A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

Fog Computing: A Taxonomy, Survey and Future Directions

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research

Routing Strategy for Internet of Vehicles based on Hierarchical SDN and Fog Computing

تم اکتشاف الحوسبة الضبابیة لحل مشكلة نقص المصادر في مستشعرات إنترنت الأشياء (IoT) ومعالجة المهام بسرعة. انترنت المركبات (IoV) هو تطبيق خاص من شبكات إنترنت الأشياء التي تتكون من أجهزة استشعار غير متجانسة موجودة في المركبات. تقوم هذه المستشعرات بنقل المهام إلى خوادم الحوسبة الضابية التي تعالجها وتعطي الإجابات للمستشعرات.  على اي حال، فإن حركة المركبات تؤثر على عملية تسليم هذه الاجابات. عندما تخرج السيارة المصدرة للمهمة من مجال خادم ضبابي معين خلال وقت معالجة هذه المهمة، فأنه لن يتم وصول الاجابة لتلك السيارة بشكل صحيح. لذلك، يحتاج إلى حساب المسار الأمثل لتلك السيارة. تتسبب هذه العملية في تجاوز الموعد النهائي للمهمة وتقليل الإنتاجية. للتغلب على هذه المشكلة، يقدم هذا البحث معمارية هرمية مبنية على الشبكات المعرفة بالبرمجيات (SDN) وحوسبة الضباب لشبكة IoV. تتألف هذه المعمارية من طبقة سيارات IoV, بيئة حوسبة ضبابية ووحدات تحكم SDN شبه مركزية ووحدة تحكم SDN  مركزية. علاوة على ذلك ، تم اقتراح إستراتيجية توجيهية تسمى إستراتيجية توجيه ذات تأخير جيد بالاعتماد على الحوسبة الضبابية و ال SDN لشبكات ال IoV (DRSFI) .تقوم وحدات التحكم SDN بتنفيذ DRSFI لحساب المسارات مع أدنى تأخير مع الأخذ بنظر الاعتبار قيد النطاق الترددي المتاح وموقع وسرعة المركبة. من نتائج محاكاة سيناريوهات مختلفة مع سرعات حركة متنوعة وعداد مختلفة من المهام، استنتجنا أن النظام المقترح أفضل من نظام IoV-Fog-Central SDN  ونظام IoV-Fog من حيث متوسط التأخير من البداية إلى النهاية و النسبة المئوية لخسارة الحزم والنسبة المئوية للإرسال الناجح.The fog computing is invited to solve the lack of resources problem in the sensors of Internet of Things (IoT) and handle the tasks quickly. Internet of Vehicles (IoV) is a special application of IoT networks that composed of heterogeneous sensors that are found in vehicles. These sensors transfer the tasks to the fog servers that process them and give the responses to the sensors. However, the mobility of vehicles effects on the delivery operation of responses. When the source vehicle of a task exited from the domain of some fog server through the processing time of this task, the response will not be reached to that vehicle correctly. Therefore, it is need to compute the optimal path to that vehicle. This process causes exceeding the task deadline and decreasing the throughput. To overcome this issue, this paper produces a hierarchical architecture based on Software Defined Network (SDN) and fog computing for IoV networks. This architecture consists of IoV vehicles, fog computing framework, semi-central SDN controllers and central SDN controller layers. Moreover, a routing strategy is proposed called Delay-Efficient Routing strategy based on SDN and Fog computing for IoV (DRSFI). The SDN controllers perform DRSFI to compute the routes with minimum delay with taking into consideration the available bandwidth constraint and the location and speed of the vehicle. From the results of simulation of different scenarios with various mobility speeds and various number of tasks, we concluded that the proposed system is better than IoV-Fog-central SDN system and IoV-Fog system in terms of average delay from end to end, percentage of packet loss and percentage of successfully transmission. &nbsp

Smart handoff technique for internet of vehicles communication using dynamic edge-backup node

© 2020 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/electronics9030524A vehicular adhoc network (VANET) recently emerged in the the Internet of Vehicles (IoV); it involves the computational processing of moving vehicles. Nowadays, IoV has turned into an interesting field of research as vehicles can be equipped with processors, sensors, and communication devices. IoV gives rise to handoff, which involves changing the connection points during the online communication session. This presents a major challenge for which many standardized solutions are recommended. Although there are various proposed techniques and methods to support seamless handover procedure in IoV, there are still some open research issues, such as unavoidable packet loss rate and latency. On the other hand, the emerged concept of edge mobile computing has gained crucial attention by researchers that could help in reducing computational complexities and decreasing communication delay. Hence, this paper specifically studies the handoff challenges in cluster based handoff using new concept of dynamic edge-backup node. The outcomes are evaluated and contrasted with the network mobility method, our proposed technique, and other cluster-based technologies. The results show that coherence in communication during the handoff method can be upgraded, enhanced, and improved utilizing the proposed technique.Published onlin