e.g., using approaches for self-adaptive systems as outlined in [61]).
- The considered cases all come with a feasible number of parameters. It remains open how the measurement behaves in high-dimensional spaces (i.e., how the dimensionality problem manifests in this context). However, in such high-dimensional scenarios, the self-adaptation mechanism itself may reach its limits.
- The measure is meant as an approach to analyzing and observing the system behavior at runtime. The determined values will be used as indicators later—which in turn may serve as basis for steering the self-adaptation behavior later (e.g., based on identifying an appropriate level of adaptation).
- The question is still if there can be something like an ’optimal’ degree of self-adaptation. We assume that such a value will not be based on quality attributes only, i.e., those that are already considered in the utility function or in the decision mechanism. In contrast, we user acceptance or stability of the solutions are candidates to consider in such an assessment.
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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ID | Parameter Name | Description | Parameter Range |
---|---|---|---|
1 | move-dist | The distance an agent moves if it must find a new | [1;5] |
location for its farmstead. | |||
2 | move-threshold | The households moves to a new unoccupied area if | [0;1] |
its energy level drops below this threshold. | |||
3 | fission-rate | Amount of energy needed to reproduce a new household | [1;2] |
Defined about the initial household energy. | |||
4 | farm-dist | Maximum distance an agent will travel to a farm | [1;20] |
Radius of area a farmstead claims ownership, if | |||
this is not already occupied by another farmstead | |||
5 | min-fertility | Minimum fertility of a patch for being considered | [0;1] |
as next farmstead location. |
Distribution | Reference Distributions | Current Distribution | |
---|---|---|---|
Distribution | Reference Distributions | Current Distribution | |
---|---|---|---|
Parameter | Standard Configuration |
---|---|
Delay | |
AllowedHelloLoss | |
HelloInterval | |
HelloInterval | |
Packet count | |
Minimum difference | |
NACK timeout | |
NACK retries |
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Tomforde, S.; Goller, M. To Adapt or Not to Adapt: A Quantification Technique for Measuring an Expected Degree of Self-Adaptation. Computers 2020, 9, 21. https://doi.org/10.3390/computers9010021
Tomforde S, Goller M. To Adapt or Not to Adapt: A Quantification Technique for Measuring an Expected Degree of Self-Adaptation. Computers. 2020; 9(1):21. https://doi.org/10.3390/computers9010021
Chicago/Turabian StyleTomforde, Sven, and Martin Goller. 2020. "To Adapt or Not to Adapt: A Quantification Technique for Measuring an Expected Degree of Self-Adaptation" Computers 9, no. 1: 21. https://doi.org/10.3390/computers9010021