Abstract: Allocation of resources is described for example, where the resources are computers, communications network resources or advertisement slots. In an example a weighted proportional resource allocation mechanism is described in which a resource provider seeks to maximize revenue whilst users seek to maximize their satisfaction in terms of the utility of any resource allocation they receive minus any payment they make for the resource allocation. In an example, the provider determines discrimination weights (using information about resource constraints and other factors). For example, the discrimination weights are published to the users; the users submit bids for the resources in the knowledge of the discrimination weights and the provider allocates the resources according to the bids and the discrimination weights. In an example keyword auctions for sponsored search are considered where the resources are advertisement slots and where the constraints include the relative positions of the advertisements.

Abstract: Crowdsourcing is described, for example, where solutions to tasks such as designing a logo, writing a piece of code or answering a question are solicited by making open calls to large scale communities. In an example, a crowdsourcing node models a plurality of contests as all-pay auctions, each contest having a task and a reward. In examples, the crowdsourcing node is arranged to set rewards for the contests so that the optimal results are received for each contest owner, those owners having provided a budget and using a utility function for each of the contests. In examples, the crowdsourcing node is arranged to recommend contests to potential participants so that those participants can more easily decide which tasks to take on next.

Abstract: Ranking systems are described. In an embodiment a large scale data center has peta bytes of items and a query engine is provided to find the top k most frequently occurring items. In embodiments, samples are taken from the data center at least until a specified number of samplings is met, or until a stopping rule is met. In examples, the samples form a sample sketch which is used to find the top k most frequently occurring items without the need to examine every item in the data center. In other examples, the number of samplings or stopping rule is varied to provide ranks or frequencies. In other embodiments the ranking system operates on items having values to find separators which divide the items into bins such that the proportion of the items in each bin is different. For example, a data set may be apportioned to different types of processor.

Abstract: An end-to-end congestion control is provided that emulates a different service differentiation than the common low-normal priority. This protocol is referred to as 4CP (Competitive, Considerate Congestion Control). The target service differentiation enables provisioning of per-flow average bandwidth guarantees to “normal” traffic, but not at the expense of potentially starving the “low” priority traffic (4CP). It thus features incentive compatibility to file-transfer applications that are throughput-greedy but want to be considerate to other traffic. 4CP is implemented and configured as a sender-only adaptation of standard TCP, and requires no special network feedback. Configuration of the bandwidth guarantee is either statically configured or automatically adjusted by 4CP. The automatic mode aims to be TCP-friendly over appropriately large timescale.

Abstract: Sampling rules for information dissemination are described which may be applied in a system containing a number of nodes arranged into groups. A target address is selected using one of two methods: selection of an address from the entire address space of the system and selection of an address from a part of the address space which corresponds to set of groups of nodes. The set of groups of nodes is updated when information is successfully disseminated to a node at a target address selected using the first of the two methods. Rules to determine which of the two methods are used for any particular selection operation are also described.

Abstract: A method of ranking and suggesting objects, such as tags, is described in which a group of objects are suggested to a user in relation to a particular information object, such as a web page or file. A user either selects one of the suggestions or enters an alternative object and on receipt of data identifying the particular object selected by the user, the group of suggestions is updated based on whether the object selected by the user was one of the suggestions or not. The updating of the group may also be based on other factors, such as ranking parameters associated with each of the objects.

Abstract: An end-to-end congestion control is provided that emulates a different service differentiation than the common low-normal priority. This protocol is referred to as 4CP (Competitive, Considerate Congestion Control). The target service differentiation enables provisioning of per-flow average bandwidth guarantees to “normal” traffic, but not at the expense of potentially starving the “low” priority traffic (4CP). It thus features incentive compatibility to file-transfer applications that are throughput-greedy but want to be considerate to other traffic. 4CP is implemented and configured as a sender-only adaptation of standard TCP, and requires no special network feedback. Configuration of the bandwidth guarantee is either statically configured or automatically adjusted by 4CP. The automatic mode aims to be TCP-friendly over appropriately large timescale.