Business model requirements and challenges in the mobile telecommunication sector

The telecommunications business is undergoing a critical revolution, driven by innovative technologies, globalization, and deregulation. Cellular networks and telecommunications bring radical changes to the way telecom businesses are conducted. Globalization, on the other hand, is tearing down legacy barriers and forcing monopolistic national carriers to compete internationally. Moreover, the noticeable progress of many countries towards deregulation coupled with liberalization is significantly increasing telecom market power and allowing severe competition. The implications of this transition have changed the business rules of the telecom industry. In addition, entrants into the cellular industry have had severe difficulties due to inexistent or weak Business Models (BMs). Designing a BM for a mobile network operator is complex and requires multiple actors to balance different and often conflicting design requirements. Hence, there is a need to enhance operators’ ability in determining what constitutes the most viable business model to meet their strategic objectives within this turbulent environment. In this paper, the authors identify the main mobile BM dimensions along with their interdependencies and further analysis provides mobile network operators with insights to improve their business models in this new ‘boundary-less’ landscape

Bayesian Optimization with Unknown Constraints

Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions. Many real-world optimization problems of interest also have constraints which are unknown a priori. In this paper, we study Bayesian optimization for constrained problems in the general case that noise may be present in the constraint functions, and the objective and constraints may be evaluated independently. We provide motivating practical examples, and present a general framework to solve such problems. We demonstrate the effectiveness of our approach on optimizing the performance of online latent Dirichlet allocation subject to topic sparsity constraints, tuning a neural network given test-time memory constraints, and optimizing Hamiltonian Monte Carlo to achieve maximal effectiveness in a fixed time, subject to passing standard convergence diagnostics.Comment: 14 pages, 3 figure

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres