A Survey of Distributed Data Aggregation Algorithms (1110.0725v1)

Abstract: Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting values result from the distributed computation of functions like COUNT, SUM and AVERAGE. Some application examples can found to determine the network size, total storage capacity, average load, majorities and many others. In the last decade, many different approaches have been proposed, with different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of aggregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, justifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally defines the concept of aggregation, characterizing the different types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.

Distributed Data Aggregation Algorithms: A Systematic Survey and Analysis

The paper "A Survey of Distributed Data Aggregation Algorithms" by Paulo Jesus, Carlos Baquero, and Paulo Sérgio Almeida offers an extensive review of distributed data aggregation algorithms, presenting their theoretical underpinnings and practical implications. This survey serves to systematically categorize and evaluate various aggregation techniques, reflecting on their efficiency, robustness, and adaptability in different network environments.

Key Contributions and Highlights

This paper delivers three primary contributions to the field of distributed systems:

1. Formal Definition of Aggregation: The concept of aggregation is carefully defined, with attention paid to various types of aggregation functions such as decomposable and non-decomposable functions, as well as duplicate-sensitivity and idempotence properties. This nuanced understanding is critical for both theoretical explorations and practical application development.
2. Comprehensive Taxonomy: A taxonomy suitable for the classification of distributed data aggregation algorithms is proposed, split into two perspectives: communication and computation. This taxonomy enables a deeper understanding of how different algorithms operate and their relative strengths and weaknesses.
3. Practical Guidelines: The survey provides valuable insights into the selection and application of aggregation techniques, offering guidance on which algorithms are better suited for specific scenarios based on their communication protocol and computation method.

Core Algorithm Categories

The surveyed algorithms are grouped into several main categories:

• Hierarchical Approaches: These require a specific network topology, typically efficient and suited for environments with minimal faults. However, they struggle with robustness in dynamic settings.
• Sketch-based Methods: These utilize data structures like hash or min-k sketches, providing fault-tolerant aggregation at the cost of some accuracy due to probabilistic error.
• Averaging Techniques: Typically implemented via gossip protocols, these methods are robust and self-stabilizing, able to accommodate errors and changes in network topology, though potentially less efficient.
• Sampling Techniques: Commonly used for estimating network size through probabilistic sampling methods such as capture-recapture and random walks.
• Complex Aggregation Functions via Digests: These algorithms allow the approximation of more complex statistical aggregates such as quantiles, though they tend to require additional computational resources.

Theoretical and Practical Implications

From a theoretical perspective, this survey underscores the intricate balance between algorithm efficiency, fault tolerance, and applicability in scalable systems. The robustness of averaging techniques in dynamic and faulty environments is particularly highlighted. In contrast, sketch-based approaches are accepted as reliable and fast, offering reasonable approximations.

Practically, the paper guides the selection of appropriate algorithms for particular applications. For instance, in wireless sensor networks (WSN), where energy efficiency is paramount, hierarchical approaches are recommended, whereas averaging and sketches provide better solutions in failure-prone, dynamic networks.

Future Prospects in Distributed Data Aggregation

Emerging challenges include improving algorithms to handle churn and continuous data changes with lower resource consumption while maintaining accuracy. Innovations in complex aggregate computation and the development of universally applicable algorithms will be crucial.

Overall, this survey not only catalogues the existing landscape of distributed data aggregation algorithms but also sets the stage for their future evolution in ever-more complex distributed computing environments. While no single algorithm emerges as a panacea, this work provides a solid foundation for understanding and advancing data aggregation in distributed systems.