Harvesting Time-Series Data from Service-Based Systems Hosted in MANETs

TL;DR

This paper introduces an epidemic, delay-tolerant data harvesting method for time-series monitoring in MANETs, enabling reliable, efficient, and timely data collection for network analysis despite connectivity challenges.

Contribution

It proposes a novel synchronization overlay and data transfer approach tailored for MANETs, improving data availability and reducing network overhead for time-sensitive monitoring.

Findings

Significantly better data availability compared to other methods

Reduces network overhead in MANET environments

Effective for time-critical, perishable data analysis

Abstract

We are concerned with reliably harvesting data collected from service-based systems hosted on a mobile ad hoc network (MANET). More specifically, we are concerned with time-bounded and time-sensitive time-series monitoring data describing the state of the network and system. The data are harvested in order to perform an analysis, usually one that requires a global view of the data taken from distributed sites. For example, network- and application-state data are typically analysed in order to make operational and maintenance decisions. MANETs are a challenging environment in which to harvest monitoring data, due to the inherently unstable and unpredictable connectivity between nodes, and the overhead of transferring data in a wireless medium. These limitations must be overcome to support time-series analysis of perishable and time-critical data. We present an epidemic, delay tolerant, and intelligent method to efficiently and effectively transfer time-series data between the mobile nodes of MANETs. The method establishes a network-wide synchronization overlay to transfer increments of the data over intermediate nodes in periodic cycles. The data are then accessible from local stores at the nodes. We implemented the method in Java~EE and present evaluation on a run-time dependence discovery method for Web Service applications hosted on MANETs, and comparison to other four methods demonstrating that our method performs significantly better in both data availability and network overhead.