Spectrum Sharing Opportunities of Full-Duplex Systems using Improper Gaussian Signaling

Sharing the licensed spectrum of full-duplex (FD) primary users (PU) brings strict limitations on the underlay cognitive radio operation. Particularly, the self interference may overwhelm the PU receiver and limit the opportunity of secondary users (SU) to access the spectrum. Improper Gaussian signaling (IGS) has demonstrated its superiority in improving the performance of interference channel systems. Throughout this paper, we assume a FD PU pair that uses proper Gaussian signaling (PGS), and a half-duplex SU pair that uses IGS. The objective is to maximize the SU instantaneous achievable rate while meeting the PU quality-of-service. To this end, we propose a simplified algorithm that optimizes the SU signal parameters, i.e, the transmit power and the circularity coefficient, which is a measure of the degree of impropriety of the SU signal, to achieve the design objective. Numerical results show the merits of adopting IGS compared with PGS for the SU especially with the existence of week PU direct channels and/or strong SU interference channels

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

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

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