Cloud computing scalability helps enterprises keep their cloud infrastructure performing at optimal levels, regardless of resource requirements or abrupt usage spikes. However, this scalability can be very difficult to unlock. The cloud is complex, visibility is hard to achieve, and costs can spiral. How can companies handle this complexity and effectively scale their cloud compute?
It’s impossible to discuss cloud computing without mentioning big data. Businesses consume big data and derive insights from that data through analytics. These insights help enterprises power business-critical processes, streamline innovation cycles, and drive strategic decisions.
To gather and process enormous volumes of big data, businesses traditionally required a bigger IT infrastructure to house more servers. The advent of cloud computing enabled enterprises to shift from physical servers and move their processes and IT stacks—including Kafka, Hadoop, and Spark—to the cloud, allowing them to consume and analyze more big data for analytics and insights, even in real time.
However, many businesses soon realize that processing and analyzing big data in the cloud presents technological requirements that are so complex that their cloud IT infrastructure design struggles to cope. Touted as the solution to cloud IT performance woes, effectively scaling it is harder than it sounds. Scaling without an effective framework or the right tech stack can quickly go wrong.
More Cloud Providers, More Problems
According to Accenture, 87% of organizations have implemented hybrid cloud initiatives, while 93% have adopted a multi-cloud stance. A hybrid cloud is a combination of public and private cloud services, typically implemented to orchestrate one IT system between both. On the other hand, multi-cloud involves the use of multiple cloud products or services from one or more cloud providers (such as AWS or Microsoft Azure).
While definitions are different, both cloud implementations entail the use of more than one cloud service provider. This is so that enterprises can access tools and resources that one vendor can’t provide. Put simply, they get the best tools and services of either system.
However, one of the biggest drawbacks of both hybrid and multi-cloud deployments is the resource management’s added complexity. Default cloud computing scalability configurations vary per vendor. If left unoptimized, resource consumption can quickly get out of control.
Making this more complicated is that no standardized billing mechanism exists across cloud services providers. This makes it increasingly difficult to assess the cost of resources when mixing and matching across clouds.
Managing Cloud Computing Costs: A Real Challenge
Although cloud vendors claim to be cost-cutters, according to our survey, over 39% of business and IT leaders rank “cost management and containment” as their biggest issue with cloud computing and big data. The same survey revealed “complexity” as the second most pressing concern.
Why is this?
As businesses move to the cloud, the spending model also shifted from capital expenditure (CapEx) to operational expenditure (OpEx). While the operational expenditure model may seem to be better on paper, it can have disastrous cost management issues. But how so?
Switching to OpEx eliminates capital expenses such as data centers, physical servers, and other expensive networking facilities and equipment. On paper, OpEx promises significant savings.
However, OpEx is extremely fluid. What does this mean for scaling cloud compute? Cloud teams have free rein when it comes to cloud expenses, particularly if they don’t have any spending checks or governance models in place. They can scale uncontrollably, leading to jacked-up cloud bills that are way beyond their initial allocated budget.
Why the Move to the Cloud?
Despite the complexities of unlocking real cloud computing scalability, enterprises are still moving their business-critical processes and apps to the cloud.
Aside from the promise of lesser costs, flexibility ranks as one of the most highlighted benefits of the cloud. Enterprises can spin up their services up and down effortlessly and at will. With limitless storage capacity, users can quickly expand their storage to meet their needs.
Scalability ensures that compute resources are available when traffic volume increases and workloads intensify.
Because cloud computing virtually eliminates restrictions that are present in on-prem infrastructure, workload/app performance is significantly improved.
Capabilities That Impact Scalability in Cloud Computing
Cloud computing scalability is now beyond human capabilities. For enterprises to scale their cloud computing effectively and achieve optimal performance, they have to rely on autoscaling and observability.
With our managed autoscaling feature, your cloud infrastructure instantly scales your compute, database, and storage resources based on the configurations and rules you’ve set.
The autoscaling mechanism activates when specific metrics like network usage, storage, or traffic registers above or below the normal threshold. It scales based on your rules and not the default configurations of the cloud vendor. With you in control of the scaling capability, your applications, workloads, and tasks have adequate resources to consume, ensuring SLAs are met.
Observability treats your cloud teams to a comprehensive, detailed, and real-time view of your enterprise’s cloud infrastructure and all its processes.
Having a clear picture of your cloud streamlines scalability in cloud computing. It enables your IT teams and developers to focus on fixing bugs, rather than spending valuable time scouring the infrastructure to find them. From a centralized location, your cloud users can quickly view and address performance issues across the platform.
Observability lets users find resource-extensive applications and users, and implement adjustments to drive down costs.
True observability helps you understand why an issue occurs, on top of knowing what caused it. (For more information, check out our webinar here.) Within the context of scaling cloud compute, observability allows your cloud teams to fully understand your systems so they can scale dynamically.
Managing the Cloud Bill after the Shock
Enterprises that weren’t prepared for the complexities of cloud computing find themselves spending more than budgeted once they make the move. Our research revealed that over one-third of businesses experienced cloud budget overruns by as much as 40%.
Faced with sticker shock, enterprises either (1) repatriate to their previous on-premises IT architecture or (2) improve their access to monitoring and management tools for better visibility and control over their cloud IT infrastructure.
Cloud repatriation, or unclouding, refers to the process of pulling off workloads and applications from the cloud and moving them back to their physical infrastructure environments.
According to the numbers, this is a big trend. A recent study on this cloud reversal discovered that 72% of organizations ordered for their applications to be repatriated, with high cost and performance issues as prevailing factors.
The same report attributed cloud repatriation to the “insufficient planning” of organizations before their shift to the cloud. To remain competitive in a technologically advanced landscape, many business leaders dived head-first into the cloud without performing any of the evaluation and planning necessary to ensure migration success.
Achieving Cloud Computing Scalability
Improving visibility and manageability across multiple/hybrid clouds is now a must for organizations seeking to continue cloud investment. To do this, you’ll need tools that specialize in:
Observability. As the number of businesses moving and deploying cloud-hosted processes, workloads, and dynamic microservice architectures increases, the need for observability becomes more obvious. Cloud users must be able to see how their big data is performing and quickly identify issues. More importantly, they need to understand why issues occur.
Autoscaling. Autoscaling ensures your applications, workloads, and processes are adequately provisioned with compute and other resources. As resource consumption increases, the platform automatically scales to meet the growing need, effectively preventing outages, lags, and downtime. However, autoscaling becomes a problem when it is performed using vendors’ configurations as it can result in the misallocation and mismanagement of resources. To completely maximize the potential of autoscaling, it must be performed with the most ideal scaling configurations.
Chargeback. Chargeback helps enterprises control IT spending by placing IT resources’ costs on the departments or employees that consume them. This is quite effective in situations where dedicated IT resources are shared by different departments and individuals.
In instances where IT resources are shared and used by multiple parties without a standard method to measure and charge resource consumption, users will likely provision and consume more resources than necessary. This can be problematic if users have free rein to allocate compute and there is no ceiling to prevent overspend.
Implementing chargebacks makes users aware of their cloud spend, thus encouraging them to take control. IT administrators can use the data from chargebacks to capture insights they can then use to improve utilization rates and reduce the number of resources they have to manage.
Hot vs. Cold Storage. Effective cloud computing scalability relies heavily on data to improve and optimize the performance of business-critical operations and applications.
Typically, data that are constantly accessed and processed are stored in hot data storage mediums. These include more durable, faster, highly powerful, very expensive SSDs. Reversely, data that are seldom used are placed in cold storage, located far back in terms of priority.
But this setup requires constant monitoring. Hot data can become cold data in an instant. Data stored in cold drives require more time to reach, extract, and process. In scenarios where you have to scale and need data from cold storage, the process may take longer, resulting in slow performance and delays.
The Pepperdata Advantage
Achieving cloud computing scalability that is fully optimized for performance and costs can be difficult, given how complex scaling is. To scale dynamically, enterprises need a platform that can manage the complicated facets of cloud computing, and provide the most optimal scaling configurations based on real-time data.
Cloud vendors offer autoscaling. However, their configurations are aggressive and unoptimized, resulting in resource wastage and runaway costs. Pepperdata Capacity Optimizer with managed autoscaling performs a powerful analysis of resource usage per node in real time, identifying scaling permutations that offer the best performance with the least amount of resources.
Capacity Optimizer not only helps enterprises achieve effective cloud computing scalability, it also:
- Shortens troubleshooting time by 90% by utilizing targeted performance insights.
- Recommends optimal configurations to achieve peak efficiency for every application.
- Instantly detects bottlenecks and sends alerts for quick resolution and minimal SLA impact.
Pepperdata also offers cloud users a chargeback reporting feature, giving them complete visibility into their memory and CPU utilization. Chargeback data helps users monitor their usage, costs, and evaluate trends. Report intervals can be set from minutes to weeks.
With chargeback reporting, enterprises see deep into their resource usage; discover the most intensive apps, processes, and workloads; and attribute the costs accurately to the right unit and/or individual. Insights from chargeback reports also help users predict consumption trends and better allocate resources for future consumption.
Highlight hot data and partitions with Pepperdata Platform Spotlight. Using Platform Spotlight, enterprises can generate a complete and detailed data temperature report. Highlighted in the report are the:
- Age and size of HDFS data files
- Exact file names for each temperature
- Files that don’t match their current policy based on access frequency
Data temperature reporting enables the system to identify and recommend data for hot storage based on access frequency. By storing hot files in the SSDs, the system can scale its resources, workloads, and apps much quicker, reducing lags and downtime instances.
Pepperdata offers users a comprehensive and unified application performance management and performance optimization solution. With it, users have access to a holistic and detailed picture of their cloud infrastructure and all its processes, components, applications, and more.
Download the What is Scalability in Cloud Computing whitepaper for more information and expert insights.