Chapter 8 Self-Organized Load Balancing through Swarm Intelligence

Šešum-Čavić, Vesna; Kühn, Eva

doi:10.1007/978-3-642-20344-2_8

Cited by 12 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The local node pattern has many similarities with local load balancing [25] as on one single computer site there is no load to be distributed or clustered, yet. Its main purpose is to model the autonomous agents as independent workers that actively compete for work.…”

Section: Local Node Patternmentioning

confidence: 99%

A Space-Based Generic Pattern for Self-Initiative Load Clustering Agents

Kühn

Marek

Scheller

et al. 2012

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Load clustering is an important problem in distributed systems, which proper solution can lead to a significant performance improvement. It differs from load balancing as it considers a collection of loads, instead of normal data items, where a single load can be described as a task. Current approaches that treat load clustering mainly lack of provisioning a general framework and autonomy. They are neither agentbased nor configurable for many topologies. In this paper we propose a generic framework for self-initiative load clustering agents (SILCA) that is based on autonomous agents and decentralized control. SILCA is a generic architectural pattern for load clustering. The SILCA framework is the corresponding implementation and thus supports exchangeable policies and allows for the plugging of different algorithms for load clustering. It is problem independent, so the best algorithm or combination of algorithms can be found for each specific problem. The pattern has been implemented on two levels: In its basic version different algorithms can be plugged, and in the extended version different algorithms can be combined. The flexibility is proven by means of nine algorithms. Further contributions are the benchmarking of the algorithms, and the working out of their best combinations for different topologies.

show abstract

Section: Local Node Patternmentioning

confidence: 99%

A Space-Based Generic Pattern for Self-Initiative Load Clustering Agents

Kühn

Marek

Scheller

et al. 2012

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

Routing algorithm for sparse unstructured P2P networks using honey bee behavior

Verma,

Thakur,

Kumar

et al. 2024

Int J Communication

View full text Add to dashboard Cite

SummaryUnstructured peer‐to‐peer (P2P) networks pose unique challenges for efficient and scalable routing. In this study, we introduce a novel routing algorithm inspired by the foraging behavior of honey bees named Honey Bee Optimization in P2P Networks (HBO_P2P) to address the inherent limitations of routing in unstructured P2P networks, focusing on improving packet delivery, minimizing hop count, reducing message overhead, and optimizing overall throughput. To evaluate the performance of our proposed algorithm, we conducted comprehensive experiments comparing it with existing algorithms commonly used in P2P networks, namely, particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimization (ACO). After the simulation, we got the results as follows: Our algorithm outperforms ACO, GA, and PSO by exhibiting the highest number of data hops indicating potential efficiency in route optimization. Routing overhead is also minimal as compared to ACO, GA, and PSO. The average data packet delay is also low in our algorithm as compared to ACO, GA, and PSO. HBO_P2P achieves the highest throughput, nearly reaching 100 Mbps. While ACO and GA exhibit similar throughput of around 80 Mbps, and PSO has the lowest throughput, approximately 60 Mbps.

show abstract

Handling Complexity in Some Typical Problems of Distributed Systems by Using Self-organizing Principles

Šešum-Čavić

2021

Studies in Computational Intelligence

View full text Add to dashboard Cite

Chapter 8 Self-Organized Load Balancing through Swarm Intelligence

Cited by 12 publications

References 29 publications

A Space-Based Generic Pattern for Self-Initiative Load Clustering Agents

A Space-Based Generic Pattern for Self-Initiative Load Clustering Agents

Routing algorithm for sparse unstructured P2P networks using honey bee behavior

Handling Complexity in Some Typical Problems of Distributed Systems by Using Self-organizing Principles

Contact Info

Product

Resources

About