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

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

Dual-phase just-in-time workflow scheduling in P2P grid systems

This paper presents a fully decentralized justin-time workflow scheduling method in a P2P Grid system. The proposed solution allows each peer node to autonomously dispatch inter-dependent tasks of workflows to run on geographically distributed computers. To reduce the workflow completion time and enhance the overall execution efficiency, not only does each node perform as a scheduler to distribute its tasks to execution nodes (or resource nodes), but the resource nodes will also set the execution priorities for the received tasks. By taking into account the unpredictability of tasks' finish time, we devise an efficient task scheduling heuristic, namely dynamic shortest makespan first (DSMF), which could be applied at both scheduling phases for determining the priority of the workflow tasks. We compare the performance of the proposed algorithm against seven other heuristics by simulation. Our algorithm achieves 20%~60% reduction on the average completion time and 37.5%~90% improvement on the average workflow execution efficiency over other decentralized algorithms. © 2010 IEEE.published_or_final_versionProcessing (ICPP 2010), San Diego, CA., 13-16 September 2010. In Proceedings of the 39th ICCP, 2010, p. 238-24

Remodelling Scientific Workflows for Cloud

Viimastel aastatel on hakanud teaduslikes kogukondades huvi pilvearvutuse vastu kasvama. Teaduskatsete läbiviimisel pilves on mitmeid eeliseid nagu elastsus, paindlikkus ja hooldatavus, kuid varasemad uuringud näitavad, et üks suurimaid probleeme teadusprogrammide jooksutamisel pilves on omavaheliste masinate andmevahetuse suurus. Üks lahendus sellele probleemile oleks tuvastada komponendid, mis omavahel palju suhtlevad ning panna nad pilves ühte kohta jooksma, et vähendada omavahelist andmevahetust. Antud bakalaureuse töös jagati (partitsioneeriti) Montage töövoo osad pilves asuvate virtuaalmasinate vahel ning rakendati valmis kirjutatud P2P süsteemi, et vähendada pilves olevat suhtlust. Tänu P2P süsteemile ja teadusprogrammi partitsioneerimisele vähendati kogu suhtlust pilves kuni 80%.In recent years, cloud computing has raised significant interest in the scientific community. Running scientific experiments in the cloud has its advantages like elasticity, scalability and software maintenance. However, the communication latencies are observed to be the major hindrance for migrating scientific computing applications to the cloud. The problem escalates further when we consider scientific workflows, where significant data is exchanged across different tasks. One way to overcome this problem is to reduce the data communication by partitioning and scheduling the workflow and adapting a peer-to-peer file sharing among the nodes. Different size Montage workflows were considered for the analysis of this problem. From the study it was observed that the partitioning along with the peer-to-peer file sharing reduced the data communication in the cloud up to 80