A Location-Aware Strategy for Agents Negotiating Load-balancing

International audienceWe study a novel location-aware strategy for distributed systems where cooperating agents perform the load-balancing. The strategy allows agents to identify opportunities within a current unbalanced allocation , which in turn triggers concurrent and one-to-many negotiations amongst agents to locally reallocate some tasks. The tasks are reallocated according to the proximity of the resources and they are performed in accordance with the capabilities of the nodes in which agents are situated. This dynamic and ongoing negotiation process takes place concurrently with the task execution and so the task allocation process is adaptive to disruptions (task consumption, slowing down nodes). We evaluate the strategy in a multi-agent deployment of the MapReduce design pattern for processing large datasets. Empirical results demonstrate that our strategy significantly improves the overall runtime of the data processing

Using Negotiation to Reduce Redundant Autonomous Mobile Program Movements

Distributed load managers exhibit thrashing where tasks are repeatedly moved between locations due to incomplete global load information. This paper shows that systems of Autonomous Mobile Programs (AMPs) exhibit the same behaviour, identifying two types of redundant movement and terming them greedy effects. AMPs are unusual in that, in place of some external load management system, each AMP periodically recalculates network and program parameters and may independently move to a better execution environment. Load management emerges from the behaviour of collections of AMPs. The paper explores the extent of greedy effects by simulation, and then proposes negotiating AMPs (NAMPs) to ameliorate the problem. We present the design of AMPs with a competitive negotiation scheme (cNAMPs), and compare their performance with AMPs by simulation

Redundant movements in autonomous mobility: experimental and theoretical analysis

<p>Distributed load balancers exhibit thrashing where tasks are repeatedly moved between locations due to incomplete global load information. This paper shows that systems of autonomous mobile programs (AMPs) exhibit the same behaviour, and identifies two types of redundant movement (greedy effect). AMPs are unusual in that, in place of some external load management system, each AMP periodically recalculates network and program parameters and may independently move to a better execution environment. Load management emerges from the behaviour of collections of AMPs.</p> <p>The paper explores the extent of greedy effects by simulating collections of AMPs and proposes negotiating AMPs (NAMPs) to ameliorate the problem. We present the design of AMPs with a competitive negotiation scheme (cNAMPs), and compare their performance with AMPs by simulation. We establish new properties of balanced networks of AMPs, and use these to provide a theoretical analysis of greedy effects.</p&gt

An adaptive multi-agent system for task reallocation in a MapReduce job

International audienceWe study the problem of task reallocation for load-balancing of MapReduce jobs in applications that process large datasets. In this context, we propose a novel strategy based on cooperative agents used to optimise the task scheduling in a single MapReduce job. The novelty of our strategy lies in the ability of agents to identify opportunities within a current unbalanced allocation, which in turn trigger concurrent and one-to-many negotiations amongst agents to locally reallocate some of the tasks within a job. Our contribution is that tasks are reallocated according to the proximity of the resources and they are performed in accordance to the capabilities of the nodes in which agents are situated. To evaluate the adaptivity and responsiveness of our approach, we implement a prototype test-bed and conduct a vast panel of experiments in a heterogeneous environment and by exploring varying hardware configurations. This extensive experimentation reveals that our strategy significantly improves the overall runtime over the classical Hadoop data processing

Autonomous mobility in multilevel networks

Autonomous Mobile Programs (AMPs) are mobile agents that are aware of their resource needs and sensitive to the execution environment. AMPs are unusual in that, instead of using some external load management system, each AMP periodically recalculates network and program parameters and independently moves to a new location if it provides a better execution environment. Dynamic load management emerges from the behaviour of collections of AMPs. AMPs have previously been measured using mobile languages like Java Voyager on local area networks (LANs). The thesis develops an accurate simulation for AMPs on networks and validates it by reproducing the behaviour of collections of AMPs on homogeneous and heterogeneous LANs. The analysis shows that AMPs exhibit thrashing like other distributed load balancers. This thrashing is investigated in collections of AMPs, and two types of redundant movement (greedy effect) are identified. The thesis explores the extent of greedy effects by simulating collections of AMPs, and proposes negotiating AMPs (NAMPs) to ameliorate the problem. The design of AMPs with a competitive negotiation scheme (cNAMPs) is presented, followed by a performance comparison AMPs and cNAMPs using simulation. To estimate the significance of the greedy effects the properties of balanced states are established, such as independent balance, singleton optimality, and consecutive optimality. The balanced states are characterised for homogeneous and heterogeneous networks where AMPs are analysed as the general case. The significance of the cNAMP greedy effect is established by conducting a worst case analysis of redundant movements, and the maximum number, and probability of, redundant movements are calculated for homogeneous and heterogeneous networks. One of three theorems proves that in a heterogeneous network of q subnetworks the number of redundant movements does not exceed q − 1. i The thesis proposes and evaluates a multilevel cNAMP architecture that abstracts over network topologies to effectively distribute cNAMPs in large networks. The thesis investigates alternatives for implementation of this multilevel architecture and proposes a fusion-based scheme where information is first available to neighbour nodes. These neighbour nodes modify the information and pass it to remote locations. The effectiveness of the scheme is evaluated by simulating networks with up to five levels, varying the number of locations from 5 to 336, and the number of cNAMPs from 8 to 3360. The experiments investigate the effects depending on the number of levels, topologies, number of locations, number of cNAMPs, work of cNAMPs, type of cNAMPs, speed of locations, and type of rebalancing. The architecture is found to be effective because it delivers performance close to the hypothetical, e.g. each additional level increases mean cNAMP completion time by just 2%

An adaptive agent-based system for deregulated smart grids

The power grid is undergoing a major change due mainly to the increased penetration of renewables and novel digital instruments in the hands of the end users that help to monitor and shift their loads. Such transformation is only possible with the coupling of an information and communication technology infrastructure to the existing power distribution grid. Given the scale and the interoperability requirements of such future system, service-oriented architectures (SOAs) are seen as one of the reference models and are considered already in many of the proposed standards for the smart grid (e.g., IEC-62325 and OASIS eMIX). Beyond the technical issues of what the service-oriented architectures of the smart grid will look like, there is a pressing question about what the added value for the end user could be. Clearly, the operators need to guarantee availability and security of supply, but why should the end users care? In this paper, we explore a scenario in which the end users can both consume and produce small quantities of energy and can trade these quantities in an open and deregulated market. For the trading, they delegate software agents that can fully interoperate and interact with one another thus taking advantage of the SOA. In particular, the agents have strategies, inspired from game theory, to take advantage of a service-oriented smart grid market and give profit to their delegators, while implicitly helping balancing the power grid. The proposal is implemented with simulated agents and interaction with existing Web services. To show the advantage of the agent with strategies, we compare our approach with the “base” agent one by means of simulations, highlighting the advantages of the proposal

A new approach for multi-agent coalition formation and management in the scope of electricity markets

This paper presents a new methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using the multi-agent market simulator MASCEM, taking advantage of its ability to provide the means to model and simulate VPP (Virtual Power Producers). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. The new features include the development of particular individual facilitators to manage the communications amongst the members of each coalition independently from the rest of the simulation, and also the mechanisms for the classification of the agents that are candidates to join the coalition. In addition, a global study on the results of the Iberian Electricity Market is performed, to compare and analyze different approaches for defining consistent and adequate strategies to integrate into the agents of MASCEM. This, combined with the application of learning and prediction techniques provide the agents with the ability to learn and adapt themselves, by adjusting their actions to the continued evolving states of the world they are playing in