Sustainability and Community Networks

Community networks are IP-based computer networks that are operated by a community as a common good. In Europe, the most well-known community networks are Guifi in Catalonia, Freifunk in Berlin, Ninux in Italy, Funkfeuer in Vienna and the Athens Wireless Metropolitan Network in Greece. This paper deals with community networks as alternative forms of Internet access and alternative infrastructures and asks: What does sustainability and unsustainability mean in the context of community networks? What advantages do such networks have over conventional forms of Internet access and infrastructure provided by large telecommunications corporations? In addition what disadvantages do they face at the same time? This article provides a framework for thinking dialectically about the un/sustainability of community networks. It provides a framework of practical questions that can be asked when assessing power structures in the context of Internet infrastructures and access. It presents an overview of environmental, economic, political and cultural contradictions that community networks may face as well as a typology of questions that can be asked in order to identify such contradictions.

Distributed parallelization of greedy Mobile Network Optimization algorithms

The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.