Solid cancer tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that block this tumor-induced development of new blood vessels (angiogenesis) is an important approach in cancer treatment. However, the complexity of angiogenesis and the difficulty in implementing and evaluating medical changes prevent the discovery of novel and effective new therapies. This paper presents a massively parallel computational search-based approach for the discovery of novel potential cancer treatments, using a high fidelity simulation of angiogenesis. Discovering new therapies is viewed as multi-objective combinatorial optimization over two competing objectives: minimizing the medical cost of the intervention while minimizing the oxygen provided to the cancer tumor by angiogenesis. Results show the effectiveness of the search process in finding simple interventions that are currently in use and more interestingly, discovering some new approaches that are counterintuitive yet effective.;Distributed systems are becoming more prevalent as the demand for connectivity increases. Developers are faced with the challenge of creating software systems that meet these demands and adhere to good software practices. Technologies of today aid developers in this, but they may cause applications to suffer performance problems and require developers to abandon basic software concepts, such as modularization, performance, and maintainability. This work presents the Vitruvian framework that provides solutions to common distribution goals, and distributes applications using replication and transparency at varying stages of application development.