Towards edge computing as a service: dynamic formation of the micro data-centers

Abstract Data centers are large-scale data storage and processing systems. It is made up of a number of servers that must be capable of handling large amount of data. As a result, data centers generate a significant quantity of heat, which must be cooled and kept at an optimal temperature to avoid overheating. To address this problem, thermal analysis of the data center is carried out using numerical methods. The CFD model consists of a micro data center, where conjugate heat transfer effects are studied. A micro data center consists of servers aligned with air gaps alternatively and cooling air is passed between the air gaps to remove heat. In the present work, the design of data center rack is made in such a way that the cold air is in close proximity to servers. The temperature and airflow in the data center are estimated using the model. The air gap is optimally designed for the cooling unit. Temperature distribution of various load configurations is studied. The objective of the study is to find a favorable loading configuration of the micro data center for various loads and effectiveness of distribution of load among the servers.

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Disaggregation – the new way to build mega (and micro) data centers

2015 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS) ◽

10.1109/ancs.2015.7110114 ◽

2015 ◽

Cited By ~ 1

Author(s):

Yuval Bachar

Keyword(s):

Data Centers ◽

Micro Data

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AI-enabled IoT-Edge Data Analytics for Connected Living

ACM Transactions on Internet Technology ◽

10.1145/3421510 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1-20

Author(s):

Zhihan Lv ◽

Liang Qiao ◽

Sahil Verma ◽

Kavita

Keyword(s):

Data Processing ◽

Real Time ◽

Data Centers ◽

Edge Computing ◽

Time Data ◽

Processing Task ◽

Light Load ◽

Hash Algorithm ◽

Real Time Data ◽

Real Time Data Processing

As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and data centers. First, the edge computing framework and key technologies are analyzed to improve the performance of real-time data processing applications. In the system scenario where the collaborative deployment tasks of multi-edge nodes and data centers are considered, the stream processing task deployment process is formally described, and an efficient multi-edge node-computing center collaborative task deployment algorithm is proposed, which solves the problem of copy-free task deployment in the task deployment problem. Furthermore, a heterogeneous edge collaborative storage mechanism with tight coupling of computing and data is proposed, which solves the contradiction between the limited computing and storage capabilities of data and intelligent terminals, thereby improving the performance of data processing applications. Here, a Feasible Solution (FS) algorithm is designed to solve the problem of placing copy-free data processing tasks in the system. The FS algorithm has excellent results once considering the overall coordination. Under light load, the V value is reduced by 73% compared to the Only Data Center-available (ODC) algorithm and 41% compared to the Hash algorithm. Under heavy load, the V value is reduced by 66% compared to the ODC algorithm and 35% compared to the Hash algorithm. The algorithm has achieved good results after considering the overall coordination and cooperation and can more effectively use the bandwidth of edge nodes to transmit and process data stream, so that more tasks can be deployed in edge computing nodes, thereby saving time for data transmission to the data centers. The end-to-end collaborative real-time data processing task scheduling mechanism proposed here can effectively avoid the disadvantages of long waiting times and unable to obtain the required data, which significantly improves the success rate of the task and thus ensures the performance of real-time data processing.

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The Pivotal Role of Edge Computing With Machine Learning and Its Impact on Healthcare

Deep Neural Networks for Multimodal Imaging and Biomedical Applications - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-7998-3591-2.ch014 ◽

2020 ◽

pp. 219-236

Author(s):

Muthukumari S. M. ◽

George Dharma Prakash E. Raj

Keyword(s):

Medical Devices ◽

Data Centers ◽

Global Market ◽

Edge Computing ◽

Wireless Sensor ◽

Surveillance Systems ◽

Patient Health ◽

Consumption Cost ◽

The Internet Of Things

The global market for IoT medical devices is expected to hit a peak of 500 billion by the year 2025, which could signal a significant paradigm shift in healthcare technology. This is possible due to the on-premises data centers or the cloud. Cloud computing and the internet of things (IoT) are the two technologies that have an explicit impact on our day-to-day living. These two technologies combined together are referred to as CloudIoT, which deals with several sectors including healthcare, agriculture, surveillance systems, etc. Therefore, the emergence of edge computing was required, which could reduce the network latency by pushing the computation to the “edge of the network.” Several concerns such as power consumption, real-time responses, and bandwidth consumption cost could also be addressed by edge computing. In the present situation, patient health data could be regularly monitored by certain wearable devices known as the smart telehealth systems that send an enormous amount of data through the wireless sensor network (WSN).

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