10 260 510 760 50 100 150 200 250 300 350 400 450 K il ob yt es
No. of SDM from discovery servers (among 500)
A1-‐MP2P A1-‐MWeb A1-‐UCWeb A2-‐MP2P A2-‐MWeb A2-‐UCWeb
Figure 14: Bandwidth measurement comparison
Figure 14 illustrates the bandwidth measurement comparison between the two approaches (A1 and A2) recorded by SCORPII for the CPI calculation. The mea- surement of each approach consists of three cost elements: (1) MP2P, correspond- ing to the bandwidth cost of retrieving SDMs directly from proximal nodes; (2)
MWeb denotes the bandwidth cost of the mobile Internet connection; (3) UCWeb denotes the bandwidth cost of ScoUC for retrieving SDMs from discovery servers. Since A1 did not use Cloud at all. Hence, A1-UCWeb is always consuming 0 band- width. Although A2 used ScoUC to retrieve SDMs, it also consumed the mobile Internet bandwidth because the communication between ScoMH and ScoUC con- sumed a minor bandwidth, in which the number is around 9.5 kilobytes and it is too little to be shown on the gure.
0% 10% 20% 30% 40% 50% 1 2 3 4 5 6 7 8 9 0 CP U u sa ge Timestamp Approach 1 Approach 2
Figure 15: CPU usage comparison
Figure 15 illustrates the CPU usage comparison between A1 and A2 when 200 (among 500 nodes; the rest SDM were already retrieved directly from the smart objects) service discovery processes involved retrieving and processing SDMs from discovery servers. As the gure shows, A1 consumed CPU over 10% for 4 timestamps, and A2 consumed CPU over 10% for 2 timestamps because A2 distributed its tasks to ScoUC. The CPU cost element in our test case was based on how long the CPU usage retains over 10%.
0.00 5.00 10.00 15.00 20.00 25.00 30.00 50 100 150 200 250 300 350 400 450 Se co n d s
No. of SDM from discovery servers
Approach 1 Approach 2
5.2.1 Context-aware Cost Element Weighing
To attribute weights to the mentioned cost elements, the regression coe cients in equation (5) must be found. To obtain these, we used multiple linear regres- sion, using the context parameters as explanatory variables and the weight as the dependent variable, as already explained in section 3.3.
Table 1: Context parameters used in the regression training dataset Cost element Context parameters ad-
dressed in A1 Context parameters ad-dressed in A2 CPU cost CPU load, Battery level,
Connection type CPU load, instance type Mobile internet
cost Data plan cost, Connectiontype, Signal strength Data plan cost, Connectiontype, Signal strength Cloud instance
cost ∅ Cloud infrastructure load,Cloud endpoint distance For multiple linear regression, we need a dataset of observed weight values, then tting the linear equation optimally to that dataset gives us the coe cients. However, we were unable to obtain a dataset based on real-life data traces and as such, we decided to generate the dataset, using certain user preferences based on expert knowledge to de ne the behaviour how the weight values vary in response to context values.
First, we xed a set of context parameters for each cost element, as shown in Table 1. The context parameters for a given cost element may vary per approach. In some cases (such as with Cloud instance cost for A1 ), the context parameter set may be empty. In this case, the weight defaults to the value 1.
To obtain a meaningful dataset, in which the context parameter values aect the weights in a realistic manner, we used fuzzy rules and fuzzy inference. In order to do fuzzy inference, we rst had to de ne the context parameters as fuzzy variables.
The values of the context parameters have been normalized to the interval [0.0, 1.0]. Then, a fuzzy representation for the context parameter could be created. In addition, a fuzzy representation of the weight itself is also required in order to do fuzzy inference. An overview of the created fuzzy variables for our test scenario can be seen in Table 2.
In order to embed various desired qualities into the dataset, based on expert advice, fuzzy rules were de ned. An example rule is "If the battery is high and the signal strength is low, then cloud instance cost weight is high" or "If battery is medium and data plan cost is high, then the CPU cost weight is low".
Table 2: Fuzzy representation of the context parameters and weights Cost Ele-
ment A1 fuzzy context variables A2 fuzzy context variables
CPU Cost
Mobile in- ternet cost
With such rules and fuzzy variables in place, we used Mamdani fuzzy infer- ence [MA75] to infer weight values according to the fuzzy rules and a randomly generated set of raw fuzzi ed context parameter values. The inference process combines the distributions of the fuzzy rules in uencing the weight into a single fuzzy output distribution. Taking the centroid of this resulting distribution will give us an exact numeric value for the weight.
● ● ● ● ● ● ● ● ● 0.85 0.90 0.95 1.00 1.05 1.10 1.15 50 100 150 200 250 300 350 400 450 No. of SDM (among 500) CPI V alue ●Approach 1Approach 2
Figure 17: CPI comparison of the two approaches in the test scenario with Context A
These inferred weight values were then used with their corresponding context parameters values as training examples to t the multiple linear regression lines. The resulting values for our experiment can be seen in Table 3.
Table 3: Weight formulas with regression coe cients plugged in Cost element Weight formula with our test case
regression coe cients A1 CPU 0.435 + 0.1158 × CpuLoad + 0.1301 × ConnectionType − 0.1196 × Battery A2 CPU 0.4935 + 0.2265 × CpuLoad − 0.23 × InstanceType A1 Mobile bandwidth 0.558 − 0.121 × CpuLoad + 0.125
× DataPlan − 0.117 × Battery + 0.006 × Signal A2 Mobile bandwidth 0.507 − 0.239 × ConnectionType + 0.23 × DataPlan − 0.004 × Signal A2 Cloud bandwidth 0.462 + 0.117 × EndpointDistance − 0.122 × ConnectionType + 0.08 × CloudLoad
Table 4: Final weights of the cost elements with Context A Cost element A1 weight A2 weight
CPU 0.61 0.316
Mobile Bandwidth 0.428 0.2898 Cloud Bandwidth 1 0.405