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The need to move from reactive to proactive resource planning has been highlighted by industry analysts, academia and enterprises. Proactive resource planning provides business users with a view of future jobs, which in turn will help them to plan their workforce utilisation appropriately in order to reduce costs and improve customer satisfaction. This paper presents the application of FOS, an integrated service management system, for managing the resources of BT. FOS incorporates applications for reliable workload forecasting, optimised workforce planning, as well as advance tools for visualising and communicating the outputs to end users.

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Abstract. MOEA/D is a novel and successful Multi-Objective Evolutionary Algorithms(MOEA) which utilizes the idea of problem decomposition to tackle the complexity from multiple objectives. It shows better performance than most nowadays mainstream MOEA methods in various test problems, especially on the quality of solution's distribution in the Pareto set. This paper aims to bring the strength of metamodel into MOEA/D to help the solving of expensive black-box multiobjective problems. Gaussian Random Field Metamodel(GRFM) is chosen as the approximation method. The performance is analyzed and compared on several test problems, which shows a promising perspective on this method.

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Cloud computing is a novel paradigm which provides on demand, scalable and pay-as-you-use computing resources in a virtualized form. With cloud computing, users are able to access large pools of resources anywhere without any limitation. In order to use the provided facilities by the cloud in an efficient way, the management of resources is an undeniable fact that should be considered in different aspects. Among all those aspects, resource allocation has received much attentions. Given the fact that the cloud is heterogeneous, the allocation of resources has to become more sophisticated. As a first promising work to deal with that problem, Dominant Resource Fairness (DRF) has been proposed which takes into account dominant shares of users. Although DRF has a sort of desirable fairness properties, it has some limitations that have already been identified in the literature. Unfortunately, DRF and its recent developments are not intuitively fair with respect to various resource demands. In this paper, we propose a Multi-level Fair Dominant Resource Scheduling (MLF-DRS) algorithm as a new allocation model inspired by Max-Min fairness and proportionality. Unlike other works that they equalize dominant shares of different resource types which leads to starvation in the maximization of allocation for some users, our algorithm guarantees that each user receives the resources they desire for based on dominant shares. As can be deducted from the mathematical proofs, MLF-DRS provides a full utilization of resources and meets some of the desirable fair allocation properties and it is applicable to be used in a naïve extension form in the presence of multiple servers as well.

The recent growth in the deployment of wireless systems and technologies shows that in this rapidly evolving and expanding environment, radio resource management (RRM) is fast becoming the most significant aspect in the provisioning of Quality of Service (QoS) for wireless networks. As radio spectrum is a finite resource, new approaches are therefore needed to maximize the existing spectrum to ensure the wireless users' and network providers' QoS requirements are met. In this paper we propose a novel radio spectrum trading via derivatives contracts as a means to address short-term demands for spectrum whilst ensuring end-to-end QoS is met.