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Load Balancing

by Blog

Load Balancing

In today's digital age, where websites and applications are expected to be fast, efficient, and reliable, load balancing has emerged as a critical component of modern computing infrastructure. Load balancing significantly ensures that server resources are utilized optimally, maximizing performance and preventing system failures. This blog post will explore the concept of load balancing, its benefits, and its various techniques.

Load balancing evenly distributes incoming network traffic across multiple servers to avoid overburdening any single server. By dynamically allocating client requests, load balancers help ensure that no single server becomes overwhelmed, enhancing the overall performance and availability of the system. This distribution of traffic also helps maintain seamless user experiences during peak usage periods.

Load balancing, at its core, involves distributing incoming network traffic across multiple servers or resources to prevent any single component from becoming overwhelmed. By intelligently managing the workload, load balancing improves resource utilization, enhances scalability, and provides fault tolerance. Whether it's a website, a cloud service, or a complex network infrastructure, load balancing acts as a vital foundation for seamless operations.

Round Robin: The Round Robin method evenly distributes traffic across available servers in a cyclic manner. It ensures that each server gets an equal share of requests, promoting fairness and preventing any single server from being overloaded.

Least Connection: The Least Connection approach directs incoming requests to the server with the fewest active connections. This strategy helps balance the load by distributing traffic based on the current workload of each server, ensuring a more even distribution of requests.

Weighted Round Robin: Weighted Round Robin assigns different weights to servers based on their capacity and performance. Servers with higher weights receive a larger proportion of traffic, allowing for efficient utilization of resources and optimal performance.

Improved Performance: Load balancing ensures that servers or resources are not overwhelmed with excessive traffic, resulting in improved response times and faster processing of requests. This leads to an enhanced user experience and increased customer satisfaction.

Scalability and Flexibility: Load balancing allows for easy scaling of resources by adding or removing servers based on demand. It provides the flexibility to adapt quickly to changing workload conditions, ensuring efficient resource allocation and optimal performance.

High Availability and Fault Tolerance:By distributing traffic across multiple servers, load balancing enhances fault tolerance and minimizes the impact of server failures. If one server becomes unavailable, the load balancer redirects traffic to the remaining servers, ensuring uninterrupted service availability.

Conclusion: Load balancing is a critical component of modern computing, enabling businesses to achieve optimal performance, scalability, and high availability. By intelligently managing network traffic, load balancing ensures efficient resource utilization and enhances the overall user experience. Whether it's a small website or a large-scale cloud infrastructure, implementing a robust load balancing solution is crucial for maintaining seamless operations in today's digital landscape.

Matt Conran

Highlights: Load Balancing

In computing, you’ll do something similar. You receive many requests on your website, which puts a lot of strain on it. There’s nothing unusual about having a website. If no one visits your website, there is no point in having one.

You run into problems when your server is overloaded with people turning on their appliances. At this point, things can go wrong; if too many people visit your site, your performance will suffer. Slowly, as the number of users increases, it will become unusable. That’s not what you wanted.

The solution to this problem lies in more resources. The choice between scaling up and scaling out depends on whether you want to replace your current server with a larger one or add another smaller one.

The scaling-up process

Scaling up is quite common when an application needs more power. The database may be too large to fit in memory, the disks are full, or that more requests are causing the database to require more processing power.

Scaling up is generally easy because databases have historically had severe problems when run on multiple computers. If you try to make things work on various machines, they fail. What is the best method for sharing tables between machines? This problem has led to the development of several new databases, such as MongoDB and CouchDB.

However, it can be pretty expensive to scale up. A server’s price usually increases when you reach a particular specification. A new type of processor (that looks and performs like the previous one but costs much more than the old one) comes with this machine, a high-spec RAID controller, and enterprise-grade disks. Scaling up might be cheaper than scaling out if you upgrade components, but you’ll most likely get less bang for your buck this way. Nevertheless, if you need a couple of extra gigabytes of RAM or more disk space, or if you just want to boost the performance of a particular program, this might be the best option.

Scaling Out

Scaling out refers to having more than one machine. Scaling up has the disadvantage that you eventually reach an insurmountable limit. A single machine can’t hold all the processing power and memory it needs. If you need more, what happens?

If you have a lot of visitors, people will say you’re in an envious position if a single machine can’t handle the load. As strange as it may sound, this is a good problem! Scaling out means you can add machines as you go. You’ll run out of space and power at some point, but scaling out will undoubtedly provide more computing power than scaling up.

Scaling out also means having more machines. Therefore, if one machine fails, other machines can still carry the load. Whenever you scale up, if one machine fails, it affects everything else.

There is one big problem with scaling out. You have three machines and a single cohesive website or web application. How can you make the three machines work together to give the impression of one machine? It’s all about load balancing!

Finally, load balancing

Now, let’s get back to load balancing. The biggest challenge in load balancing is making many resources appear as one. How can you make three servers look and feel like a single website to the customer?

How does the Web work?

This journey begins with an examination of how the Web functions. Under the covers of your browser, what happens when you click Go? The book goes into great detail, even briefly discussing the TCP (Transmission Control Protocol) layer. While someone might be able to make an awe-inspiring web application, they may not be as familiar with the lower-level details that make it all function. Fortunately, this isn’t an issue since kickass software doesn’t require knowledge of the Internet’s inner workings. It would be best if you had a much better understanding of how it works to make your software scream past the competition quickly.

Lack of Visibility

Existing service provider challenges include a lack of network visibility into customer traffic. They are often unaware of the granular details of traffic profiles, leading them to over-provision bandwidth and link resilience. There are a vast amount of over-provisioned networks. Upgrades at a packet and optical layer occur without complete traffic visibility and justification. Many core networks are left at half capacity, just in a spike. Money is wasted on underutilization that could be spent on product and service innovation. You might need the analytical information for many reasons, not just bandwidth provisioning. 

Network Analytics 

Popular network analytic capability tools are sFlow and NetFlow. Nodes capture and send sFlow information to a sFlow collector, where the operator can analyze it with the sFlow collector’s graphing and analytical tools. An additional tool that can be used is a centralized SDN controller, such as an SD-WAN Overlay, that can analyze the results and make necessary changes to the network programmatically. A centralized global viewpoint enabling load balancing can aid in intelligent multi-domain Traffic Engineering (TE) decisions.

Before you proceed, you may find the following posts of interest:

  1. Transport SDN
  2. What Does SDN Mean
  3. Load Balancer Scaling
  4. Network Traffic Engineering
  5. Application Delivery Architecture



Load Balancing.

Key Load Balancing Discussion Points:


  • Introduction to Load Balancing and how it can be used.

  •  Discussion on traffic forwarding and traffic engineering.

  • Discussion on distributed routing protocols.

  • Highlighting Northstar SD-WAN controller. Use case.

  • A final note on network topologies.

Back to basics with load balancing

One use case for load balancers to solve is availability. At some stage in time, machine failure happens. This is 100%. Therefore, you should avoid single points of failure whenever feasible. This signifies that machines should have replicas. In the case of front-end web servers, there should be at least two. When you have replicas of servers, a machine loss is not a total failure of your application. Therefore, your customer should notice as little during a machine failure event as possible.

Benefits of Load Balancing:

Load balancing offers several critical benefits in modern computing:

1. Improved Scalability: By distributing client requests across multiple servers, load balancers enable systems to handle increased traffic without compromising performance. This scalability is essential for websites and applications that experience high user demand or rapid growth.

2. Enhanced Reliability: Load balancers provide fault tolerance by automatically rerouting traffic to healthy servers in case of failures or outages. This redundancy ensures that users can access the system even if individual servers go offline, minimizing service disruptions.

3. Optimal Resource Utilization: By evenly distributing traffic, load balancers ensure that server resources are utilized efficiently. This results in better overall system performance and cost-effectiveness, as organizations can make the most of their server infrastructure without unnecessary overprovisioning.

4. Geographic Load Balancing: With the increasing globalization of internet services, load balancers can distribute traffic based on geographic locations. This approach helps reduce latency and provides users faster response times, irrespective of location.

Load Balancing Techniques:

Several load-balancing techniques are commonly used in modern computing environments. These include:

1. Round Robin: In this method, requests are distributed sequentially to each server in a cyclic manner. Although simple, round-robin can be less effective if servers have different capabilities or workload capacities.

2. Least Connection: This technique directs client requests to the server with the fewest active connections. By dynamically considering server capacity, this method ensures that incoming traffic is distributed more evenly.

3. Weighted Round Robin: Weighted round robin assigns varying weights to servers based on their capabilities. Servers with higher capacities receive more traffic, enabling organizations to optimize resource utilization.

4. IP Hash: This technique consistently routes requests to the same server using the client’s IP address. IP hash helps maintain session persistence, ensuring that a client’s data remains on the same server throughout their session.

Load Balancing and Traffic Engineering

We need network traffic engineering for load balancing that allows packets to be forwarded over non-shortest paths. Tools such as Resource Reservation Protocol (RSVP) and Fast Re-Route (FRR) enhance the behavior of TE. IGP-based TE uses a distributed routing protocol to discover the topology and run algorithms to find the shortest path. MPLS/RSVP-TE enhances standard TE and allows more granular forwarding control and the ability to differentiate traffic types for CoS/QoS purposes.

Constrained Shortest Path First

The shortest path algorithm called Constrained Shortest Path First (CSPF) provides label switch paths (LSP) to take any available path in the network. The MPLS control plane is distributed and requires a distributed IGP and label allocation protocol. The question is whether a centralized controller can solve existing traffic engineering problems. It will undoubtedly make orchestrating a network more manageable.

The contents of a TED have IGP scope domain visibility. Specific applications for TE purposes require domain-wide visibility to make optimal TE decisions. The IETF has defined the Path Computation Element (PCE) used to compute end-to-end TE paths.

Link and TE attributes are shared with external components. Juniper’s SD-WAN product, NorthStar, adopts these technologies and promises network-wide visibility and enhanced TE capabilities. 

Load Balancing with NorthStar SD-WAN controller

NorthStar is a new SD-WAN product by Juniper aimed at Service Providers and large enterprises that follow the service provider model. It is geared for the extensive network that owns Layer 2 links. NorthStar is an SD-WAN Path Computation Engine (PCE), defined in RFC 5440, that learns network state by Path Computation Element Protocol (PCEP).

It provides centralized control for path computation and TE purposes, enabling you to run your network more optimally. In addition, NorthStar gives you a programmable network with global visibility. It allowed you to spot problems and deploy granular control over traffic.

load balancing

They provide a simulation environment where they learn about all the traffic flows on the network. This allows you to simulate what “might” happen in specific scenarios. With a centralized view of the network, they can optimize flows throughout it, enabling a perfectly engineered and optimized network.

The controller can find the extra and unused capacity, allowing the optimization of underutilized spots in the network. The analytics provided is helpful for forecasting and capacity planning. It has an offline capability, providing offline versions of your network with all its traffic flows.

It takes inputs from:

  1. The network determines the topology and views link attributes.
  2. Human operators.
  3. Requests by Northbound REST API.

These inputs decide TE capabilities and where to place TE LSP in the network. In addition, it can modify LSP and create new ones, optimizing the network traffic engineering capabilities.

Understand network topology

Traditional networks commonly run IGP and build topology tables. It can get overly complicated when you have a multi-area or multi-IGP running on the network. For network-wide visibility, NorthStar recommends BGP-LS. BGP-LS enables routers to export the contents of the TE database to BGP. It uses a new address family, allowing BGP to carry node and link attributes (metric, max amount of bandwidth, admin groups, and affinity bits) related to TE. BGP-LS can be used between different regions.

As its base is BGP, you can use scalable and high-availability features, such as route reflection, to design your BGP-LS network. While BGP is very scalable, its main advantage is reduced network complexity.

While NorthStar can peer with existing IGP (OSPF and ISIS), BGP-LS is preferred. Knowing the topology and attributes, the controller can set up LSP; for example, if you want a diverse LSP, it can perform a diverse LSP path computation. 

LSP & PCEP

There are three main types of LSPs in a NorthStar WAN-controlled network:

  1. A Vanilla-type LSP. It is a standard LSP, configured on the ingress router and signaled by RSVP.
  2. A delegated LSP is configured on the ingress router and then delegated to the controller, which is authorized to make changes to this LSP.
  3. The controller initiates the third LSP via a human GUI or Northbound API operation.

PCEP (Path Computation Elements Protocol) communicates between all nodes and the controller. It is used to set up and modify LSP and enable dynamic and inter-area, inter-domain traffic, and engineered path setup. It consists of two entities, PCE and PCC. Path Computation Client (PCC) and Path Computation Element (PCE) get established over TCP.

Once the session is established, PCE builds the topology database (TED) using the underlying IGP or BGP-LS. BGP-LS has enhanced TLV capabilities that have been added for PCE to learn and build this database. RSVP is still used to signal the LSP.

As the demand for fast and reliable web services grows, load balancing has become an essential component of modern computing infrastructure. By evenly distributing incoming network traffic across multiple servers, load balancers enhance scalability, reliability, and resource utilization. With various load-balancing techniques, organizations can choose the most suitable method to optimize their system’s performance and deliver an exceptional user experience. Embracing load balancing is vital for businesses seeking to stay competitive in today’s digital landscape.

 

Summary: Load Balancing

Load balancing, the art of distributing workloads across multiple resources, is critical in optimizing performance and ensuring seamless user experiences. In this blog post, we explored the concept of load balancing, its significance in modern computing, and various strategies for effective load balancing implementation.

Understanding Load Balancing

Load balancing is a technique employed in distributed systems to evenly distribute incoming requests across multiple servers, networks, or resources. Its primary goal is to prevent any single resource from becoming overwhelmed, thus improving overall system performance, availability, and reliability.

Types of Load Balancing Algorithms

There are several load-balancing algorithms, each with its strengths and use cases. Let’s delve into some popular ones:

1. Round Robin: This algorithm distributes incoming requests equally among available resources in a circular manner, ensuring each resource receives a fair share of the workload.

2. Least Connections: In this algorithm, incoming requests are directed to the resource with the fewest active connections, effectively balancing the load based on current utilization.

3. Weighted Round Robin: This algorithm assigns servers different weights, allowing for a proportional distribution of workloads based on their capabilities.

Load Balancing Strategies and Approaches

When implementing load balancing, it’s crucial to consider the specific requirements and characteristics of the system. Here are a few common strategies:

1. Server-Side Load Balancing: This approach involves dedicated hardware or software acting as an intermediary between client requests and servers, distributing the load based on predefined rules or algorithms.

2. DNS Load Balancing: By manipulating DNS responses, this strategy distributes incoming requests across multiple IP addresses associated with different servers, achieving load balancing at the DNS level.

3. Content-Aware Load Balancing: This advanced technique analyzes the content of incoming requests and directs them to the most appropriate server based on factors like geographic location, user preferences, or server capabilities.

Load Balancing Best Practices

Implementing load balancing effectively requires following some best practices:

1. Monitoring and Scaling: Regularly monitor the performance of resources and scale them up or down based on demand to ensure optimal load distribution.

2. Redundancy and Failover: Implement redundancy mechanisms and failover strategies to ensure high availability in case of resource failures or disruptions.

3. Security Considerations: Implement proper security measures to protect against potential threats or vulnerabilities from load-balancing configurations.

Conclusion:

Load balancing is a crucial aspect of modern computing, enabling efficient resource utilization, improved performance, and enhanced user experiences. By understanding the various load-balancing algorithms, strategies, and best practices, organizations can master the art of load-balancing and unlock the full potential of their distributed systems.

OpenStack written on the keyboard button

Openstack Architecture in Cloud Computing

by Blog

OpenStack Architecture in Cloud Computing

Cloud computing has revolutionized businesses' operations by providing flexible and scalable infrastructure for hosting applications and storing data. OpenStack, an open-source cloud computing platform, has gained significant popularity due to its robust architecture and comprehensive services.

In this blog post, we will explore the architecture of OpenStack and how it enables organizations to build and manage their own private or public clouds.

At its core, OpenStack comprises several interconnected components, each serving a specific purpose in the cloud infrastructure. The architecture follows a modular approach, allowing users to select and integrate the components that best fit their requirements.

OpenStack architecture is designed to be modular and scalable, allowing businesses to build and manage their own private or public clouds. At its core, OpenStack consists of several key components, including Nova, Neutron, Cinder, Glance, and Keystone. Each component serves a specific purpose, such as compute, networking, storage, image management, and identity management, respectively.

Matt Conran

Highlights: OpenStack Architecture in Cloud Computing

OpenStack: The cloud operation system

Cloud operating systems such as OpenStack are best viewed as public and private clouds, respectively. In this era of cloud computing, we are moving away from virtualization and software-defined networking (SDN). Any organization can build a cloud infrastructure using OpenStack without committing to a vendor. Despite being open source, OpenStack has the support of many heavyweights in the industry, such as Rackspace, Cisco, VMware, EMC, Dell, HP, Red Hat, and IBM. If a brand name acquires OpenStack, it won’t disappear overnight or lose its open-source status.

OpenStack is also an application and toolset that provides identity management, orchestration, and metering. Despite supporting several hypervisors, such as VMware ESXi, KVM, Xen, and Hyper-V, OpenStack is not a hypervisor. Thus, OpenStack does not replace these hypervisors; it is not a virtualization platform but a cloud management platform.

OpenStack is composed of many modular components, each of which is governed by a technical committee. OpenStack’s roadmap is determined by a board of directors driven by its community.

Openstack services

 

OpenStack Modularity

OpenStack is highly modular. Components provide specific services, such as instance management, image catalog management, network management, volume management, object storage, and identity management. A minimal OpenStack deployment can provision instances from images and connect them to networks. Identity management controls cloud access. Some clouds are only used for storage.

There is an object storage component and, again, an identity component. The OpenStack community does not refer to services by their functions, such as services, images, etc. Instead, these components are referred to by their nicknames. Server functions are officially called compute, but everyone calls them Nova. It’s pretty fitting since NASA co-founded OpenStack. Glance is the image service, Neutron is the network service, and Cinder is the volume service. Swift provides object storage, while Keystone includes identity management, which keeps everything together.

The role of decoupling

The key to cloud computing is decoupling virtual resources from physical ones. The ability to abstract processors, memory, etc., from the underlying hardware enables on-demand/elastic provisioning and increased efficiency. This abstraction process has driven the cloud and led to various popular cloud flavors such as IaaS – Infrastructure-as-as-Service, PaaS – Platform-as-as-Service, and SaaS – Software-as-as-Service, a base for OpenStack foundations.

The fundamentals have changed, and the emerging way of consuming I.T. ( compute, network, storage ) is the new “O.S.” for the data center in the cloud. The cloud cannot operate automatically and needs a management suite to control and deploy service-oriented infrastructures. Different companies deploy different teams that specialize only in managing cloud computing. Those without an in-house team get it outsourced by firms like Global Storage. 

SDN Abstraction

These platforms rely on a new networking architecture known as software-defined networking. Traditional networking relies on manual administration, and its culture is based on a manual approach. Networking gear is managed box by box, and administrators maintain singular physical network hardware and connectivity. SDN, on the other hand, abstracts the network.

The switching infrastructure may still contain physical switch components but is managed like one switch. The data plane is operated as an entire entity rather than a loosely coupled connected device. SDN approach is often regarded as a prerequisite and necessary foundation for scalable cloud computing.

SDN and OpenFlow

Related: You may find the following post of interest:

  1. OpenStack Neutron Security Groups
  2. OpenStack Neutron
  3. Network Security Components
  4. Hyperscale Networking



Openstack Architecture in Cloud Computing.

Key Openstack Architecture in Cloud Computing Discussion Points:


  • Introduction to OpenStack architecture in cloud computing and what is involved.

  • Highlighting the components of cloud computing.

  • Critical points on OpenStack foundations and operations.

  • Technical details on the use of APIs.

  • Technical details for the OpenStack deployment details.

Back to Basics: Cloud Adoption.

The adoption of cloud technology has transformed how companies run their IT services. By leveraging new strategies for resource use, several cloud solutions came into play with different categories: private, public, hybrid, and community.

OpenStack falls into the private cloud category. However, deploying OpenStack is still tricky, requiring a good understanding of its beneficial returns to a given organization regarding automation, orchestration, and flexibility.

The New Data Center Paradigm

n cloud computing, infrastructure services such as Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) are provided. Agility, speed, and self-service are the challenges the public cloud sets. Most companies have expensive IT systems, which they have developed and deployed over the years, but these systems are siloed and require human intervention. As public cloud services become more agile and faster, IT systems struggle to keep up. Today’s agile service delivery environment may make the traditional data center model and siloed infrastructure unsustainable. To achieve next-generation data center efficiency, enterprise data centers must focus on speed, flexibility, and automation.

Fully Automated Infrastructure

Admins and operators can deploy fully automated infrastructures with a software infrastructure within a minute. Next-generation data centers reduce infrastructure to a single, significant, agile, scalable, and automated unit. The result is an infrastructure that is programmable, scalable, and multi-tenant-aware. In this regard, OpenStack stands out as the next generation of data center operating systems. Several sizeable global cloud enterprises, such as VMware, Cisco, Juniper, IBM, Red Hat, Rackspace, PayPal, and eBay, have benefited from OpenStack. Many are running a private cloud based on OpenStack in their production environment. Your IT infrastructure should use OpenStack if you wish to be a part of an innovative, winning cloud company.

The main components of OpenStack are:

While different services cater to various needs, they follow a common theme in their design:

  • In OpenStack, Python is used to develop most services, making it easier for them to be developed rapidly.

  • REST APIs are available for all OpenStack services. The APIs are the primary communication interfaces for other services and end users.

  • Different components may be used to implement the OpenStack service. A message queue communicates between the service components and has several advantages, including queuing requests, loose coupling, and load distribution.

1. Nova: Nova is the compute service responsible for managing and provisioning virtual machines (VMs) and other instances. It provides an interface to control and automate the deployment of instances across multiple hypervisors.

2. Neutron: Neutron is a networking service that enables the creation and management of virtual networks within the cloud environment. It offers a range of networking options, including virtual routers, load balancers, and firewalls, allowing users to customize their network configurations.

3. Cinder: Cinder provides block storage to OpenStack instances. It allows users to create and manage persistent storage volumes, which can be attached to cases for data storage. Cinder supports various storage backends, including local disks and network-attached storage (NAS) devices.

4. Swift: Swift is an object storage service that provides scalable and durable storage for unstructured data. It enables users to store and retrieve large amounts of data, making it suitable for applications that require high scalability and fault tolerance.

5. Keystone: Keystone serves as the identity service for OpenStack, providing authentication and authorization mechanisms. It manages user credentials and assigns access rights to the various components and services within the cloud infrastructure.

6. Glance: Glance is an image service that enables users to discover, register, and retrieve virtual machine images. It provides a catalog of images that can be used to launch instances, making it easy to create and manage VM templates.

7. Horizon: Horizon is the web-based dashboard for OpenStack, providing a graphical user interface (GUI) for managing and monitoring the cloud infrastructure. It allows users to perform administrative tasks like launching instances, managing networks, and configuring security settings.

These components work together to provide a comprehensive cloud computing platform that offers scalability, high availability, and efficient resource management. OpenStack’s architecture is designed to be highly modular and extensible, allowing users to add or replace components per their specific requirements.

Additional Details on OpenStack Components

Keystone

Architecturally, Keystone is the most straightforward service in OpenStack. OpenStack’s core component provides an identity service that enables tenant authentication and authorization. By authorizing communication between OpenStack services, Keystone ensures that the correct user or service can access the requested OpenStack service. Keystone integrates with numerous authentication mechanisms, including usernames, passwords, tokens, and authentication-based systems. It can also be integrated with existing backends like Lightweight Directory Access Protocol (LDAP) and Pluggable Authentication Module (PAM).

Swift

Swift is one of the storage services that OpenStack users can use. REST APIs provide access to its object-based storage service. Object storage differs from traditional storage solutions, such as file shares and block-based access, in that it treats data as objects that can be stored and retrieved. An overview of Object Storage can be summarized as follows. In the Object Store, data is split into smaller chunks and stored in separate containers. A cluster of storage nodes maintains redundant copies of these containers to provide high availability, auto-recovery, and horizontal scalability.

Cinder

Another way to provide storage to OpenStack users may be to use the Cinder service. This service manages persistent block storage, which provides block-level storage for virtual machines. Virtual machines can use Cinder raw volumes as hard drives.

Some of the features that Cinder offers are as follows:

  • Volume management: This allows the creation or deletion of a volume

  • Snapshot management: This allows the creation or deletion of a snapshot of volumes

  • Attaching or detaching volumes from instances

  • Cloning volumes

  • Creating volumes from snapshots 

  • Copy of images to volumes and vice versa

Like Keystone services, Cinder features can be delivered by orchestrating various backend volume providers, such as IBM, NetApp, Nexenta, and VMware storage products, through configurable drivers.

Manila

As well as the blocks and objects we discussed in the previous section, OpenStack has had a file-share-based storage service called Manila since the Juno release. Storage is provided as a remote file system. Unlike Cinder, it is similar to the Storage Area Network (SAN) service as opposed to the Network File System (NFS) we use on Linux. The Manila service supports NFS, SAMBA, and CIFS as backend drivers. The Manila service orchestrates shares on the share servers.

Glance

An OpenStack user can launch a virtual machine from the Glance service based on images and metadata. Depending on the hypervisor, various image formats are supported. With Glance, you can access images for KVM/Qemu, XEN, VMware, Docker, etc.

When you’re new to OpenStack, you might wonder, What’s the difference between Glance and Swift? Both handle storage. How do they differ? What is the need for such a solution?

Swift is a storage system, whereas Glance is an image registry. In contrast, Glance keeps track of virtual machine images and their associated metadata. Metadata can include kernels, disk images, disk formats, etc. Glance uses REST APIs to make this information available to OpenStack users. Images can be stored in Glance utilizing a variety of backends. Directories are the default approach, but other methods, such as NFS and Swift, can be used in massive production environments.

In contrast, Swift is a storage system. This solution allows you to store data such as virtual disks, images, backup archiving, and more.

As an image registry, Glance serves as a resource for users. Glance focuses on an architectural approach to storing and querying image information via the Image Service API. In contrast, storage systems typically offer highly scalable and redundant data stores, whereas Glance allows users (or external services) to register virtual disk images. You, as a technical operator, must find the right storage solution at this level that is cost-effective and performs well.

OpenStack Features

    • Scalability and Elasticity

OpenStack’s architecture enables seamless scalability and elasticity, allowing businesses to allocate and manage resources dynamically based on their needs. By scaling up or down on demand, organizations can efficiently handle periods of high traffic and optimize resource utilization.

    • Multi-Tenancy and Isolation

One of OpenStack’s standout features is its robust multi-tenancy support, which enables the creation of isolated environments for different users or projects within a single infrastructure. This ensures enhanced security, privacy, and efficient resource allocation across various departments or clients.

    • Flexible Deployment Models

OpenStack offers a variety of deployment options, including private, public, and hybrid clouds. This flexibility allows businesses to choose the most suitable model based on their specific requirements, whether maintaining complete control over their infrastructure or leveraging the benefits of public cloud providers.

    • Comprehensive Service Catalog

With an extensive service catalog, OpenStack provides a wide range of services such as compute, storage, networking, and more. Users can quickly provision and manage these services through a unified dashboard, simplifying the management and deployment of complex infrastructure components.

    • Open and Vendor-Agnostic

OpenStack’s open-source nature ensures vendor-agnosticism, allowing organizations to choose hardware, software, and services from various vendors. This eliminates the risk of vendor lock-in and fosters a competitive market, driving innovation and cost-effectiveness.

OpenStack Architecture in Cloud Computing

OpenStack Fundations and Origins

OpenStack Foundations is a software platform for orchestrating and automating data center environments. It provides APIs enabling users to create virtual machines, network topologies, and scale applications to business requirements. It does not just let you control your cloud; you may make it available to customers for unique self-service and management.

It’s a collection of projects (each with a specific mission) to create a shared cloud infrastructure maintained by a community. It enables any organization type to build its public or private cloud stack. A key differentiator from OpenStack and other platforms is that it’s open-source, run by an independent community continually updating and reviewing publicly accessible information. The key to its adoption is that customers do not fear vendor lock-in.

The pluggable framework is supported by multiple vendors, allowing customers to move away from the continuous path of yearly software license renewal costs. There is real momentum behind it. The lead-up to OpenStack and cloud computing started with Amazon Web Service (AWS) in 2006. They offered a public IaaS and virtual instances with an API. However, there was no SLA or data guarantee, so research academies mainly used it.

NASA and Rackspace

Historically, OpenStack was founded by NASA and Rackspace. NASA was creating a project called Nebula, which was used for computing. Rackspace was involved in a storage project ( object storage platform ) called Cloud Files. The two projects mentioned above led to a community of collaborating developers working on open projects and components.

There are plenty of vendors behind it and across the entire I.T. stack. For servers, we have Dell and H.P.; Storage consists of NetApp and SolidFire; Networking has Cisco and Software with VMware and IBM.

Initially, OpenStack foundations started with three primary services: NOVA computer service, SWIFT storage service, and GLANCE virtual disk image service. Soon after, many additional services, such as network connectivity, were added. The initial implementations were simple, providing only basic networking via Linux Layer 2 VLANs and IPtables.

Now, with the Neutron networks, you can achieve a variety of advanced topologies and rich network policies. Most networking is based on tunneling ( GRE or VXLAN ). Tunnels are used within the hypervisor, so it fits nicely with multi-tenancy. Tunnels are created between the host over the Layer 3 network within the hypervisor. As a result, tenancy V.M.s can spin up where they want and communicate over the tunnel.

What is an API?

The application programming interface ( API ) is the engine under the cloud hood. The messenger takes requests, tells the systems what you want to do, and then returns the response to you—ultimately creating connectivity.

openstack foundations

Each core project (compute, network, etc.) will expose one or more HTTP/RESTful interfaces for public or managed access. This is known as a Northbound REST API. Northbound API faces some programming interfaces. It conceptualizes lower-level detail functions. Southbound faces the forwarding plane and allows components to communicate with a lower-level part.

For example, a southbound protocol could be OpenFlow or NETCONF. Northbound and southbound are software directions from the reference point of the network operating systems. We now have an East-West interface. At the time of writing, this protocol is not fully standardized, but eventually, it will be used to communicate between federations of controllers for state synchronization and high availability.

OpenStack Architecture: The Foundations

  1. OpenStack Compute – Nova is comparable to AWS EC2. She is used to provisioning instances for applications.
  2. OpenStack Storage – Swift is comparable to AWS S3. Provides object storage functions for application objects.
  3. OpenStack Storage – Cinder is comparable to AWS Elastic Block Storage. Provides persistent block storage functions for stateless instances.
  4. OpenStack Orchestration – Heat is comparable to AWS Cloud formation. Orchestrates deployment of cloud services
  5. OpenStack Networking—Neutron Network is comparable to AWS VPC and ELB. It creates networks, topologies, ports, and routers.

There are others, such as Identity, Image Service, Trove, Ceilometer, and Sahara.

Each OpenStack foundation component has an API that can be called from either CURL, Python, or CLI. CURL is a command-line tool that lets you send HTTP requests and receive responses. Python is a widely used programming language within the OpenStack ecosystem. It automates scripts to create and manage resources in your OpenStack cloud. Finally, command-line interfaces (CLI) can access and send requests to APIs.

OpenStack Architecture & Deployment

OpenStack has a very modular design, and the diagram below displays key OpenStack components. Logically, it can be divided into three groups: a) Control, b) Network, and c) Compute. All of the features use a database or a message bus. The database can either be MySQL, MariaDB, or PostgreSQL. The message bus can be RabbitMQ, Qpid, and ActiveMQ.

The messaging and database could run on the same control node for small or DevOps deployments but could be separated for redundancy. The cloud controller on the left consists of numerous components, which are often disaggregated into separate nodes. It is the logical interface to the cloud and provides the API service.

Openstack Deployment

The network controller includes the networking service Neutron. It offers an API for orchestrating network connectivity. Extension plugins provide additional network services such as VPNs, NAT, security firewalls, and load balancing. Generally, it is separate from the cloud controller, as traffic may flow through it. The compute nodes are the instances. This is where the application instances are deployed. 

Leverage vagrant 

Vagrant is a valuable tool for setting up Dev OpenStack environments to automate and build virtual machines ( with OpenStack ). It’s a wrapper around a virtualization platform, so you are not running the virtualization in Vagrant. The Vagrant V.M. gives you a pure environment to work with as it isolates dependencies from other V.M. applications. Nothing can interfere with the V.M., offering a full testing scope. An excellent place to start is Devstack. It’s the best tool for setting up small single-node non-production/testing installs.

Summary: OpenStack Architecture in Cloud Computing

In the fast-evolving world of cloud computing, OpenStack has emerged as a powerful open-source platform that enables efficient management and deployment of cloud infrastructure. Understanding the architecture of OpenStack is essential for developers, administrators, and cloud enthusiasts alike. This blog post delved into the various components and layers of OpenStack architecture, providing a comprehensive overview of its inner workings.

Section 1: OpenStack Components

OpenStack comprises several key components, each serving a specific purpose in the cloud infrastructure. These components include:

1. Nova (Compute Service): Nova is the heart of OpenStack, responsible for managing and provisioning virtual machines (VMs) and controlling compute resources.

2. Neutron (Networking Service): Neutron handles networking functionalities, providing virtual network services, routers, and load balancers.

3. Cinder (Block Storage Service): Cinder offers block storage capabilities, allowing users to attach and manage persistent storage volumes to their instances.

4. Swift (Object Storage Service): Swift provides scalable and durable object storage, ideal for storing large amounts of unstructured data.

Section 2: OpenStack Architecture Layers

The OpenStack architecture is structured into multiple layers, each playing a crucial role in the overall functioning of the platform. These layers include:

1. Infrastructure Layer: This layer comprises the physical hardware resources such as servers, storage devices, and switches that form the foundation of the cloud infrastructure.

2. Control Layer: The control layer comprises services that manage and orchestrate the infrastructure layer. It includes components like Nova, Neutron, and Cinder, which control and coordinate resource allocation and network connectivity.

3. Application Layer: At the topmost layer, the application layer consists of software applications and services that run on the OpenStack infrastructure. These can range from web applications to databases, all utilizing the underlying resources OpenStack provides.

Section 3: OpenStack Deployment Models

OpenStack offers various deployment models to cater to different needs and requirements. These models include:

1. Public Cloud: OpenStack is operated and managed by a third-party service provider in a public cloud deployment, offering cloud services to multiple organizations or individuals over the internet.

2. Private Cloud: A private cloud deployment involves setting up an OpenStack infrastructure exclusively for a single organization. It provides enhanced security and control over data and resources.

3. Hybrid Cloud: A hybrid cloud deployment combines both public and private clouds, allowing organizations to leverage the benefits of both models. This provides flexibility and scalability while ensuring data security and control.

Conclusion:

OpenStack architecture is a complex yet robust framework that powers cloud computing environments. Understanding its components, layers, and deployment models is crucial for effectively utilizing and managing OpenStack infrastructure. Whether you are a developer, administrator, or simply curious about cloud computing, exploring OpenStack architecture opens up a world of possibilities for building scalable and efficient cloud environments.