Hybrid clouds for data-Intensive, 5G-Enabled IoT applications: an overview, key issues and relevant architecture

Hybrid cloud multi-access edge computing (MEC) deployments have been proposed as efficient means to support Internet of Things (IoT) applications, relying on a plethora of nodes and data. In this paper, an overview on the area of hybrid clouds considering relevant research areas is given, providing technologies and mechanisms for the formation of such MEC deployments, as well as emphasizing several key issues that should be tackled by novel approaches, especially under the 5G paradigm. Furthermore, a decentralized hybrid cloud MEC architecture, resulting in a Platform-as-a-Service (PaaS) is proposed and its main building blocks and layers are thoroughly described. Aiming to offer a broad perspective on the business potential of such a platform, the stakeholder ecosystem is also analyzed. Finally, two use cases in the context of smart cities and mobile health are presented, aimed at showing how the proposed PaaS enables the development of respective IoT applications.Peer ReviewedPostprint (published version

Resource provisioning and scheduling algorithms for hybrid workflows in edge cloud computing

In recent years, Internet of Things (IoT) technology has been involved in a wide range of application domains to provide real-time monitoring, tracking and analysis services. The worldwide number of IoT-connected devices is projected to increase to 43 billion by 2023, and IoT technologies are expected to engaged in 25% of business sector. Latency-sensitive applications in scope of intelligent video surveillance, smart home, autonomous vehicle, augmented reality, are all emergent research directions in industry and academia. These applications are required connecting large number of sensing devices to attain the desired level of service quality for decision accuracy in a sensitive timely manner. Moreover, continuous data stream imposes processing large amounts of data, which adds a huge overhead on computing and network resources. Thus, latency-sensitive and resource-intensive applications introduce new challenges for current computing models, i.e, batch and stream. In this thesis, we refer to the integrated application model of stream and batch applications as a hybrid work ow model. The main challenge of the hybrid model is achieving the quality of service (QoS) requirements of the two computation systems. This thesis provides a systemic and detailed modeling for hybrid workflows which describes the internal structure of each application type for purposes of resource estimation, model systems tuning, and cost modeling. For optimizing the execution of hybrid workflows, this thesis proposes algorithms, techniques and frameworks to serve resource provisioning and task scheduling on various computing systems including cloud, edge cloud and cooperative edge cloud. Overall, experimental results provided in this thesis demonstrated strong evidences on the responsibility of proposing different understanding and vision on the applications of integrating stream and batch applications, and how edge computing and other emergent technologies like 5G networks and IoT will contribute on more sophisticated and intelligent solutions in many life disciplines for more safe, secure, healthy, smart and sustainable society

On the Future of Cloud Engineering

Ever since the commercial offerings of the Cloud started appearing in 2006, the landscape of cloud computing has been undergoing remarkable changes with the emergence of many different types of service offerings, developer productivity enhancement tools, and new application classes as well as the manifestation of cloud functionality closer to the user at the edge. The notion of utility computing, however, has remained constant throughout its evolution, which means that cloud users always seek to save costs of leasing cloud resources while maximizing their use. On the other hand, cloud providers try to maximize their profits while assuring service-level objectives of the cloud-hosted applications and keeping operational costs low. All these outcomes require systematic and sound cloud engineering principles. The aim of this paper is to highlight the importance of cloud engineering, survey the landscape of best practices in cloud engineering and its evolution, discuss many of the existing cloud engineering advances, and identify both the inherent technical challenges and research opportunities for the future of cloud computing in general and cloud engineering in particular

Trusted resource allocation in volunteer edge-cloud computing for scientific applications

Data-intensive science applications in fields such as e.g., bioinformatics, health sciences, and material discovery are becoming increasingly dynamic and demanding with resource requirements. Researchers using these applications which are based on advanced scientific workflows frequently require a diverse set of resources that are often not available within private servers or a single Cloud Service Provider (CSP). For example, a user working with Precision Medicine applications would prefer only those CSPs who follow guidelines from HIPAA (Health Insurance Portability and Accountability Act) for implementing their data services and might want services from other CSPs for economic viability. With the generation of more and more data these workflows often require deployment and dynamic scaling of multi-cloud resources in an efficient and high-performance manner (e.g., quick setup, reduced computation time, and increased application throughput). At the same time, users seek to minimize the costs of configuring the related multi-cloud resources. While performance and cost are among the key factors to decide upon CSP resource selection, the scientific workflows often process proprietary/confidential data that introduces additional constraints of security postures. Thus, users have to make an informed decision on the selection of resources that are most suited for their applications while trading off between the key factors of resource selection which are performance, agility, cost, and security (PACS). Furthermore, even with the most efficient resource allocation across multi-cloud, the cost to solution might not be economical for all users which have led to the development of new paradigms of computing such as volunteer computing where users utilize volunteered cyber resources to meet their computing requirements. For economical and readily available resources, it is essential that such volunteered resources can integrate well with cloud resources for providing the most efficient computing infrastructure for users. In this dissertation, individual stages such as user requirement collection, user's resource preferences, resource brokering and task scheduling, in lifecycle of resource brokering for users are tackled. For collection of user requirements, a novel approach through an iterative design interface is proposed. In addition, fuzzy interference-based approach is proposed to capture users' biases and expertise for guiding their resource selection for their applications. The results showed improvement in performance i.e. time to execute in 98 percent of the studied applications. The data collected on user's requirements and preferences is later used by optimizer engine and machine learning algorithms for resource brokering. For resource brokering, a new integer linear programming based solution (OnTimeURB) is proposed which creates multi-cloud template solutions for resource allocation while also optimizing performance, agility, cost, and security. The solution was further improved by the addition of a machine learning model based on naive bayes classifier which captures the true QoS of cloud resources for guiding template solution creation. The proposed solution was able to improve the time to execute for as much as 96 percent of the largest applications. As discussed above, to fulfill necessity of economical computing resources, a new paradigm of computing viz-a-viz Volunteer Edge Computing (VEC) is proposed which reduces cost and improves performance and security by creating edge clusters comprising of volunteered computing resources close to users. The initial results have shown improved time of execution for application workflows against state-of-the-art solutions while utilizing only the most secure VEC resources. Consequently, we have utilized reinforcement learning based solutions to characterize volunteered resources for their availability and flexibility towards implementation of security policies. The characterization of volunteered resources facilitates efficient allocation of resources and scheduling of workflows tasks which improves performance and throughput of workflow executions. VEC architecture is further validated with state-of-the-art bioinformatics workflows and manufacturing workflows.Includes bibliographical references