Managing Service-Heterogeneity using Osmotic Computing

Computational resource provisioning that is closer to a user is becoming increasingly important, with a rise in the number of devices making continuous service requests and with the significant recent take up of latency-sensitive applications, such as streaming and real-time data processing. Fog computing provides a solution to such types of applications by bridging the gap between the user and public/private cloud infrastructure via the inclusion of a "fog" layer. Such approach is capable of reducing the overall processing latency, but the issues of redundancy, cost-effectiveness in utilizing such computing infrastructure and handling services on the basis of a difference in their characteristics remain. This difference in characteristics of services because of variations in the requirement of computational resources and processes is termed as service heterogeneity. A potential solution to these issues is the use of Osmotic Computing -- a recently introduced paradigm that allows division of services on the basis of their resource usage, based on parameters such as energy, load, processing time on a data center vs. a network edge resource. Service provisioning can then be divided across different layers of a computational infrastructure, from edge devices, in-transit nodes, and a data center, and supported through an Osmotic software layer. In this paper, a fitness-based Osmosis algorithm is proposed to provide support for osmotic computing by making more effective use of existing Fog server resources. The proposed approach is capable of efficiently distributing and allocating services by following the principle of osmosis. The results are presented using numerical simulations demonstrating gains in terms of lower allocation time and a higher probability of services being handled with high resource utilization.Comment: 7 pages, 4 Figures, International Conference on Communication, Management and Information Technology (ICCMIT 2017), At Warsaw, Poland, 3-5 April 2017, http://www.iccmit.net/ (Best Paper Award

Data Processing in Cyber-Physical-Social Systems Through Edge Computing

© 2013 IEEE. Cloud and Fog computing have established a convenient and widely adopted approach for computation offloading, where raw data generated by edge devices in the Internet of Things (IoT) context is collected and processed remotely. This vertical offloading pattern, however, typically does not take into account increasingly pressing time constraints of the emerging IoT scenarios, in which numerous data sources, including human agents (i.e., Social IoT), continuously generate large amounts of data to be processed in a timely manner. Big data solutions could be applied in this respect, provided that networking issues and limitations related to connectivity of edge devices are properly addressed. Although edge devices are traditionally considered to be resource-constrained, main limitations refer to energy, networking, and memory capacities, whereas their ever-growing processing capabilities are already sufficient to be effectively involved in actual (big data) processing. In this context, the role of human agents is no longer limited to passive data generation, but can also include their voluntary involvement in relatively complex computations. This way, users can share their personal computational resources (i.e., mobile phones) to support collaborative data processing, thereby turning the existing IoT into a global cyber-physical-social system (CPSS). To this extent, this paper proposes a novel IoT/CPSS data processing pattern based on the stream processing technology, aiming to distribute the workload among a cluster of edge devices, involving mobile nodes shared by contributors on a voluntary basis, and paving the way for cluster computing at the edge. Experiments on an intelligent surveillance system deployed on an edge device cluster demonstrate the feasibility of the proposed approach, illustrating how its distributed in-memory data processing architecture can be effective

Fog Computing Challenges: A Systematic Review

This paper reports on a study of major Australian organisations that are using programs to achieve strategic transformation of their work. While the program management literature has focused on the coordination of the multiple projects and related operational activities within the programs, little is known about how these programs deploy efforts to coordinate activities in response to contextual pressures. This exploratory, multi-case study asserts that a significant effort is needed to coordinate responses to factors external to the program. In addition, this study shows the key internal and external forces that combine in shifting the locus of effort in coordinating and integrating multiple activities and projects in major transformation programs

Cooperative-hierarchical based edge-computing approach for resources allocation of distributed mobile and IoT applications

Using mobile and Internet of Things (IoT) applications is becoming very popular and obtained researchers’ interest and commercial investment, in order to fulfill future vision and the requirements for smart cities. These applications have common demands such as fast response, distributed nature, and awareness of service location. However, these requirements’ nature cannot be satisfied by central systems services that reside in the clouds. Therefore, edge computing paradigm has emerged to satisfy such demands, by providing an extension for cloud resources at the network edge, and consequently, they become closer to end-user devices. In this paper, exploiting edge resources is studied; therefore, a cooperative-hierarchical approach for executing the pre-partitioned applications’ modules between edges resources is proposed, in order to reduce traffic between the network core and the cloud, where this proposed approach has a polynomial-time complexity. Furthermore, edge computing increases the efficiency of providing services, and improves end-user experience. To validate our proposed cooperative-hierarchical approach for modules placement between edge nodes’ resources, iFogSim toolkit is used. The obtained simulation results show that the proposed approach reduces network’s load and the total delay compared to a baseline approach for modules’ placement, moreover, it increases the network’s overall throughput

A Time-Sensitive IoT Data Analysis Framework

This paper proposes a Time-Sensitive IoT Data Analysis (TIDA) framework that meets the time-bound requirements of time-sensitive IoT applications. The proposed framework includes a novel task sizing and dynamic distribution technique that performs the following: 1) measures the computing and network resources required by the data analysis tasks of a time-sensitive IoT application when executed on available IoT devices, edge computers and cloud, and 2) distributes the data analysis tasks in a way that it meets the time-bound requirement of the IoT application. The TIDA framework includes a TIDA platform that implements the above techniques using Microsoft’s Orleans framework. The paper also presents an experimental evaluation that validates the TIDA framework’s ability to meet the time-bound requirements of IoT applications in the smart cities domain. Evaluation results show that TIDA outperforms traditional cloud-based IoT data processing approaches in meeting IoT application time-bounds and reduces the total IoT data analysis execution time by 46.96%

Data-Centric Resource Management in Edge-Cloud Systems for the IoT

A major challenge in emergent scenarios such as the Cloud-assisted Internet of Things is efficiently managing the resources involved in the system while meeting requirements of applications. From the acquisition of physical data to its transformation into valuable services or information, several steps must be performed, involving the various players in such a complex ecosystem. Support for decentralized data processing on IoT devices and other devices near the edge of the network, in combination with the benefits of cloud technologies has been identified as a promising approach to reduce communication overhead, thus reducing delay for time sensitive IoT applications. The interplay of IoT, edge and cloud to achieve the final goal of producing useful information and value-added services to end user gives rise to a management problem that needs to be wisely tackled. The goal of this work is to propose a novel resource management framework for edge-cloud systems that supports heterogeneity of both devices and application requirements. The framework aims to promote the efficient usage of the system resources while leveraging the Edge Computing features, to meet the low latency requirements of emergent IoT applications. The proposed framework encompasses (i) a lightweight and data-centric virtualization model for edge devices, (ii) a set of components responsible for the resource management and the provisioning of services from the virtualized edge-cloud resources

Elastic provisioning of network and computing resources at the edge for IoT services

The fast growth of Internet-connected embedded devices demands new system capabilities at the network edge, such as provisioning local data services on both limited network and computational resources. The current contribution addresses the previous problem by enhancing the usage of scarce edge resources. It designs, deploys, and tests a new solution that incorporates the positive functional advantages offered by software-defined networking (SDN), network function virtual-ization (NFV), and fog computing (FC). Our proposal autonomously activates or deactivates embedded virtualized resources, in response to clients’ requests for edge services. Complementing existing literature, the obtained results from extensive tests on our programmable proposal show the superior performance of the proposed elastic edge resource provisioning algorithm, which also assumes a SDN controller with proactive OpenFlow behavior. According to our results, the maximum flow rate for the proactive controller is 15% higher; the maximum delay is 83% smaller; and the loss is 20% smaller compared to when the non-proactive controller is in operation. This improvement in flow quality is complemented by a reduction in control channel workload. The controller also records the time duration of each edge service session, which can enable the ac-counting of used resources per session.info:eu-repo/semantics/publishedVersio