2013/11/08 by Evangelos Pournaras, Martijn Warnier, Frances Brazier · 1 citation
Computer Science · Engineering · #Caching and Content Delivery #Peer-to-Peer Network Technologies #Opportunistic and Delay-Tolerant Networks #Distributed computing #Computer science #Data aggregator #Bloom filter #Overhead (engineering) #Abstraction #Middleware (distributed applications) #Computation #Service (business) #Representation (politics) #Function (biology) #Node (physics) #Recursion (computer science) #Computer network #Wireless sensor network #Algorithm #Engineering
paper · pdf · doi:10.1186/2194-3206-1-19
openalex publication_date 2013/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Abstract Purpose 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. Methods 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. Results 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. Conclusions Experimental evaluation under various aggregation and resource-constrained settings shows that DIAS is an efficient and accurate decentralized aggregation service.