Now showing 1 - 10 of 36
  • Publication
    dSUMO: Towards a Distributed SUMO
    Microscopic urban mobility simulations consist of modelling a city's road network and infrastructure, and to run autonomous individual vehicles to understand accurately what is going on in the city. However, when the scale of the problem space is large or when the processing time is critical, performing such simulations might be problematic as they are very computationally expensive applications. In this paper, we propose to leverage the power of many computing resources to perform quicker or larger microscopic simulations, keeping the same accuracy as the classical simulation running on a single computing unit. We have implemented a distributed version of SUMO, called dSUMO. We show in this paper that the accuracy of the simulation in SUMO is not impacted by the distribution and we give some preliminary results regarding the performance of dSUMO compared to SUMO.
  • Publication
    A Hybrid Algorithm for Multi-objective Test Case Selection
    Testing is crucial to ensure the quality of software systems – but testing is an expensive process, so test managers try to minimise the set of tests to run to save computing resources and speed up the testing process and analysis. One problem is that there are different perspectives on what is a good test and it is usually not possible to compare these dimensions. This is a perfect example of a multi-objective optimisation problem, which is hard — especially given the scale of the search space here. In this paper, we propose a novel hybrid algorithm to address this problem. Our method is composed of three steps: a greedy algorithm to find quickly some good solutions, a genetic algorithm to increase the search space covered and a local search algorithm to refine the solutions. We demonstrate through a large scale empirical evaluation that our method is more reliable (better whatever the time budget) and more robust (better whatever the number of dimensions considered) – in the scenario with 4 objectives and a default execution time, we are 178% better in hypervolume on average than the state-of-the-art algorithms.
  • Publication
    Assessing the Robustness of Conversational Agents using Paraphrases
    Assessing a conversational agent’s understanding capabilities is critical, as poor user interactions could seal the agent’s fate at the very beginning of its lifecycle with users abandoning the system. In this paper we explore the use of paraphrases as a testing tool for conversational agents. Paraphrases, which are different ways of expressing the same intent, are generated based on known working input by per- forming lexical substitutions. As the expected outcome for this newly generated data is known, we can use it to assess the agent’s robustness to language variation and detect potential understanding weaknesses. As demonstrated by a case study, we obtain encouraging results as it appears that this approach can help anticipate potential understanding shortcomings and that these shortcomings can be addressed by the generated paraphrases.
      447Scopus© Citations 12
  • Publication
    Scalable Anti-KNN: Decentralized Computation of k-Furthest-Neighbor Graphs with HyFN
    The decentralized construction of k-Furthest-Neighbor graphs has been little studied, although such structures can play a very useful role, for instance in a number of distributed resource allocation problems. In this paper we define KFN graphs; we propose HyFN, a generic peer-to-peer KFN construction algorithm, and thoroughly evaluate its behavior on a number of logical networks of varying sizes.
  • Publication
    Towards the Automatic Detection of Efficient Computing Assets in a Heterogeneous Cloud Environment
    (Institute of Electrical and Electronic Engineers (IEEE), 2013-06-03) ; ; ; ;
    In a heterogeneous cloud environment, the manual grading of computing assets is the first step in the process of configuring IT infrastructures to ensure optimal utilization of resources. Grading the efficiency of computing assets is however, a difficult, subjective and time consuming manual task. Thus, an automatic efficiency grading algorithm is highly desirable. In this paper, we compare the effectiveness of the different criteria used in the manual grading task for automatically determining the efficiency grading of a computing asset. We report results on a dataset of 1,200 assets from two different data centers in IBM Toronto. Our preliminary results show that electrical costs (associated with power and cooling) appear to be even more informative than hardware and age based criteria as a means of determining the efficiency grade of an asset. Our analysis also indicates that the effectiveness of the various efficiency criteria is dependent on the asset demographic of the data centre under consideration.
  • Publication
    iVMp: an Interactive VM Placement Algorithm for Agile Capital Allocation
    (Institute of Electrical and Electronic Engineers (IEEE), 2013-06-03) ; ; ; ;
    Server consolidation is an important problem in any enterprise, where capital allocators (CAs) must approve any cost saving plans involving the acquisition or allocation of new assets and the decommissioning of inefficient assets. Our paper describes iVMp an interactive VM placement algorithm, that allows CAs to become 'agile' capital allocators that can interactively propose and update constraints and preferences as placements are recommended by the system. To the best of our knowledge this is the first time that this interactive VM placement recommendation problem has been addressed in the academic literature. Our results show that the proposed algorithm finds near optimal solutions in a highly efficient manner.
  • Publication
    Exact and Hybrid Solutions for the Multi-objective VM Reassignment Problem
    Machine Reassignment is a challenging problem for constraint programming (CP) and mixed integer linear programming (MILP) approaches, especially given the size of data centres. Hybrid solutions mixing CP and heuristic algorithms, such as, large neighbourhood search (CBLNS), also struggle to address the problem given its size and number of constraints. The multi-objective version of the Machine Reassignment Problem is even more challenging and it seems unlikely for CP, MILP or hybrid solutions to obtain good results in this context. As a result, the first approaches to address this problem have been based on other optimisation methods, including metaheuristics. In this paper we study three things: (i) under which conditions a mixed integer optimisation solver, such as IBM ILOG CPLEX, can be used for the Multi-objective Machine Reassignment Problem; (ii) how much of the search space can a well-known hybrid method such as CBLNS explore; and (iii) can we find a better hybrid approach combining MILP or CBLNS and another recent metaheuristic proposed for the problem (GeNePi). We show that MILP can handle only small or medium scale data centres, and with some relaxations, such as, an optimality tolerance gap and a limited number of directions explored in the search space. CBLNS on the other hand struggles with the problem in general but achieves reasonable performance for large instances of the problem. However, we show that our hybridisation improves both the quality of the set of solutions (CPLEX+GeNePi and CBLNS+GeNePi improve the solutions by +17.8% against CPLEX alone and +615% against CBLNS alone) and number of solutions (8.9 times more solutions than CPLEX alone and 56.76 times more solutions than CBLNS alone), while the processing time of CPLEX+GeNePi and CBLNS+GeNePi increases only by 6% and 16.4% respectively. Overall, the study shows that CPLEX+GeNePi is the best algorithm for small instances (CBLNS+GeNePi only gets 45.2% of CPLEX+GeNePi’s hypervolume) while CBLNS+GeNePi is better than the others on large instances (that CPLEX+GeNePi cannot address).
      425Scopus© Citations 12
  • Publication
    An Adaptive VM Provisioning Method for Large-Scale Agent-Based Traffic Simulations on the Cloud
    (Institute of Electrical and Electronic Engineers (IEEE), 2014-12-18) ; ; ;
    Using the Cloud for large-scale distributed simulations, such as agent-based traffic simulations, sounds like a good idea, as it is possible to provision and release easily processing nodes (e.g., Virtual machines) in the Cloud. However, the question is complex as it involves users' objectives, such as, time to process the simulation and cost of the simulation, and because the workload evolves in distributed simulations, in each node and the whole system, and this impact the resource provisioning plans. This paper proposes two main contributions: (i) a method for efficient utilization of computational resources for distributed agent-based simulations, providing a mechanism that adapts the resource provisioning to users' objectives and workload evolution, and (ii) a staged asynchronous migration technique to limit the migration overhead when the number of workers change. Our preliminary experimental results on a 24 hour scenario of traffic in the city of Tokyo show that our system outperforms a static provisioning by 12% in average and 23% during periods when workload changes a lot.
      430Scopus© Citations 15
  • Publication
    PIT-HOM: an Extension of Pitest for Higher Order Mutation Analysis
    Mutation testing is a well-known, effective, fault-based testing criterion. First order mutation introduces defects in the form of a single small syntactic change. While the technique has been shown to be effective, it has some limits. Higher order mutation, where the faults introduced include multiple changes, has been proposed as a way to address some of these limits. Although the technique has shown promising results, there is no practical tool available for the application and study of higher order mutation on Java programs. In this paper we present PIT-HOM, an extension of Pitest (PIT) for higher order mutation. Pitest is a practical mutation analysis tool for Java, applicable on real-world codebases. PIT-HOM combines mutants in a same class to create higher order mutants of user-defined orders, it runs the mutants and reports the results in an easy to process format. We validate PIT-HOM using two small Java programs and report its performance as well as some characteristics of the mutants it creates.
      462Scopus© Citations 3
  • Publication
    Towards a Gamified System to Improve Translation for Online Meetings
    Translation of online meetings (e.g., Skype conversations) is a useful feature that can help users to understand each other. However translations can sometimes be inaccurate or they can miss the context of the discussion. This is for instance the case in corporate environments where some words are used with special meanings that can be obscure to other people. This paper presents the prototype of a gamified application that aims at improving translations of and for online meetings. In our system, users play to earn points and rewards – and they try to propose and vote for the most accurate translations in context. Our system uses various techniques to split conversations in various semantically coherent segments and label them with relevant keyphrases. This is how we extract a description of the context of a sentence and we use this context to: (i) weight users' expertise and their translation (e.g., an AI specialist is more likely than a lay person to give a correct translation for a sentence about deep learning) (ii) map the various translations of words and phrases and their context, so that we can use them during online meetings.