This paper describes and analyzes the application of a simulated evolution (SE) approach to the problem of matching and scheduling of coarse-grained tasks in a heterogeneous suite of machines. The various steps of the SE algorithm are first discussed. Goodness functionrequired by SE is designed and explained. Then experimental results applied on various types of workloads are analyzed. Workloads are characterized according to the connectivity, heterogeneity, and communication-to-cost ratio of the task graphs. The performance of SE is also compared with a genetic algorithm (GA) approach for the same problem with respect to the quality of solutions generated, and timing requirements of the algorithms.

Femtocells (FCs) are introduced to enhance the indoor coverage and system capacity of traditional cellular networks. However, the network performance could be significantly deteriorated due to the increase in cochannel interference in dense deployment. In the literature, three spectrum usage schemes that deal with the cross-tier interference have been proposed, i.e., orthogonal assignment, underlay, and controlled underlay using mainly closed-access FCs. This paper targets the optimization of resource allocation, together with the selection of base stations (BSs) in two-tier networks assuming hybrid access FCs, to reduce the cross-tier interference. Moreover, this model aims to achieve effective spatial reuse between the macrocell (MC) and FCs while guaranteeing quality-of-service (QoS) transmissions by means of joint power control in both tiers. Simulations are conducted to show a comparison with the other three approaches.

In this paper, we consider the problem of resource allocation in two-tier networks taking into consideration nondense femtocell deployments. The following limitations can be remarked from the prior work in the field of resource allocation: (1) resources are underutilized due to the equal power distribution in macrocell, (2) access to public users in femtocells is restricted to avoid depriving own subscribers transmissions, and (3) degradation of signal-to-noise ratio due to noise effects has not been evaluated. To overcome these limitations, we propose a joint power and bandwidth allocation among two tiers together with base station selection using genetic algorithm. Our solution is able to: (1) maximize the overall system throughput, (2) find an appropriate serving base station for each user, and (3) bandwidth and power assigned to each user. Simulations were conducted and a comparison with a Weighted Water Filling algorithm is carried out.