Memory Management in Smart Home Gateway
Beizhong Chen, Ibrahim Kamel, Ivan Marsic · InTech eBooks · 2010
In this chapter, we have considered the problem of managing services and bundles in home gateways with limited amount of main memory. Because of the different architecture of home gateway using OSGi from the traditional computer architecture, a key difference between our problem and the traditional memory management is that the dependencies among different services have to be taken into consideration for a higher customers' satisfaction. We use a dependency graph to model the relationship among services. This chapter proposes two algorithms. The first one is an extension of Knapsack problem which finds the optimal solution in a polynomial time. The second one is a heuristic that spans the dependency graph and tries to free the required amount of memory while minimizing the number of terminated services. We compared the proposed techniques with the traditional memory management algorithms such as the best fit and worst fit. Our experimental results indicate that SD (service dependency) heuristic is a good candidate for use in practical environments, as its performance is close to the optimal solution in terms of the number of stopped services. SD heuristic performs much better than the traditional memory management techniques. From the execution time point of view, SD heuristic is almost as fast as the traditional memory management techniques. In this chapter, we have not taken into account of the priorities of different services. Our future work will focus on extending the proposed model to include the service priority. Different services may have different priority which determined by their specific characteristics or set by users. For example, an Internet game should not force out from the gateway a home security service (which is much more important than the internet game). Each service defines a priority value that reflects the importance of this service relative to other services. We will introduce the priority as a new factor in both the heuristic and the optimal solution.