Cost Optimization Strategies on AWS for Telco BSS Workloads | AWS for Industries

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Communication service providers (CSPs) are evolving their IT frameworks for Business Support Systems (BSS) to capitalize on the promising economic benefits of 5G, IoT, Mobile Virtual Network Operator (MVNO), and Ultra Low Latency Gaming Applications. This evolution aims for operational agility, reduced time to market and operational costs, improved efficiency, and accelerated innovation. Each CSP’s cloud adoption journey is distinct—some initiate their journey with rehosting (lift-and-shift) while others focus on re-platforming or modernization. Regardless of the approach, an effective cloud cost optimization strategy is crucial to the modernization plan and the realization of monetization opportunities. The AWS Well-Architected Framework’s cost optimization pillar provides actionable guidance to operate systems that deliver business value. This enables customers to manage costs effectively and optimize spending continually while developing modern, scalable applications to meet customer demands. In this post, we explore the essential cost optimization strategies through the lens of telecom BSS.

Core Cost Optimization Strategies

Cost optimization is an ongoing endeavor, and CSPs’ Cloud Center of Excellence (CCoE) teams must implement strategies to consistently reduce costs. Regardless of migration strategy and architecture, five fundamental pillars apply across almost all environments. These include:

1. Right Sizing – Tailoring compute instances to align with the performance and capacity requirements of workloads at the lowest possible cost.
2. Increasing Elasticity – Dynamically adjusting resources to meet demand and deactivating them when not in use.
3. Leveraging Appropriate Pricing Models – Utilizing options such as Savings Plans, Spot Instances, and On-Demand pricing to tailor costs to the nature of the workload.
4. Optimizing Storage – Choosing the right storage solutions based on performance needs, such as utilizing Amazon Elastic Block Store (Amazon EBS) or the Amazon Simple Storage Service (Amazon S3) family for regulatory compliance.
5. Governance Mechanisms – Implementing strategies to manage and oversee these optimizations effectively.

Right Sizing

Right sizing involves matching instance types and sizes with the performance and capacity requirements of workloads while minimizing costs. It also entails reviewing deployed instances to identify opportunities for downsizing or decommissioning without sacrificing performance. Different telecom BSS components often necessitate varying instance types. For instance, rating and charging applications that utilize enterprise-class and in-memory databases have distinct requirements compared to compute-heavy billing applications.

Traditional on-premises telecom workloads are often designed for peak capacity, leading to oversizing when transitioning to AWS Cloud. Organizations may initially lift and shift their environments without proper right sizing. The result is frequently oversized instances and unnecessary expenditures on unused resources.

Additionally, it’s vital to right size database machines across various environments—Development, Testing, Pre-Production, and Production—as each has different SLA and performance needs. For example, in the US East region, using db.m4.2xlarge with a bring-your-own-enterprise license can save CSPs $522.50 USD monthly in a development environment. Tools like AWS Trusted Advisor can assist in identifying and stopping underutilized Amazon RDS instances.

Increase Elasticity

Elasticity refers to the capability to provision resources as applications demand them and relinquish resources when they are no longer necessary. BSS workloads require this flexibility to facilitate real-time rating and manage uneven billing cycles, especially with the advent of 5G digital services. CSPs should ensure their BSS systems, such as Configure Price Quote (CPQ) and Order Manager (OM), can scale efficiently in response to workload fluctuations.

To implement elasticity, CSPs must identify variable workloads and their operational limits. They should utilize AWS Autoscaling with services like Amazon Elastic Compute Cloud (Amazon EC2) and incorporate horizontal pod and cluster autoscaling for containerized applications. Scheduled scaling can also be effective for predictable workload changes, such as nightly builds or billing calculations. Once tasks are completed, resources can be shut down to minimize costs. CSPs should continually test elasticity to ensure it meets load variance requirements.

Leverage Right Pricing Model

AWS offers various pricing models that enable CSPs to pay for resources in the most cost-effective manner that aligns with their requirements. Selecting the appropriate pricing model is crucial for maximizing savings. For example, if CSPs’ BSS applications utilize container or serverless architecture, they can save up to 72% on Amazon EC2 and AWS Fargate when opting for AWS Savings Plans with a one- or three-year commitment. Furthermore, purchasing Reserved Instances (RIs) on Amazon RDS, Amazon Redshift, Amazon ElastiCache, and Amazon OpenSearch Service can yield savings of up to 72% compared to on-demand pricing.

Optimize Storage

CSPs must align their storage solutions with usage patterns, data types, access frequencies, and IOPS requirements. For instance, mediation systems may be designed to handle backlogs of Call Detailed Records (CDRs) while Order Capture applications might deal with infrequent large orders. Due to regulatory mandates, CSPs may also need to retain historical data for extended periods. Amazon S3 offers various storage classes tailored for different use cases, enabling CSPs to optimize storage costs effectively.

By implementing these cost optimization strategies, CSPs can take significant steps toward reducing overall operational expenditures while enhancing the performance of their BSS workloads.

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