This paper studies the problem of load-balancing the demand for content in a peer-to-peer network across heterogeneous peer nodes that hold replicas of the content. Previous decentralized load balancing techniques in distributed systems base their decisions on periodic updates containing information about load or available capacity observed at the serving entities. We show that these techniques do not work well in the peer-to-peer context; either they do not address peer node heterogeneity, or they suffer from significant load oscillations. We propose a new decentralized algorithm, Max-Cap, based on the maximum inherent capacities of the replica nodes and show that unlike previous algorithms, it is not tied to the timeliness or frequency of updates. Yet, Max-Cap can handle the heterogeneity of a peer-to-peer environment without suffering from load oscillations.
later extend this work to balance load across a set of widely distributed heterogeneous web servers @cite_2 . This work proposes the use of adaptive TTLs, where the TTL for a DNS mapping is set inversely proportional to the domain's local client request rate for the mapping of interest (as reported by the domain's local name server). The TTL is at the same time set to be proportional to the chosen web server's maximum capacity. So web servers with high maximum capacity will have DNS mappings with longer TTLs, and domains with low request rates will receive mappings with longer TTLs. Max-Cap, the algorithm proposed in this thesis, also uses the maximum capacities of the serving replica nodes to allocate requests proportionally. The main difference is that in the work by , the root DNS scheduler acts as a centralized dispatcher setting all DNS mappings and is assumed to know what the request rate in the requesting domain is like. In the peer-to-peer case the authority node has no idea what the request rate throughout the network is like, nor how large is the set of requesting nodes.
@cite_2
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With ever increasing Web traffic, a distributed multi server Web site can provide scalability and flexibility to cope with growing client demands. Load balancing algorithms to spread the requests across multiple Web servers are crucial to achieve the scalability. Various domain name server (DNS) based schedulers have been proposed in the literature, mainly for multiple homogeneous servers. The presence of heterogeneous Web servers not only increases the complexity of the DNS scheduling problem, but also makes previously proposed algorithms for homogeneous distributed systems not directly applicable. This leads us to propose new policies, cabled adaptive TTL algorithms, that take into account both the uneven distribution of client request rates and heterogeneity of Web servers to adaptively set the time-to-live (TTL) value for each address mapping request. Extensive simulation results show that these strategies are robust and effective in balancing load among geographically distributed heterogeneous Web servers.