Energy expenses are becoming an increasingly important fraction of data center operating costs. At the same time, the energy expense per unit of computation can vary significantly between two different locations. In this paper, we characterize the variation due to fluctuating electricity prices and argue that existing distributed systems should be able to exploit this variation for significant economic gains. Electricity prices exhibit both temporal and geographic variation, due to regional demand differences, transmission inefficiencies, and generation diversity. Starting with historical electricity prices, for twenty nine locations in the US, and network traffic data collected on Akamai's CDN, we use simulation to quantify the possible economic gains for a realistic workload. Our results imply that existing systems may be able to save millions of dollars a year in electricity costs, by being cognizant of locational computation cost differences.

Locating content in decentralized peer-to-peer systems is a challenging problem. Gnutella, a popular file-sharing application, relies on flooding queries to all peers. Although flooding is simple and robust, it is not scalable. In this paper, we explore how to retain the simplicity of Gnutella, while addressing its inherent weakness: scalability. We propose a content location solution in which peers loosely organize themselves into an interest-based structure on top of the existing Gnutella network. Our approach exploits a simple, yet powerful principle called interest-based locality, which posits that if a peer has a particular piece of content that one is interested in, it is very likely that it will have other items that one is interested in as well. When using our algorithm, called interest-based shortcuts, a significant amount of flooding can be avoided, making Gnutella a more competitive solution. In addition, shortcuts are modular and can be used to improve the performance of other content location mechanisms including distributed hash table schemes. We demonstrate the existence of interest-based locality in five diverse traces of content distribution applications, two of which are traces of popular peer-to-peer file-sharing applications. Simulation results show that interest-based shortcuts often resolve queries quickly in one peer-to-peer hop, while reducing the total load in the system by a factor of 3 to 7.

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Using more than 12,000 servers in over 1,000 networks, Akamai's distributed content delivery system fights service bottlenecks and shutdowns by delivering content from the Internet's edge. As Web sites become popular, they're increasingly vulnerable to the flash crowd problem, in which request load overwhelms some aspect of the site's infrastructure, such as the frontend Web server, network equipment, or bandwidth, or (in more advanced sites) the back-end transaction-processing infrastructure. The resulting overload can crash a site or cause unusually high response times -both of which can translate into lost revenue or negative customer attitudes toward a product or brand.Our company, Akamai Technologies, evolved out of an MIT research effort aimed at solving the flash crowd problem (www.akamai.com/en/html/about/history. html). Our approach is based on the observation that serving Web content from a single location can present serious problems for site scalability, reliability, and performance. We thus devised a system to serve requests from a variable number of surrogate origin servers at the network edge.1 By caching content at the Internet's edge, we reduce demand on the site's infrastructure and provide faster service for users, whose content comes from nearby servers.When we launched the Akamai system in early 1999, it initially delivered only Web objects (images and documents). It has since evolved to distribute dynamically generated pages and even applications to the network's edge, providing customers with on-demand bandwidth and computing capacity. This reduces content providers' infrastructure requirements, and lets them deploy or expand services more quickly and easily. Our current system has more than 12,000 servers in over 1,000 networks. Operating servers in many locations poses many technical challenges, including how to direct user requests to appropriate servers, how to handle failures, how to monitor and control the servers, and how to update software across the sys- Global Deployment of Data Centerstem. Here, we describe our system and how we've managed these challenges. Existing ApproachesResearchers have explored several approaches to delivering content in a scalable and reliable way. Local clustering can improve fault-tolerance and scalability. If the data center or the ISP providing connectivity fails, however, the entire cluster is inaccessible to users. To solve this problem, sites can offer mirroring (deploying clusters in a few locations) and multihoming (using multiple ISPs to connect to the Internet). Clustering, mirroring, and multihoming are common approaches for sites with stringent reliability and scalability needs. These methods do not solve all connectivity problems, however, and they do introduce new ones:I It is difficult to scale clusters to thousands of servers. I With multihoming, the underlying network protocols -in particular the border gateway protocol (BGP) 2 -do not converge quickly to new routes when connections fail. I Mirroring requires synchronizing the site amo...

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