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

Software Development in the Post-PC Era: Towards Software Development as a Service

Abstract. Software systems affect all aspects of our modern life andare revolutionizing the way we live. Over the years, software developmenthas evolved to meet the needs of new types of applications and toembrace new technological disruptions. Today, we witness the rise of mobilitywhere the role of the conventional high-specification PC is declining.Some refer to this era as the Post-PC era. This technological shift,powered by a key enabling technology - cloud computing, has opened new opportunities for human advancement (e.g. the Internet of Things).Consequently, the evolving landscape of software systems drives the need for new methods for conceiving them. Such methods need to a) address the challenges and requirements of this era and b) embrace the benefitsof new technological breakthroughs. In this paper, we list the characteristics of the Post-PC era from the software development perspective. In addition, we describe three motivating trends of software development processes. Then, we derive a list of requirements for the future software development approach from the characteristics of the Post-PC era and from the motivating trends. Finally, we propose a reference architecturefor cloud-based software process enactment as an enabler for Software Development as a Service (SDaaS). The architecture is thefirst step to address the needs that we have identified

Proactive cloud management for highly heterogeneous multi-cloud infrastructures

Various literature studies demonstrated that the cloud computing paradigm can help to improve availability and performance of applications subject to the problem of software anomalies. Indeed, the cloud resource provisioning model enables users to rapidly access new processing resources, even distributed over different geographical regions, that can be promptly used in the case of, e.g., crashes or hangs of running machines, as well as to balance the load in the case of overloaded machines. Nevertheless, managing a complex geographically-distributed cloud deploy could be a complex and time-consuming task. Autonomic Cloud Manager (ACM) Framework is an autonomic framework for supporting proactive management of applications deployed over multiple cloud regions. It uses machine learning models to predict failures of virtual machines and to proactively redirect the load to healthy machines/cloud regions. In this paper, we study different policies to perform efficient proactive load balancing across cloud regions in order to mitigate the effect of software anomalies. These policies use predictions about the mean time to failure of virtual machines. We consider the case of heterogeneous cloud regions, i.e regions with different amount of resources, and we provide an experimental assessment of these policies in the context of ACM Framework

Model-Driven Process Enactment for NFV Systems with MAPLE

The Network Functions Virtualization (NFV) advent is making way for the rapid deployment of network services (NS) for telecoms. Automation of network service management is one of the main challenges currently faced by the NFV community. Explicitly defining a process for the design, deployment, and management of network services and automating it is therefore highly desirable and beneficial for NFV systems. The use of model-driven orchestration means has been advocated in this context. As part of this effort to support automated process execution, we propose a process enactment approach with NFV systems as the target application domain. Our process enactment approach is megamodel-based. An integrated process modelling and enactment environment, MAPLE, has been built into Papyrus for this purpose. Process modelling is carried out with UML activity diagrams. The enactment environment transforms the process model to a model transformation chain, and then orchestrates it with the use of megamodels. In this paper we present our approach and environment MAPLE, its recent extension with new features as well as application to an enriched case study consisting of NS design and onboarding process.Comment: 27 pages, 14 figures, 1 tabl

Automatic deployment and reproducibility of workflow on the Cloud using container virtualization

PhD ThesisCloud computing is a service-oriented approach to distributed computing that has many attractive features, including on-demand access to large compute resources. One type of cloud applications are scientific work ows, which are playing an increasingly important role in building applications from heterogeneous components. Work ows are increasingly used in science as a means to capture, share, and publish computational analysis. Clouds can offer a number of benefits to work ow systems, including the dynamic provisioning of the resources needed for computation and storage, which has the potential to dramatically increase the ability to quickly extract new results from the huge amounts of data now being collected. However, there are increasing number of Cloud computing platforms, each with different functionality and interfaces. It therefore becomes increasingly challenging to de ne work ows in a portable way so that they can be run reliably on different clouds. As a consequence, work ow developers face the problem of deciding which Cloud to select and - more importantly for the long-term - how to avoid vendor lock-in. A further issue that has arisen with work ows is that it is common for them to stop being executable a relatively short time after they were created. This can be due to the external resources required to execute a work ow - such as data and services - becoming unavailable. It can also be caused by changes in the execution environment on which the work ow depends, such as changes to a library causing an error when a work ow service is executed. This "work ow decay" issue is recognised as an impediment to the reuse of work ows and the reproducibility of their results. It is becoming a major problem, as the reproducibility of science is increasingly dependent on the reproducibility of scientific work ows. In this thesis we presented new solutions to address these challenges. We propose a new approach to work ow modelling that offers a portable and re-usable description of the work ow using the TOSCA specification language. Our approach addresses portability by allowing work ow components to be systematically specifed and automatically - v - deployed on a range of clouds, or in local computing environments, using container virtualisation techniques. To address the issues of reproducibility and work ow decay, our modelling and deployment approach has also been integrated with source control and container management techniques to create a new framework that e ciently supports dynamic work ow deployment, (re-)execution and reproducibility. To improve deployment performance, we extend the framework with number of new optimisation techniques, and evaluate their effect on a range of real and synthetic work ows.Ministry of Higher Education and Scientific Research in Iraq and Mosul Universit

Software development in the post-PC era : towards software development as a service

PhD ThesisEngineering software systems is a complex task which involves various stakeholders and requires planning and management to succeed. As the role of software in our daily life is increasing, the complexity of software systems is increasing. Throughout the short history of software engineering as a discipline, the development practises and methods have rapidly evolved to seize opportunities enabled by new technologies (e.g., the Internet) and to overcome economical challenges (e.g., the need for cheaper and faster development). Today, we are witnessing the Post-PC era. An era which is characterised by mobility and services. An era which removes organisational and geographical boundaries. An era which changes the functionality of software systems and requires alternative methods for conceiving them. In this thesis, we envision to execute software development processes in the cloud. Software processes have a software production aspect and a management aspect. To the best of our knowledge, there are no academic nor industrial solutions supporting the entire software development process life-cycle(from both production and management aspects and its tool-chain execution in the cloud. Our vision is to use the cloud economies of scale and leverage Model-Driven Engineering (MDE) to integrate production and management aspects into the development process. Since software processes are seen as workflows, we investigate using existing Workflow Management Systems to execute software processes and we find that these systems are not suitable. Therefore, we propose a reference architecture for Software Development as a Service (SDaaS). The SDaaS reference architecture is the first proposal which fully supports development of complex software systems in the cloud. In addition to the reference architecture, we investigate three specific related challenges and propose novel solutions addressing them. These challenges are: Modelling & enacting cloud-based executable software processes. Executing software processes in the cloud can bring several benefits to software develop ment. In this thesis, we discuss the benefits and considerations of cloud-based software processes and introduce a modelling language for modelling such processes. We refer to this language as EXE-SPEM. It extends the Software and Systems Process Engineering (SPEM2.0) OMG standard to support creating cloudbased executable software process models. Since EXE-SPEM is a visual modelling language, we introduce an XML notation to represent EXE-SPEM models in a machine-readable format and provide mapping rules from EXE-SPEM to this notation. We demonstrate this approach by modelling an example software process using EXE-SPEM and mapping it to the XML notation. Software process models expressed in this XML format can then be enacted in the proposed SDaaS architecture. Cost-e cient scheduling of software processes execution in the cloud. Software process models are enacted in the SDaaS architecture as workflows. We refer to them sometimes as Software Workflows. Once we have executable software process models, we need to schedule them for execution. In a setting where multiple software workflows (and their activities) compete for shared computational resources (workflow engines), scheduling workflow execution becomes important. Workflow scheduling is an NP-hard problem which refers to the allocation of su cient resources (human or computational) to workflow activities. The schedule impacts the workflow makespan (execution time) and cost as well as the computational resources utilisation. The target of the scheduling is to reduce the process execution cost in the cloud without significantly a ecting the process makespan while satisfying the special requirements of each process activity (e.g., executing on a private cloud). We adapt three workflow scheduling algorithms to fit for SDaaS and propose a fourth one; the Proportional Adaptive Task Schedule. The algorithms are then evaluated through simulation. The simulation results show that the our proposed algorithm saves between 19.74% and 45.78% of the execution cost, provides best resource (VM) utilisation and provides the second best makespan compared to the other presented algorithms. Evaluating the SDaaS architecture using a case study from the safety-critical systems domain. To evaluate the proposed SDaaS reference architecture, we instantiate a proof-of-concept implementation of the architecture. This imple mentation is then used to enact safety-critical processes as a case study. Engineering safety-critical systems is a complex task which involves multiple stakeholders. It requires shared and scalable computation to systematically involve geographically distributed teams. In this case study, we use EXE-SPEM to model a portion of a process (namely; the Preliminary System Safety Assessment - PSSA) adapted from the ARP4761 [2] aerospace standard. Then, we enact this process model in the proof-of-concept SDaaS implementation. By using the SDaaS architecture, we demonstrate the feasibility of our approach and its applicability to di erent domains and to customised processes. We also demonstrate the capability of EXE-SPEM to model cloud-based executable processes. Furthermore, we demonstrate the added value of the process models and the process execution provenance data recorded by the SDaaS architecture. This data is used to automate the generation of safety cases argument fragments. Thus, reducing the development cost and time. Finally, the case study shows that we can integrate some existing tools and create new ones as activities used in process models. The proposed SDaaS reference architecture (combined with its modelling, scheduling and enactment capabilities) brings the benefits of the cloud to software development. It can potentially save software production cost and provide an accessible platform that supports collaborating teams (potentially across di erent locations). The executable process models support unified interpretation and execution of processes across team(s) members. In addition, the use of models provide managers with global awareness and can be utilised for quality assurance and process metrics analysis and improvement. We see the contributions provided in this thesis as a first step towards an alternative development method that uses the benefits of cloud and Model-Driven Engineering to overcome existing challenges and open new opportunities. However, there are several challenges that are outside the scope of this study which need to be addressed to allow full support of the SDaaS vision (e.g., supporting interactive workflows). The solutions provided in this thesis address only part of a bigger vision. There is also a need for empirical and usability studies to study the impact of the SDaaS architecture on both the produced products (in terms of quality, cost, time, etc.) and the participating stakeholders