Engineering a Highly Scalable Object-aware Process Management Engine Using Distributed Microservices

Scalability of information systems has been a research topic for many years and is as relevant as ever with the dramatic increases in digitization of business processes and data. This also applies to process-aware information systems, most of which are currently incapable of scaling horizontally, i.e., over multiple servers. This paper presents the design science artifact that resulted from engineering a highly scalable process management system relying on the object-aware process man-agement paradigm. The latter allows for distributed process execution by conceptually encapsulating process logic and data into multiple in-teracting objects that may be processed concurrently. These objects, in turn, are represented by individual microservices at run-time, which can be hosted transparently across entire server clusters. We present mea-surement data that evaluates the scalability of the artifact on a compute cluster, demonstrating that the current prototypical implementation of the run-time engine can handle very large numbers of users and process instances concurrently in single-case mechanism experiments with large amounts of simulated user input. Finally, the development of scalable process execution engines will further the continued maturation of the data-centric business process management field

End-to-End Stochastic QoS Performance Under Multi-layered Satellite Network

To meet the growth of real-time and multimedia traffic, the next generation of satellite networks with a guarantee of quality of service (QoS) is indeed, urgent. In this paper, we support the multi-layered satellite network as the scenario, owing to dynamic topology and distinct classification of the generated traffics. We map the satellite network system into a tandem queuing model, which the purpose is devoted to use a mathematical tool for evaluating the performance bounds of per-flow end-to-end networks. For delay-sensitive traffics, we compare two different arrival models–Poisson process and self-similar process. Meanwhile, we apply traditional scheduling strategy to MEO nodes while considering link impairment between a pair of satellites. Finally, we analyze, in a numerically way, which parameters (and how they) influence the per-flow end-to-end performance bounds. Our analysis can be used as a reference to China’s future satellite topology and routing algorithms designed and, optimization given a network with performance requirements and constraints

Asymptotics of insensitive load balancing and blocking phases

We study a single class of traffic acting on a symmetric set of processor-sharing queues with finite buffers, and we consider the case where the load scales with the number of servers. We address the problem of giving robust performance bounds based on the study of the asymptotic behaviour of the insensitive load balancing schemes, which have the desirable property that the stationary distribution of the resulting stochastic network depends on the distribution of job-sizes only through its mean. It was shown for small systems with losses that they give good estimates of performance indicators, generalizing henceforth Erlang formula, whereas optimal policies are already theoretically and computationally out of reach for networks of moderate size. We characterize the response of symmetric systems under those schemes at different scales and show that three amplitudes of deviations can be identified according to whether ρ 1. A central limit scaling takes place for a sub-critical load; for ρ= 1 , the number of free servers scales like nθθ+1 (θ being the buffer depth and n being the number of servers) and is of order 1 for super-critical loads. This further implies the existence of different phases for the blocking probability. Before a (refined) critical load ρc(n)=1-an-θθ+1, the blocking is exponentially small and becomes of order n-θθ+1 at ρc(n). This generalizes the well-known quality-and-efficiency-driven regime, or Halfin—Whitt regime, for a one-dimensional queue and leads to a generalized staffing rule for a given target blocking probability.Fil: Jonckheere, Matthieu Thimothy Samson. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; ArgentinaFil: Prabhu, Balakrishna J.. Centre National de la Recherche Scientifique; Franci