Today applications offer extensive interactive content and users expect quick rendering and page load speeds. This brings a number of challenges to the fore and the infrastructures of many organizations no longer meet the needs for advanced performance. Issues like speed, latency, and scalability are some of the challenges that need addressing. A number of different solutions, including operational in-memory solutions, are available to help scale networks and improve security, performance, and productivity in a cost-effective way.
In-Memory Data Grid
An in-memory data grid (IMDG) offers a cost-effective way to accelerate applications and services. It consists of a cluster of computers pooling random access memory (RAM) so applications can share data with other applications running in the cluster.
Distributed in-memory caching means the IMDG retains all data in memory, which means applications can achieve far greater speeds.
The IMDG is designed for running large-scale applications that need more RAM. High application performance is possible by using the RAM and processing power of many computers running tasks in parallel. It can handle millions of people or devices accessing a system at the same time and all expecting an instant response.
An IMDG also allows organizations to build applications that can process huge amounts of data all at the same time. It simplifies application development by having all data at its disposal.
Each computer on the cluster has specialized software that coordinates data access for applications. It has its own view of data and data structures in-memory but the view is shared across all the computers.
The distributed design of an IMDG allows scalability of both data and the application load just by adding a new node to the cluster. It is possible to scale up quickly when loading spikes and scale down when resources are not required anymore. By being able to scale up and down like this, drives operational efficiency and reduces hardware requirements.
Some examples of use cases built on IMDGs include simulations, e-commerce engines, and payment processing systems.
In-memory databases (IMDBs) also enable high-speed applications but they are designed for more storage-focused applications where only small data subsets need to be retrieved at a given time. When data points are needed, they are retrieved, processed, and written back to the IMDB.
There are two general use cases for IMDBs that use separate technologies. The first use case stores data in the table, row, or column format and is like a version of a relational database but with more speed due to the in-memory advantage. These IMDBs typically emphasize analytics. Another use case is caching, and IMDBs offering this typically store data in a non-relational format.
An IMDB offers faster reads than disk-based databases but with an IMDB, the engine that runs the business models lives on an application and the data lives on the server-side. The data has to travel over the network, and this makes it significantly slower than an IMDG where the application and the data run in the same memory space. An IMDB is also limited when it comes to scalability and some require customers to replace their existing databases.
Cloud computing has revolutionized several sectors that were dominated in the past by large, expensive legacy applications. Factors such as cloud computing and changing end-user demands have resulted in organizations reconsidering their legacy applications.
Proprietary cloud stacks can deliver great functionality using containerized microservices, but they are incompatible with legacy applications. However, most organizations cannot afford to start from scratch and walk away from their existing investments.
Fortunately, traditional and cloud-based approaches can co-exist. The best approach for many organizations is to enhance their existing infrastructure and improve their staff productivity by training them and giving them the tools to support cloud-based capabilities.
Modern applications mostly use common programming languages and tools so IT staff can work on a variety of applications. Legacy applications, on the other hand, were often built around proprietary tools from a single vendor.
Modern applications combine traditional app services and emerging containerized apps so they should be on the same infrastructure. Organizations often see initial cost savings when they migrate to the cloud but if they want to continue to see savings, optimization and ongoing enhancements are essential.
Cloud providers keep introducing new features and applications. Organizations need to ensure they are only paying for the right ones for their workloads. There is also a need to size according to application usage as it is always possible to resize if required. Setting size at a higher capacity than the actual requirement is a waste of money.
Older applications function in an environment that may include legacy databases, operating systems, libraries and may even use specific hardware. These legacy applications become more expensive to maintain as they get older and so does the underlying infrastructure. Modern applications designed for cloud platforms don’t have the same issues.
Many legacy applications, such as CRM and ERP, were used to solve extensive business problems. With new technologies, like standardized APIs, it is easier for organizations to integrate smaller applications. They can buy only what they need and integrate it rapidly, rather than using massive applications that cover all eventualities.
Multiple Clouds Solutions
A consistent operational framework improves productivity, reduces costs, and increases security, compliance, and governance. When applications are not consistent across public and private clouds, IT may place compatibility considerations ahead of other more important ones like operational, technical, business, and financial considerations.
Hybrid cloud consistency needs to extend across all environments, from public cloud to edge and on-premises. This helps to limit operational silos, reduce risks, increase efficiency and reduce costs.
A Final Word
Business agility, customer satisfaction, employee productivity, and business revenue all depend on business-critical applications and services working optimally. The demand for low latency, scalability, agility, and speed means that organizations need new solutions that can offer this as their legacy systems are unable to cope. The above solutions offer some ways to accelerate applications and services in a cost-effective manner.