Adaptive use of task assignment models in team-based mobile business processes

Most mobile business processes are executed under uncertain and dynamic working environments. This makes the traditional centralized approach for the management of mobile tasks inappropriate to respond to the changes in working environment quickly as collecting the changing information from geographically distributed workforces in real time is expensive if not impossible. This raises the need of a distributed approach in the management of mobile tasks. This paper proposes a distributed architecture for team-based coordination support for mobile task management. In this architecture, tasks are managed via peer-to-peer style coordination between team members who have better understanding on the changing working environment than a centralised system. The novelty of the design of the architecture is explained by applying it to a real business process in the UK

Teams organization and performance analysis in autonomous human-robot teams

This paper proposes a theory of human control of robot teams based on considering how people coordinate across different task allocations. Our current work focuses on domains such as foraging in which robots perform largely independent tasks. The present study addresses the interaction between automation and organization of human teams in controlling large robot teams performing an Urban Search and Rescue (USAR) task. We identify three subtasks: perceptual search-visual search for victims, assistance-teleoperation to assist robot, and navigation-path planning and coordination. For the studies reported here, navigation was selected for automation because it involves weak dependencies among robots making it more complex and because it was shown in an earlier experiment to be the most difficult. This paper reports an extended analysis of the two conditions from a larger four condition study. In these two "shared pool" conditions Twenty four simulated robots were controlled by teams of 2 participants. Sixty paid participants (30 teams) were recruited to perform the shared pool tasks in which participants shared control of the 24 UGVs and viewed the same screens. Groups in the manual control condition issued waypoints to navigate their robots. In the autonomy condition robots generated their own waypoints using distributed path planning. We identify three self-organizing team strategies in the shared pool condition: joint control operators share full authority over robots, mixed control in which one operator takes primary control while the other acts as an assistant, and split control in which operators divide the robots with each controlling a sub-team. Automating path planning improved system performance. Effects of team organization favored operator teams who shared authority for the pool of robots. © 2010 ACM

A performance comparison of the contiguous allocation strategies in 3D mesh connected multicomputers

The performance of contiguous allocation strategies can be significantly affected by the distribution of job execution times. In this paper, the performance of the existing contiguous allocation strategies for 3D mesh multicomputers is re-visited in the context of heavy-tailed distributions (e.g., a Bounded Pareto distribution). The strategies are evaluated and compared using simulation experiments for both First-Come-First-Served (FCFS) and Shortest-Service-Demand (SSD) scheduling strategies under a variety of system loads and system sizes. The results show that the performance of the allocation strategies degrades considerably when job execution times follow a heavy-tailed distribution. Moreover, SSD copes much better than FCFS scheduling strategy in the presence of heavy-tailed job execution times. The results also show that the strategies that depend on a list of allocated sub-meshes for both allocation and deallocation have lower allocation overhead and deliver good system performance in terms of average turnaround time and mean system utilization

Many Server Scaling of the N-System Under FCFS-ALIS

The N-System with independent Poisson arrivals and exponential server-dependent service times under first come first served and assign to longest idle server policy has explicit steady state distribution. We scale the arrival and the number of servers simultaneously, and obtain the fluid and central limit approximation for the steady state. This is the first step towards exploring the many server scaling limit behavior of general parallel service systems

Optimal performance of parallel-server systems with job size prediction errors

[EN] Modern communication networks integrate distributed computing architectures, in which customers are processed in parallel. We show how to minimize the waiting time of customer’s jobs by leveraging a simple threshold-based job dispatching policy. The optimal policy leverages the SITA routing, which assigns jobs to servers according to the size of the job. Moreover, the optimal policy permits to optimize system performance even when the job size is not known a priori and is estimated by means of error-prone predictors.The work of Josu Doncel has been supported by the Department of Education of the Basque Government through the Consolidated Research Group MATHMODE (IT1294-19), by the Marie Sklodowska-Curie grant agreement No 777778 and by the Spanish Ministry of Science and Innovation with reference PID2019-108111RB-I00 (FEDER/AEI). The work of Vincenzo Mancuso has been supported by the Ramon y Cajal grant RYC-2014-16285 from the Spanish Ministry of Economy and Competitiveness, and by the Region of Madrid through the TAPIR-CM program (S2018/TCS-4496)

AWSQ: an approximated web server queuing algorithm for heterogeneous web server cluster

With the rising popularity of web-based applications, the primary and consistent resource in the infrastructure of World Wide Web are cluster-based web servers. Overtly in dynamic contents and database driven applications, especially at heavy load circumstances, the performance handling of clusters is a solemn task. Without using efficient mechanisms, an overloaded web server cannot provide great performance. In clusters, this overloaded condition can be avoided using load balancing mechanisms by sharing the load among available web servers. The existing load balancing mechanisms which were intended to handle static contents will grieve from substantial performance deprivation under database-driven and dynamic contents. The most serviceable load balancing approaches are Web Server Queuing (WSQ), Server Content based Queue (QSC) and Remaining Capacity (RC) under specific conditions to provide better results. By Considering this, we have proposed an approximated web server Queuing mechanism for web server clusters and also proposed an analytical model for calculating the load of a web server. The requests are classified based on the service time and keep tracking the number of outstanding requests at each webserver to achieve better performance. The approximated load of each web server is used for load balancing. The investigational results illustrate the effectiveness of the proposed mechanism by improving the mean response time, throughput and drop rate of the server cluster