Time-Aware Service Ranking Prediction in the Internet of Things Environment

ABSTRACT

With the rapid development of the Internet of things (IoT), building IoT systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedures of building IoT systems, QoS-aware service selection is an important concern, which requires the ranking of a set of functionally similar services according to their QoS values. In reality, however, it is quite expensive and even impractical to evaluate all geographically-dispersed IoT services at a single client to obtain such a ranking. Nevertheless, distributed measurement and ranking aggregation have to deal with the high dynamics of QoS values and the inconsistency of partial rankings.

To address these challenges, we propose a time-aware service ranking prediction approach named TSRPred for obtaining the global ranking from the collection of partial rankings. Specifically, a pairwise comparison model is constructed to describe the relationships between different services, where the partial rankings are obtained by time series forecasting on QoS values. The comparisons of IoT services are formulated by random walks, and thus, the global ranking can be obtained by sorting the steady-state probabilities of the underlying Markov chain. Finally, the efficacy of TSRPred is validated by simulation experiments based on large-scale real-world datasets.

PRELIMINARIES

Figure 1. Quality of service (QoS) Matrix

In this paper, we need analyze the QoS dataset to obtain the global service ranking. Assume there are n IoT services S = {s1, s2, …,sn} invoked by m users U = {u1, u2, …, um}. Each service has its QoS attributes monitored over some time, which include response time, throughput, etc. When a user invokes a IoT service, we can obtain the QoS information during t time intervals. By integrating all the QoS information from users, we form a three-dimensional user-service-time matrix as shown in figure 1.

MODEL OF SERVICE RANKING PREDICTION

Figure 2. Framework of service ranking prediction. QoS: quality of service; DTMC: discrete-time Markov chain

In this context, we put forward an approach to predict the global service ranking from partial service rankings, namely TSRPred, which can be decomposed into the following three phases, including pairwise comparison, time series forecasting, and service ranking. The framework of TSRPred is illuminated by Figure 2.

As Figure 2 shows, during the procedure of our approach, we obtain the original QoS data collected from the candidate IoT services at first, and then we compare the services by pairwise comparison model, which can fill the gap of inconsistent measurements. Once the pairwise comparison model is constructed, the comparisons are transformed to the QoS future value forecasting.

Figure 3. Pairwise comparison model

In this model, we focus on how to collect all partial rankings to obtain the global service ranking. The model demonstration is shown in Figure 3. It is assumed the set of candidate IoT service contains n IoT services with similar functionality, which are geographically dispersed in different locations.

In sight of this characteristic, we deploy m different clients to rate these services, and each of them only rates a portion of the candidate services. Thus the partial ranking of each subset service is rated at a client, finally, a centralized client is used to collect all the partial rankings and obtain the global service ranking.

ALGORITHMS FOR OBTAINING GLOBAL RANKING

In the previous section, a pairwise comparison model is constructed to obtain the partial service rankings, where the QoS differentials are forecasted by time series models. Furthermore, the discrete-time Markov chain based on random walks is modeled to derive the global ranking. In this section, we will detail the algorithms for obtaining global ranking.

Our approach for obtaining service ranking can shield the methodologies of how to rate the services, and obtain the global service ranking with limits and noise information. The procedures for obtaining global ranking are illuminated as follows.

Step 1 In the first step, our approach selects all services pairs based on the constructed pairwise comparison model.

Step 2 The future values of QoS differentials can be estimated by the fitted time series model for obtaining the partial service rankings.

Step 3 All partial rankings are aggregated and the transition matrix is calculated by the formula (6).

Step 4 Furthermore, DTMC with transition matrix P can be solved by π·P = π.

Step 5 Finally, the global service ranking is derived through steady-state probabilities ranking.

CASE STUDY

Figure 5. QoS Series

At first, we use the comparison between services with ID 4386 and 4009 invoked by client 1 to investigate the procedure of time series forecasting. In our approach, we should analyze the QoS comparison of ranked services. The QoS comparison series is shown in Figure 5.

Figure 8. Case study of the markov chain

Since the future comparison values are obtained by time series forecasting, we will construct the Markov chain based on the forecasting values. For instance, services with ID 4386 and 4009 are invoked by client 1 and 2, so the average of forecasting value 0.3263 is used to construct the Markov chain, which can be found in Figure 8. Finally, we can obtain the steady-state probabilities and sort them to derive the global ranking. The predicted global ranking is [S3242, S4386, S4009, S1], which is the same as the actual ranking.