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

Overload Control in a Token Player for a Fuzzy Workflow Management System

The underlying proposal of this work is to control overload of a Token Player in a Fuzzy Workflow Management System. In order to accomplish this, possibility theory is used to measure how much the Token Player can be overloaded. The model used in this work is built using Colored Petri net and the simulation is made using CPN Tools. Finally, is possible to control overload of the Token Player in a Fuzzy Workflow Management System, nevertheless more time is spent to achieve the end of activities

Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution

Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, applications running on top of the cloud infrastructure are more or less black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. Yet, in most cases, application developers in turn have limited knowledge of the cloud infrastructure. In this paper, we propose learning adaptation rules during runtime. To this end, we introduce FQL4KE, a self-learning fuzzy cloud controller. In particular, FQL4KE learns and modifies fuzzy rules at runtime. The benefit is that for designing cloud controllers, we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to specify cloud controllers by simply adjusting weights representing priorities in system goals instead of specifying complex adaptation rules. The applicability of FQL4KE has been experimentally assessed as part of the cloud application framework ElasticBench. The experimental results indicate that FQL4KE outperforms our previously developed fuzzy controller without learning mechanisms and the native Azure auto-scaling

Analysis Of Aircraft Arrival Delay And Airport On-time Performance

While existing grid environments cater to specific needs of a particular user community, we need to go beyond them and consider general-purpose large-scale distributed systems consisting of large collections of heterogeneous computers and communication systems shared by a large user population with very diverse requirements. Coordination, matchmaking, and resource allocation are among the essential functions of large-scale distributed systems. Although deterministic approaches for coordination, matchmaking, and resource allocation have been well studied, they are not suitable for large-scale distributed systems due to the large-scale, the autonomy, and the dynamics of the systems. We have to seek for nondeterministic solutions for large-scale distributed systems. In this dissertation we describe our work on a coordination service, a matchmaking service, and a macro-economic resource allocation model for large-scale distributed systems. The coordination service coordinates the execution of complex tasks in a dynamic environment, the matchmaking service supports finding the appropriate resources for users, and the macro-economic resource allocation model allows a broker to mediate resource providers who want to maximize their revenues and resource consumers who want to get the best resources at the lowest possible price, with some global objectives, e.g., to maximize the resource utilization of the system

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users’ requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This article provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions

An Adaptive Mediation Framework for Workflow Management in the Internet of Things

Tärkavad värkvõrksüsteemid koosnevad arvukast hulgast heterogeensetest füüsilistest seadmetest, mis ühenduvad Internetiga. Need seadmed suudavad pidevalt ümbritseva keskkonnaga suhelda ja osana lõppkasutaja rakendusestest edendada valdkondi nagu tark kodu, e-tervis, logistika jne. Selleks, et integreerida füüsilisi seadmeid värkvõrgu haldussüssteemidega, on töövoo haldussüsteemid kerkinud esile sobiva lahendusena. Ent töövoo haldussüsteemide rakendamine värkvõrku toob kaasa reaalajas teenuste komponeerimise väljakutseid nagu pidev teenusavastus ja -käivitus. Lisaks kerkib küsimus, kuidas piiratud resurssidega värkvõrgu seadmeid töövoo haldussüsteemidega integreerida ning kuidas töövooge värkvõrgu seadmetel käivitada. Tööülesanded (nagu pidev seadmeavastus) võivad värkvõrgus osalevatele piiratud arvutusjõudluse ja akukestvusega seadmetele nagu nutitelefonid koormavaks osutuda. Siinkohal on võimalikuks lahenduseks töö delegeerimine pilve. Käesolev magistritöö esitleb kontekstipõhist raamistikku tööülesannete vahendamiseks värkvõrgurakendustes. Antud raamistikus modelleeritakse ning käitatakse tööülesandeid kasutades töövoogusid. Raamistiku prototüübiga läbi viidud uurimus näitas, et raamistik on võimeline tuvastama, millal seadme avastusülesannete pilve delegeerimine on kuluefektiivsem. Vahel aga pole töövoo käitamistarkvara paigaldamine värkvõrgu seadmetele soovitav, arvestades energiasäästlikkust ning käituskiirust. Käesolev töö võrdles kaht tüüpi töövookäitust: a) töövoo mudeli käitamine käitusmootoriga ning b) töövoo mudelist tõlgitud programmikoodi käitamine. Lähtudes katsetest päris seadmetega, võrreldi nimetatud kahte meetodit silmas pidades süsteemiressursside- ning energiakasutust.Emerging Internet of Things (IoT) systems consist of great numbers of heterogeneous physical entities that are interconnected via the Internet. These devices can continuously interact with the surrounding environment and be used for user applications that benefit human life in domains such as assisted living, e-health, transportation etc. In order to integrate the frontend physical things with IoT management systems, Workflow Management Systems (WfMS) have gained attention as a viable option. However, applying WfMS in IoT faces real-time service composition challenges such as continuous service discovery and invocation. Another question is how to integrate resource-contained IoT devices with the WfMS and execute workflows on the IoT devices. Tasks such as continuous device discovery can be taxing for IoT-involved devices with limited processing power and battery life such as smartphones. In order to overcome this, some tasks can be delegated to a utility Cloud instance. This thesis proposes a context-based framework for task mediation in Internet of Things applications. In the framework, tasks are modelled and executed as workflows. A case study carried out with a prototype of the framework showed that the proposed framework is able to decide when it is more cost-efficient to delegate discovery tasks to the cloud. However, sometimes embedding a workflow engine in an IoT device is not beneficial considering agility and energy conservation. This thesis compared two types of workflow execution: a) execution of workflow models using an embedded workflow engine and b) execution of program code translations based on the workflow models. Based on experiments with real devices, the two methods were compared in terms of system resource and energy usage

An adaptive trust based service quality monitoring mechanism for cloud computing

Cloud computing is the newest paradigm in distributed computing that delivers computing resources over the Internet as services. Due to the attractiveness of cloud computing, the market is currently flooded with many service providers. This has necessitated the customers to identify the right one meeting their requirements in terms of service quality. The existing monitoring of service quality has been limited only to quantification in cloud computing. On the other hand, the continuous improvement and distribution of service quality scores have been implemented in other distributed computing paradigms but not specifically for cloud computing. This research investigates the methods and proposes mechanisms for quantifying and ranking the service quality of service providers. The solution proposed in this thesis consists of three mechanisms, namely service quality modeling mechanism, adaptive trust computing mechanism and trust distribution mechanism for cloud computing. The Design Research Methodology (DRM) has been modified by adding phases, means and methods, and probable outcomes. This modified DRM is used throughout this study. The mechanisms were developed and tested gradually until the expected outcome has been achieved. A comprehensive set of experiments were carried out in a simulated environment to validate their effectiveness. The evaluation has been carried out by comparing their performance against the combined trust model and QoS trust model for cloud computing along with the adapted fuzzy theory based trust computing mechanism and super-agent based trust distribution mechanism, which were developed for other distributed systems. The results show that the mechanisms are faster and more stable than the existing solutions in terms of reaching the final trust scores on all three parameters tested. The results presented in this thesis are significant in terms of making cloud computing acceptable to users in verifying the performance of the service providers before making the selection