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

Scheduling with Predictions and the Price of Misprediction

In many traditional job scheduling settings, it is assumed that one knows the time it will take for a job to complete service. In such cases, strategies such as shortest job first can be used to improve performance in terms of measures such as the average time a job waits in the system. We consider the setting where the service time is not known, but is predicted by for example a machine learning algorithm. Our main result is the derivation, under natural assumptions, of formulae for the performance of several strategies for queueing systems that use predictions for service times in order to schedule jobs. As part of our analysis, we suggest the framework of the "price of misprediction," which offers a measure of the cost of using predicted information

Load Balancing via Random Local Search in Closed and Open systems

In this paper, we analyze the performance of random load resampling and migration strategies in parallel server systems. Clients initially attach to an arbitrary server, but may switch server independently at random instants of time in an attempt to improve their service rate. This approach to load balancing contrasts with traditional approaches where clients make smart server selections upon arrival (e.g., Join-the-Shortest-Queue policy and variants thereof). Load resampling is particularly relevant in scenarios where clients cannot predict the load of a server before being actually attached to it. An important example is in wireless spectrum sharing where clients try to share a set of frequency bands in a distributed manner.Comment: Accepted to Sigmetrics 201

Flexible Task coordination for mobile workforce

With the advancement of networking and mobile devices, more and more mobile business processes are automated and supported using the technologies. Mobile businesses processes are naturally exposed to uncertainty and dynamic changes that require distributed coordination. In large business organizations, the complexity of the processes also makes central control difficult due to the large number of variables to consider and mobile workers involved. To this end, this paper presents a flexible coordination mechanism for mobile workforce where multiple task assignment models are used together to adapt to dynamic changes and achieve efficiency. The overall system is flexible in that the assignment models are easily added because they are constructed as components, and the switch between assignment models are easy using manual or automated transition between the models. An example application of the model is presented using a real telecommunication organization in Europe where field workers install and repair telecommunication networks for customers

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)

Size-Based Routing Policies: Non-Asymptotic Analysis and Design of Decentralized Systems

Size-based routing policies are known to perform well when the variance of the distribution of the job size is very high. We consider two size-based policies in this paper: Task Assignment with Guessing Size (TAGS) and Size Interval Task Assignment (SITA). The latter assumes that the size of jobs is known, whereas the former does not. Recently, it has been shown by our previous work that when the ratio of the largest to shortest job tends to infinity and the system load is fixed and low, the average waiting time of SITA is, at most, two times less than that of TAGS. In this article, we first analyze the ratio between the mean waiting time of TAGS and the mean waiting time of SITA in a non-asymptotic regime, and we show that for two servers, and when the job size distribution is Bounded Pareto with parameter α=1, this ratio is unbounded from above. We then consider a system with an arbitrary number of servers and we compare the mean waiting time of TAGS with that of Size Interval Task Assignment with Equal load (SITA-E), which is a SITA policy where the load of all the servers are equal. We show that in the light traffic regime, the performance ratio under consideration is unbounded from above when (i) the job size distribution is Bounded Pareto with parameter α=1 and an arbitrary number of servers as well as (ii) for Bounded Pareto distributed job sizes with α∈(0,2)\{1} and the number of servers tends to infinity. Finally, we use the result of our previous work to show how to design decentralized systems with quality of service constraints.Josu Doncel has received funding from the Department of Education of the Basque Government through the Consolidated Research Group MATHMODE (IT1294-19), from the Marie Sklodowska-Curie grant agreement No 777778, and from the Spanish Ministry of Science and Innovation with reference PID2019-108111RB-I00 (FEDER/AEI). Eitan Bachmat’s work was supported by the German Science Foundation (DFG) through the grant, Airplane Boarding, (JA 2311/3-1)

On the nature and impact of self-similarity in real-time systems

In real-time systems with highly variable task execution times simplistic task models are insufficient to accurately model and to analyze the system. Variability can be tackled using distributions rather than a single value, but the proper charac- terization depends on the degree of variability. Self-similarity is one of the deep- est kinds of variability. It characterizes the fact that a workload is not only highly variable, but it is also bursty on many time-scales. This paper identifies in which situations this source of indeterminism can appear in a real-time system: the com- bination of variability in task inter-arrival times and execution times. Although self- similarity is not a claim for all systems with variable execution times, it is not unusual in some applications with real-time requirements, like video processing, networking and gaming. The paper shows how to properly model and to analyze self-similar task sets and how improper modeling can mask deadline misses. 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