Making Actionable Decisions at the Network s Edge

Transcription

1 DIGITAL SYSTEMS & TECHNOLOGY Making Actionable Decisions at the Network s Edge In the evolving hyper-connected world of the Internet of Things, immense new possibilities are emerging from interlinked ecosystems that can make fast, actionable decisions unconstrained by traditional analytical processes. Edge analytics is fast emerging as a way of extending the limits of cloud-enabled decision-making. November 2017

2 EXECUTIVE SUMMARY The Internet of Things (IoT) is enabling innovative new connected ecosystems to emerge that amplify the value organizations provide to end customers. Smart cities, smart infrastructure, driverless cars and real-time guidance using location intelligence are just a few of the ways these new solutions touch our everyday lives. However, what makes such connected ecosystems tick, succeed and evolve is their ability to process in near-real time the huge amounts of data generated at the network s edge by sensors and instrumented devices. The emergence of the cloud has made building such ecosystems possible, but additional time and enhanced connectivity is still required to inform fact-based decisions after analysis. Sending data to the cloud and awaiting analytical results costs precious milliseconds, damaging real-time responsiveness. Billions of devices produce data persistently, but managing and making sense of this big data requires a huge investment in computational analytics, storage and networking software, as well as powerful computing platforms. Organizations that hue to traditional approaches will be unable to tap the IoT s full potential. A new and evolving technique commonly known as edge analytics is rapidly emerging as the go-to mechanism for overcoming existing infrastructure limitations. While edge analytics derives from cloud analytics, it goes one step further by democratizing the ability to analyze data not just in cloud data centers but at the point of data collection, on devices themselves and in the gateways that interconnect enterprise ecosystems. Edge analytics has reached a major inflection point. In data-warehouse-oriented analytics, data is typically sent, via gateways, to cloud systems where the entire analytics process 2 Making Actionable Decisions at the Network s Edge

3 occurs. Data visualization is presented after a series of steps performed in the cloud. While this works fine in situations that do not require decisions to be made quickly, digital-era demands are quickly changing the business calculus. In edge analytics, data is collected and analyzed close to the source of data generation. So, instead of sending data to the cloud for analysis and then waiting for a response, edge analytics brings more computation to the edge, saving on time as well as cost of data transmission. Simple model-based analytics can be conducted on the device/sensor itself while more complex analytics that require data from multiple devices can be performed on IoT gateways, and finally the most sophisticated form of analytics commonly called big data analytics can be handled on the cloud. This analytics hierarchy reduces the complexity and burden on the network and the data centers. Distributing the analysis of data to the edge is a powerful way of unlocking IoT value. Edge analytics is not just about gaining operational efficiencies or making the business more scalable. Many businesses do not require complex or sophisticated data analytics, but do need speed and automation. Any delay in delivering results or loss of data due to connectivity failure can cause reputational and even financial damage to the organization. It is another reason why gateways need to be placed closer to the source so the data generated can be cleaned, batched and sent back to decision-makers in shorter timeframes. This white paper explores how to build a connected ecosystem that not only has a brain in the cloud, but also reflex actions at the edge. It also offers some case illustrations to help decision-makers envision edge analytics art of the possible. Making Actionable Decisions at the Network s Edge 3

4 THE CLOUD ON EDGE Large organizations have benefitted from the concept of cloud computing. But as the IoT grows, along with the amount of the data generated to inform real-time decision-making, it is essential to access this data quickly but without incurring huge investments of time and money. This is where edge analytics comes into play. The trick is to incorporate both models to their best effectiveness: deploy edge analytics where time is of the essence, and use cloud analytics where security and data volumes are the deciding factors. It is imperative that IoT strategies make use of the best of both cloud computing techniques and edge analytics processing to optimize IoT ecosystems (see Figure 1). Edge vs. Cloud Cloud Analytics Edge Analytics Cloud Cloud Data Data Internet Internet Data Data Edge Network Network Devices Devices Figure 1 4 Making Actionable Decisions at the Network s Edge

5 The trick is to incorporate both models to their best effectiveness: deploy edge analytics where time is of the essence, and use cloud analytics where security and data volumes are the deciding factors. It is imperative that IoT strategies make use of the best of both cloud computing techniques and edge analytics processing to optimize IoT ecosystems. Making Actionable Decisions at the Network s Edge 5

6 In the IoT age, nearly every connected and instrumented devices generates huge amounts of data. The underlying metadata, however, is useless unless it is analyzed for meaning. AN INTRODUCTION TO EDGE ANALYTICS ARCHITECTURE The hierarchy of edge analytics can be represented as a three-tiered architecture (see Figure 2, next page). The flow of data begins with sourcing of raw data from smart devices or sensors followed by more sophisticated analysis on gateways at the edge of the network and finally some heavy lifting, or big data analysis, using complex cloud computing models. Tier 1: The Sourcing of Raw Data In the IoT age, nearly every connected and instrumented devices generates huge amounts of data. The underlying metadata, however, is useless unless it is analyzed for meaning. Much of the data collected does not require complex analytics, hence data from these devices can be analyzed on the edge - i.e., close to the source of data generation to deliver near-instant automated results. Tier 2: Processing Data on the Network s Edge Edge analytics deploys gateways on the edge of the network. These gateways connect, collect and analyze data in near-real time. The outcome of this analysis can be transferred back to the devices immediately or can be stored in a small, low-cost memory device. The stored data can be further transferred or routed to the cloud for advanced analytics. Distributing analytics on the network to different edge nodes has many advantages. It decreases the complexity that companies face while computing huge amounts of real-time data and increases the scalability by distributing the computation workload across multiple edge nodes. Tier 3: Sophisticated Cloud Computing Filtered data from the edge of the network is transferred to the cloud for more complex processing. Data is sent to the cloud from multiple gateways to store, process or analyze. Generally, data that does not require an instant response is transferred to the cloud for heavy-duty processing. EDGE ANALYTICS BUSINESS DRIVERS With the deluge of data has come the need to use it quickly and in real time. IoT devices pump out data in huge amounts and with the increasing number of smart connected devices, the need for edge computing is clear. According to Gartner, there will be 20.4 billion IoT devices by the year In this scenario, it is important to identify which business drivers spur a demand for ongoing edge analytics. 6 Making Actionable Decisions at the Network s Edge

7 Three-Tier Edge Analytics Architecture Tier 3: Analytics on the Cloud Transfer of Data Tier 2: Analytics on the Edge Tier 1: Data Sourcing Figure 2 High data volumes: Huge amounts of data in the cloud will spur scalability issues and pose a problem of bandwidth, increasing storage costs. This factor makes it the primary business driver of edge analytics. Latency: This can be a killer where predictions drive decisions and actions in real time. It may not be prudent to have predictions made in the cloud and then sent back to ground zero. In the IoT world, prediction has to be made within 100 milliseconds, which can be accomplished only with edge analytics. Making Actionable Decisions at the Network s Edge 7

8 Robotics: Autonomous systems, which are often used to inform decisions such as object recognition, shortest path and control of actuators, must be made in real time. Edge analytics is an essential component in these deployments. Power saving: Continuous data transmission to the cloud leads to huge power consumption. Avoiding the transmission of data is one way to conserve power in low-power IoT devices. Regulatory problems: Countries such as Germany have passed regulations to keep the data generated in their country within their political boundaries. 2 Cloud servers are often located thousands of miles away from the data s originating country. With such new regulations, taking the data to the cloud for analytics will become an issue. Edge analytics again will come in handy in such situations. ADDRESSING THE REQUIREMENTS In the digital age, data has become the most valuable asset to organizations. Data-driven companies are heavily dependent on sophisticated and heavy-duty analytical techniques to achieve business objectives and stay competitive with rivals, globally. And edge analytics, without proper implementation know-how, will not add any value to envisioned business outcomes. Hence it is imperative that the aforementioned business drivers are properly addressed to advance corporate goals. High Volume Data As the pressure increases on CIOs to reduce the volume of data transferred to the cloud, there is a growing need for decentralized distributed computational power at the edge of the networks, close to the generation sources, to monitor data volumes. Monitoring this high volume of data at the edge can deliver meaningful insights in the following ways: Threshold crossing alerts (TCAs): The majority of the data received at the edge may not be of much interest, assuming the system is working normally. To filter out such data, companies can install a tool or software with predefined threshold values for parameters. When the parameter value crosses the threshold value, it will trigger an alert to the monitoring tool. Using this approach, companies can save a lot of time and money when evaluating this data. Summary extraction: With this technique, companies can extract a summary of the analyzed data at the network s edge. This summary can then be sent to the cloud for more sophisticated analysis. Companies can set a timer to extract the summary on a periodic basis. Doing so can dramatically reduce the amount of data being sent to the cloud. Parameterized models: This more sophisticated technique is an advanced version of summary extraction. Here, companies run an appropriate algorithm on the data periodically and extract only the parameters for the model and not the complete summary. This method is used with advanced computing techniques to transfer only the parameters to the cloud. In the example below, only after the model is extracted in the cloud are the parameters used in the next iteration onwards. This limits the amount of data sent to the cloud.»» Parameterized model: ax +bx 2 +cx 3 +d, Extracted Parameters (coefficients): a:5, b:8, c:3, d:9. 8 Making Actionable Decisions at the Network s Edge

9 QUICK TAKE The Case for Connected Energy Large corporate office complexes and buildings benefit from hardware/software tools that monitor energy generation and consumption in real time. The data is typically collected from all energy sources onto one IoT device at the so-called edge of the corporate network. On this platform, the data (kilowatt units) is continuously monitored around a predefined threshold limit. When the power generated falls below this threshold, an alarm is activated. In this technique, data is not always sent to the central servers only when an abnormal situation occurs. This real-time monitoring technique helps corporations maximize their use of renewable energy. It saves bandwidth and space storage in the cloud. It provides environmental benefits by helping companies reduce their carbon footprints and meet regulatory targets on energy consumption. By preventing energy waste, corporations can monetize unused power by selling it to the grid after work hours and during the weekends. Making Actionable Decisions at the Network s Edge 9

10 Comparing the Human Nervous System & Edge Computing Data Sources CLOUD EDGE Gateways & Sensors Low Latency Decisions Edge analytics simulates the working of the human nervous system: The edge environment simulates the spinal cord and the cloud simulates the human brain. Most of the sensory signals that require instant reflex action are responded to directly by the spinal cord without transmitting the signals to the brain. Similarly, data requests that demand immediate action are analyzed on the network edge and are responded to immediately. This swift analysis drastically reduces the latency overhead that results when data is sent to the cloud for analysis. The spinal cord transmits only those signals to the brain that do not require an impulsive reflex action, or those signals that can be handled only by the brain. Similarly, the edge filters the data that is sent to the cloud. The results of the edge analysis that require more heavy-duty analytical treatment are routed to the cloud through the gateways at the edge of the network. Figure 3! In our Digital Technologies Lab, we have built solutions using edge analytics that have reduced decision-making time to a few hundred milliseconds, from a few seconds. Low latency can be achieved using two techniques: Static reflex: Reflex action at the edge is triggered by the prediction made by the computing model. This model can be a static version, unchanging over time. A static model is used when the data environment does not change much and the corresponding data falls into fixed patterns. The upside of this approach is the model needs to be refreshed only infrequently. Adaptive reflex: Adaptive models are more sophisticated, and learn as they evolve. Such adaptive models change and enhance their analyzing capabilities according to the data set. These adaptive models are used to handle more complex analysis where data changes are quite frequent. 10 Making Actionable Decisions at the Network s Edge

11 In-Place Analytics In-place analytics tools/software are designed to analyze and process data in its local environment. By employing in-place analytics on native data, organizations can filter down these large data sets collected from their IoT devices. The result generated from the analysis is more targeted and helps organizations gain quick access to key facts about operational matters, thus empowering them to make more informed strategic decisions. Working with smaller data collections also helps organizations avoid duplication of data and to more efficiently dispose of data that isn t business critical. QUICK TAKE Indoor Navigation Indoor navigation tools provide navigational services inside a building when and where GPS is not available. The objective is to provide the shortest path between two points as people move from place to place. This technology displays real-time locations, similar to how GPS works. Since the technology operates with minimal latency, a model is trained using predictive algorithms that run on a server at the network s edge, and which are then distributed to the user s mobile phone during runtime (when the person is moving). Indoor navigation functions primarily on a hard-coded reflex action since the models are pre-configured and changed infrequently. Making Actionable Decisions at the Network s Edge 11

12 Three-Tier Edge Analytics Architecture Gateway Gateway Training Data Training Data Training Sensors Model Convergence Prediction Temporary Model Model Convergence Prediction Sensors Action Action Edge Layer Cloud Layer Edge Layer Figure 4 One of the approaches to implement in-place analytics is geo-distributed machine learning (GDML) (see Figure 4). GDML: In the GDML technique, each compute node runs a local component of the algorithm on the data available and calculates interim results local values of objective function, gradient and direction.»» The interim results are communicated to a central node on the cloud layer.»» At the central node, a global component of the algorithm aggregates all the results it receives from the network s edges and calculates global approximate objective function, gradient and direction.»» Global approximate values are broadcast back to the edge layer.»» This iteration is repeated until a desired convergence in the model is achieved. THE ROAD AHEAD FOR BUSINESSES Edge analytics provides an appropriate platform for numerous IoT services and applications, such as driverless cars, smart grids/buildings and smart cities, as well as wireless sensors and actuators networks (WSANs). Every business opportunity across any industry that requires low latency and communications accuracy including automotive, consumer electronics, energy and utilities, and healthcare will find the implementation of edge analytics extremely helpful. 12 Making Actionable Decisions at the Network s Edge

13 An enormous number of applications are available for edge analytics, and with IoT gaining force in the coming years, businesses interacting directly with consumers will have to realign their business models to earn strategic advantage over their competitors. These businesses should know that it would become extremely difficult to build a growth strategy based on the existing legacy systems as they are very expensive to maintain and have low response times. In the future, cloud-only computing will continue to be costly and time-consuming. The expected growth in IoT-based applications will encourage vendors to commercialize edge analytics to deliver on the promises of lower latency, near-real-time response rates and more-optimized user experience than existing cloud analytics systems can provide. QUICK TAKE The Case for Smart Cities In a smart city, traffic lights at each intersection are equipped with various sensors to monitor passing cars and predict whether a driver will jump the red signal. To develop this model, the data collected from all the traffic signals must be analyzed in order to identify the patterns. This can be done by collecting the data from all the traffic signals at a centralized server, or we can use the GDML approach to develop the model. In GDML, the data collected in each traffic signal is kept locally only the objective functions generated locally are sent to the central server. Once a converged model is arrived at, it is distributed to all traffic signals, and it is then used for prediction. This approach drastically reduces data transmission rates and at the same time generates an effective and predictive global model. Making Actionable Decisions at the Network s Edge 13

14 FOOTNOTES REFERENCES Making Actionable Decisions at the Network s Edge

15 ABOUT THE AUTHORS Ranga Vangipuram Chief Architect, Cognizant Global Technology Office Ranga Vangipuram is a Chief Architect within the Cognizant Global Technology Office s Digital Technologies Lab. He has 30 years of experience in the manufacturing, telecomm and IT industries, with nine of those years spent in the U.S. telecom sector. Ranga previously led new product development and innovation in Cognizant s social, mobile, analytics and cloud (SMAC) group. He is passionate about emerging technologies and specializes in providing technological solutions to business problems. Ranga s current area of focus is in IoT and data science, with a focus on the connected energy, indoor navigation, edge analytics and location-aware-things spaces. He has a master s degree in engineering from Anna University. Ranga can be reached at Ranga.Vangipuram@cognizant.com Ashish Anand is a Senior Business Analyst within Cognizant s Global Technology Office (GTO). He is part of the GTO marketing and alliances team. Ashish s passion is creating fresh perspectives on future technologies. He received a postgraduate degree in business management from the Institute of Management Technology, Ghaziabad. Ashish can be reached at Ashish.Anand6@cognizant.com Ashish Anand Senior Business Analyst, Cognizant Global Technology Office Making Actionable Decisions at the Network s Edge 15

16 GLOBAL TECHNOLOGY OFFICE Global Technology Office (GTO) is a core business unit of Cognizant, with a mandate to power technology-driven transformation. GTO s mission is to power and accelerate Cognizant s capability to harness transformative technologies and enable our customers, people and processes to navigate the shift in the work ahead. As part of GTO, Cognizant Technology Labs is the specialist group that researches, pilots and prototypes emerging technologies with the greatest potential to spark transformative business innovation. Our labs focus on pursuing tomorrow s technologies to put our clients ahead, and extending our footprint in specific areas where we believe technology trends are headed. We develop solutions that bring real value to our clients businesses, and help them own and embrace emerging technologies in ways that make sense for their organizations. We also draw on our existing building blocks and emerging technology expertise to accelerate the development of new applications. ABOUT COGNIZANT Cognizant (NASDAQ-100: CTSH) is one of the world s leading professional services companies, transforming clients business, operating and technology models for the digital era. Our unique industry-based, consultative approach helps clients envision, build and run more innovative and efficient businesses. Headquartered in the U.S., Cognizant is ranked 205 on the Fortune 500 and is consistently listed among the most admired companies in the world. Learn how Cognizant helps clients lead with digital at or follow World Headquarters 500 Frank W. Burr Blvd. Teaneck, NJ USA Phone: Fax: Toll Free: European Headquarters 1 Kingdom Street Paddington Central London W2 6BD England Phone: +44 (0) Fax: +44 (0) India Operations Headquarters #5/535 Old Mahabalipuram Road Okkiyam Pettai, Thoraipakkam Chennai, India Phone: +91 (0) Fax: +91 (0) Copyright 2017, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by any means,electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein is subject to change without notice. All other trademarks mentioned herein are the property of their respective owners. Codex 2938