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.

This paper presents SoftRate, a wireless bit rate adaptation protocol that is responsive to rapidly varying channel conditions. Unlike previous work that uses either frame receptions or signal-to-noise ratio (SNR) estimates to select bit rates, SoftRate uses confidence information calculated by the physical layer and exported to higher layers via the SoftPHY interface to estimate the prevailing channel bit error rate (BER). Senders use this BER estimate, calculated over each received packet (even when the packet has no bit errors), to pick good bit rates. SoftRate's novel BER computation works across different wireless environments and hardware without requiring any retraining. SoftRate also uses abrupt changes in the BER estimate to identify interference, enabling it to reduce the bit rate only in response to channel errors caused by attenuation or fading. Our experiments conducted using a software radio prototype show that SoftRate achieves 2× higher throughput than popular frame-level protocols such as SampleRate [4] and RRAA [24]. It also achieves 20% more throughput than an SNR-based protocol trained on the operating environment, and up to 4× higher throughput than an untrained SNR-based protocol. The throughput gains using SoftRate stem from its ability to react to channel variations within a single packet-time and its robustness to collision losses.

This paper presents the design and implementation of the Intentional Naming System (INS), a resource discovery and service location system for dynamic and mobile networks of devices and computers. Such environments require a naming system that is (i) expressive, to describe and make requests based on specific properties of services, (ii) responsive, to track changes due to mobility and performance, ( iii) robust, to handle failures, and (iv) easily configurable. INS uses a simple language based on attributes and values for its names. Applications use the language to describe what they are looking for (i.e., their intent), not where to find things (i.e., not hostnames). INS implements a late binding mechanism that integrates name resolution and message routing, enabling clients to continue communicating with end-nodes even if the name-to-address mappings change while a session is in progress. INS resolvers self-configure to form an application-level overlay network, which they use to discover new services, perform late binding, and maintain weak consistency of names using soft-state name exchanges and updates. We analyze the performance of the INS algorithms and protocols, present measurements of a Java-based implementation, and describe three applications we have implemented that demonstrate the feasibility and utility of lNS.

A Resilient Overlay Network (RON) is an architecture that allows distributed Internet applications to detect and recover from path outages and periods of degraded performance within several seconds, improving over today's wide-area routing protocols that take at least several minutes to recover. A RON is an application-layer overlay on top of the existing Internet routing substrate. The RON nodes monitor the functioning and quality of the Internet paths among themselves, and use this information to decide whether to route packets directly over the Internet or by way of other RON nodes, optimizing application-specific routing metrics.Results from two sets of measurements of a working RON deployed at sites scattered across the Internet demonstrate the benefits of our architecture. For instance, over a 64-hour sampling period in March 2001 across a twelve-node RON, there were 32 significant outages, each lasting over thirty minutes, over the 132 measured paths. RON's routing mechanism was able to detect, recover, and route around all of them, in less than twenty seconds on average, showing that its methods for fault detection and recovery work well at discovering alternate paths in the Internet. Furthermore, RON was able to improve the loss rate, latency, or throughput perceived by data transfers; for example, about 5% of the transfers doubled their TCP throughput and 5% of our transfers saw their loss probability reduced by 0.05. We found that forwarding packets via at most one intermediate RON node is sufficient to overcome faults and improve performance in most cases. These improvements, particularly in the area of fault detection and recovery, demonstrate the benefits of moving some of the control over routing into the hands of end-systems.

The recently developed notion of TCP-compatibility has led to a number of proposals for alternative congestion control algorithms whose long-term throughput as a function of a steady-state loss rate is similar to that of TCP. Motivated by the needs of some streaming and multicast applications, these algorithms seem poised to take the current TCP-dominated Internet to an Internet where many congestion control algorithms co-exist. An important characteristic of these alternative algorithms is that they are slowly-responsive , refraining from reacting as drastically as TCP to a single packet loss.However, the TCP-compatibility criteria explored so far in the literature considers only the static condition of a fixed loss rate. This paper investigates the behavior of slowly-responsive, TCP-compatible congestion control algorithms under more realistic dynamic network conditions, addressing the fundamental question of whether these algorithms are safe to deploy in the public Internet. We study persistent loss rates, long- and short-term fairness properties, bottleneck link utilization, and smoothness of transmission rates.