Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

Dynamic bandwidth allocation in multi-class IP networks using utility functions.

PhDAbstact not availableFujitsu Telecommunications Europe Lt

Elastic calls in an integrated services network: the greater the call size variability the better the QoS

We study a telecommunications network integrating prioritized stream calls and delay tolerant elastic calls that are served with the remaining (varying) service capacity according to a processor sharing discipline. The remarkable observation is presented and analytically supported that the expected elastic call holding time is decreasing in the variability of the elastic call size distribution. As a consequence, network planning guidelines or admission control schemes that are developed based on deterministic or lightly variable elastic call sizes are likely to be conservative and inefficient, given the commonly acknowledged property of e.g.\ \textsc{www}\ documents to be heavy tailed. Application areas of the model and results include fixed \textsc{ip} or \textsc{atm} networks and mobile cellular \textsc{gsm}/\textsc{gprs} and \textsc{umts} networks. \u

Model-driven Scheduling for Distributed Stream Processing Systems

Distributed Stream Processing frameworks are being commonly used with the evolution of Internet of Things(IoT). These frameworks are designed to adapt to the dynamic input message rate by scaling in/out.Apache Storm, originally developed by Twitter is a widely used stream processing engine while others includes Flink, Spark streaming. For running the streaming applications successfully there is need to know the optimal resource requirement, as over-estimation of resources adds extra cost.So we need some strategy to come up with the optimal resource requirement for a given streaming application. In this article, we propose a model-driven approach for scheduling streaming applications that effectively utilizes a priori knowledge of the applications to provide predictable scheduling behavior. Specifically, we use application performance models to offer reliable estimates of the resource allocation required. Further, this intuition also drives resource mapping, and helps narrow the estimated and actual dataflow performance and resource utilization. Together, this model-driven scheduling approach gives a predictable application performance and resource utilization behavior for executing a given DSPS application at a target input stream rate on distributed resources.Comment: 54 page

Analysis of packet scheduling for UMTS EUL - design decisions and performance evaluation

The UMTS Enhanced Uplink (EUL) provides higher capacity, increased data rates and smaller latency on the communication link from users towards the network. In this paper we present a performance comparison of three distinct EUL scheduling schemes (one-by-one, partial parallel and full parallel) taking into account both the packet level characteristics and the flow level dynamics due to the (random) user behaviour.\ud Using a very efficient hybrid analytical and simulation approach we analyse the three schemes with respect to performance measures such as mean file transfer time and fairness. In UMTS, a significant part of the system capacity will be used to support non-elastic voice traffic. Hence, part of our investigation is dedicated to the effects that the volume of voice traffic has on the performance of the elastic traffic supported by the EUL. Finally, we evaluate the impact that implementation specifics of a full parallel scheduler has on these measures.\ud \ud Our main conclusion is that our partial parallel scheduler, which is a hybrid between the one-by-one and full parallel, outperforms the other two schedulers in terms of mean flow transfer time, and is less sensitive to volume and nature of voice traffic. However, under certain circumstances, the partial parallel scheduler exhibits a somewhat lower fairness than the alternatives