Cloud computing and edge computing
212420432
Network Management in Cloud and Edge Computing
Data center has many features such as virtualized resource environment, modular infrastructure, automated operation and maintenance management, rapid expansion capability, efficient resource utilization, and reliable redundant backup. While edge computing has advantages in the latency due to the short distance to end users, a variety of business applications have exploded, which has placed higher demands on the basic functions and service performance of the edge servers. This book will analyze these two trends in detail. This chapter introduces the research background, content summary, key contributions, and arrangements of the remaining chapters. 1.1 Research Background The rapid growth of service chains is changing the landscape of cloud based applications. Different stand alone components are now handled by cloud servers, providing cost efficient and reliable services to Internet users. It is known that the workloads from service chains are more complex than the traditional noninteractive (or batch) workloads: for non interactive workloads, such as scientific computing and image processing, they can be processed on one specific server and do not need interactions from other servers. Being not strictly time sensitive, they can be scheduled to run anytime as long as the work could finish before a soft deadline. But for the interactive workloads from service chains, they should go through multiple stand alone components to apply corresponding functions, and this unavoidably introduces additional latency. Meanwhile these interactive chained services typically process real time user requests, such as business transactional and complex gaming control. Therefore, the performance for service chains is in urgent need to be ensured. Nowadays, data centers have become the cornerstones of modern computing infrastructure and one dominating paradigm in the externalization of IT resources. The data center tasks always consist of rich and complex flows which traverse different parts of the network at potentially different times. To minimize the network contention among different tasks, task serialization was widely suggested. This (c) Springer Nature Singapore Pte Ltd. 2020 Y. Zhang, K. Xu, Network Management in Cloud and Edge Computing, https:/doi.org/10.1007/978 981 15 0138 8_1 1
Yuchao Zhang Ke Xu
227171892
Development of Smart Vegetable Growing Cabinet with IoT, Edge Computing and Cloud Computing
In recent years, there has been an increasing interest in smart farming. One of the most significant current discussions in smart farming is how to fully control the plant grow. This research aims to assist in facilitating the cultivation of soilless vegetables by adopting Internet of Things, Edge computing and Cloud computing technologies to plant hydroponics vegetables. Today people are interested in consuming more hydroponics vegetables. Also, hydroponics vegetables are expensive on the market. This hydroponics vegetable cabinet is made so that they can be grown at home without buying from the market and fresh. This smart vegetable cabinet can control various electrical appliances within the cabinet for example, water pump control, LED Grow Light control and automatic ventilator operation at the set value. Moreover, we can check various component values inside the cabinet such as pH, EC in water, humidity and temperature inside the cabinet. In addition, they can control devices, check their values, take photos, or view images from a web page. This vegetable grower can be planted in inaccessible places such as homes or restaurants. This vegetable grower utilizes the Grow Light LED, which provides the light that plants need. Also, this intelligent vegetable grower is not very large, easy to move, suitable for those who have less space but want to grow their own vegetables. The results from the research are satisfactory.
ICIP 2020
Khanista Namee Chumpol Kamjumpol Witoon Pimsiri
197660220
An emotion recognition system based on edge computing and cloud computing applied in the kindergarten
As much attention is paid to the safety and education of children in the kindergarten, the importance of analyzing emotions of children is realized. With the adventure of the Internet of Things, more and more visual sensors are connected to the network, which makes the emotion analysis available through the recognition of images. It is necessary to response quickly when negative emotions of children are recognized, which means the processing of images containing negative emotions is delay sensitive. The processing of an enormous amount of images brings challenge for emotion recognition based on traditional cloud computing. Cloud computing is of high scalability and sufficient capability, but cannot meet the need of real time detection due to the time delay of transmission and computing of large amounts of images. Emerging edge computing can help to solve the problem by offloading computing from cloud center to edge nodes. However, because the resource of edge nodes is limited, only part of the computing can be placed at the edge nodes. In our work, an emotion recognition system combined with edge computing and cloud computing is proposed to apply in the kindergarten. Due to delay sensitive negative emotions in this case, a priority scheduling algorithm is also proposed to improve the computing effectiveness of the edge. In this way, the low latency of the edge and the high scalability of the cloud are utilized to reduce the time delay and improve the capacity of the emotion recognition system. The experiments in the real scene of the kindergarten verify that the system performs well.
ACM TUR C
Ruizhi Zhang Chujie Tian Yang Ji
51930296
Proceedings of the 2018 Workshop on Theory and Practice for Integrated Cloud, Fog and Edge Computing Paradigms
TOPIC@PODC
131970835
Session details: Edge Computing and Edge Cloud
Timothy M Cockerill
174801960
An Expression Analysis Service based on Edge Cloud Computing
Facial Expression Analysis (FEA) is widely applied in many areas such as marketing, health care and on line learning. For considering performance and cost, FEA currently is implemented as a cloud service. However, uploading the streaming of face images from the end of networks to clouds will probably result in that the network bandwidth in Internet is exhaustively consumed, and finally the quality of the FEA service will degrade to an unacceptable level due to high network latency. To avoid this problem, this paper is aimed at proposing an expression analysis service system based on the architecture of edge cloud computing. The proposed system can efficiently provide users with the services of expression analysis while it need not consume a large amount of network bandwidth for data transmission from user clients to data centers. Moreover, it can provide a higher precision of expression analysis than related works by a method combining MTCNN, OpenFace and BPNN, and it can effectively select edge servers for reducing the time of expression analysis.
Proceedings of the 2019 2nd International Conference on Information Science and Systems
Tyng-Yeu Liang Chi-Hong Wu Yu Ting Chiang
197660696
A wide deep event model for complex event processing in edge and cloud computing environment
With the rapid development of smart IoT devices, it is a trend for complex event processing in large scale IoT application to be intelligent capacity, complex event processing will involve more intelligent collaboration and evolution. However, the studies on traditional complex event processing focus more on performance and efficiency, and fewer consider intelligent collaboration and evolution. In this paper, aiming at the edge and cloud computing in large scale IoT application, a wide deep event model and its suitable complex event processing architecture are proposed to mainly improve intelligent collaboration and evolution. The model and architecture fully consider the feature of the edge and cloud computing, combine with deep learning and wide deep learning and make them seamless integration, making the architecture have better intelligent collaboration and evolution.
ACM TUR C
Shuqing He Haifeng Wang Yunpeng Cao De Yu Zhao
201724711
High Performance Computing Applications in Numerical Simulation and Edge Computing: ACM ICS 2018 International Workshops, HPCMS and HiDEC, Beijing, China, June 12, 2018, Revised Selected Papers
The explosive growth of massive data generation from Internet of Things in industrial, agricultural and scientific communities has led to a rapid increase in cloud data centers for data analytics. The ubiquitous and pervasive demand for near data processing urges the edge computing paradigm in recent years. Edge computing is promising for less network backbone bandwidth usage and thus less data center side processing, as well as enhanced service responsiveness and data privacy protection. Computation offloading plays a crucial role in network packets transmission and system responsiveness through dynamic task partitioning between cloud data centers and edge servers and edge devices. In this paper a thorough literature review is conduct to reveal the state of the art of computation offloading in edge computing. Various aspects of computation offloading, including energy consumption minimization, Quality of Services (QoS) and Quality of Experiences (QoE) are surveyed. Resource scheduling approaches, gaming and tradeoffing among system performance and system overheads for offloading decision making are also reviewed.
Yinghui Zhao
22104546
Cognitive IoT Gateways: Automatic Task Sharing and Switching between Cloud and Edge/Fog Computing
Fog computing, also known as Edge computing, is an emerging computational paradigm, increasingly utilized in Internet of Things (IoT) applications, particularly those that cannot be served efficiently using Cloud computing due to limitations such as bandwidth, latency, Internet connectivity. At present, the norm is the static allocation of tasks by developers of an application, where some IoT applications are allocated to be performed on the Cloud, some on the Fog, and some on a hybrid Cloud Fog. The applications are pre programmed and predefined to be run on a platform, and this is unchangeable at run time. IoT gateways, which are devices that bridge the IoT local network and the Internet, are in a position to make dynamic adjustments and allocation decision between platforms based upon real time conditions such as an IoT applications' performance. However, currently there is no (or very little) intelligence embedded into IoT gateways. This paper proposes cognitive IoT gateways powered by cognitive analytics and machine learning to improve the performance of IoT applications. These IoT devices are able to automatically learn and decide when and where to run an application, be that on the Cloud or on the Fog. The dynamic task sharing and platform interchanging will enable the IoT applications to be optimized for multiple objectives including task performance.
SIGCOMM Posters and Demos
Fatemeh Jalali Olivia J Smith Timothy M Lynar Frank Suits
218675963
On the impact of clustering for IoT analytics and message broker placement across cloud and edge
With edge computing emerging as a promising solution to cope with the challenges of Internet of Things (IoT) systems, there is an increasing need to automate the deployment of large scale applications along with the publish/subscribe brokers they communicate over. Such a placement must adjust to the resource requirements of both applications and brokers in the heterogeneous environment of edge, fog, and cloud. In contrast to prior work focusing only on the placement of applications, this paper addresses the problem of jointly placing IoT applications and the pub/sub brokers on a set of network nodes, considering an application provider who aims at minimizing total end to end delays of all its subscribers. More specifically, we devise two heuristics for joint deployment of brokers and applications and analyze their performance in comparison to the current cloud based IoT solutions wherein both the IoT applications and the brokers are located solely in the cloud. As an application provider should consider not only the location of the application users but also how they are distributed across different network components, we use von Mises distributions to model the degree of clustering of the users of an IoT application. Our simulations show that superior performance of our heuristics in comparison to cloud based IoT operation is most pronounced under a high degree of clustering. When users of an IoT application are in close network proximity of the IoT sensors, cloud based IoT unnecessarily introduces latency to move the data from the edge to the cloud and vice versa while processing could be performed at the edge or the fog layers.
EdgeSys@EuroSys
Daniel Happ Suzan Bayhan