In recent years we have witnessed a great interest in large distributed computing platforms, also known as clouds. While these systems offer enormous computing power, they are major energy consumers. In existing data centers CPUs are responsible for approximately half of the energy consumed by the servers. A promising technique for saving CPU energy consumption is dynamic speed scaling, in which the speed at which the processor is run is adjusted based on demand and performance constraints. In this paper we look at the problem of allocating the demand in the network of processors (each being capable to perform dynamic speed scaling) to minimize the global energy consumption/cost subject to a performance constraint. The nonlinear dependence between the energy consumption and the performance as well as the high variability in the energy prices result in a nontrivial resource allocation. The problem can be abstracted as a fully distributed convex optimization with a linear constraint. On the theoretical side, we propose two low-overhead fully decentralized algorithms for solving the problem of interest and provide closed-form conditions that ensure stability of the algorithms. Then we evaluate the efficacy of the optimal solution using simulations driven by the real-world energy prices. Our findings indicate a possible cost reduction of 10 − 40% compared to power-oblivious 1/N load balancing, for a wide range of load factors.