EDGE COMPUTING EXPLAINED:
The emergence of Internet-of-Things (IoT) devices, autonomous farming, smart cities, smart homes, self-driving vehicles, user developed & supported software, automated manufacturing/printing, interactive education and the likes opened the floodgates for a new era of computing. Many of these new solutions, security cameras and autonomous farming for example, brought-in so much data that even the seemingly boundless computing capabilities of the cloud were not enough to maintain timely results. This new era of computing is – edge computing.
WHAT IS EDGE COMPUTING?
“Edge computing” is a type of distributed architecture in which data storage and processing occurs close to the source of data and close to the user of the data, i.e., at the “edge” of the networked computing ecosystem. This approach reduces the need to bounce data back and forth between the cloud and data user while maintaining consistent performance.
With regards to infrastructure, edge computing is a network of local data centers used to store and process edge computing data.
The term “edge” originates from the network diagrams. In it, “edge” is a point at which traffic comes in and goes out of the computing ecosystem. Since its location is at the edges of the diagram – its name reflects this fact.
Edge computing is enabled by a wide range of new technologies such as easy-to-build AI software (enables local user-specific applications), software defined networking, high-performance local area networks & more.
Edge computing solutions are the fastest growing part of the market. In 2018, Gartner predicted that by 2020 75% of enterprise-generated data will be created and processed outside the traditional, centralized data center or cloud. FNT Software’s recent survey noted that “44% of the data centers say they have either already deployed some form of edge computing capacity technology or will do so in the near term”.
THE UNDERSERVED EDGE — Unfortunately, the data centers supporting edge computing technology are nearly all located in the world’s largest metropolitan areas and they support edge solutions in those metro areas only.
There are very few high-quality, edge computing data centers in smaller communities. Those that do exist are usually part of a pilot usage such as autonomous farming or smart vehicle pilots. But, edge computing solutions are mostly being deployed in smaller communities by placing solutions indevice (i.e. in smart phones) or on-premise (i.e. with servers in a data closet at the business site) solutions. Thus, smaller communities remain largely underserved, with no local high-quality data centers that can support and deliver edge computing solutions.
EDGE COMPUTING VS CLOUD COMPUTING: WHAT’S THE DIFFERENCE?
Edge computing is a kind of expansion of cloud computing architecture – an optimized solution for decentralized infrastructure. The main difference between cloud and edge computing is in the mode of infrastructure.
- Cloud is centralized.
- Edge is decentralized.
The edge computing framework’s purpose is to be an efficient workaround for the high workload data processing and transmissions that are prone to cause significant system bottlenecks.
- Since applications and data are closer to the source, the turnaround is quicker, and the system performance is better.
The critical requirement for the implementation of edge computing data processing is the time-sensitivity of data. Here’s what it means:
- When data is required for the proper functioning of the device (such as self-driving cars, drones, et al.); and
- When information stream is a requirement for proper data analysis and related activities (such as virtual assistants and wearable IoT devices).
The time-sensitivity factor has formed two significant approaches to edge computing:
- Point of origin processing – when data processing happens within the IoT device itself (for example, as in self-driving cars); and
- Intermediary server processing – when data processing is going through a nearby local server (as with virtual assistants). In addition to that, there is “non-time-sensitive” data required for all sorts of data analysis and storage that can be sent straight to the cloud-like any other type of data.
The intermediary server method is also used for remote/branch office configurations when the target user base is geographically diverse (in other words – all over the place).
- In this case, the intermediary server replicates cloud services on the spot, and thus keeps performance consistent and maintains the high performance of the data processing sequence.
WHY EDGE COMPUTING MATTERS?
There are several reasons for the growing adoption of edge computing:
- The increasing use of mobile computing and “the internet of things” devices.
- The decreasing cost of hardware.
- Internet of Things devices requires a high response time and considerable bandwidth for proper operation.
- Cloud computing is centralized. Transmitting and processing massive quantities of raw data puts a significant load on the network’s bandwidth.
- In addition to this, the constant movement of large quantities of data back and forth is beyond reasonable cost-effectiveness.
- On the other hand, processing data on the spot, and then sending valuable data to the center, is a far more efficient solution.
BENEFITS AND CHALLENGES OF EDGE COMPUTING?
VINCENT EDGE DATA CENTERS COMPUTING BENEFITS
The benefits of edge computing form five categories:
- Speed – edge computing allows processing data on the spot or at a local data center, thus reducing latency. As a result, data processing is faster than it would be when the data is ping-ponged to the cloud and back.
- Security – There is a fair share of concerns regarding the security of IoT (more on that later). However, there is an upside too. The thing is – standard cloud architecture is centralized. This feature makes it vulnerable for DDoS and other troubles (check out our article on cloud security threats to know more). At the same time, edge computing spreads storage, processing, and related applications on devices and local data centers. This layout neutralizes the disruption of the whole network.
- Scalability – a combination of local data centers and dedicated devices can expand computational resources and enable more consistent performance. At the same time, this expansion doesn’t strain the bandwidth of the central network.
- Versatility – edge computing enables the gathering of vast amounts of diverse valuable data. Edge computing handles raw data and allows the device service. In addition to this, the central network can receive data already prepared for further machine learning or data analysis.
- Reliability – with the operation proceedings occurring close to the user, the system is less dependent on the state of the central network.
EDGE COMPUTING CHALLENGES (SOLVED BY VINCENT EDGE DATA CENTERS)
Edge computing brings much-needed efficiency to IoT data processing. This aspect helps to maintain its timely and consistent performance.
However, there are also a couple of challenging issues that come with the good stuff.
Overall, five key challenges come with the implementation of edge computing applications. Let’s take a closer look:
- Network bandwidth – the traditional resource allocation scheme provides higher bandwidth for data centers, while endpoints receive the lower end. With the implementation of edge computing, these dynamics shift drastically as edge data processing requires significant bandwidth for proper workflow. The challenge is to maintain the balance between the two while maintaining high performance.
- Geolocation – edge computing increases the role of the area in the data processing. To maintain proper workload and deliver consistent results, companies need to have a presence in local data centers.
- Security. Centralized cloud infrastructure enables unified security protocols. On the contrary, edge computing requires enforcing these protocols for remote servers, while security footprint and traffic patterns are harder to analyze.
- Data Loss Protection and Backups. Centralized cloud infrastructure allows the integration of a system-wide data loss protection system. The decentralized infrastructure of edge computing requires additional monitoring and management systems to handle data from the edge.
IN CONCLUSION?
The adoption of cloud computing brought data analytics to a new level. The interconnectivity of the cloud-enabled a more thorough approach to capturing and analyzing data. With edge computing, things have become even more efficient. As a result, the quality of business operations has become higher but rural and smaller communities are not well supported. Edge computing is a viable solution for data-driven operations that require lightning-fast results and a high level of flexibility, depending on the current state of things.
GLOSSARY OF TERMS
Auction Markets: An auction market is a market in which buyers indicate the highest price they are willing to pay and sellers indicate the lowest price they are willing to accept. A trade occurs when the buyer and seller agree on a price.
Bonds: In finance, a bond is an instrument of indebtedness of the bond issuer to the holders. The most common types of bonds include municipal bonds and corporate bonds.
Common Stocks: Common stock is a form of corporate equity ownership, a type of security.
Correlated/Correlation: When two sets of data are strongly linked together we say they have a High Correlation. The word Correlation is made of Co- (meaning “together”), and Relation. Correlation is Positive when the values increase together, and. Correlation is Negative when one value decreases as the other increases.
Demographics: Demography is the statistical study of populations, especially human beings. Demography encompasses the study of the size, structure, and distribution of these populations, and spatial or temporal changes in them in response to birth, migration, aging, and death.
Depreciation: In accountancy, depreciation refers to two aspects of the same concept: first, the actual decrease in value of fair value of an asset, such as the decrease in value of factory equipment each year as it is used and wears, and second, the allocation in accounting statements of the original cost of the assets to periods in which the assets are used.
Direct Ownership: Direct Ownership means ownership by an owner but excluding any such ownership with or through Associates and Affiliates of such owner. The terms “directly own” and “directly owned” have correlative meanings.
Diversification/Diversified: Portfolio diversification means investing in multiple different asset classes and risk levels in an effort to mitigate overall investment risk.
Exchange-Traded Funds (ETFs): An exchange-traded fund is an investment fund traded on stock exchanges, much like stocks. An ETF holds assets such as stocks, commodities, or bonds and generally operates with an arbitrage mechanism designed to keep it trading close to its net asset value, although deviations can occasionally occur.
Interest Expense: Interest expense relates to the cost of borrowing money. It is the price that a lender charges a borrower for the use of the lender’s money. On the income statement, interest expense can represent the cost of borrowing money from banks, bond investors, and other sources.
Investment Portfolio: An investment portfolio is a basket of assets that can hold stocks, bonds, cash and more. Investors aim for a return by mixing these securities in a way that reflects their risk tolerance and financial goals.
Investment Vehicles: An investment vehicle is a product used by investors to gain positive returns. Investment vehicles can be low risk, such as certificates of deposit (CDs) or bonds, or they can carry a greater degree of risk, such as stocks, options, and futures.
Mortgage-Backed Security (MBS): A mortgage-backed security is a type of asset-backed security which is secured by a mortgage or collection of mortgages. The mortgages are aggregated and sold to a group of individuals that securitizes, or packages, the loans together into a security that investors can buy.
Mutual Funds: A mutual fund is a professionally managed investment fund that pools money from many investors to purchase securities. These investors may be retail or institutional in nature. Mutual funds have advantages and disadvantages compared to direct investing in individual securities.
Paper Investments: A number of different kinds of popular investments in the United States qualify as paper investments. These include stocks, bonds, mutual funds, certificates of deposits, and money market accounts. Shares of stock are pieces of paper that relate a certain percentage of ownership in a publicly-traded company.
Private Placements: Private placement is a funding round of securities which are sold not through a public offering, but rather through a private offering, mostly to a small number of chosen investors. Generally, these investors include friends and family, accredited investors, and institutional investors.
Shares: In financial markets, a share is a unit used as mutual funds, limited partnerships, and real estate investment trusts. The owner of shares in the company is a shareholder of the corporation. A share is an indivisible unit of capital, expressing the ownership relationship between the company and the shareholder.
Socially Responsible Investing (SRI): Socially responsible investing, or social investment, also known as sustainable, socially conscious, “green” or ethical investing, is any investment strategy which seeks to consider both financial return and social/environmental good to bring about social change regarded as positive by proponents.
Stocks: Stock of a corporation, is all of the shares into which ownership of the corporation is divided. Shares are collectively known as “stock”. A single share of the stock represents fractional ownership of the corporation in proportion to the total number of shares.
Stock Trading: Stock trading, also known as refers to the buying and selling of shares in a particular company; if you own the stock, you own a piece of the company.
Volatility: In finance, volatility is the degree of variation of a trading price series over time, usually measured by the standard deviation of logarithmic returns. Historic volatility measures a time series of past market prices.
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