How can the emma platform help organizations rightsize or select the best instance?
Organizations seek performance optimization and cost-efficiency while managing their cloud workloads. Learn about the important factors and challenges companies should consider while rightsizing cloud instances. Discover how the emma multi-cloud management platform helps organizations make optimal decisions regarding cloud resource allocation and instance rightsizing.
Cloud computing impacts diverse industries because of its flexibility, scalability, and cost-efficient pricing. However, organizations find it challenging to select the best cloud instance that fits their business requirements and manage their workloads appropriately.
According to HashiCorp State of Cloud Strategy Survey 2022, 94% of the companies overspend in the cloud. Two top reasons for their cloud wastage are underutilized and overprovisioned resources. Understanding these cloud inefficiencies is necessary for cloud cost management and performance optimization.
In this blog, we’ll explain the concept of cloud instance rightsizing in detail. We’ll explore major factors organizations should consider while rightsizing, discuss key challenges they can typically face, and how the emma platform streamlines cloud rightsizing across vendors.
Rightsizing cloud instances involves more than choosing the right size and type; it involves a holistic approach encompassing various vital factors. These considerations ensure that an organization's cloud infrastructure is robust and cost-effective. Here are some essential factors to consider when rightsizing cloud instances:
Operational needs
Organizations must analyze their application workloads to estimate their operational needs before selecting an instance type. Operational requirements include CPU, memory, storage, and network needs. To assess operational needs realistically, organizations must record their usage over time. Typically, workload usage contains spikes where network traffic or resource utilization goes beyond the usual observations. Hence, workload assessment must include such information.
Scaling type: vertical or horizontal
Scalability determines how flexible cloud workloads are and their ability to adapt to changes in demand. Horizontal scaling means adding more instances, whereas adding more power to existing instances is vertical scaling. In horizontal scaling, workload distribution is managed across multiple nodes. Hence, the goal is to ensure the system remains available even if part of it fails. Smaller applications or those with budget constraints typically opt for vertical scaling, while large-scale applications, like e-commerce platforms, employ horizontal scaling.
Balance between underprovisioning vs. overprovisioning
Selecting a smaller instance type – or underprovisioning – could lead to performance issues due to its inability to handle the workload. While overprovisioning could lead to underutilized resources. These issues are more visible for on-premise deployments compared to the public cloud. In on-premise environments, internal IT teams have to manage workloads, which can prove challenging depending on their business processes. Striking a balance between requirements and resources is a key consideration while rightsizing cloud instances.
Downtime risk
Downtime means cloud resources become unavailable for some period. It implies that the task the particular instance was performing is interrupted. There can be many reasons for downtime in the cloud, such as infrastructure failures, software issues, resource limitations, etc. Downtime is also possible while right-sizing cloud instances. Hence, organizations should have a proper downtime risk management strategy to avoid project delays. Regular monitoring and backing up application data are necessary risk management strategies.
Monitoring performance metrics
Organizations should use a cloud management dashboard to monitor performance metrics like CPU utilization, memory utilization, requests per minute, network throughput, latency, disk I/O, etc. These parameters help gauge the underprovisioning or underutilization of cloud resources.
Automation
Automation in cloud management can significantly streamline workload operations. It enables organizations to dynamically provision and de-provision resources based on real-time demands. Organizations can set customized policies for workload automation. Also, it helps in defining accurate budget allocation since automation reduces errors and time consumption.
Cloud instance rightsizing challenges
One size fits all thinking
Each cloud workload is unique. A single instance configuration cannot work well for all scenarios. Hence, organizations must evaluate specific cloud workload requirements for each application. Adopting a generic approach can lead to compromised performance and increased cloud waste. Due to a lack of comparative analysis and intelligent recommendations, it becomes challenging for organizations to rightsize their cloud instances.
Lack of long-term planning
Businesses evolve and scale with time. Cloud strategy must align with long-term business goals. While developing an application, organizations must understand and anticipate how the utilization of cloud resources will behave in the future. Cloud forecasting should be based on data-driven approaches. Hence, organizations must employ robust tools to monitor the performance of their cloud workloads. A lack of long-term cloud planning could lead to disruptions and cost inefficiencies, making it more challenging to rightsize cloud resources appropriately.
Lack of data-informed cloud strategy
At all times, organizations must be aware of their entire cloud ecosystem, including workloads, services, and applications. Since cloud requirements continuously change, it becomes more challenging to keep track. As a result, organizations lose a holistic view of their cloud environments and their cloud strategy gets misaligned from their business goals. Any attempts at rightsizing cloud instances in such a scenario – especially based on intuition, can lead to underprovisioning or idle cloud resources. This is why organizations always need reliable data-driven insights to keep track of usage patterns across all services and workloads in order to recommend the best possible cloud instance.
emma is a no-code multi-cloud management platform that helps organizations manage their cloud resources without much effort. The emma platform provides data-driven insights and actionable strategies to make decision-making easier for cloud practitioners. Let’s discuss how organizations can leverage the emma platform for cloud instance rightsizing.
Cloud instance rightsizing is the key to maintaining a feasible and cost-effective cloud strategy. The emma platform helps organizations select the best instance using a data-driven approach.
One of the unique features of the emma platform is using machine learning models to learn historical cloud computing patterns and usage. The model learns about underutilized or overprovisioned resources by analyzing historical data usage. emma’s ML engine can then intelligently recommend the right instance type that fits an organization’s current needs.
Moreover, the model can predict future instance type and size requirements. For example, how many resources would the organizations require in a week or month? These predictive capabilities eliminate unnecessary costs and are pivotal in effective cloud cost management.
Selecting the best instance type according to workload requirements helps in performance optimization. When the instance matches computational needs, it allows faster processing.
The emma platform helps organizations in rightsizing, considering various factors such as CPU, memory, storage, and network capabilities. Through its advanced analysis, emma offers valuable recommendations tailored to an organization's needs. Besides monitoring the overall cloud usage trend, emma can predict future demand (increase or decrease) in cloud requirements.
Based on demand estimates, organizations can proactively respond to positive and negative spikes by adjusting their cloud infrastructure. This enhances the efficiency of applications and ensures cost-effective use of resources, striking a balance between performance and cost.
Cost comparison between cloud instances helps organizations make informed decisions and control their cloud spend. The emma platform helps organizations compare various instance types and provides detailed cost breakdowns. It also considers variables such as on-demand pricing and spot instances.
On-demand pricing allows organizations to pay for computing capacity, depending on the instances they run. On the other hand, spot instances let organizations use unused or excess computing capacity at a significant discount. For instance, organizations can receive discounts of up to 90% by using Amazon EC2 spot instance, compared to the on-demand price. This range of options allows companies to select the best package that fits their needs.
Moreover, the emma platform allows users to compare the costs of different cloud service providers. This cost comparison ensures organizations choose cloud instances that best align with their budgetary requirements and long-term cloud cost management strategies.
According to the State of Cloud Report 2022, about 54% of organizations say lack of visibility is the top reason for cloud waste. Moreover, various challenges and questions arise for cloud practitioners and IT managers adopting cloud resources for business operations, such as:
How can I comprehensively view all my cloud expenses across different providers?
Are there underutilized resources I'm still paying for?
How can I identify and rectify network bottlenecks?
How can I automate provisioning and scaling based on demand?
The emma platform provides intuitive statistics dashboards for monitoring compute resources, providing comprehensive visibility into the status of all virtual resources. They offer a summary of resource usage across all cloud vendors, such as total virtual machines in a project, virtual machine locations, the number of operating systems, storage usage per VM, average CPU usage, etc. These dashboards aggregate usage information across different cloud instances, services, and projects to present a detailed cost breakdown. Users can also monitor the usage of each compute instance separately. The emma platform also analyzes usage information to provide recommendations for existing instances, such as resizing overutilized instances, deleting unused or forgotten VMs, and increasing or relocating VM loads.
Organizations use cloud resources with various parameters, such as peak load, seasonality, idle time, etc. Workload profiling refers to the process of gathering and analyzing this data. This helps organizations make data-informed decisions to rightsize cloud instances.
emma’s machine learning model, a system that learns an organization’s workload patterns, helps allocate optimal cloud resources. Its workload profiling helps organizations manage cloud costs by achieving resource efficiency, avoiding overprovisioning, and allocating the right resources to handle workload demands effectively. This feature helps organizations in achieving a balance between performance and cost.
Instance lifecycle management refers to the provisioning, monitoring, optimizing, and decommissioning of cloud resources. Lifecycle management is a complex and hard-to-track activity without having a robust cloud management platform.
The emma platform helps businesses in cloud lifecycle management by offering remote management tools that provide visibility into instance utilization and usage patterns over time. This helps in maintaining performance and cloud cost management. By monitoring instance usage and patterns, businesses can rightsize, downsize, or upgrade cloud instances depending on the evolving business needs.
Intelligent instance selection means choosing the best instance type and size according to evolving business needs. Manual estimation could lead to underprovisioning or underutilizing cloud resources, which in turn hampers performance and increases cloud waste.
emma’s intelligent machine learning engine helps organizations rightsize their instance types. By analyzing historical data, workload characteristics, and performance metrics using intelligent algorithms and machine learning, the emma platform recommends the best instance types for specific applications or workloads. This approach optimizes performance and ensures resource efficiency and cost-effectiveness while simplifying the instance selection process.
Using the emma multi-cloud management platform, organizations can make informed decisions about rightsizing tasks and focus on other business operations. emma’s rightsizing solutions give organizations a streamlined cloud experience by providing visibility into cost analysis and breakdown across multiple vendors. The platform recommends the best instance type according to current workload needs and forecasts the future workload environment. These comprehensive cloud rightsizing and instance selection utilities align an organization’s cloud strategy with its business goals.
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