Hot Data Centers vs. Cool Peers

Most networked applications use either a largely centralized architecture (e.g. iTunes) or a p2p architecture (e.g. BitTorrent). The popularity of centralized Internet applications such as Search and web portals has fueled the growth of large data centers. Modeling and minimizing the power consumption of large data centers is the hot new research area (pun intended). However, no attention has been paid to the power consumption of p2p systems. This despite the fact that programs like BitTorrent account for up to 95% of Internet traffic today. There are two main reasons why the power consumption of p2p applications has not yet been studied. First, these applications do not consume a large amount of power at a single location. Second, no central entity pays for the power consumed by applications like BitTorrent. Yet, it is undeniable that systems like BitTorrent do consume power, and quite likely, a lot of power. End hosts consume more power as they do more work and routers consume more power as they route additional traffic [3]. Thus one many wonder: is it more energy efficient to download a song from BitTorrent rather than iTunes? To answer such questions, one needs to build models that can predict the power consumption of p2p applications. In this paper, we present an outline of a model that allows us to compare the energy consumed by a p2p system to its centralized counterpart. Note that it is not our aim to advocate one system architecture over another. Many issues such as manageability, reliability and ease of deployment must be taken into account when making high-level architectural decisions. Indeed, it may be entirely infeasible to deploy applications such as web search in a p2p manner. Thus, this model is best viewed as an exercise driven primarily by academic curiosity. And yet, this exercise is not entirely without practical interest. Today energy costs represents a significant component of the capital and operational costs of data centers, and hence it is important to understand whether, and when, alternate system architectures might prove more cost effective. For example, perhaps in controlled environments such as enterprise networks it is reasonable to harness user desktops to take on some of the tasks traditionally assigned to server rooms and data centers? More broadly, models like this help us understand the energy impact of the different design decisions that go into building a large scale system. For example, our model shows that numerous factors such as power consumption of routers, whether the application is communication vs. computation heavy and the efficiency of the protocol design affect the overall power consumption. To the best of our knowledge, this is the first attempt to model the energy consumption of complex networked systems in their entirety. We hope our initial exploration spurs further research on this topic.