A generic and adaptive aggregation service for large-scale decentralized networks

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.

Self-Repairable Smart Grids Via Online Coordination of Smart Transformers

“…Moreover an expression of the phase shift distribution factors in AC would make the model more accurate. Finally, fully decentralized sensing mechanisms studied in earlier work [40] could make the proposed model computationally scalable to large-scale network with all components interacting and self-regulating power flow in a collective fashion.…”

Overlay Service Computing - Modular and Reconfigurable Collective Adaptive Systems

“…Overlay services can be realised as distributed middleware systems. Section 5 illustrates two representative examples of overlay services: (i) self-organisation of overlay networks in tree topologies [30] and (ii) aggregation of dynamically changing information distributed in the network [29].…”

“…• DIAS, the Dynamic Intelligent Aggregation Service [29]. These two overlay services have a generic application scope, yet, earlier work illustrates evidence about their applicability in the domain of demand-side energy management [25,28,31].…”

“…Figure 5.6 illustrates performance trade-offs between accuracy and communication cost for the adaptation strategies of DIAS. The accuracy measures how close the estimates of the aggregates, e.g., summation, is to the actual values [29]. Two scenarios are illustrated regarding how selected states change during runtime: (i) synchronous and (ii) asynchronous changes.…”

“…DIAS can compute a wide range of aggregation functions at a performance comparable with the one shown in this section. In contrast to related methodologies reviewed in earlier work [29], DIAS does not require any architectural changes to compute a different aggregation function. It is because of the ASMA architectural modularity that the aggregation process is separated from routing.…”