Will innovation happen in the cloud, at the edge, or elsewhere?
Will innovation happen in the cloud, at the edge, or elsewhere?
Release time：2018-04-14
Views：8698
Innovation is essential for businesses to stay relevant and avoid disruption, but where will it happen?
Industry experts believe that innovation will not happen in the cloud, but at the edge. However, edge computing is just an extension of cloud computing. So what does this mean? Because cloud and edge computing may work together.
In addition, people are concerned about whether technologies such as facial recognition technology used in Apple's recently launched iPhone X mobile phone will bring greater risks to users' personal information.
Prior to this, Apple's smart devices used fingerprint recognition technology, while some Android smart devices used iris recognition technology. So the plot of science fiction novels quickly became scientific fact.
Businesses need to plan ahead, especially when they need to deal with the EU's "General Data Protection Regulation (GDPR)" that comes into effect in five months. To ensure that retailers, government agencies, emergency services, and other organizations do not violate regulatory standards, people need to consider whether the use of technologies such as facial recognition, license plate recognition, and vehicle sensors can meet the requirements and requirements of GDPR.
Empowering Citizens
Jim McGann, VP of Marketing and Business Development at Index Engines, offered his thoughts on these legal provisions: “GDPR puts the power over personal data in the hands of citizens. So, companies that do business in the EU (including the US) must comply with this regulation.”
He added that GDPR raises a key issue for organizations in terms of data management. Many times, organizations have a hard time finding personal data in their systems or paper records. And often they have no way of knowing whether the data needs to be kept, deleted, modified or corrected. Therefore, GDPR will push the responsibility of organizations to a new level due to the possibility of huge fines.
However, he offers advice on adopting relevant solutions: “We provide information management solutions and apply policies to ensure that an organization’s business complies with data protection regulations. There are petabytes of data that need to be sorted, but organizations don’t really have an understanding of what data exists. Index Engines provides a service to cleanse this data by looking at different data sources to understand what can be cleansed. Many organizations can free up 30% of their data, which allows them to manage the data more effectively. Once an organization can manage the data effectively, they can implement policies and measures for it, because most companies know what types of files contain personal data.”
Clearing the data
McGann continued: “Much of this data is very sensitive, so a lot of companies don’t want to talk about it, but we also do a lot of work through legal consulting firms to get organizations to comply with regulations.”
For example, Fortune 500 electronics manufacturer Index Engine completed a data cleansing job and found that 40% of its data no longer contained any business value. As a result, the company decided to purge it.
He points out: “This saves data center management costs: they get positive results by cleaning up the data, but if it is a public company, you can’t just delete the data at will because there are compliance issues.” In some cases, files need to be kept for up to 30 years. He advises, “Enterprises need to ask whether the files have business value or any compliance requirements.” For example, if there is no legal reason to keep the data, then it can be deleted. Some companies are also migrating their data to the cloud so that the data can be deleted from the data center.
In this process, many companies need to check whether the data has business value in order to make their data migration decisions. Organizations need to consider what is in their files - whether it is edge computing or cloud computing for data management, backup and storage.
Ensure information compliance
It is therefore important for organizations to explore how to prevent new technologies from being used in ways that consumers and citizens don’t like, and consider how to use this data to create value for the organization and consumers. And organizations that use this data need to pay attention to information security in providing, using, protecting, and improving digital services.
For example, facial recognition technology has many applications, and its role is not only to allow users to unlock applications on their smartphones, but also to pay for things. With facial recognition technology on smartphones, the image is saved in a locally deployed data center. Despite this, people still need to keep a certain amount of data on the database, and this data also needs to be protected to prevent hackers from using personal data for malicious attacks.
Innovation in edge computing
With the increasing investment of organizations in autonomous vehicles and smart cities, and the development of connected car technologies such as automatic emergency braking (AEB), 2018 will also require consideration of the location of innovation and whether there is a balance between regulatory compliance and innovation.
In addition, there is a growing belief that innovation will occur at the edge rather than in the cloud, and that edge computing is just an extension of cloud computing. Even if data is to be analyzed close to the source, a large amount of data still needs to be analyzed elsewhere. Data and network latency is a historical obstacle, and people hope that the impact of latency can be reduced or eliminated.
Edge computing can extend the capabilities of data centers, allowing a large number of smaller data centers to store, manage, and analyze data, while allowing some data to be managed and analyzed locally by a disconnected device or sensor (such as a connected autonomous car). Once a network connection is present, its data can be backed up to the cloud for further action.
Data Acceleration
Reducing network latency and data delays can improve customer experience. However, due to the high probability of data being transmitted to the cloud, network latency and packet loss can have a considerable negative impact on data throughput. Without machine intelligence solutions such as PORTrock IT, the effects of latency and packet loss can inhibit data and backup performance.
If the database of facial recognition technology cannot quickly transmit citizenship and immigration information, this can cause airport delays and possible accidents or technical problems with autonomous vehicles.
With the advent of autonomous vehicle technology, the data generated by cars will be moving between vehicles in a continuous manner. Some of this data, such as critical status and safety data, requires a fast response turnaround, while other data is often road information such as traffic flow and driving speed. Autonomous vehicles send all their safety-critical data back to a central cloud location over a 4G or 5G network, which can add a significant amount of data latency to the turnaround due to network latency before the data begins to be received. There is currently no easy and cost-effective way to reduce latency between networks. The speed of light is the main factor that people cannot change. Therefore, it is critical to effectively and efficiently manage network and data latency.
The Challenge of Large Data Volumes
Hitachi says autonomous vehicles will create approximately 2PB of data per day. It is expected that connected cars will create approximately 25TB bytes of data per hour. Consider that there are currently more than 800 million cars in the United States, China, and Europe. Therefore, if half of them have full network connectivity in the near future, assuming an average of 3 hours of use per day, this will create 37.5 billion gigabytes of data per day.
If, as expected, the majority of new cars in the mid-2020s are autonomous vehicles, the above figures will be insignificant. Obviously, not all data can be immediately transmitted back to the cloud without some degree of data verification and reduction. There has to be a compromise, and edge computing can support this technology, which can be applied to autonomous vehicles.
From a physical point of view, storing the increasing amount of data will be a challenge. The size and scale of the data is sometimes very important. This gives rise to financial and economic issues of cost per GB. For example, although electric vehicles are considered to be the mainstream of the future, power consumption is bound to increase.
In addition, it is necessary to ensure that the large amount of data created by individuals or devices does not violate data protection legislation.
Industry experts believe that innovation will not happen in the cloud, but at the edge. However, edge computing is just an extension of cloud computing. So what does this mean? Because cloud and edge computing may work together.
In addition, people are concerned about whether technologies such as facial recognition technology used in Apple's recently launched iPhone X mobile phone will bring greater risks to users' personal information.
Prior to this, Apple's smart devices used fingerprint recognition technology, while some Android smart devices used iris recognition technology. So the plot of science fiction novels quickly became scientific fact.
Businesses need to plan ahead, especially when they need to deal with the EU's "General Data Protection Regulation (GDPR)" that comes into effect in five months. To ensure that retailers, government agencies, emergency services, and other organizations do not violate regulatory standards, people need to consider whether the use of technologies such as facial recognition, license plate recognition, and vehicle sensors can meet the requirements and requirements of GDPR.
Empowering Citizens
Jim McGann, VP of Marketing and Business Development at Index Engines, offered his thoughts on these legal provisions: “GDPR puts the power over personal data in the hands of citizens. So, companies that do business in the EU (including the US) must comply with this regulation.”
He added that GDPR raises a key issue for organizations in terms of data management. Many times, organizations have a hard time finding personal data in their systems or paper records. And often they have no way of knowing whether the data needs to be kept, deleted, modified or corrected. Therefore, GDPR will push the responsibility of organizations to a new level due to the possibility of huge fines.
However, he offers advice on adopting relevant solutions: “We provide information management solutions and apply policies to ensure that an organization’s business complies with data protection regulations. There are petabytes of data that need to be sorted, but organizations don’t really have an understanding of what data exists. Index Engines provides a service to cleanse this data by looking at different data sources to understand what can be cleansed. Many organizations can free up 30% of their data, which allows them to manage the data more effectively. Once an organization can manage the data effectively, they can implement policies and measures for it, because most companies know what types of files contain personal data.”
Clearing the data
McGann continued: “Much of this data is very sensitive, so a lot of companies don’t want to talk about it, but we also do a lot of work through legal consulting firms to get organizations to comply with regulations.”
For example, Fortune 500 electronics manufacturer Index Engine completed a data cleansing job and found that 40% of its data no longer contained any business value. As a result, the company decided to purge it.
He points out: “This saves data center management costs: they get positive results by cleaning up the data, but if it is a public company, you can’t just delete the data at will because there are compliance issues.” In some cases, files need to be kept for up to 30 years. He advises, “Enterprises need to ask whether the files have business value or any compliance requirements.” For example, if there is no legal reason to keep the data, then it can be deleted. Some companies are also migrating their data to the cloud so that the data can be deleted from the data center.
In this process, many companies need to check whether the data has business value in order to make their data migration decisions. Organizations need to consider what is in their files - whether it is edge computing or cloud computing for data management, backup and storage.
Ensure information compliance
It is therefore important for organizations to explore how to prevent new technologies from being used in ways that consumers and citizens don’t like, and consider how to use this data to create value for the organization and consumers. And organizations that use this data need to pay attention to information security in providing, using, protecting, and improving digital services.
For example, facial recognition technology has many applications, and its role is not only to allow users to unlock applications on their smartphones, but also to pay for things. With facial recognition technology on smartphones, the image is saved in a locally deployed data center. Despite this, people still need to keep a certain amount of data on the database, and this data also needs to be protected to prevent hackers from using personal data for malicious attacks.
Innovation in edge computing
With the increasing investment of organizations in autonomous vehicles and smart cities, and the development of connected car technologies such as automatic emergency braking (AEB), 2018 will also require consideration of the location of innovation and whether there is a balance between regulatory compliance and innovation.
In addition, there is a growing belief that innovation will occur at the edge rather than in the cloud, and that edge computing is just an extension of cloud computing. Even if data is to be analyzed close to the source, a large amount of data still needs to be analyzed elsewhere. Data and network latency is a historical obstacle, and people hope that the impact of latency can be reduced or eliminated.
Edge computing can extend the capabilities of data centers, allowing a large number of smaller data centers to store, manage, and analyze data, while allowing some data to be managed and analyzed locally by a disconnected device or sensor (such as a connected autonomous car). Once a network connection is present, its data can be backed up to the cloud for further action.
Data Acceleration
Reducing network latency and data delays can improve customer experience. However, due to the high probability of data being transmitted to the cloud, network latency and packet loss can have a considerable negative impact on data throughput. Without machine intelligence solutions such as PORTrock IT, the effects of latency and packet loss can inhibit data and backup performance.
If the database of facial recognition technology cannot quickly transmit citizenship and immigration information, this can cause airport delays and possible accidents or technical problems with autonomous vehicles.
With the advent of autonomous vehicle technology, the data generated by cars will be moving between vehicles in a continuous manner. Some of this data, such as critical status and safety data, requires a fast response turnaround, while other data is often road information such as traffic flow and driving speed. Autonomous vehicles send all their safety-critical data back to a central cloud location over a 4G or 5G network, which can add a significant amount of data latency to the turnaround due to network latency before the data begins to be received. There is currently no easy and cost-effective way to reduce latency between networks. The speed of light is the main factor that people cannot change. Therefore, it is critical to effectively and efficiently manage network and data latency.
The Challenge of Large Data Volumes
Hitachi says autonomous vehicles will create approximately 2PB of data per day. It is expected that connected cars will create approximately 25TB bytes of data per hour. Consider that there are currently more than 800 million cars in the United States, China, and Europe. Therefore, if half of them have full network connectivity in the near future, assuming an average of 3 hours of use per day, this will create 37.5 billion gigabytes of data per day.
If, as expected, the majority of new cars in the mid-2020s are autonomous vehicles, the above figures will be insignificant. Obviously, not all data can be immediately transmitted back to the cloud without some degree of data verification and reduction. There has to be a compromise, and edge computing can support this technology, which can be applied to autonomous vehicles.
From a physical point of view, storing the increasing amount of data will be a challenge. The size and scale of the data is sometimes very important. This gives rise to financial and economic issues of cost per GB. For example, although electric vehicles are considered to be the mainstream of the future, power consumption is bound to increase.
In addition, it is necessary to ensure that the large amount of data created by individuals or devices does not violate data protection legislation.
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