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MAS4Data : Multiagent systems for analyzing very large data sets



Data Science aims at processing large volumes of data to extract knowledge or insights. The volume and velocity of the available data to analyze requires to parallelize the processing as it can be done with the MapReduce design pattern (Dean and Ghemawat, 2008). The latter takes its name from the functions on which it is based: the map function which filters the data and the reduce function which aggregates them. The most popular framework for MapReduce is Hadoop but many other implementations exist, such as the cluster computing framework Spark (Zaharia et al., 2016), or the distributed NoSQL database Riak built from Amazon Dynamo (DeCandia et al., 2007).

Data flows can have periodic (daily, weekly or seasonal) and event-triggered peak data loads. These peaks can be challenging to manage. In the existing frameworks, an efficient task distribution (i.e. the key partitioning) requires prior knowledge of the data distribution. The partitioning is a priori fixed and so the workload is not necessarily uniformly distributed. In a similar way, the usage of an heterogeneous cluster environment can lead to a workload unevenly distributed between the reducers.

These skews have been studied and corrected with:

  1. a pre-processing of the data which raises the problem of building a representative sample;

  2. a history of the information on the previous runs which is not adapted with a variation of the data and treatments over time;

  3. a system requiring an expertise on the data (a priori knowledge) in order to set up the tools.

Multiagent systems

By contrast, multiagent systems are inherently adaptive and thus particularly suitable when workloads constantly evolve.

The SMAC team (Multiagent systems and behaviours) of the laboratory CRIStAL use the multiagent systems paradigm (MAS), in particular for complex distributed problem solving. For this purpose, a MAS is composed of many entities which aim at solving a complex problem which cannot be done by a single entity. A MAS is characterized by the the fact that:

  1. each agent has incomplete information and limited capabilities;

  2. there is no central authority;

  3. data are decentralized;

  4. the computations are asynchronously performed.

In a MAS, the autonomy of agents allows the system to dynamically adapt to the variations, eventually to the disturbances of their environment. For this reason, we defend the thesis according to which MAS are particularly appropriate for adapting to unknown data, flows that are constantly evolving and dynamic computing environments.

Scientific project

This project adopts an interdisciplinary approach, mixing Computer Science and Economy, to identify:

  • the challenges related to data management, including computing on large volumes of data and data-oriented parallelism;
  • the challenges related to the knowledge discovery including information retrieval and complex queries on large data.

As part of the MAS4Data project we are working on the design of a multiagent system implementing the MapReduce design pattern where the dynamic allocation of tasks is based on negotiation between reducers. This system is dynamic, decentralized, with neither history nor a priori knowledge of the data and it does not require a parameterization dependent on the data. Our MAS performs many distributed and concurrent auctions which involve adaptive agents taking decisions based on local information.


Dean, J. and Ghemawat, S. (2008), Mapreduce: simplified data processing on large clusters, Commun. ACM 51(1), 107–113.

Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M. J., Ghodsi, A., Gonzalez, J., Shenker, S. and Stoica, I. (2016), Apache spark: a unified engine for big data processing, Commun. ACM 59(11), 56– 65.

DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P. and Vogels, W. (2007), Dynamo: Amazon’s highly available key-value store, in Proc. of the 21st ACM SIGOPS Symposium on Operating Systems Principles (SOSP ’07), pp. 205–220.


The video shows some examples of tasks allocation with our MAS. We consider here real-world weather data and we count the number of records by temperature in the whole dataset.

You can observe the task negotiations during the reducing phase. We use here the default Hadoop partitioning as a reference point in order to illustrate several features of our MAS:

  1. The basic adaptation of our multiagent system, where each reducer is a negotiating agent, improves the tasks allocation;

  2. The extension of our multiagent system, where the tasks are divisible, allows the negotiation of expensive tasks;

  3. The multi-auctions extension, which allows reducers to be a bidder in more than one auction at the same time, improves the efficiency of the negotiation process.

Please note that in the video:

  • the tasks with a black border are currently performed by the reducer while the tasks with a green border are already performed;

  • the cheap tasks are graphically bigger than they should be in order to be see properly. Thus, the unfairness of the tasks allocation is a graphical effect.



  • Negotiation Strategy of Divisible Tasks for Large Dataset Processing Quentin Baert, Anne-Cécile Caron, Maxime Morge, Jean-Christophe Routier in the 15th European Conference on Multi-Agent Systems (EUMAS'2017). Evry, 14-15 December 2017.

Abstract: MapReduce is a design pattern for processing large datasets on a cluster. Its performances depend on some data skews and on the runtime environment. In order to tackle these problems, we propose an adaptive multiagent system. The agents interact during the data processing and the dynamic task allocation is the outcome of negotiations. These negotiations aim at improving the workload partition among the nodes within a cluster and so decrease the runtime of the whole process. Moreover, since the negotiations are iterative the system is responsive in case of node performance variations. In this full original paper, we show how, when a task is divisible, an agent may split it in order to negotiate its subtasks.

Abstract: MapReduce is a design pattern for processing large datasets distributed on a cluster. Its performances are linked to the data structure and the runtime environ- ment. Indeed, data skew can yield an unfair task allocation, but even when the initial allocation produced by the partition function is well balanced, an unfair allocation can occur during the reduce phase due to the heterogeneous performance of nodes. For these reasons, we propose an adaptive multi-agent system. In our approach, the reducer agents interact during the job and the task re-allocation is based on negotiation in order to decrease the workload of the most loaded reducer and so the runtime. In this paper, we propose and evaluate two negotiation strategies. Finally, we experiment our multi-agent system with real-world datasets over heterogeneous runtime environment.

Abstract: Many companies are using MapReduce applications to pro- cess very large amounts of data. Static optimization of such applications is complex because they are based on user-defined operations, called map and reduce, which prevents some algebraic optimization. In order to optimize the task allocation, several systems collect data from previous runs and predict the performance doing job profiling. However they are not effective during the learning phase, or when a new type of job or data set appears. In this paper, we present an adaptive multi-agent system for large data sets analysis with MapReduce. We do not preprocess data and we adopt a dynamic approach, where the reducer agents interact during the job. In order to decrease the workload of the most loaded reducer - and so the execution time - we propose a task re-allocation based on negotiation.


This project is supported by the CNRS Challenge Mastodons.