In the realm of Internet of Things (IoT) architectures where diversified services exist, latency related network challenges remain as critical problems in cloud based systems. To address these challenges, fog computing has emerged as a viable solution for Quality of Service (QoS) oriented design. Leveraging heterogeneous services with different requirements, fog layer transmits decision outputs with traffic patterns composed of multi-priority data packets. To fully harness the benefits of fog computing, it is crucial to model such data traffic and devise an efficient scheduling approach for its management. Additionally, employing effective management of cloud resources for handling incoming data traffic is pivotal to enhance the design of latency based QoS aware services. In this paper, we present a fog enabled service architecture and its novel IoT data traffic management mechanism for the mitigation of latency based QoS problems. To this end, we first elaborate on the service components and data traffic characteristics of the architecture with an in-depth analysis of arrival-service model to give insights into the starvation issue in multi-priority scheduling. We present a Complex Event Processing (CEP) based scheduling approach for handling multi-priority IoT data traffic with QoS perspective and a dynamic resource scaling mechanism for handling decision data traffic on the cloud. Finally, we discuss the test results obtained from implementation of the proposed policies and services in a real test-bed environment with two IoT use-cases. The results reveal that the proposed architecture significantly improves the wait time based QoS requirements and resource utilization compared to the baseline system.

Studies on the Internet of Things have contributed to the development of air quality monitoring applications within the smart city paradigm. Furthermore, fog computing helps eliminate the data density bottleneck that may arise as smart city applications evolve. In this study, a fog computing-based, location-aware air quality monitoring system has been proposed, and its performance was evaluated through preliminary tests using real data. The proposed system is compared with a centralized cloud-based application scenario. The results indicate that the proposed system can serve up to 960 clients in the presence of 120 air quality monitoring stations in the established test environment. Additionally, the study revealed that the proposed model outperforms the cloud-based model in terms of latency and service load stability characteristics.

In this demonstration, we will construct and visualize a scalable fog computing-based multi-layer Internet of Things (IoT) platform that supports various application scenarios. Within the platform, we assess its performance in terms of latency, wait time, and processing load as QoS metrics. We examine scenarios where applications generating different alarm levels, resulting in multi-priority decision data traffic across clients and sensor nodes. This proposal provides an overview of the platform, including its layered design, the demo setup, and how QoS is addressed across the different layers.

Recent Machine Learning (ML) techniques enable new features on video analytic applications. Still, video pro-cessing requires intensive computing power and communication bandwidth. Processing tasks are shifted to Edge nodes close to video sources to reduce bandwidth usage. Thus, critical events can be detected in real-time and notified to users. However, the amount of resources at Edge is restricted. Insufficient resources can lower the prediction accuracy of ML models. In this paper, we designed a dynamic allocation framework and implemented it to improve resource utilization at the Edge node while the prediction accuracy is maintained.

Internet of Things (IoT) services have grown substantially in recent years. Consequently, IoT service providers (SPs) are emerging in the market and competing to offer their services. Many IoT applications utilize these services in an integrated manner with different Quality-of-Service (QoS) requirements. Thus, the provisioning of end-to-end QoS is getting more indispensable for IoT platforms. However, provisioning the system by using only QoS metrics without considering user experiences is not sufficient. Recently, Quality of Experience (QoE) model has become a promising approach to quantify actual user experiences of services. A holistic design approach that considers constraints of various QoS/QoE metrics together is needed to satisfy requirements of these applications and services. Besides, IoT services may operate in environments with limited resources. Therefore, effective management of services and system resources is essential for QoS/QoE support. This paper provides a comprehensive survey for the state-of-the-art studies on IoT services with QoS/QoE perspective. Our contributions are threefold: 1) QoE-driven architecture is demonstrated by classifying vital components according to QoE-related functions in prior studies; 2) QoE metrics and QoE optimization objectives are classified by corresponding system and resource control problems in the architecture; and 3) QoE-aware resource management e.g., QoE-aware offloading, placement and data caching policies with recent Machine Learning approaches are extensively reviewed.

Fog computing has the benefits to handle and reduce data traffic load towards the central cloud in IoT systems. These benefits are facilitated with the help of offloaded fog services that participate in the decision making processes. Besides, fog-based systems have the potential to mitigate scalability bottlenecks that occur in cloud-based systems. In this study, we elaborate on fog based design for a scalable real time air quality monitoring and alert generation system. We established an emulation test bed with real data collected from air quality sensing nodes deployed around Bangkok and vicinity areas to understand the behavior of the proposed solution in terms of waiting time characteristics. We analyzed the performance of the system in two design scenarios; first scenario is built with the proposed fog solution and the second one is the cloud-based approach. We present the performance results revealing the advantages of the proposed model, for the number of air box nodes scaling up to 120 and the number of client nodes up to 200.

In this paper, we conduct feasibility studies on the average delay space for Cloud computing, and we propose a heuristic method to control the vector of average delays, subject to predefined delay constraints. Our work is strongly motivated by the fact that delay control plays a critical role to improve Service Level Agreements (SLA) between users and Cloud service providers, which is necessary for empowering online business. Specifically, our main contributions are two-fold: First, the feasible regions of various routing algorithms for the system's dispatcher are investigated in depth. Second, a simple heuristic algorithm is designed, to move the average delay point along the feasible direction until achieving the delay constraints. Average delay is dependent on multiple factors such as job size, inter-arrival time, flow rate, and the dispatching rules of the system. Therefore, we vary their distribution, parameters and routing rules to examine how the feasible regions move or change. After establishing the feasible delay space, then by moving along the feasible directions, we show that a simple heuristic algorithm can achieve the delay constraints for a two queue system.

A massive amount of energy consumption currently stems from the transportation sector. Therefore, improvements in power usage by commuting vehicles are being studied and becoming an increasingly popular research topic. In particular, there is a growing need to model the envisioned smart infrastructure, including charging stations, some of which might include energy storage devices and swappable, pre-charged batteries. For such new stations, power management is indeed crucial for operation costs, driver convenience, and overall smart grid efficiency. Information technology, communications and vehicle intelligence need to play a crucial role in this process. In this paper, we describe a quantitative model and propose a guiding and control system for the charging of PHEVs in a future smart infrastructure. Specifically, we describe an algorithm that can be used for the joint guidance and power control of smarter electric vehicles in the smart grid. We envision it as part of a larger Smart Guide for the Smart Grid (SGSG) system. Its function is to guide PHEV drivers, directing them to the appropriate charging station, while attempting to achieve an optimization goal at the same time. Our algorithm aims at a joint guiding and power control, in order to heuristically maximize the weighted sum of the average of throughput and energy cost consumption from multiple vehicle charging stations, while satisfying a cost constraint at each station, as well as system stability.

Heterogeneous computing platforms such as Grid and Cloud computing are becoming prevalent and available online. As a result, resource management in these platforms is fundamentally critical to their global performance. Under the assumption of jobs comprised of subtasks forming DAG jobs, we focus on how to increase utilization and achieve near-optimal throughput performance on heterogeneous platforms. Our analysis and proposed algorithm are analytically derived and establish that, by aggregating multiple jobs using good scheduling, a near-optimal throughput can be achieved. Consequently, its limit is asymptotically converging to a certain value and can be written in the form of the service time of subtasks. Furthermore, our analysis shows how to explicitly compute the optimal throughput of computing systems, an important task for such a complex scheduling problem. In addition, we derive a simple super-job scheduling and show that its performance in term of throughput is better than the well-known Heterogeneous Earliest-Finish-Time (HEFT) algorithm.

With the advent of cloud computing, organizations tend to buy services from data centers of major cloud vendors. In contrast, community cloud computing as described in give the alternative way to reduce the costs or even obtain free resources, by sharing among communities. Because the utilization of computing resources in an organization is not constantly 100%, other members of the community can exploit these excessive resources. In this paper, we design an algorithm for admission control and resource allocation, in order to deal with unreliably excessive computing resources. Furthermore, we introduce a social price in order to manage the allocation more efficiently both in term of social relations and of revenue.