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The fast growth in the amount of connected devices with computing capabilities in the past years has enabled the emergence of a new computing layer at the Edge. Despite being resource-constrained if compared with cloud servers, they offer lower latencies than those achievable by Cloud computing. The combination of both Cloud and Edge computing paradigms can provide a suitable infrastructure for complex applications' quality of service requirements that cannot easily be achieved with either of these paradigms alone. These requirements can be very different for each application, from achieving time sensitivity or assuring data privacy to storing and processing large amounts of data. Therefore, orchestrating these applications in the Cloud-Edge computing raises new challenges that need to be solved in order to fully take advantage of this layered infrastructure. This paper proposes an architecture that enables the dynamic orchestration of applications in the Cloud-Edge continuum. It focuses on the application's quality of service by providing the scheduler with input that is commonly used by modern scheduling algorithms. The architecture uses a distributed scheduling approach that can be customized in a per-application basis, which ensures that it can scale properly even in setups with high number of nodes and complex scheduling algorithms. This architecture has been implemented on top of Kubernetes and evaluated in order to asses its viability to enable more complex scheduling algorithms that take into account the quality of service of applications.

Advancements in the areas of Cloud Computing, Internet of Things (IoT), and hybrid Human-Computer systems have made feasible the creation of a highly integrated human-machine world. The concept of elasticity plays a crucial role in fulfilling this vision, enabling systems to address various requirements reflecting performance, security, and business concerns. However, elastic systems are still in their inception, and numerous challenges need to be addressed in their development and management. In this article we present an overview of our experience on elastic systems, with a focus on elastic cloud systems. In the quest for designing and managing elastic systems, several challenges need to be addressed, such as: (i) enabling the systems to fulfill different requirements from multiple involved stakeholders, (ii) designing elastic systems considering various degrees of elasticity capabilities provided by different technologies and environments, (iii) understanding behavioral relationships in elastic systems, and their effects on stakeholder requirements, (iv) monitoring costs and analyzing cost efficiency of elastic systems, (v) controlling the elasticity of such systems at runtime in order to fulfill stakeholders' requirements, and (vi) supporting system elasticity through operations management. We present the techniques we have adopted in order to tackle the above challenges. We introduce our solution for creating elastic systems, following their complete lifecycle, from design-time to operations management. [ABSTRACT FROM AUTHOR]

As multiple types of distributed, heterogeneous cloud computing environments have proliferated, cloud software can leverage diverse types of infrastructural, platform and data resources with di ff erent cost and quality models. This introduces a multidimensional elasticity perspective for cloud software that would greatly meet changing demands from the user. However, we argue that current techniques are not enough for dealing with multi-dimensional elasticity in distributed cloud environments. We present our approach to the realization of multi-dimensional elasticity by introducing novel concepts and a roadmap to achieve them. [ABSTRACT FROM AUTHOR]

Cloud applications can benefit from the on-demand capacity of cloud infrastructures, which offer computing and data resources with diverse capabilities, pricing, and quality models. However, state-of-the-art tools mainly enable the user to specify “if-then-else” policies concerning resource usage and size, resulting in a cumbersome specification process that lacks expressiveness for enabling the control of complex multilevel elasticity requirements. In this article, first we propose SYBL, a novel language for specifying elasticity requirements at multiple levels of abstraction. Second, we design and develop the rSYBL framework for controlling cloud services at multiple levels of abstractions. To enforce user-specified requirements, we develop a multilevel elasticity control mechanism enhanced with conflict resolution. rSYBL supports different cloud providers and is highly extensible, allowing service providers or developers to define their own connectors to the desired infrastructures or tools. We validate it through experiments with two distinct services, evaluating rSYBL over two distinct cloud infrastructures, and showing the importance of multilevel elasticity control. [ABSTRACT FROM AUTHOR]

To optimize the cost and performance of complex cloud services under dynamic requirements, workflows and diverse cloud offerings, we rely on different elasticity control processes. An elasticity control process, when being enforced, produces effects in different parts of the cloud service. These effects normally evolve in time and depend on workload characteristics, and on the actions within the elasticity control process enforced. Therefore, understanding the effects on the behavior of the cloud service is of utter importance for runtime decision-making process, when controlling cloud service elasticity. In this paper, we present a novel methodology and a framework for estimating and evaluating cloud service elasticity behaviors. To estimate the elasticity behavior, we collect information concerning service structure, deployment, service runtime, control processes, and cloud infrastructure. Based on this information, we utilize clustering techniques to identify cloud service elasticity behavior, in time, and for different parts of the service. Knowledge about such behavior is utilized within a cloud service elasticity controller to substantially improve the selection and execution of elasticity control processes. These elasticity behavior estimations are successfully being used by our elasticity controller, in order to improve runtime decision quality. We evaluate our framework with three real-world cloud services in different application domains. Experiments show that we are able to estimate the behavior in 89.5% of the cases. Moreover, we have observed improvements in our elasticity controller, which takes better control decisions, and does not exhibit control oscillations. [ABSTRACT FROM AUTHOR]

As a virtualized environment, the cloud offers considerable flexibility. Maximizing its benefits requires incorporating elasticity as a "first-class" property throughout the cloud software development life cycle. [ABSTRACT FROM PUBLISHER]

In the past, elasticity and commitment in business processes were underexplored. But as businesses increasingly exploit pay-per-use resources in the cloud for on-demand needs, elasticity and commitment have become important issues. Here, the authors discuss the value of using elastic resources and commitments to create more dynamic organizations that can easily balance the need to be adaptable and flexible, while also retaining a high level of manageability. [ABSTRACT FROM PUBLISHER]

Abstract: This paper introduces a new, octree-based algorithm to assist in the automated conversion of laser scanning point cloud data into solid models appropriate for computational analysis. The focus of the work is for typical, urban, vernacular structures to assist in better damage prediction prior to tunnelling. The proposed FaçadeVoxel algorithm automatically detects boundaries of building façades and their openings. Next, it checks and automatically fills unintentional occlusions. The proposed method produced robust and efficient reconstructions of building models from various data densities. When compared to measured drawings, the reconstructed building models were in good agreement, with only 1% relative errors in overall dimensions and 3% errors in openings. In addition, the proposed algorithm was significantly faster than other automatic approaches without compromising accuracy. [Copyright &y& Elsevier]

For solving complex problems, in many cases, software alone might not be sufficient and we need hybrid systems of software and humans in which humans not only direct the software performance but also perform computing and vice versa. Therefore, we advocate constructing "social computers" which combine software and human services. However, to date, human capabilities cannot be easily programmed into complex applications in a similar way like software capabilities. There is a lack of techniques to conceptualize and program human and software capabilities in a unified way. In this paper, we explore a new way to virtualize, provision and program human capabilities using cloud computing concepts and service delivery models. We propose novel methods for conceptualizing and modeling clouds of human-based services (HBS) and combine HBS with software-based services (SBS) to establish clouds of hybrid services. In our model, we present common APIs, similar to well-developed APIs for software services, to access individual and team-based compute units in clouds of HBS. Based on that, we propose a framework for utilizing SBS and HBS to solve complex problems. We present several programming primitives for hybrid services, also covering forming hybrid solutions consisting of software and humans. We illustrate our concepts via some examples of using our cloud APIs and existing cloud APIs for software. [ABSTRACT FROM AUTHOR]

Smart cities encompass services in diverse business and technological domains. Presently, most of these services are delivered through domain-specific, tightly coupled systems, which entail limited scalability and extensibility. The authors propose Web-scale service delivery that addresses these limitations and encourage the creation of novel services based on a domain-independent, cloud-based service-delivery platform. [ABSTRACT FROM PUBLISHER]

Automated conversion of point cloud data from laser scanning into formats appropriate for structural engineering holds great promise for exploiting increasingly available aerially and terrestrially based pixelized data for a wide range of surveying-related applications from environmental modeling to disaster management. This paper introduces a point-based voxelization method to automatically transform point cloud data into solid models for computational modeling. The fundamental viability of the technique is visually demonstrated for both aerial and terrestrial data. For aerial and terrestrial data, this was achieved in less than 30 s for data sets up to 650,000 points. In all cases, the solid models converged without any user intervention when processed in a commercial finite-element method program. [ABSTRACT FROM AUTHOR]

Enabling and controlling the elasticity of cloud computing applications is challenging. The developer must deal with daunting tasks using low-level code to implement strategies trading off costs versus quality of service. Programming directives can substantially reduce this overhead by delegating control of elasticity to middleware systems while allowing developers to focus on defining suitable strategies. This Web extra lists example directives in the Simple-Yet-Beautiful Language (SYBL). [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to examine how cloud-based information systems and services can support emerging and future requirements for sustainability governance of facilities. Design/methodology/approach – The authors present basic elements of cloud-based sustainability governance platforms, conduct a survey of existing industrial platforms and research works, discuss distinguishable and common characteristics of cloud computing platforms for sustainability governance, and give views on future research. Findings – Cloud computing emerges as a potential candidate for supporting sustainability governance. However, several techniques must be provided in order to support multiple stakeholders, complex analysis and compliance processes. Research limitations/implications – The number of industrial platforms and research works in the survey is limited, as is information about industrial platforms. Furthermore, industrial platforms are continuously updated, thus some information might be outdated. Originality/value – There exists no survey for understanding how cloud computing could be used for sustainability governance. The paper not only helps to understand state-of-the-art in using cloud computing for sustainability governance but also discusses main components, stakeholders and requirements for cloud-based sustainability governance platforms. [ABSTRACT FROM AUTHOR]

Abstract: The emerging cloud computing models for Internet-of-Things have fostered the development of lightweight applications using cloud services for monitoring and optimizing devices and equipment hosted in distributed facilities. Such applications – called bots in our work – can be composed and deployed with multiple types of governance policies from cloud platforms to distributed hosting environments and they can access not only local data and devices but also cloud data and features. Therefore, it is a great challenge to govern them. In this paper, we discuss governance issues and state-of-the-art on supporting the emerging Bot-as-a-Service in sustainability governance platforms. Based on that we outline our approaches to policy development and enforcement for the Bot-as-a-Service model [Copyright &y& Elsevier]

Abstract: With the offer from cloud computing providers, scientists have the opportunity to utilize pay-as-you-go resources together with their own and shared resources. However, scientists need to decide which parts of their applications should be executed in cloud computing systems in order to balance the trade-off between cost, time and resource requirements. In this paper, we present a service for estimating, monitoring and analyzing costs associated with scientific applications in the cloud. Cost models associated with different application execution models are proposed and these cost models can be composed to determine costs of different scenarios. We present techniques to estimate costs for service dependency and to monitor costs associated with typical scientific applications. Experiments with real-world applications are performed to illustrate the usefulness of our techniques. Our service could eventually be integrated into cloud resource management and execution services to support on-the-fly resource scheduling. [ABSTRACT FROM AUTHOR]

Cloud computing's success has made on-demand computing with a pay-as-you-go pricing model popular. However, cloud computing's focus on resources and costs limits progress in realizing more flexible, adaptive processes. The authors introduce elastic processes, which are based on explicitly modeling resources, cost, and quality, and show how they improve on the state of the art. [ABSTRACT FROM PUBLISHER]