Abstract
We consider a network of computer data centers on the earth surface delivering computing as a service to a big number of users. The problem is to assign users to data centers to minimize the total communication distance between compu-ting resources and their users in the face of capacity constrained datacenters. In this paper, we extend the classical pla-nar Voronoi Diagram to a hyperbolic Voronoi Diagram on the sphere. We show that a solution to the distance minimi-zation problem under capacity constraints is given by a hyperbolic spherical Voronoi Diagram of data centers. We also present numerical algorithms, computer implementation and results of simulations illustrating our solution. We note applicability of our solution to other important assignment problems, including the assignment of population to regional trauma centers, location of airbases, the distribution of the telecommunication centers for mobile telephones in global telephone companies, and others.
Highlights
Cloud Computing could transform the way we use computer hardware by eliminating the need to own a computer
We show that a solution to the distance minimization problem under capacity constraints is given by a hyperbolic spherical Voronoi Diagram of data centers
Our result is described below in Theorem 3.1, where we show that the solution to the minimization problem in the face of capacity constraints is given by a new, extended form of Voronoi Diagram, hyperbolic Voronoi Diagrams on the sphere
Summary
Cloud Computing could transform the way we use computer hardware by eliminating the need to own a computer. With the transition to the cloud, students’ desktops must be moved from local in-classroom computers to large, centralized datacenters. These facilities are typically hundreds, if not thousands, of miles away from the schools and the homes of students. The decision of where to put a user is made individually for each connection request This approach works well for accessing websites where the content is replicated across all cloud locations, such as search engines, social network sites, news, and weather. When based in a cloud, students’ desktops require a continual static binding to the network access point of the student. The solution for balancing students across cloud facilities must take into account occasional capacity constraints and allow weighting of assignment to potentially more distant facilities to compensate
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