Reliable Task Allocation with Load Balancing in Multiplex Networks

Abstract

In multiplex networks, agents are connected by multiple types of links; a multiplex network can be split into more than one network layer that is composed of the same type of links and involved agents. Each network link type has a bias for communicating different types of resources; thus, the task's access to the required resources in multiplex networks is strongly related to the network link types. However, traditional task allocation and load balancing methods only considered the situations of agents themselves and did not address the effects of network link types in multiplex networks. To solve this problem, this article considers both link types and agents, and substantially extends the existing work by highlighting the effect of network layers on task allocation and load balancing. Two multiplex network-adapted models of task allocation with load balancing are presented: network layer-oriented allocation and agent-oriented allocation. This article also addresses the unreliability in multiplex networks, which includes the unreliable links and agents, and implements a reliable task allocation based on a negotiation reputation and reward mechanism. Our findings show that both of our presented models can effectively and robustly satisfy the task allocation objectives in unreliable multiplex networks; the experiments prove that they can significantly reduce the time costs and improve the success rate of tasks for multiplex networks over the traditional simplex network-adapted task allocation model. Lastly, we find that our presented network layer-oriented allocation performs much better in terms of reliability and allocation time compared to our presented agent-oriented allocation, which further explains the importance of network layers in multiplex networks.

A Survey of Task Allocation and Load Balancing in Distributed Systems

“…Therefore, in the systems, there may be several network layers, where each network layer is composed of the same type of links and the involved nodes. By considering this new situation, Jiang et al [63] proposed the network layeroriented task allocation model, which considers not only the contextual nodes but also the contextual layers.…”

Multiagent-Based Allocation of Complex Tasks in Social Networks

“…A typical way to solve the bi-objective optimization problem is to transform the problem into a single objective problem [12] [14]. In this paper, we adopt this idea by combining the two objective functions (i.e., social waiting cost and social communication cost) into a single objective function (i.e., the social execution cost).…”

“…Because our problem cannot be solved optimally within polynomial time unless P=NP, in this paper, we propose an efficient distributed social complex task allocation model by utilizing the multiagent technology, which also provides a provable performance guarantee. Multiagent technologies have been employed widely for distributed problem solving [1] [12][13][14][15][19] [21][22][23][24][25][26][27][28][29][30][31]. Motivated by the studies [22][23][24], in our model, we use the mobile and cooperative agent to carry the subtask to search for the suitable target node.…”

“…To achieve dependable resources with the least resource access time for undependable social networks, Jiang et al [12] propose a reputation-based negotiation mechanism. Recently, a network-layer oriented task allocation model is presented for minimizing the task execution time in multiplex networks [14]. By being aware of the community structure in networks, Wang and Jiang [15] propose a community-aware task allocation model (where agents can cooperate with other agents in the same community) to improve social welfare while incurring a few of negotiation overhead.…”

“…1(b), the connections among the engineers represent communication possibilities and the number attached to each connection represents the communication cost. The smaller the communication cost, the less effort is required for efficient commu- nication [12][13][14][15].…”