  In cloud computing, an important concern is to allocate the available
resources of service nodes to the requested tasks on demand and to make the
objective function optimum, i.e., maximizing resource utilization, payoffs and
available bandwidth. This paper proposes a hierarchical multi-agent
optimization (HMAO) algorithm in order to maximize the resource utilization and
make the bandwidth cost minimum for cloud computing. The proposed HMAO
algorithm is a combination of the genetic algorithm (GA) and the multi-agent
optimization (MAO) algorithm. With maximizing the resource utilization, an
improved GA is implemented to find a set of service nodes that are used to
deploy the requested tasks. A decentralized-based MAO algorithm is presented to
minimize the bandwidth cost. We study the effect of key parameters of the HMAO
algorithm by the Taguchi method and evaluate the performance results. When
compared with genetic algorithm (GA) and fast elitist non-dominated sorting
genetic (NSGA-II) algorithm, the simulation results demonstrate that the HMAO
algorithm is more effective than the existing solutions to solve the problem of
resource allocation with a large number of the requested tasks. Furthermore, we
provide the performance comparison of the HMAO algorithm with the first-fit
greedy approach in on-line resource allocation.
