This article will share key highlights about
• Microservices Architecture
• Performance Testing benefits
• Tools Used for Performance Analysis
• Google Cloud Offerings with Best Practices
• Overcoming a few challenges during adoption and Indium success stories

Microservices Architecture and Performance Testing Benefits

Microservice architecture refers to a method of software development in which a large software application is decomposed into several independently deployable services. Each service represents a specific business feature or domain that can be developed, deployed, and scaled independently. The mode of communication will be through well-defined APIs that make use of transport protocols such as HTTP or messaging queue systems.

By breaking down a monolithic application into smaller, specialised services, microservice architecture offers several benefits:

• Scalability: Microservices allow individual services to be scaled independently based on their specific resource requirements. This scalability enables applications to handle varying workloads and accommodate increased traffic and user demands.
• Flexibility and Agility: Microservices facilitate rapid development and deployment by enabling teams to work independently on different services. Each service can be developed, tested, and deployed separately, allowing for faster iteration and continuous delivery of new features and updates.
• Fault Isolation: In a monolithic application, a single bug or issue can impact the entire system. A microservices architecture isolates services from each other, minimising the impact of failures.
• Technology Diversity: Microservices allow for the use of different technologies and programming languages for different services. This flexibility allows teams to choose the most suitable tools and technologies for each service, depending on their specific requirements and expertise.

Performance testing plays a critical role in ensuring the effectiveness and reliability of microservice architecture. Here’s why performance testing is essential in this context:

A Glimpse at Performance Testing Tools for Micro Services

Some of the popular Load Testing tools are mentioned below.

• Apache JMeter
• Locust
• Gatling
• ReadyAPI
• Postman (a recent release has included Load testing features)

Some of the popular Monitoring tools are mentioned below.

• AppDynamics APM Tool
• Dynatrace APM Tool
• New Relic APM Tool
• Nagios, ELK Stack, and Grafana (Open-Sourced)

Indium has well-trained specialists and core expertise in using the above tools. Please refer to this link to learn more about Indium’s Offerings for Performance Testing and engineering.

Core Google Cloud Services for Micro Services Performance Testing

Best Practices for Adopting Google Cloud for Microservices

Challenges and Mitigation during the Google Cloud adoption process

During the adoption process of Google Cloud’s microservices architecture, organizations may encounter specific challenges. Here are a few common challenges and ways they can be overcome:

1. Migration Complexity:

Migrating existing monolithic applications to a microservices architecture on Google Cloud can be complex. It involves breaking down the monolith into smaller services and redesigning the application architecture. This process requires careful planning and coordination.

Overcoming the Challenge:

• Conduct a thorough analysis of the existing application to identify service boundaries and dependencies.
• Utilize tools and frameworks like Google Kubernetes Engine (GKE) and Istio for managing and orchestrating microservices.
• Gradually migrate services to the microservices architecture, starting with less critical components, and incrementally move towards a fully distributed system.
• Employ testing methodologies, such as canary deployments and A/B testing, to ensure a smooth transition and minimize disruptions.

2. Operational Complexity:

Operating and managing a microservices architecture can be challenging, especially when dealing with multiple services, deployments, and dependencies. Ensuring high availability, monitoring, and fault tolerance across the distributed system requires robust operational practices.

Overcoming the Challenge:

• Leverage Google Cloud’s managed services, such as GKE, to simplify the management of microservices infrastructure.
• Implement observability practices using tools like Cloud Monitoring and Logging to gain visibility into the performance and health of microservices.
• Employ automated deployment and scaling mechanisms, such as Kubernetes Horizontal Pod Autoscaler (HPA) and Google Cloud’s Load Balancing, to handle fluctuating workloads.
• Establish robust incident management and alerting processes to address issues promptly and minimize downtime.

3. Data Management and Consistency:

Microservices architecture often involves distributed data management, which introduces challenges in maintaining data consistency, synchronisation, and managing transactions across services.

Overcoming the Challenge:

• Utilise appropriate data storage solutions provided by Google Cloud, such as Cloud Firestore, Cloud Spanner, or Cloud Bigtable, depending on the specific requirements of each microservice.
• Implement event-driven architectures and message queues, such as Cloud Pub/Sub, for asynchronous communication and eventual consistency between services.
• Employ data replication and synchronisation techniques, such as Change Data Capture (CDC), to ensure data integrity and consistency across services.
• Implements transactional patterns like the Saga Pattern or two-phase commits when strong consistency is required across multiple microservices.

4. Security and Access Control:

Securing microservices and managing access control across the distributed system can be challenging due to the increased complexity of the architecture and the need to protect sensitive data and communication channels.

Overcoming the Challenge:

• Employ Google Cloud Identity and Access Management (IAM) to manage access control and permissions for different microservices.
• Implement secure communication channels using encryption protocols like SSL or TLS.
• Utilise Google Cloud’s security services, such as Cloud Security Command Centre and Cloud Armour, to monitor and protect against security threats.
• Implement security best practises like input validation, secure coding practises, and regular vulnerability assessments to mitigate risks.

Indium also has a detailed cloud adoption framework that can be used by small and large firms. The Cloud Maturity Assessment model helps us determine where we are in our cloud journey and what strategies to undertake moving forward. Kindly refer to the link to learn more about it.

Success Stories

For testing the performance of microservices, many organisations have used Google Cloud. Here are a few examples of how Indium has successfully adopted Google Cloud services, which have made “Happy Customers“.

Conclusion

In summary, performance testing is crucial in a microservices architecture to validate scalability, assess service interactions, evaluate load balancing strategies, ensure resilience and failure handling, and optimise resource utilisation. It helps identify performance bottlenecks, improve system reliability, and deliver a smooth and responsive user experience in complex, distributed environments.

The post Microservices Performance Testing using Google Cloud appeared first on Indium.

Let’s look at a few reasons why modernizing a data warehouse is essential and highlight the benefits it brings.

Traditional data warehouses have served organizations well for many years. These systems typically involve batch processing, where data is extracted from various sources, transformed, and loaded into the warehouse periodically. While this approach has been effective for historical analysis and reporting, it falls short when it comes to real-time decision-making. With the rise of technologies like the Internet of Things (IoT), social media, and streaming data, organizations require access to up-to-the-minute insights to gain a competitive edge.

Why Modernize a Data Warehouse?

Modernizing a data warehouse is crucial for several reasons. First and foremost, it enables organizations to harness the power of real-time data. By integrating data from multiple sources in real-time, businesses can gain immediate visibility into their operations, customer behavior, market trends, and more. This empowers decision-makers to respond quickly to changing circumstances and make data-driven decisions that drive growth and efficiency.

Moreover, modernizing a data warehouse enhances scalability and agility. Traditional data warehouses often struggle to handle the increasing volumes and varieties of data generated today. However, by adopting modern technologies like cloud computing and distributed processing, organizations can scale their data warehousing infrastructure as needed, accommodating growing data volumes seamlessly. This flexibility allows businesses to adapt to evolving data requirements and stay ahead of the competition.

The Need for Modernizing a Data Warehouse

Evolving Business Landscape: The business landscape is experiencing a significant shift, with organizations relying more than ever on real-time insights for strategic decision-making. Modernizing your data warehouse enables you to harness the power of real-time data, empowering stakeholders with up-to-the-minute information and giving your business a competitive edge.

Enhanced Agility and Scalability: Traditional data warehouses often struggle to accommodate the growing volume, velocity, and variety of data. By modernizing, organizations can leverage scalable cloud-based solutions that offer unparalleled flexibility, allowing for the seamless integration of diverse data sources, accommodating fluctuations in demand, and enabling faster time-to-insight.

Accelerated Decision-Making: Making informed decisions swiftly can mean the difference between seizing opportunities and missing them. A modernized data warehouse empowers organizations with real-time analytics capabilities; enabling stakeholders to access and analyze data in near real-time. This empowers them to make quick decisions swiftly, leading to better outcomes and increased operational efficiency.

Benefits of Modernizing a Data Warehouse

Real-Time Decision-Making: Modernizing a data warehouse enables organizations to make timely decisions based on the most up-to-date information. For example, an e-commerce company can leverage real-time data on customer browsing behavior and purchasing patterns to personalize recommendations and optimize marketing campaigns in the moment.

Enhanced Customer Experience: By analyzing real-time data from various touchpoints, organizations can gain deeper insights into customer preferences and behaviors. This knowledge can drive personalized interactions, targeted promotions, and improved customer satisfaction. For instance, a retail chain can use real-time data to optimize inventory levels and ensure products are available when and where customers need them.

Operational Efficiency: Real-time data management allows organizations to monitor key performance indicators (KPIs) and operational metrics in real-time. This enables proactive decision-making, rapid issue identification, and effective resource allocation. For example, a logistics company can leverage real-time data to optimize route planning, reduce delivery times, and minimize fuel consumption.

Wrapping Up

Modernizing a data warehouse is no longer an option but a necessity in today’s data-driven landscape. By adopting real-time data management, organizations can unlock the power of timely insights, enabling faster and more informed decision-making. The benefits extend beyond operational efficiency to include improved customer experience, enhanced competitiveness, and the ability to seize new opportunities as they arise. As technology continues to advance, organizations must prioritize data warehouse modernization to stay agile, remain relevant, and  flourish in a world that is increasingly centered around data.

The post Driving Business Success with Real-Time Data: Modernizing Your Data Warehouse appeared first on Indium.

The 1-Click Deployment Framework for Mendix applications on public cloud(s) simplifies and accelerates the deployment process. With just a single click, you can seamlessly deploy your Mendix applications onto public cloud platforms, unlocking the benefits of scalability, reliability, and cost-efficiency. This framework eliminates the complexities of traditional deployment methods and empowers organizations to launch their Mendix applications quickly and efficiently on the public cloud, enabling faster time-to-market and enhanced agility. Experience the ease and convenience of deploying your Mendix applications with a single click on the public cloud.

Let’s look at a use case and the remedy:

• Mendix MPC customers are unable to employ a flexible custom build process. The Mendix native build pipeline does not let clients implement their own build process because Mendix MPC maintains total control over CI/CD.
• The customer won’t have any control over the application, infrastructure, or security in Mendix MPC. They are forced to pick and choose which security features to use.

Solution:

• Deploying a Mendix application in any public cloud provides one-click deployment, total control over the infrastructure, high availability, and built-in security features. The one-click deployment framework for Indium is reliable and has been tested across multiple clouds with minimal to no adjustments.
• With the most flexible and secure cloud computing environment currently available, such as AWS/Azure/GCP, this architecture gives you the control and assurance you need to safely manage your organization.
• You can become more adept at upholding fundamental security and compliance standards, such as those relating to data localization, protection, and confidentiality, with the help of public clouds.

The rigidity of this structure was examined in this blog post using AWS, the current market leader in public cloud adoption. We can see how the customer has the freedom to choose the infrastructure and the application to be deployed thanks to the powerful integration of the trio Jenkins, Mendix, and AWS.

How to use our own framework to deploy the Mendix application.

1. Set up a VPC with two availability zones and private and public subnets.

2. To secure the nodes and application while preventing external connections, private subnets were created for Kubernetes nodes.

3. We can utilize CloudWatch and Grafana for log monitoring.

4. Configuring Jenkins to automate the CI/CD pipeline.

5. Integrating Jenkins with the Mendix team server.

6. Create a Docker image using the Mendix Docker file and our application code.

7. Upload the Docker image to artefacts like the Docker Hub, ECR, or ACR.

8. Create YAML Scripts to deploy the application. These scripts pass parameters like the database host name and password and the Mendix admin password as secrets using a secrets manager.

9. Using YAML, deploy the docker image in EKS and get the saved images from the artefacts.

10. For high availability and dependability, use EKS’s load balancer, replica sets, and autoscaling.

Also read: How to Secure an AWS Environment with Multiple Accounts

Architectural Overview:

Jenkins begins downloading code from Team servers after a developer clicks a single button, using Mendix docker files and source code to create a docker image that is then used to deploy in Elastic Kubernetes Service in AWS. 

Benefits of Mendix Application Deployment on Public Cloud

 1. Giving the client the ability to take charge of the CI/CD process.

2. The isolated Kubernetes environment allows users to create and administer their own cloud Virtual Private Cloud (VPC), with the potential to increase security.

3. The application auto-scales loads based on traffic and is highly accessible.

4. Logs are simple to monitor, and setting warnings for high CPU usage is simple. 

Conclusion

In conclusion, the 1-Click Deployment Framework for Mendix applications on public cloud(s) revolutionizes the way organizations deploy their applications. By simplifying the deployment process and providing a seamless experience, this framework empowers businesses to leverage the scalability and reliability of the public cloud. With just a single click, organizations can effortlessly launch their Mendix applications, accelerating time-to-market and driving business agility. Embrace the power of 1-Click Deployment and unlock the full potential of your Mendix applications on the public cloud.

The post 1 Click Deployment Framework for Mendix Application on Public Cloud(s) appeared first on Indium.

Introduction:

AMQP stands for Advanced Message Queuing Protocol, and it is an open standard application layer protocol. AMQP Message Protocol also deals with publishers and subscribers for the consumer.

One of the key features of AMQP is the message broker, which acts as an intermediary between sender and receiver. The broker receives messages from senders, stores them, and delivers them to their intended recipients based on predefined routing rules. The broker provides a range of features such as message persistence, message acknowledgment, and message prioritisation to ensure reliable and efficient message delivery. 

Several industries, including telecommunications, healthcare, and financial services, use AMQP. It has been widely adopted as a messaging protocol due to its reliability, interoperability, and flexibility.

Now there are four different exchange types:

• Direct Exchange
• Fan Out Exchange
• Topic Exchange and
• Header Exchange

Direct Exchange:

A direct exchange works by matching the routing key, when there is a match, the message is delivered to the queue. Each message sent to a direct exchange must have a routing key. 

If the routing key match, the message can be forwarded to the queue of the message.

For example, suppose there are three nodes named node A, node B, and node C, and a direct exchange named X. If node A is connected to X with a routing key of “key 1”, node B is connected to X with a routing key of “key 2”, and node C is connected to X with a routing key of “key 3”, then when a message is sent to X with a routing key of “key 2”, the message will be routed to node B.

Fan Out Exchange:

A fanout exchange works by sending messages to all of its bound queues. When a message is sent to a fanout exchange, the exchange simply copies it and sends it to all the currently bound queues.

For example, A real-time example of a Fanout Exchange can be a social media platform where a user posts a message that needs to be sent to all the users.

Topic Exchange:

When a message is sent to a topic exchange, the exchange will forward the message to all the queues. If queues have a binding key that matches the routing key, the message is routed to that queue, and finally each customer will receive the message from the queue.

Header Exchange:

A header exchange works by allowing the sender to attach a set of header attributes to each message. The header exchange looks at the headers and compares them to the header values specified in the bindings of each queue. If there is a match between the header of the message and the bindings of a queue, the message is delivered to that queue.       

Also read: Internet of Things in the Automotive Industry Blog.

Advantages of AMQP:

Message orientation, queuing, routing (including publish and subscribe and point-to-point), dependability, and security are the characteristics that set AMQP apart.

It employs techniques to ensure the secure transmission of critical data.

Flexibility:

AMQP includes publisher and subscriber request responses among the many message patterns it supports and point-to-point messaging, which makes it suitable for a variety of business use cases.

These services are provided using AMQP:

Healthcare services:

AMQP can be used to transmit medical data from wearable and implantable devices to healthcare providers, enabling remote monitoring and personalised treatment. It can be used to transmit patient data, test results, and other medical information securely and in real time. By using AMQP, healthcare providers can establish a reliable and secure communication channel to exchange data and messages between different services. The transfer of patient information among various healthcare providers, including hospitals, clinics, and laboratories

Financial services:

AMQP can be used to build reliable and secure messaging systems for financial institutions, including stock exchanges, banks, and trading platforms. It can be used to transmit market data, trade orders, and other financial information securely and efficiently. By using AMQP, financial services providers can improve the speed and efficiency of their communication systems and reduce the risk of delays or errors.

Internet of Things (IoT) services:

the AMQP protocol is designed for reliable, interoperable, and secure communication between different components of distributed applications, including Internet of Things (IoT) devices.

Device-to-cloud communication:

The AMQP protocol enables IoT devices to transmit messages to cloud services for further processing and analysis. For instance, a temperature sensor can utilise AMQP to transmit temperature readings to a cloud-based analytics service.

Overall, AMQP provides a flexible and scalable messaging infrastructure that can support various IoT services, from simple device-to-cloud communication to complex event processing and analytics.

Security:

AMQP provides a range of security features, such as authentication and encryption, to protect messages and prevent unauthorised access.

Conclusion

AMQP is a powerful messaging protocol that enables different applications to communicate with each other reliably, securely, and flexibly. With its client-server architecture and components such as a broker, exchange, queue, producer, and consumer, AMQP provides a robust framework for message-oriented middleware.

The post Seamless Communication: Exploring the Advanced Message Queuing Protocol (AMQP) appeared first on Indium.

Since businesses have to pay based on use, it lowers the overall cost of development as well. The charges may be in 100-millisecond increments because of its dynamic, real-time, and precise provisioning. Scaling is automated, and based on demand and growth in user base. Servers start up and end as needed. Serverless infrastructure does not need code to be uploaded to servers or any backend to be configured for releasing a working version of the application. As a result, developers can release new products quickly by uploading bits of code, the complete code, or one function at a time. Developers can also push updates, and make patches, fixes, and new feature additions quickly.

The code can run from anywhere and on any server close to the end user since it does not have to be hosted on an origin server. This approach reduces latency.

To learn more about our services and solutions, please click here and our expert team will contact you.

Snowflake Goes Serverless

Snowflake’s Data Cloud provides Software-as-a-Service (SaaS)-based data storage, processing, and analytic solutions. It is built on a new, natively designed SQL query engine with innovative architecture that provides novel features and unique capabilities over and above the traditional functionality of an enterprise analytics database.

Getting Started with Snowflake Serverless Architecture

All you need to do is sign up for a Snowflake account. Upload data and run queries without planning for capacity, provisioning the servers, or assessing the number of Snowflake instances you will need. Just one is enough. Snowflake manages all the needs automatically, without manual intervention. With increasing usage, Snowflake storage also auto-scales based on the need. This ensures that you have enough disk space. Server maintenance is also taken care of by Snowflake, which prevents and manages disk and server failures.

Serverless Task

One of these features is Serverless Tasks, where Snowflake provides a fully-managed serverless compute model for tasks, freeing developers of the responsibility of managing virtual warehouses. Based on the workload needs, the compute resources resize and scale up or down automatically. The ideal size of the compute resources for a workload is calculated based on past runs of the same task using a dynamic statistical analysis, with a provision equivalent to an XXLARGE warehouse, if required. Common compute resources are shared by multiple workloads in the customer account. The only requirement is for the user to specify the option for enabling the serverless compute model when creating a task. The syntax for creating a task, CREATE TASK, is similar to that in virtual warehouses managed by the user.

Read this amazing blog on: Chatbots play an integral role in e-commerce, allowing businesses to…

Serverless Credit Usage

Serverless credit usage emanates from features depending on the compute resources provided by Snowflake and is not a user-managed virtual warehouse. These compute resources are automatically resized and scaled up or down, as required, by Snowflake.

This is an efficient model as users pay based on the duration for which the resources are used for these features to run. In user-managed virtual warehouses, users pay for running them even when idle and sometimes end up over-utilizing resources. This can prove to be costly.

Snowflake offers transparent billing for serverless compute resources as the cost of each line item is given and the charges are calculated based on total resource usage, measured based on compute-hours credit usage. The rate of credits consumed per compute hour depends on the serverless feature.

Snowflake’s Serverless Features

Snowflake offers the following managed compute resources:

Automatic Clustering

Each clustered table background maintenance is automated, including clustering initially and reclustering as required.

External Tables

The external table metadata is automatically refreshed with the latest set of associated files in the external stage and path.

Materialized Views

Background synchronization for each materialized view is automated and changes made to the base table for viewing.

Query Acceleration Service

Portions of eligible queries are executed using Snowflake-managed compute resources.

Replication

Data copying between accounts, including the initial replication and maintenance as required, is automated.

Search Optimization Service

Background maintenance of the search optimization service’s search access paths is automated.

Snowpipe

File loading requests processing for each pipe object is automated.

Tasks

SQL code execution is given access to Snowflake-managed compute resources.

For businesses seeking to reduce release time cycles, to improve efficiency and productivity, to cut down on development costs, and to gain competitive advantage, Snowflake Serverless Architecture is an ideal solution.

Indium Software, a rapidly growing technology services company, helps businesses and developers take advantage of Snowflake’s serverless solution to optimize resource utilization while minimizing costs. Our team of solution providers combine cross-domain expertise with technical skills and experience across Cloud Engineering, DevOps, Application Engineering, Data and Analytics, and Digital Assurance. We provide bespoke solutions to help businesses latest technologies and improve delivery cycles.

If you’d like to speed up time to market by leveraging Snowflake serverless architecture, contact Indium now for designing and implementing the solution, contact us by click this link:

FAQs

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One of the key factors driving the success of the cloud application is the use of containers, which are light, fast, and portable, unlike virtual machines. They improve the testability of the applications, are more secure, and allow workloads to be isolated inside cost-effective clusters. This helps with faster development and deployment to meet the ever-changing needs of the customers.

To leverage containers successfully, developers need a managed container platform such as Kubernetes from Google. Kubernetes enables the building of customized platforms that align with the organization’s governance needs regarding project creation, nodes being used, and the libraries and repositories that can be tapped by providing a governed and secure framework.

Kubernetes refers to an open-source model that helps create and scale reliable apps and services in a secure environment and adds value through innovation using standardized plugins and extensions. This is expected to help the global Kubernetes solutions market grow from USD 1747.20 million in 2021 at a CAGR of 17.70% to reach USD 5467.40 million by 2028.

Automating Scaling and Deployment

Kubernetes or K8s automate the deployment and management of cloud-native applications on public cloud platforms or on-premises infrastructure and orchestrate containerized applications to run on a cluster of hosts. Some of the functions of Kubernetes include:

• Distributing application workloads across a Kubernetes cluster
• Automating dynamic container networking needs
• Allocating storage and persistent volumes to running containers
• Enabling automatic scaling
• Maintaining the desired state of applications
• Providing resiliency

Kubernetes Architecture

The applications are encapsulated in the containers in a portable form, which makes it easy to deploy. The Kubernetes architecture is made up of clusters that include a minimum of one control plane and one worker node and are designed to run containerized applications.

The control plane’s responsibilities include exposing the Kubernetes API through the API server and managing the nodes contained in the cluster. It manages the cluster and identifies and responds to cluster events.

Kubernetes Pod is the smallest unit of execution for an application in Kubernetes, has one or more containers, and runs on worker nodes.

Kubernetes allows two kinds of scaling:

Horizontal Scaling: Horizontal Pod Autoscaler allows the adding of new nodes or increasing the replicated count of pods to the existing cluster of computing resources. The number of pods needed is calculated based on the metrics specified at the outset such as CPU and RAM consumption or other custom metrics.

Vertical Scaling: In this, attributed resources are modified for each node in the cluster, and the resource requests and limits are adjusted as per current application requirements using Vertical Pod Autoscaler.

Container Orchestration: Kubernetes container lifecycle management encompassing provisioning, deployment, scaling, networking, and load balancing is enabled through orchestration. This automates the tasks essential for running containerized workloads and services.

Kubernetes Features

Some of the key features of K8 that enable orchestrating containers across multiple hosts, automating cluster management, and optimizing resource utilization include:

Auto-scaling: It enables the automated scaling up and down of containerized applications and their resources based on need.

Lifecycle Management. It enables automated deployments and updates, rollback to earlier versions, pausing or continuing a deployment, and so on.

Declarative Model: When the desired state is declared, K8s maintain that state and recover in case of failures by working in the background.

Self-healing and Resilience: Application self-healing is made possible by automated placement, restart, replication, and scaling.

Persistent Storage: Storage can be mounted and added dynamically.

Load Balancing: Several types of internal and external load balancing is supported for diverse needs.

DevSecOps Support: Kubernetes facilitates DevSecOps to improve developer productivity, simplify and automate container operations across clouds, and integrate security through the container life cycle.

Some of the key benefits of Kubernetes include:

• Faster time to release by simplifying the development lifecycle
• Cost-effectiveness through automatic modulation of resource allocation
• Making applications scalable and available

Advantage Indium

Indium Software is a cutting-edge cloud engineering company with a team of experts that can help with the migration and modernization of applications. Developing microservices and containerizing applications is one of our strengths. We are a Kubernetes solution provider, working closely with our customers and developing cloud-native applications and modernizing apps using the Kubernetes platform.

Our DevSecOps expertise further helps us to leverage Kubernetes for faster development and deployment of applications with security integrated into the process. Our experts analyze and understand the business needs and facilitate smooth management of clusters for scaling up and down based on the need for greater availability at lower costs.

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