A DFA‐based approach for the deployment of BPaaS fragments in the cloud

Abstract

SummaryCloud computing is an emerging technology that is largely adopted by the current computing industry. With the growing number of Cloud services, Cloud providers' main focus is how to best offer efficient services (eg, SaaS, BPaaS, mobile services, etc) in order to hook the eventual customers. To meet this goal, services arrangement and placement in the cloud is becoming a serious problem because an optimal placement of these applications and their related data in accordance with the available resources can increase companies' benefits. Since there is a widespread deployment of business processes in the cloud, the hereinafter conducted research works aim to enhance the business processes' outsourcing by providing an optimized placement scheme that would attract cloud customers. In the light of these facts, the purpose of this paper is to deal with the BPaaS placement problem while optimizing both the total execution time and cloud resources' usage. To do so, we first determine the redundant BPaaS fragments using a DNA Fragment Assembly technique. We apply a variant of the Genetic Algorithm to resolve it. Then, we propose a placement algorithm, which produces an optimized placement scheme on the basis of the determined fragments relations. We follow that by an implementation of the whole placement process and a set of experimental results that have shown the feasibility and efficiency of the proposed approach.

Predictive BPaaS management with quantum and neural computing

“…Several BPaaS management tasks have been addressed by researchers. We cite, as examples, the placement of BPaaS fragments, 5 the decomposition, 11 and adaptation 7 of BPaaS services. Other researches have considered various objectives together, such as the CloudSocket project, 12 in which the design, allocation, execution, and evaluation of BPaaS have been treated.…”

“…To perform the different BPaaS management tasks, techniques like DNA assembly, genetic algorithms, fuzzy logic, multimodeling, fuzzy clustering, decision models, and affinity rules have been applied. For example, in reference, 5 the BPaaS placement task has been treated as a DNA fragment assembly problem and optimized using memetic genetic algorithm. Fuzzy logic was applied in reference, 7 to deal with the dynamicity and volatility of cloud environments and to ensure QoS preservation.…”

“…By considering the context of BPaaS, we notice that the majority of the approaches have generically treated the cloud environment. Except Hedhli and Mezni 5 and Moreno-Vozmediano et al, 13 researchers have not specified their vision of multicloud environment. Indeed, a strategical service management in such environment brings several advantages to both providers and customers, like the high scalability of business services that are dispersed over different cloud regions, in addition to the possibility of parallelizing various process components.…”

“…It is the same for other constraints like BPaaS execution time that has been covered only in reference. 5 Add to that, the BPaaS cost that has been optimized in reference 7 is of paramount importance. In addition, the collaboration in the context of BPaaS, as stated in reference, 15 seems to be highly recommended in order to deal with sophisticated scenarios.…”

“…BPaaS management has been widely and diversely treated. Some researchers have proposed generic frameworks allowing the BPaaS design, 3 modeling, 4 allocation, evaluation, 5 and reuse. 6 Other researches have concentrated on the trust and the security of the BPaaS management systems.…”