Software-Defined Cloud Computing: Architectural Elements and Open Challenges

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi, Indi

Smart Decision-Making via Edge Intelligence for Smart Cities

Smart cities are an ambitious vision for future urban environments. The ultimate aim of smart cities is to use modern technology to optimize city resources and operations while improving overall quality-of-life of its citizens. Realizing this ambitious vision will require embracing advancements in information communication technology, data analysis, and other technologies. Because smart cities naturally produce vast amounts of data, recent artificial intelligence (AI) techniques are of interest due to their ability to transform raw data into insightful knowledge to inform decisions (e.g., using live road traffic data to control traffic lights based on current traffic conditions). However, training and providing these AI applications is non-trivial and will require sufficient computing resources. Traditionally, cloud computing infrastructure have been used to process computationally intensive tasks; however, due to the time-sensitivity of many of these smart city applications, novel computing hardware/technologies are required. The recent advent of edge computing provides a promising computing infrastructure to support the needs of the smart cities of tomorrow. Edge computing pushes compute resources close to end users to provide reduced latency and improved scalability — making it a viable candidate to support smart cities. However, it comes with hardware limitations that are necessary to consider. This thesis explores the use of the edge computing paradigm for smart city applications and how to make efficient, smart decisions related to their available resources. This is done while considering the quality-of-service provided to end users. This work can be seen as four parts. First, this work touches on how to optimally place and serve AI-based applications on edge computing infrastructure to maximize quality-of-service to end users. This is cast as an optimization problem and solved with efficient algorithms that approximate the optimal solution. Second, this work investigates the applicability of compression techniques to reduce offloading costs for AI-based applications in edge computing systems. Finally, this thesis then demonstrate how edge computing can support AI-based solutions for smart city applications, namely, smart energy and smart traffic. These applications are approached using the recent paradigm of federated learning. The contributions of this thesis include the design of novel algorithms and system design strategies for placement and scheduling of AI-based services on edge computing systems, formal formulation for trade-offs between delivered AI model performance and latency, compression for offloading decisions for communication reductions, and evaluation of federated learning-based approaches for smart city applications

Addressing Application Latency Requirements through Edge Scheduling

Abstract Latency-sensitive and data-intensive applications, such as IoT or mobile services, are leveraged by Edge computing, which extends the cloud ecosystem with distributed computational resources in proximity to data providers and consumers. This brings significant benefits in terms of lower latency and higher bandwidth. However, by definition, edge computing has limited resources with respect to cloud counterparts; thus, there exists a trade-off between proximity to users and resource utilization. Moreover, service availability is a significant concern at the edge of the network, where extensive support systems as in cloud data centers are not usually present. To overcome these limitations, we propose a score-based edge service scheduling algorithm that evaluates network, compute, and reliability capabilities of edge nodes. The algorithm outputs the maximum scoring mapping between resources and services with regard to four critical aspects of service quality. Our simulation-based experiments on live video streaming services demonstrate significant improvements in both network delay and service time. Moreover, we compare edge computing with cloud computing and content delivery networks within the context of latency-sensitive and data-intensive applications. The results suggest that our edge-based scheduling algorithm is a viable solution for high service quality and responsiveness in deploying such applications