Performance-energy trade-off exploration in dynamic data types for network applications

Demanding applications are introduced to networking field, asking simultaneously for high performance and low-energy consumption, requests more imperative in wireless networks. The dynamic nature of such applications makes the dynamic memory subsystem of an embedded system a critical contributing factor to the overall energy and execution time performance. This paper presents a novel aspect in designing dynamic data types in network applications. A systematic methodology, which is tool-supported and capable of manipulating different network traces, is proposed. Plethora of possible solutions, i.e. Pareto points, in terms of energy consumption, performance and memory size usage is achieved. Eventually, alternative optimal implementations, i.e. Pareto-optimal points can be extracted. Two real-life case studies (from NetBench suite) are studied and explored thoroughly. It is proved that up to 80% energy savings and up to 20% performance, comparing with two benchmarks' original implementation, can be accomplished. Furthermore, a plethora of trade-offs among the Pareto-optimal choices reach up to 52% for energy consumption and up to 13% for performance, are achieved