Edge Computing vs Cloud Computing – What’s the Difference? (Pros and Cons). In this article we will compare Edge computing vs Cloud computing and look at their both Pros and cons.
Cloud computing is the initial network infrastructure that enables data access over the internet. Edge computing was introduced to provide more speed and less latency by utilizing and adopting the cloud computing methodologies. It is achieved by reducing the distance between the end user and the cloud, opening up new opportunities in the digital transformation era.
What is Edge Computing
Edge computing is a decentralised computing system that allows multiple devices, resources and applications to connect and work with each other. While Edge computing also connects users to the cloud, it’s like the cloud is now shifted closer to the user. These edges are the high density populations of the centre and the edges of the networks which connect the cloud and the users. Edge computing offers more control over the application and the resources while increasing the performance.
Edge computing can be used in many trenching and latency concerned applications such as autonomous vehicles, smart traffic lights and smart thermostats. All these applications need less latency, high performance efficiency and real time syncing.
OTT platforms like Netflix, Amazon prime etc., where a high number of subscribers are connected can raise high traffic if handled by a centralized server. Since these platforms use edge computing, the shared content with more demand is cached in facilities closer to the users. Hence they have instant access to them.
Pros of Edge Computing
- Reduced cost
Edge computing doesn’t have data migration and latency issues. You do not need much data to work with as it needs less bandwidth and latency.
- Smart data transfer and low bandwidth
Edge computing can intelligently filter out the most critical data when storing. Therefore, it needs a low bandwidth as there’s no need to transfer bulky data volumes with the servers
- Data security
In edge computing, sensitive and confidential data is secured from unauthorized public access since it is analysed inside the own network. The security protocols used in edge computing further increase the security.
- High performance
Data storing, processing and actioning happen within millisecond times. Thus, edge computing is faster in computations than cloud computing.
- Easy decision making
Since you have all the required data for decision making closer to you, making a decision is a matter of milliseconds.
Cons of Edge Computing
- Control and Reliability: As Edge computing is a decentralized system, users have to control most of the things there and need to pay more attention to them.
- Security concerns: There can be security threats if data is being processed outside of the edges.
- Compatibility: There can be a large amount of data to handle in some of the applications as edge computing is highly used in IoT related applications. It can be too much for an edge to process and analyze.
- Cost And Storage Capacity Needs: Edge devices need more hardware and software to build up storage requirements and gain the required performance. The cost can increase if they are all over a few geographical locations.
What is Cloud Computing
Cloud computing is a modern way of collecting and processing data in a centralized location. The devices which need to access the data or information have to be connected to the data center which are hosted in the cloud. It is very convenient for remote access and control the resources. The services provided within cloud computing are scalable which enhances security via authorized access. These services include memory, storage, processing and bandwidth. Users only have to pay for what they use.
Cloud computing services can be categorised into various models such as:
- Private Cloud
A Private Cloud is limited to a specific organization or a set of users. Hence it is a confidential system that is designed to prioritize security. Organizations that need high confidentiality and security use the private cloud computing model. Example: FBI
- Public Cloud
Public cloud computing is similar to an open cloud service. It is commercially available for any general public. That means anyone can develop and host an application there without any financial activity or motive.
- Community Cloud
The community cloud model is used to allow mutual use of cloud services for multiple organizations. It is an ideal option for smaller companies with similar requirements.
Cloud computing can be used in trendy and conventional Applications which need high responsiveness and efficiency. A few of the use cases are video Camera Systems which need high processing power and storage, Smart Lighting Systems where you can control devices located anywhere just via the internet. Apart from that, cloud computing can also be used in any smart application.
Pros of Cloud Computing
- Resources Pooling
The Cloud computing resources provide services to multiple customers with the help of a multi tenant model.
- Cloud Security
It is one of the best features of cloud computing. It creates a snapshot of the data stored so that the data may not get lost even if one of the servers gets damaged.
Cloud computing offers various service models that make it easier for you to choose a better solution that aligns with your needs.
Cloud services provide a large collection of redundant and available infrastructure and software applications. Therefore, you can improve the availability of IT services easily.
- Cost effectiveness
In cloud computing, you have no cost regarding infrastructure implementation and management. Hence, there will be no setting up and hiring cost for relevant frameworks.
The payments are also based on usage. Therefore you won’t be charged for any other services.
There is less management and administrative hassle as they are the responsibilities of the cloud service provider.
- More powerful
You can implement more powerful applications that require high performance since you have access to more data and can also integrate with other additional services or components.
Cons of Cloud Computing
- Data security
As everything related to these services is hosted in the cloud and accessible through the internet, they can be subjected to cyber attacks or thefts. Users might not be able to use the services or secure their data in such scenarios.
- Complex Integration
There can be complexities in connecting to onsite and off-site data storage and integrating with services when the applications are not designed or implemented on the cloud.
- Customization complexities
There can be additional development effort to set up the configuration if you are planning to use existing apps and services with PaaS. It can sometimes result in a complex codebase.
Users might have to do additional configurations prior to deploying existing applications in the cloud. It might require expert help and will result in a complex codebase.
- Vendor Lock in
Vendors may not provide additional support to allow data migration and other support actions in case of a requirement change or any concern in service agreements. Hence there is a probability of your application or resources getting locked into a specific vendor.
- Runtime issues
There can be a mismatch between the cloud infrastructure and the user applications like they are not optimised. Thus, there can be more runtime issues as there are more limitations and dependencies among the resources.
Edge Computing vs Cloud Computing – What's the Difference? Conclusion
Time of data: It is important to understand that cloud vs edge computing are different technologies that cannot replace one another. Edge computing is used to process time sensitive data where cloud computing processes data that is not time driven.
Location: In cloud computing, the hardware used for data storage and processing is located in data centers, distributed globally. In edge computing, data processing is done closer to the source and a user request is routed over a routers, switchesand then hitting a Point of Presence (PoP) along the path. Edge computing is preferred over cloud computing in remote locations, where there is limited or no connectivity to a centralized location.
The Choice of either cloud or edge is not a straightforward option. If you have IoT devices that are spread over multiple geographical locations and need independence, you can implement better edge computing architectures to serve them. It can be accomplished by isolating the data which will also offer more security and control. This system can be extended with cloud computing to provide a few more centralized processing and services.
Time sensitivity: if sensitive applications need to make decisions quickly and data processing tasks are moved closer to where data is generated, new data requests can be processed at significantly lower latency Edge servers can cache results for faster data access.
Edge computing is now extended to online streaming applications. It is a good solution if you have an application with such technology and requirements On the other hand, Cloud computing is the definite answer when you have to deal with a large number of details and need high processing techniques and power.
Edge computing + Cloud computing
The best way to use both architectures is to combine all the data gathered with edge computing devices and then use the storage capacity and the high processing power of the cloud to perform more actions on that data. It can improve the analytics of the application and will be beneficial for decision making in the future.
Edge computing can work in harmony with cloud computing to run, deploy and manage the IoT devices remotely. Cloud computing is very relevant today and will work alongside edge computing to provide data analytics and real time solutions for organizations.
Great solution for computing challenges for IT vendors and organizations is a cloud computing. But applying them together could be a far more comprehensive solution. It is important to add that delegating all data to the edge is also not a wise decision. That is why public cloud providers have started combining IoT strategies and technology stacks with edge computing.