What Is Cloud Cost Optimization?
Cloud cost optimization is the process of reducing the cost of using cloud computing services, without sacrificing the required performance and availability, by improving resource utilization, minimizing waste, and identifying cost-effective pricing options.
This can include:
- Right-sizing: Matching the size of cloud resources to the actual workload requirements to minimize overprovisioning and waste.
- Automated scheduling: Automatically starting and stopping cloud resources based on usage patterns to reduce the amount of time resources are unused and unnecessary.
- Cost-effective pricing options: Choosing cost-effective pricing options like reserved instances, spot instances, or changing cloud service providers (for example, see Amazon’s instance pricing options).
- Resource utilization: Monitoring and improving resource utilization to reduce the number of underutilized resources.
- Cost tracking and reporting: Tracking and analyzing cloud costs to identify areas for optimization and to provide visibility into cloud spending.
- Cost allocation and billing: Allocating cloud costs to appropriate departments and projects, and accurately tracking and billing for shared resources.
- Managing costs for cloud migrations: ensuring strategic initiatives for migrating IT resources to the cloud provide the desired return on investment.
Recommended Article: Why Choose a Multi-Cloud Strategy for AI Deployment
How Is AI Used for Cloud Cost Optimization?
Cloud cost optimization tools are continuously improving with the help of machine learning capabilities. For example:
- Predictive cost optimization: AI algorithms can analyze cloud usage patterns and resource utilization, and predict future usage and cost trends, allowing organizations to plan and allocate resources more effectively.
- Resource usage forecasting: AI algorithms can help forecast resource usage, allowing organizations to predict when it is most cost-effective to scale their resources up or down.
- Usage anomaly detection: Machine learning algorithms can detect anomalies in cloud resource usage, identify potential cost savings opportunities, and suggest optimizations.
- Automated recommendations: AI-powered cloud management tools can provide automated recommendations for cost optimization, such as choosing cost-effective pricing options, right-sizing cloud resources, and reducing resource waste.
- Cloud resource optimization: Cloud management solutions can use AI algorithms to analyze cloud resource utilization, identify underutilized resources, and suggest optimizations to reduce waste.
See this blog post for a detailed review of contemporary cloud cost optimization best practices and tools.
Two Machine Learning Models Used in Cloud Cost Optimization
Let’s take a closer look at two machine learning models commonly used under the hood in cloud cost optimization processes.
Workflow Scheduling
Hybrid Cloud Optimized Cost (HCOC) is a scheduling algorithm that aims to minimize makespan, while maintaining a reasonable cost and meeting a specified deadline. Makespan is a term used in scheduling and optimization problems, referring to the total amount of time required to complete a set of tasks or a project. In the context of workflow scheduling, makespan is the time it takes to complete all tasks within a workflow, from the start of the first task to the completion of the last task, considering task dependencies and resource constraints.
To achieve minimal makespan, the algorithm balances the use of private and public cloud resources. Executing all tasks on local resources may cause delays, while utilizing public cloud resources for all tasks may lead to excessive costs.
Background and definitions
In the HCOC algorithm, a workflow is visualized as a directed acyclic graph (DAG) – G = (V, E) – with n nodes (tasks) and associated computation and communication costs. Private and public clouds consist of heterogeneous resources with varying processing capacities and network links. A hybrid cloud combines resources from both private and public clouds.
Task scheduling maps tasks to resources within the hybrid cloud. Many applications, such as Montage, AIRSN, CSTEM, LIGO, and Chimera, are represented by DAGs. The proposed scheduling algorithm can handle such applications by leveraging public cloud resources whenever private resources are insufficient to execute the workflow.
Initial Schedule
The Path Clustering Heuristic (PCH) scheduling algorithm is used to create an initial workload schedule that considers only private resources to meet the specified deadline. If the workload does not meet the deadline, the algorithm determines which public cloud resources to use based on performance, cost, and the overall number of tasks that will be scheduled in the cloud.
The PCH algorithm computes attributes for every DAG node, such as computation cost, communication cost, priority, earliest start time, and estimated finish time. It then creates clusters of tasks within the same path in the graph, scheduling tasks on the same resource in the same cluster.
The HCOC Algorithm
HCOC consists of three main steps:
- Generate an initial workload schedule with resources from a private cloud.
- If the makespan exceeds the deadline, select tasks for rescheduling and public cloud resources to create a hybrid cloud.
- Reschedule the specified tasks in the new hybrid cloud.
The algorithm chooses the tasks to be rescheduled from the DAG’s beginning to its end based on the highest-level priority. It then determines the number of public cloud resources to request by considering price, performance, and the number of task clusters being rescheduled.
Once the initial schedule is generated, the algorithm verifies that public cloud resources are needed to meet the deadline. If the makespan exceeds the deadline, the algorithm determines the nodes to be rescheduled, considering the resources available from the public cloud. This process continues until the schedule meets the deadline or reaches a specified number of iterations.
Adaptability and robustness
The HCOC algorithm is easily adaptable to work with budgets rather than deadlines, making it suitable for prepaid systems or scenarios where the user has a fixed budget. Additionally, other scheduling heuristics can be used in place of PCH to evaluate the proposed strategies’ robustness.
You might also want to read this Guide on Cloud Run.
Optimizing Reserved Instances
Reserved Instances Optimizer (RIO) is a straightforward, efficient, and adaptable tool for optimizing cloud computing costs. RIO utilizes modern techniques from industry and research, and involves four steps: opportunity size calculation, reserved instance (RI) planning, visualization, and risk analysis. It employs a heuristic approach to find the ideal number of RIs, with the results compared to theoretical findings.
Selecting parameters
To determine the most beneficial reserved instances to purchase, RIO assesses the opportunity size for each instance type. A reserved instance is defined by a set of parameters, including operating system, size, availability zone, term length, and purchase option. The algorithm focuses exclusively on one-year term options, as three-year terms require more extensive data on demand and infrastructure planning.
Despite the availability of multiple purchase options, RIO only uses the partial upfront option, which provides a balance between initial investment and long-term cost savings. Full upfront and no upfront options are excluded due to their higher risks and lower savings, respectively.
Analyzing hourly demand
RIO processes the hourly demand for each instance type to identify the most profitable purchases. This demand refers to the number of instances per hour within a specific time range. Instead of forecasting future demand, which can be imprecise with limited data, RIO analyzes past data (e.g., the previous 30 days) to manage uncertainty. This analysis yields two values for each option: maximum profit threshold and loss threshold, which evaluate an option’s effectiveness.
The maximum profit threshold represents the cost savings achieved through the optimal number of reserved instances, while the loss threshold indicates the point at which over-provisioning costs outweigh cost savings. Both of these metrics depend on a specific time range.
Key concepts: Profit function and hill climbing
- The profit function represents the cost savings achieved by using a specified number of reserved instances. It calculates the effective hourly cost, which amortizes the per-hour cost of a given reserved instance over the term length, including upfront payments. The goal of RIO is to maximize the RI profit function while automatically and effectively identifying the thresholds using hill-climbing techniques.
- Hill-climbing is a local search heuristic that adjusts a single element in a vector at a time to maximize a target function. It works effectively because the profit function has a global optimum.
Reserved instances planning
After analyzing individual options, RIO bundles different options into a plan based on specific constraints, such as budget limitations or exploiting a smaller fraction of the overall opportunity size. The profit of a plan is calculated as the sum of the profits of its elements. RIO uses heuristic-based approaches to find approximate solutions to these planning problems.
Visualization and risk analysis
Visualizing the results is crucial for providing an effective summary of the relevant data to the decision-makers. RIO generates a report that displays the proposed plan and detailed analysis for each option in the plan, including opportunity size, loss threshold, hourly demand, and previous reserved instance utilization.
RIO analyzes the risks associated with purchasing RIs, such as decreased demand, the release of new instance types, infrastructure changes, and cloud provider price reductions. It suggests risk mitigation strategies, such as regularly iterating the purchase process, evaluating risks based on analysis results, and purchasing a fraction of the opportunity size. RIO also takes into account risk parameters like instance age and retirement, guiding decision-makers to purchase newer, more efficient instances with lower risk levels.
Conclusion
In conclusion, cloud cost optimization is a crucial function in cloud computing that requires organizations to maximize resource utilization and minimize waste. AI and machine learning play a significant role in this process, by providing organizations with the tools and data they need to analyze their cloud usage patterns and optimize their spending.
With the help of machine learning algorithms such as the Hybrid Cloud Optimized Cost (HCOC) workload scheduling algorithm and the Reserved Instance Optimizer, organizations can automate their cost optimization processes and make more informed decisions about their cloud spending. By leveraging the power of AI and machine learning, organizations can reduce costs, increase efficiency, and make the most of their cloud resources.
