Optimal Cost for Time-Aware Cloud Resource Allocation in Business Process

Cloud Computing infrastructures are being increasingly used for running business process activities due to its high performance level and low operating cost. The enterprise QoS requirements are diverse and different resources are offered by Cloud providers in various QoS-based pricing strategies. Furthermore, business process activities are constrained by hard timing constraints and if they are not executed correctly the enterprise will pay penalties costs. Therefore, finding the optimal Cloud resources allocation for a business process becomes a highly challenging problem. While optimizing the Cloud resource allocation cost, it is important to respect activities QoS requirements and temporal constraints and Cloud pricing strategies constraints. The aim of the present paper is to offer a method that assists users finding the optimal pricing strategy for Cloud resource used by business process activities. Basically, we use a binary/(0-1) linear program with an objective function under a set of constraints. In order to show its feasibility, our approach has been implemented and the results of our experiments highlight the effectiveness of our proposed solution

Anti-Pattern Specification and Correction Recommendations for Semantic Cloud Services

Given the economic and technological advantages \ they offer, cloud services are increasing being offered by \ several cloud providers. However, the lack of standardized \ descriptions of cloud services hinders their discovery. \ In an effort to standardize cloud service descriptions, \ several works propose to use ontologies. Nevertheless, \ the adoption of any of the proposed ontologies \ calls for an evaluation to show its efficiency in cloud \ service discovery. Indeed, the existing cloud providers \ describe, their similar offered services in different ways. \ Thus, various existing works aim at standardizing the \ representation of cloud computing services by proposing \ ontologies. However, since the existing proposals \ were not evaluated, they might be less adopted and considered. \ Indeed, the ontology evaluation has a direct impact \ on its understandability and reusability. In this paper, \ we propose an evaluation approach to validate our \ proposed Cloud Service Ontology (CSO), to guarantee \ an adequate cloud service discovery. To this end, this \ paper has a three-fold contribution. First, we specify a \ set of patterns and anti-patterns in order to evaluate our \ CSO. Second, we define an anti-pattern detection algorithm \ based on SPARQL queries which provides a set of \ correction recommendations to help ontologists revise \ their ontology. Finally, tests were conducted in relation \ to: (i) the algorithm efficiency and (ii) anti-pattern detection \ of design anomalies as well as taxonomic and \ domain errors within CSO

Decentralized procurement mechanisms for efficient logistics services mapping - a design science research approach

Companies tend to outsource logistics services for flexibility or platform operating costs reduction. To do so, they typically use centralized platforms to delegate the services procurement process. However, those platforms can be prone to information asymmetries between carriers and shippers which can lead to sub-optimal procurement outcomes. A more transparent and efficient way to manage the procurement of logistics services between carriers and shippers could be a decentralized platform based on blockchain and smart contracts. In this paper, we design, implement, and evaluate the potential for a decentralized logistics services procurement system, following a design science research approach. In so doing, we contribute by (1) developing such a decentralized logistics services procurement system that addresses the allocation problem, and (2) developing a set of nascent design principles guiding the elaboration of decentralized procurement mechanisms on blockchain

Cross-Collaboration Processes based on Blockchain and IoT: a survey

Cross-collaboration processes are decentralized by nature and their centralized monitoring can trigger mistrust. Nevertheless, a decentralized monitoring facility such as a blockchain-based and Internet-of-Things-aware (IoT-aware) business process management system can reduce this pitfall. However, concerns related to usability, privacy, and performance, hamper the wide adoption of these systems. To better understand the challenges at stake, this paper reviews the use of blockchain and IoT devices in cross-collaboration processes. This survey sheds some light on standard uses such as model engineering or permissioned blockchains which help adopt cross-collaboration business process management systems. Moreover, with respect to process design, two schools of thought coexist, addressing both constrained and loosely processes. Furthermore, a focus on data-centric processes appears to get some momentum, as many industries go digital. Finally, this survey underlines the need to orient future research towards a more flexible, scalable, and data-aware blockchain-based business process management system

Blockchain logging for process mining: a systematic review

Considerable progress was forcasted for collaborative business processes with the rise of blockchain programmable platforms. One of the saliant promises was auditable traces of business process execution, but practically that has posed challenges specially with regard to blockchain logs’ structure who turned out to be inadequate for process mining techniques. Approaches to answer this issue have started to emerge in the literature, some focusing on the creation process of event logs and others dealing with their retrieval from the blockchain. This work outlines the generic steps required to solve these challenges and analyzes findings in these approaches with a consideration for efficiency and future research directions

Mining and Improving Composite Web Services Recovery Mechanisms

International audienceEnsuring composite services reliability is a challenging problem. Indeed, due to the inherent autonomy and heterogeneity of Web services it is difficult to predict and reason about the behavior of the overall composite service. Generally, previous approaches develop, using their modeling formalisms, a set of techniques to analyze the composition model and check “correctness” properties. Although powerful, these approaches may fail, in some cases, to ensure CS reliable executions even if they formally validate its composition model. This is because properties specified in the studied composition model remains assumptions that may not coincide with the reality (i.e. effective CS executions). Sharing the same issue, we present a reengineering approach that starts from CS executions log to improve its recovery mechanisms. Basically, we propose a set of mining techniques to discover CS transactional behavior from an event based log. Then, based on this mining step, we use a set of rules in order to improve its reliability

Toward a correct and optimal time-aware cloud resource allocation to business processes

© 2020 Elsevier B.V. Cloud is an increasingly popular computing paradigm that provides on-demand services to organizations for deploying their business processes over the Internet as it reduces their needs to plan ahead for provisioning resources. Cloud providers offer competitive pricing strategies (e.g., on-demand, reserved, and spot) specified based on temporal constraints to accommodate organizations’ changing and last-minute demands. Despite their varieties and benefits to optimize business process deployment cost, using those pricing strategies can lead to violating time constraints and exceeding budget constraints due to inappropriate decisions when allocating cloud resources to business processes. In this paper, we present an approach to guarantee a correct and optimal time-aware allocation of cloud resources to business processes. Correct because time constraints on these processes are not violated. And, optimal because the deployment cost of these processes is minimized. For this purpose, our approach uses timed automata to formally verify the matching between business processes’ temporal constraints and cloud resources’ time availabilities and linear programming to optimize deployment costs. Experiments demonstrate the technical doability of our proposed approach

Service Querying to Support Process Variant Development

International audienceDeveloping process variants enables enterprises to effectively adapt their business models to different markets. Existing approaches focus on business process models to support the variant development. The assignment of services in a business process, which ensures the process variability, has not been widely examined. In this paper, we present an innovative approach that focuses on component services instead of process models. We target to recommend services to a selected position in a business process. We define the service composition context as the relationships between a service and its neighbors. We compute the similarity between services based on the matching of their composition contexts. Then, we propose a query language that considers the composition context matching for service querying. We developed an application to demonstrate our approach and performed different experiments on a public dataset of real process models. Experimental results show that our approach is feasible and efficient

Restriction-based fragmentation of business processes over the cloud

© 2019 John Wiley & Sons, Ltd. Despite the elasticity and pay-per-use benefits of cloud computing (aka fifth utility computing), organizations adopting clouds could be locked into single cloud providers, which is not always a “pleasant” experience when these providers stop operations. This is a serious concern for those organizations that who would like to deploy (core) business processes on the cloud along with tapping into these two benefits. To address the lock-into concern, this paper proposes an approach for decomposing business processes into fragments that would run over multiple clouds and hence multiple providers. To develop fragments, the approach considers both restrictions over owners of business processes and potential competition among cloud providers. On the one hand, restrictions apply to each task in a business process and are specialized into budget to allocate, deadline to meet, and exclusivity to request. On the other hand, competition leads cloud providers to offer flexible pricing policies that would cater to the needs and requirements of each process owner. A policy handles certain clouds\u27 properties referred to as limitedness, non-renewability, and non-shareability that impact the availability of cloud resources and hence the whole fragmentation. For instance, a non-shareable resource could delay other processes should the current process do not release this resource on time. During fragmentation, interactions between owners of processes and providers of clouds happen according to two strategies referred to as global and partial. The former collects offers about cloud resources from all providers, while the latter collects such details from particular providers. To evaluate these strategies\u27 pros and cons, a system implementing them, as well as demonstrating the technical feasibility of the fragmentation approach using credit-application case study, is also presented in the paper. The system extends BPMN2-modeler Eclipse plugin and supports interactions of processes\u27 owners with clouds\u27 providers that result to identifying the necessary fragments with focus on cost optimization

Model driven simulation of elastic OCCI cloud resources

International audienceDeploying a cloud configuration in a real cloud platform is mostly cost-and time-consuming, as large number of cloud resources have to be rent for the time needed to run the configuration. Thereafter, cloud simulation tools are used as a cheap alternative to test Cloud configuration. However, most of existing cloud simulation tools require extensive technical skills and does not support simulation of any kind of cloud resources. In this context, using a model-driven approach can be helpful as it allows developers to efficiently describe their needs at a high level of abstraction. To do, we propose, in this article, a Model-Driven Engineering (MDE) approach based on the OCCI (Open Cloud Computing Interface) standard metamodel and CloudSim toolkit. We firstly extend OCCI metamodel for supporting simulation of any kind of cloud resources. Afterward, to illustrate the extensibility of our approach, we enrich the proposed metamodel by new simulation capabilities. As proof of concept, we study the elasticity and pricing strategies of Amazon Web Services (AWS). This article benefits from OCCIware Studio to design an OCCI simulation extension and to provide a simulation designer for designing cloud configurations to be simulated. We detail the approach process from defining an OCCI simulation extension until the generation and the simulation of the OCCI cloud configurations. Finally, we validate the proposed approach by providing a realistic experimentation to study its usability, the resources coverage rate and the cost. The results is compared with the ones computed from AWS