DIAS - Dynamic Intelligent Aggregation Service
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.
dist sync Oct 4, 2015
lib sync Oct 4, 2015
src Lets call this v0.0.1 Sep 1, 2015
summaries Fixed paths in run_live script Feb 17, 2016
.gitignore cleanup to merge back with Evangelos Sep 23, 2015
LICENSE Initial commit Jan 12, 2015
README.md Update README.md Jan 13, 2015
build.xml cleanup to merge back with Evangelos Sep 23, 2015
plot.py Cleanup, works for orig/delay/live 3nodes Sep 1, 2015
run_orig.sh Lets call this v0.0.1 Sep 1, 2015


DIAS, the Dynamic Intelligent Aggregation Service

This project is the source code that prototypes DIAS as illustrated in the following published paper:

E. Pournaras, M. Warnier and F.M.T. Brazier, A Generic and Adaptive Aggregation Service for Large-scale Decentralized Networks, Complex Adaptive Systems Modeling, 1:19, 2013 © SpringerOpen

You can also read about DIAS in Chapter 5 of the following PhD thesis.

E. Pournaras, Multi-level Reconfigurable Self-organization in Overlay Services, PhD Thesis, Delft University of Technology, March 2013


Aggregation functions are used in distributed environments to make system-wide information locally available in the nodes of a network. The computation of different aggregation functions, e.g., SUMMATION, AVERAGE, MAXIMUM etc., in large-scale distributed systems is challenging and crucial for a wide range of applications. This is especially the case when the input values of these functions dynamically change during system runtime. Related approaches of decentralized aggregation are function-dependent, interaction-dependent, assume static values or cannot always tolerate duplicates and continuously changing information.


This paper introduces DIAS, the Dynamic Intelligent Aggregation Service. DIAS is an agent-based middleware that addresses these issues with a holistic approach: an efficient availability of the distributed information in every node of the network that enables the simultaneous computation of almost any aggregation function. Such an abstraction initially requires a significant communication and storage cost and has a rather large overhead. These issues are resolved by introducing an implicit local representation and storage of the explicit distributed information: aggregation memberships in bloom filters.


The performance impact of bloom filters in DIAS is critical for its applicability as it compensates and reduces the initial high communication and storage required for such an abstraction.


Experimental evaluation under various aggregation and resource-constrained settings shows that DIAS is an efficient and accurate decentralized aggregation service.


Aggregation; Adaptation; Agent; Bloom filter; Consistency