1,720,977 research outputs found
Extending the BPMN Specification to Support Cost-Centric Simulations of Business Processes
No full textBusiness Process Simulation is considered by many a very useful technique to analyze the impact of some important choices designers take at process design or optimization time, right before processes are actually implemented and deployed. In order for the simulation to provide accurate and reliable results, process models need to take into account not just the workflow dynamics, but also many other important factors that may impact on the overall performance of process execution, and that form what we refer to as the Context of a process. In this paper we formalize a new Business Process Model that encompasses all the features of a business process in terms of workflow and execution Context respectively. The model allows designers to build a cost-centric perspective of a business process. Also, we propose an extension to the Business Process Model and Notation (BPMN) specification with the aim of enhancing the power of the BPMN to also model resources and the process execution environment. In the paper we provide some details of the implementation of a novel Business Process Simulator capable of simulating the newly introduced process model. To prove the overall approach’s viability, a case study is finally discussed
Complementing the BPMN to enable data-driven simulations of business processes
No full textBusiness Process Simulation is a useful and widely adopted
technique that fits process analysts with the ability to estimate the per-
formance impact of important business decisions before the actions are
actually deployed. In order for the simulation to provide accurate and
reliable results, process models need to consider not just the workflow
dynamics, but also many important factors that may impact on the over-
all process performance, which constitute what we refer to as the Process
Context. In this paper we formalize a new Business Process Simulation
Model which strictly integrates to the BPMN 2.0 standard and encom-
passes all the features of a business process in terms of Process Workflow
and Process Context respectively. It allows designers to build a resource-
based perspective of a business process that enables the simulation of
complex data-driven behaviors. To prove the viability of the proposed
approach, a case study is finally discussed. The results obtained from
the case simulation are also reported
Web interactive non intrusive load disaggregation system for active demand in smart grids
Full text linkA Smart Grid combines the use of traditional technology with innovative digital solutions, making the management of the electricity grid more flexible. It allows for monitoring, analysis, control and communication within the supply chain to improve efficiency, reduce the energy consumption and cost, and maximize the transparency and reliability of the energy supply chain. The optimization of energy consumption in Smart Grids is possible by using an innovative system based on Non Intrusive Appliance Load Monitoring (NIALM) algorithms, in which individual appliance power consumption information is disaggregated from single-point measurements, that provide a feedback in such a way to make energy more visible and more amenable to understanding and control. We contribute with an approach for monitoring consumption of electric power in households based on both a NILM algorithm, that uses a simple load signatures, and a web interactive systems that allows an active role played by users
A framework for the management of dynamic SLAs in composite service scenarios
No full textThe advent of information and communication technology has changed the nature of business-to-business interaction among organizations. The use of electronic contracts with automated support for their management allows an increase of effectiveness and efficiency in contract processing, opening new possibilities for interaction among parties. Service Providers and their customers negotiate utility based Service Level Agreements (SLA) to determine costs and penalties based on the achieved performance levels. The global QoS to be provided to the end customer can be strongly affected by any violation on each single SLA. In order to prevent such violations, SLAs need to be flexible and dynamically adaptable. In this work we focus on the WS-Agreement specification, a Web Service protocol to establish agreements on the QoS level to be guaranteed in the provision of a service. We propose to enhance the flexibility of its approach by integrating new functionality to the protocol that enable the parties of a WS-Agreement to re-negotiate and modify its terms during the service provisio
A procurement auction market to trade residual Cloud computing capacity
No full textCurrently in the cloud market resources are mainly allocated according to the fixed-price, direct selling model. Market principles such as the supply-demand rate are not taken into consideration by cloud providers. In the literature several market-based resource allocation models and algorithms have been proposed, showing that dynamic pricing models might result more convenient for both the providers and the consumers of cloud resources. In this paper, we discuss about the use of alternative auction-based mechanisms to sell the 'residual' computing capacity, i.e., the capacity which the provider is not able to allocate through direct-selling. The design of a procurement market for computing resources is proposed: in such a market we devised an adaptive bidding strategy that suggests to the provider the right actions useful to attain its business objective. The resource overbooking mechanism is also proposed to overcome the problem of resource underutilization. A software simulator was implemented with the aim of testing and validating the proposed mechanisms. Results show that, by fine-tuning their own strategy, providers manage to pursue specific objective
TORCH: a TOSCA-Based Orchestrator of Multi-Cloud Containerised Applications
Full text linkThe growth in the number and types of cloud-based services offered to IT customers is supported by the constant entry of new actors in the market and the consolidation of disruptive technologies such as AI, Big Data and Micro-services. From the customer's perspective, in a market landscape where the cloud offer is highly diversified due to the presence of multiple competing service providers, picking the service that best accommodate their specific needs is a critical challenge. Once the choice is made, so called ``cloud orchestration tools'' (orchestrators) are required to take care of the customer application's life-cycle. While big players offer their customers proprietary orchestrators, in the literature quite a number of open-source initiatives have launched multi-cloud orchestrators capable of transparently managing applications on top of the most representative cloud platforms. In this paper, we propose TORCH, a TOSCA-based framework for the deployment and orchestration of cloud applications, both classical and containerised, on multiple cloud providers. The framework assists the cloud customer in defining application requirements by using standard specification models. Unlike other multi-cloud orchestrators, TORCH adopts a strategy that separates the provisioning workflow from the actual invocation of proprietary cloud services API. The main benefit is the possibility to add support to any cloud platforms at a very low implementation cost. In the paper, we present a prototypical implementation of TORCH and showcase its interaction with two different container-based cluster platforms. Preliminary performance tests conducted on a small-scale test-bed confirm the potential of TORC
A hierarchical Hadoop framework to handle big data in geo-distributed computing environments
No full textAdvances in the communication technologies, along with the birth of new communication paradigms leveraging on the power of the social, has fostered the production of huge amounts of data. Oldfashioned computing paradigms are unfit to handle the dimensions of the data daily produced by the countless, worldwide distributed sources of information. So far, the MapReduce has been able to keep the promise of speeding up the computation over Big Data within a cluster. This article focuses on scenarios of worldwide distributed Big Data. While stigmatizing the poor performance of the Hadoop framework when deployed in such scenarios, it proposes the definition of a Hierarchical Hadoop Framework (H2F) to cope with the issues arising when Big Data are scattered over geographically distant data centers. The article highlights the novelty introduced by the H2F with respect to other hierarchical approaches. Tests run on a software prototype are also reported to show the increase of performance that H2F is able to achieve in geographical scenarios over a plain Hadoop approach
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