Application-level network relays possess many desirable properties, including support for communication between disconnected clients, increasing bandwidth between distant clients, and enabling routing around Internet failures. One problem not considered by existing systems is how to assign client load to relay servers in order to maximize throughput of the relay-system. In this paper, we are interested in the particular case where network conditions change frequently so that the ability of clients to adapt flow is restricted and each round of activity is critical. To this end, we present an algorithm, called Aggressive Increase, AAI which improves its competitive ratio in each time round that the network conditions persists. Given a relay network where a client connects to at most N servers, if network conditions persist for log(N) rounds then the algorithm's throughput becomes constant competitive. Our results improve upon the competitive ratio of previous work (of Awerbuch, Hajiaghayi, Kleinberg, and Leighton [2]). In addition we show that the AAI algorithm performs well in simulation studies as compared with the algorithm of [2] and an adaptation of the multiplicative increase algorithm of [8]. On a variety of input graphs, we show that the AAI algorithm typically reaches close to peak bandwidth levels within only a small constant (< 10) number of rounds.

Demonstrates the power of providing a common set of operating system services to wide-area applications, including mechanisms for naming, persistent storage, remote process execution, resource management, authentication and security. On a single machine, application developers can rely on the local operating system to provide these abstractions. In the wide area, however, application developers are forced to build these abstractions themselves or to do without. This ad-hoc approach often results in individual programmers implementing non-optimal solutions, wasting both programmer effort and system resources. To address these problems, we are building a system, WebOS, that provides the basic operating systems services needed to build applications that are geographically distributed, highly available, incrementally scalable and dynamically reconfigurable. Experience with a number of applications developed under WebOS indicates that it simplifies system development and improves resource utilization. In particular, we use WebOS to implement Rent-A-Server to provide dynamic replication of overloaded Web services across the wide area in response to client demands.