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Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art
Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP
Exploiting the user interaction context for automatic task detection
Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones
Addressing challenges to teach traditional and agile project management in academia
In order to prepare students for a professional IT career, most universities attempt to provide a current
educational curriculum in the Project Management (PM) area to their students. This is usually based on
the most promising methodologies used by the software industry. As instructors, we need to balance
traditional methodologies focused on proven project planning and control processes leveraging widely
accepted methods and tools along with the newer agile methodologies. Such new frameworks
emphasize that software delivery should be done in a flexible and iterative manner and with significant
collaboration with product owners and customers. In our experience agile methodologies have
witnessed an exponential growth in many diverse software organizations, and the various agile PM tools
and techniques will continue to see an increase in adoption in the software development sector.
Reflecting on these changes, there is a critical need to accommodate best practices and current methodologies in our courses that deliver Project Management content. In this paper we analyse two of the most widely used methodologies for traditional and agile software development â the widely used
ISO/PMBOK standard provided by the Project Management Institute and the well-accepted Scrum
framework. We discuss how to overcome curriculum challenges and deliver a quality undergraduate PM
course for a Computer Science and Information systems curricula. Based on our teaching experience
in Europe and North America, we present a comprehensive comparison of the two approaches. Our research covers the main concepts, processes, and roles associated with the two PM frameworks and recommended learning outcomes. The paper should be of value to instructors who are keen to see their computing students graduate with a sound understanding of current PM methodologies and who can deliver real-world software products.Accepted manuscrip
A WOA-based optimization approach for task scheduling in cloud Computing systems
Task scheduling in cloud computing can directly
affect the resource usage and operational cost of a system. To
improve the efficiency of task executions in a cloud, various
metaheuristic algorithms, as well as their variations, have been
proposed to optimize the scheduling. In this work, for the
first time, we apply the latest metaheuristics WOA (the whale
optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that
basis, we propose an advanced approach called IWC (Improved
WOA for Cloud task scheduling) to further improve the optimal
solution search capability of the WOA-based method. We present
the detailed implementation of IWC and our simulation-based
experiments show that the proposed IWC has better convergence
speed and accuracy in searching for the optimal task scheduling
plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource
utilization, in the presence of both small and large-scale tasks
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