There are a myriad of computing resources in the World, most of which are underused. Efficiently using some of these devices raises several interesting opportunities. For instance, devices are spread everywhere, so locality can be deeply exploited. Furthermore, combining the computational capacity of all, or some, of them may conform a virtual cluster more powerful than any current data center. And, finally, by using resources already there avoids the need for a specific investment on specific infrastructures, as well as significantly reduces the operational costs. In this paper we propose a research architecture for global resources management which aims at exploiting the availability of this vast amount of heterogeneous resources at the edge of the network. We have designed the Progressive Mapping System (PMS) as a system that keeps track of the available resources and, upon an application launching, finds the best devices configuration for optimal application execution, considering both, the application performance requirements and the resources availability and performance features, while guaranteeing the Quality of Service (QoS). The PMS implements a graph of devices where nodes describe their performance features and edges describe their geographic distance as well as communication capabilities. Given an application description, which includes some performance description and quality of service requirements, the PMS system selects a subset of the global graph by solving a mixed integer quadratically constrained programming formulation, which finds the appropriate set of nodes for optimal application execution performance. One of the novelties of the PMS system is that it considers the geographic positioning of the devices and the application locality requirements to deeper exploiting locality issues. The PMS system is yet in a preliminary implementation stage. In this paper we describe the system overview, the architectural design, and discuss some research challenges which are critical for an effective PMS implementation.

Peer Reviewed