DIAS

DIAS, the Dynamic Intelligent Aggregation Service is a decentralized middleware mechanism that makes locally available in every node of a network system-wide (global) information without involving centralized computational entities. More specifically, DIAS locally computes almost any aggregation function that receives for input numerical values from all of the nodes in a network. These values represent a property of the nodes. 

[…] a decentralized mechanism that makes locally available in every node of a network system-wide (global) information without involving centralized computational entities […] e.g. AVERAGE, SUMMATION, MAXIMUM, MINIMUM, COUNT, etc […]

For example, aggregation functions, e.g. AVERAGE, SUMMATION, MAXIMUM, MINIMUM, COUNT, etc, can locally compute information such as the average load of a network, the size of network under scaling/node failures, or the node with the maximum bandwidth. In each of these cases, DIAS does not require any server or centralized authority for storage, processing or computation. In contrast to other related methodologies, aggregation in DIAS is function-independent, routing-independent and adaptive under changing input values during runtime.

In contrast to other related methodologies, aggregation in DIAS is function-independent, routing-independent and adaptive under changing input values during runtime.

DIAS achieves this abstraction and flexibility by introducing the concept of aggregation memberships. An aggregation membership provides historic information in each node about a computed input value. This information indicates if the aggregated value is new, outdated or duplicate. This distinction guarantees accurate computation of aggregates. It also provides two adaptation strategies that tune performance for a fast update of outdated values or a fast discovery of new input values.

Storing aggregation memberships explicitly is not a scalable and decentralized aggregation approach. The four aggregation memberships of DIAS managed by the disseminator and aggregator agents in two remote nodes i and j.Nevertheless, DIAS stores aggregation memberships in probabilistic data structures: the bloom filters. A bloom filter provides large space savings at the cost of false positives. A false positive incorrectly denotes that a membership exists when it actually does not. DIAS is able to detect false positives and, therefore, prevent inaccuracies related to duplicate and outdated information. Experimental evaluation shows the  performance trade-offs of DIAS and confirms its high accuracy under different experimental settings.

 

DIAS is part of my PhD studies and resulted in a journal publication.

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