17 KiB
- Mdnotes File Name: sunAIEnhancedOffloadingEdge2019
Green Annotations (17/12/2020, 23:14:22)
"The Industrial Internet of Things (IIoT) enables intelligent industrial operations by incorporating artificial intelligence (AI) and big data technologies." (Sun et al 2019:68)
"An AI-enabled framework typically requires prompt and private cloud-based service to process and aggregate manufacturing data" (Sun et al 2019:68)
"integrating intelligence into edge computing is without doubt a promising development trend" (Sun et al 2019:68)
"edge intelligence brings heterogeneity to the edge servers, in terms of not only computing capability, but also service accuracy" (Sun et al 2019:68)
"this article we introduce an intelligent computing architecture with cooperative edge and cloud computing for IIoT" (Sun et al 2019:68)
"AI enhanced offloading framework is proposed for service accuracy maximization, which considers service accuracy as a new metric besides delay, and intelligently disseminates the traffic to edge servers or through an appropriate path to remote cloud." (Sun et al 2019:68)
"performance gain of the proposed framework" (Sun et al 2019:68)
"interconnects a multitude of industrial devices, actuators, and people at work" (Sun et al 2019:68)
"IIoT incorporates artificial intelligence (AI) technologies to process and analyze data from various sources and make advanced predictive analytics, such as fault class prediction, predictive maintenance, demand forecasting." (Sun et al 2019:68)
"smart manufacturing is a large connected and complex industrial process, which produces a large amount of multi-feature data, it is difficult to construct its operation process with an accurate mathematical model" (Sun et al 2019:68)
"AI algorithms are able to extract critical features without in-depth physical understanding of the concerned system" (Sun et al 2019:68)
"in IIoT, predictive maintenance relies on machine learning to detect anomalies in systems and then predict the failure of devices by correlating and analyzing the change in the pattern" (Sun et al 2019:68)
"IIoT typically requires prompt and private computing service to process and aggregate the manufacturing data" (Sun et al 2019:68)
"integrating intelligence into the edge is without doubt a promising development trend [2]" (Sun et al 2019:68)
"distributed computing service through small-scale data centers near the edge of the network." (Sun et al 2019:68)
"edge computing provides real-time data analytics with privacy preserving, increases network capabilities, and avoids congestion of backbone networks and the Internet core" (Sun et al 2019:68)
"Personalization: Customized AI models can be developed at the edge servers, which are tailored to individual users' behaviors and requirements to deliver accurate results to the users." (Sun et al 2019:68)
This might be related to the citizen developer / lowcode (note on p.68)
"Responsiveness: While the industrial process is time-varying and unpredictable, the computing service must be prompt and more adaptive and feasible to the new situation." (Sun et al 2019:68)
This might be related to the federated learning aspect mentioned by Luciano (note on p.68)
"Privacy Preserving: Especially for IIoT, the processing information owned by industrial companies may not be willing to transmit to the remote cloud for privacy issues; thus, the edge server provides private service." (Sun et al 2019:68)
"The AI service deployed on edge servers exhibits heterogeneity in terms of service accuracy due to the limited and heterogeneous computing capability of edge servers." (Sun et al 2019:68)
"The impacts of edge intelligence on computing offloading remains untouched." (Sun et al 2019:68)
"intelligent computing architecture with cooper-" (Sun et al 2019:68)
"ative edge and cloud computing for IIoT. Then" (Sun et al 2019:69)
"AI enhanced offloading framework for service accuracy maximization is developed" (Sun et al 2019:69)
"service accuracy as a new metric besides delay," (Sun et al 2019:69)
"intelligently disseminates the traffic to edge servers or through appropriate paths to remote cloud." (Sun et al 2019:69)
"Machine learning, as an application of AI, gives devices or computer systems the ability to "learn" with data without being explicitly programmed [2]." (Sun et al 2019:69)
"supervised learning conducts classification or regression tasks from labeled data" (Sun et al 2019:69)
"unsupervised learning categorizes the unlabeled data into clusters" (Sun et al 2019:69)
"Reinforcement learning indicates agents to take actions so as to maximize the cumulative reward" (Sun et al 2019:69)
"In IIoT, machine learning algorithms are leveraged to analyze the complex manufacturing data and believer insights about predictive maintenance, industrial prognostics, and demand forecasting." (Sun et al 2019:69)
"Generally, cloud computing is employed for data processing. However, it is difficult to transmit huge amounts of data to the remote cloud; thus, approximate and distributed computing service becomes necessary." (Sun et al 2019:69)
THIS IS A RELEVANT CASE, I.E., WHEN YOU CANNOT SEND HUDGE AMOUNT OF DATA TO THE CLOUD FOR PERFORMING DATA ANALYTICS TASKS!!! (note on p.69)
"There are edge servers that are actually designed for AI-enabled computing tasks such as the NVIDIA DGX workstation" (Sun et al 2019:69)
"analyzed video streams recorded on a number of surveillance cameras" (Sun et al 2019:69)
THIS MAKES SENSE FOR THE THE SCENARIO OF LUCIANO ON SMART BUILDING. IN SUCH CASES DATA NEED TO BE ANALYSED ON THE EDGE. (note on p.69)
"The MEC application examined the video streams, classified what were normal and abnormal patterns, and then only needed to send the stream to the backbone when a potential security issue was identified." (Sun et al 2019:70)
YES! VERY IMPORTANT (note on p.70)
"AI tasks laid a heavy burden on edge servers with limited resources" (Sun et al 2019:70)
"Industrial MDs monitor the industrial parameters, and deliver the collected data to the data center for aggregation" (Sun et al 2019:70)
"The AI-enabled IIoT service includes self-monitoring, demand forecasting, fault detection, and workforce management." (Sun et al 2019:70)
"The decision is fed back to the IIoT devices and executed automatically." (Sun et al 2019:70)
"he intelligent computational architecture needs to be reshaped." (Sun et al 2019:70)
"two-layer intelligent data center, that is, edge layer and cloud layer" (Sun et al 2019:70)
THAT'S THE MAIN ARCHITECTURAL INNOVATION THAT THEY PROPOSE. (note on p.70)
"Edge Layer: It accommodates lightweight intelligent computing service for IIoT," (Sun et al 2019:70)
"Cloud Layer: It provides powerful and comprehensive computing service for IIoT at the cost of latency and communication burden." (Sun et al 2019:70)
"The interaction between the edge layer and the cloud layer is at the cost of additional communication on the backbone network." (Sun et al 2019:70)
"how to deploy the computing service between the edge layer and cloud layer, and afterward assign the computing tasks of IIoT devices according to their requirements as well as the characteristics of heterogeneous edge servers and remote cloud, needs serious consideration." (Sun et al 2019:70)
THAT'S ANOTHER IMPORTANT CHALLENGES (note on p.70)
"priority" (Sun et al 2019:71)
"accuracy" (Sun et al 2019:71)
"delay" (Sun et al 2019:71)
"(ui, ai, di), where ui is the degree of urgency of MD i, that is, priority (a scalar value within (0, 1)), and ai is the acceptable accuracy of MD i, and di is the acceptable delay of MD i" (Sun et al 2019:71)
"o choose an optimal jD i, it is imperative to estimate the delay and accuracy of offloading to available edge servers, and determine the optimal offloading option according to its requirement." (Sun et al 2019:71)
"Different from the previous research, the offloading decision depends not only on the estimated access delay but also on the accuracy the edge server can provide." (Sun et al 2019:71)
THAT'S IMPORTANT, IT IS RELATED TO THE WAY TASKS ARE DISTRIBUTED AND ASSIGNED TO THE DIFFERENT EDGE SERVERS. (note on p.71)
"near-optimal offloading framework for accuracy maximization offloading with latency constraints" (Sun et al 2019:71)
"Step 1: (Estimate the accuracy of computing task from IIoT MD i.)" (Sun et al 2019:71)
"Step 2: (Estimate the access delay of computing tasks from MD i.)" (Sun et al 2019:71)
"Step 3: (Offload to the appropriate edge servers.) According to the estimated accuracy and dela" (Sun et al 2019:71)
"For those MDs that did not find an appropriate edge server or remote server, it can be accomplished locally at the CPU of MDs. For those computing tasks with predicted delay to the remote cloud lower than its delay requirement, we tend to route the computing tasks to the remote cloud, since the remote cloud is most powerful and can provide the highest accuracy. Thus, through Step 3, the accuracy of MDs is deter" (Sun et al 2019:71)
"By the proposed offloading framework, traffic will be disseminated intelligently, according to its requirement, to the optimal edge servers or to the remote cloud through an appropriate path so that the pressure on the backbone network will be effectively alleviated." (Sun et al 2019:71)
"y edge servers and therefore does not consume much bandwidth. Unlike large-scale training in remote cloud, transfer training requires only a small amount of targeted training data to achieve high accuracy of the network." (Sun et al 2019:72)
"trAnsfEr lEArnIn" (Sun et al 2019:72)
"IoT MDs turn to the edge servers for image processing to monitor the industrial process." (Sun et al 2019:72)
SIMILE SCENARIO PER NOI??? (note on p.72)
"The transfer-learning-based computing and offloading framework is done following five phases, that is, training the source neural network with large-scale data in the remote cloud, loading the pretrained neural network, customizing the predictive model, training the predictive model with small-scale data in the edge, and offloading tasks to appropriate edge servers." (Sun et al 2019:72)
"First, the source neural networks are trained in large-scale data in remote cloud" (Sun et al 2019:72)
"Then an edge server loads the pretrained neural network from remote cloud" (Sun et al 2019:72)
"he pretrained network then transforms to a customized predictive model" (Sun et al 2019:72)
"Finally, we assess the service accuracy of the predictive model and offload the computing tasks of IIoT devices according to AMLC." (Sun et al 2019:72)
"In order to develop lightweight machine learning technologies on edge servers in IIoT, transfer learning is adopted." (Sun et al 2019:72)
"Transfer learning is a popular approach in deep learning where pre-trained models are used as the starting point to learn a new task" (Sun et al 2019:72)
IMPORTANT FOR THE THINGS ABOUT FEDERATED VS DISTRIBUTED LEARNING MENTIONED BY LUCIANO (note on p.72)
"This shows that at the edge layer, the customized predictive model on edge servers differs from each other due to different local data, even when the pretrained networks are the same." (Sun et al 2019:73)
"we find it feasible to deploy machine learning applications to edge servers and employ service accuracy as a metric in the traffic offloading of MEC, while it also faces many challenges, such as the storage of training and test data, model training, and parameter updates" (Sun et al 2019:73)
IMPORTANT CHALLENGES!!! (note on p.73)
"in AI-enabled edge computing, it is also challenging to appropriately tailor the AI-based computing service to trade off between accuracy and the constrained computing resources." (Sun et al 2019:73)
CHALLENGE (note on p.73)
"proposed an intelligent computing architecture in IIoT with cooperation between edge servers and remote cloud." (Sun et al 2019:74)
THAT'S THE IDEA OF THE PROPOSED APPROACH (note on p.74)
"AI-driven offloading framework considering service accuracy as a new metric, intelligently disseminating traffic to edge servers or remote cloud" (Sun et al 2019:74)
"AI-based computing service to trade off between accuracy and the constrained computing resources." (Sun et al 2019:74)
THAT'S A POSSIBLE FUTURE WORK (note on p.74)