Using Bayesian networks for highly available cloud-based web applications
31 Oct 2015-Journal of Reliable Intelligent Environments (Springer International Publishing)-Vol. 1, Iss: 2, pp 87-100
TL;DR: The use of this language for allocating cloud resources to maximise service dependability by definition of a model-driven approach able to guide the software engineering to define a cloud infrastructure using a semi-automated process using both high-level languages such as UML as well as Bayesian networks.
Abstract: Bayesian networks have demonstrated their capability in several applications spanning from reasoning under uncertainty in artificial intelligence to dependability modelling and analysis. This paper focuses on the use of this language for allocating cloud resources to maximise service dependability. This objective is accomplished by the definition of a model-driven approach able to guide the software engineering to define a cloud infrastructure (applications, services, virtual and concrete resources) using a semi-automated process. This process exploits both high-level languages such as UML as well as Bayesian networks. Using all their features (backward analysis, ease of usage, low analysis time), Bayesian networks are used in this process as a driver for the optimization, learning and estimation phases. The paper discusses all the issues that the application of Bayesian networks in the proposed process arises.
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TL;DR: In this paper , the authors proposed a hybrid trust prediction model to provide accurate trust-based cloud service evaluations while efficiently handling the uncertainty in the cloud service assessment data, which employs Picture Fuzzy Sets (PFSs), Naïve Bayes, Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS), and Random Forest Classifier (RFC) to predict the trust value of cloud services.
Abstract: In general, objective and subjective QoS assessment data forms the major data source for service evaluations. Especially, in the most dynamic service-oriented computing ecosystem like cloud environments, such assessment data is subjected to high uncertainty due to the dynamics in the number of cloud services and related QoS values. In such cases, the accuracy and reliability of the most efficient cloud service selection model remain questionable. Further, the complex interdependencies among the multiple QoS parameters and the trustworthiness of the cloud services complicate the process of handling uncertainty during cloud service selection. This work presents Fuzzy-Multi Attribute Decision Making (FMADM), a hybrid trust prediction model to provide accurate trust-based cloud service evaluations while efficiently handling the uncertainty in the cloud service assessment data. FMADM employs (i) Picture Fuzzy Sets (PFSs): to capture inconsistency, uncertainty, and ambiguity in the QoS data, (ii) Naïve Bayes: to recompute the weights of the QoS attributes, (iii) Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS): to model the non-linear relationship between the QoS values of cloud services and their corresponding trust result, and (iv) Random Forest Classifier (RFC): to predict the trust value of cloud services. The performance of FMADM was evaluated using Quality Web Service (QWS) v1.0 dataset under different scenarios using various quality metrics.
9 citations
01 Jan 2017
TL;DR: This book chapter aims to explore computational modeling theories in order to represent a cloud infrastructure focusing on how to estimate and model cloud availability.
Abstract: Cloud-based solution adoption is becoming an indispensable strategy for enterprises, since it brings many advantages, such as low cost. On the other hand, to attend this demand, cloud providers are facing a great challenge regarding their resource management: how to provide services with high availability relying on finite computational resources and limited physical infrastructure? Understanding the components and operations of cloud data center is a key point to manage resources in an optimal way and to estimate how physical and logical failures can impact on users’ perception. This book chapter aims to explore computational modeling theories in order to represent a cloud infrastructure focusing on how to estimate and model cloud availability.
9 citations
TL;DR: Connectivity, human, hardware, storage, and software were found to be the important categories of causes for the unavailability of systems in an industrial context using archival data and records from a student information system.
Abstract: Software unavailability can lead to disastrous consequences ranging from delays and cancellation to a loss of millions of dollars of technology. However, research on the causes that can make systems unavailable is still little. The aim of this paper is to investigate such causes in an industrial context using 2 qualitative approaches ((a) interview and (b) retrospective analysis) using archival data and records from a student information system. As a result, connectivity, human, hardware, storage, and software were found to be the important categories of causes for the unavailability. Besides, the critical lessons to be learned that relate to activities of software management and software business are also discussed. This includes vendor support, systems documentation, health check process, licensing, and software updating or upgrading. To strengthen the claim that our findings are promising, we compare our main findings with those of a previous study. This study can generally assist software engineering people including engineers, developers, project managers, vendors. Additionally, this paper dis cusses the threats to the study's validity and suggests open problems for future research.
2 citations
Journal Article•
TL;DR: A2THOS as discussed by the authors is a framework to calculate the availability of partially outsourced IT services in the presence of SLAs and to achieve a cost-optimal choice of availability levels for outsourcing IT components while guaranteeing a target availability level for the service.
Abstract: IT service availability is at the core of customer satisfaction and business success for today’s organisations. Many medium-large size organisations outsource part of their IT services to external providers, with Service Level Agreements describing the agreed availability of outsourced service components. Availability management of partially outsourced IT services is a non trivial task since classic approaches for calculating availability are not applicable, and IT managers can only rely on their expertise to fulfil it. This often leads to the adoption of non optimal solutions. In this paper we present A2THOS, a framework to calculate the availability of partially outsourced IT services in the presence of SLAs and to achieve a cost-optimal choice of availability levels for outsourced IT components while guaranteeing a target availability level for the service.
2 citations
TL;DR: The dplbnDE R package as mentioned in this paper implements differential evolution strategies for training Bayesian Network parameters using Discriminative Learning, focusing on optimizing the conditional log likelihood rather than the log-likelihood.
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Book•
01 Jan 1988TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Abstract: From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
15,671 citations
TL;DR: The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.
Abstract: Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end-users under a usage-based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd.
4,570 citations
01 Jan 2007
TL;DR: The continuity of the basic conceptual model between Abstract and Executable Processes in WSBPEL makes it possible to export and import the public aspects embodied in Abstract Processes as process or role templates while maintaining the intent and structure of the observable behavior.
2,640 citations
Additional excerpts
...Among the several techniques available for such integration, BPEL (BP execution language) [15] is the most widespread solution and took the role of standard language for services orchestration....
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TL;DR: An architectural framework and principles for energy-efficient Cloud computing are defined and the proposed energy-aware allocation heuristics provision data center resources to client applications in a way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS).
2,511 citations
"Using Bayesian networks for highly ..." refers background in this paper
...3 Some works use energy to guide the allocation of VMs by pursuing energy efficiency [19,20]....
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TL;DR: Model-driven engineering technologies offer a promising approach to address the inability of third-generation languages to alleviate the complexity of platforms and express domain concepts effectively.
Abstract: Model-driven engineering technologies offer a promising approach to address the inability of third-generation languages to alleviate the complexity of platforms and express domain concepts effectively.
1,883 citations