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Load balancing is the process of distributing network traffic across two or more instances of a workload. IT teams use load balancing to ensure each instance performs at peak efficiency, without any one instance becoming overburdened or failing due to excess network traffic.

Traditionally, a load balancer exists in a local data center as a dedicated physical network appliance. However, load balancing is more frequently performed by an application installed on a server and offered as a network service. Public cloud providers use the service paradigm and provide software-based load balancers as a distinct feature.

Once a load balancer is implemented, it acts as a network front end and often uses a single IP address to receive all network traffic intended for the target workload. The load balancer can evenly distribute the network traffic to each available workload instance, or it can throttle traffic to send specific percentages of traffic to each instance.

With a load balancer, the target workloads can be in different physical places. Cloud load balancing provides similar benefits that enable users to distribute network traffic across multiple instances within the same region or across multiple regions or availability zones.

Load balancing is defined by the layer that the network traffic is handled by based on the traditional seven-layer Open Systems Interconnection network model. Each layer corresponds to specific traffic types. Cloud load balancing is most commonly performed at Layer 4 (transport or connection layer) or Layer 7 (application layer).

For example, AWS' Network Load Balancer service operates at Layer 4 to direct data from transport layer protocols, including Transmission Control Protocol (TCP), User Datagram Protocol (UDP) and Transport Layer Security (TLS). Google Cloud Platform (GCP) refers to this as TCP/UDP Load Balancing, while Microsoft calls its Layer 4 service Azure Load Balancer. Since traffic is handled at a lower level of the network stack, Layer 4 load balancing provides the best performance. Cloud load-balancing services can handle millions of network requests per second and ensure low latencies. They are, therefore, great options for erratic or unpredictable network traffic patterns.

At the top of the network stack, Layer 7 handles more complex traffic, such as HTTP and HTTPS requests. Each of the major cloud providers has its own feature or service for this:

Since this traffic is much higher up the network stack, IT teams can implement more advanced options, such as content- or request-based routing decisions. This type of cloud load balancing works well with modern application instances and architectures, including microservices and container-based workloads.

The choice of a cloud load balancer should extend beyond traffic types alone. Cloud providers also differentiate load-balancing services based on scope and framework. For example, GCP suggests global load-balancing services when workloads are distributed across multiple regions, while regional load-balancing services are a good fit when all workloads are in the same region. Similarly, GCP suggests external load balancers when traffic is coming into the workloads from the internet and internal load balancers when traffic is intended for use within GCP.

Be sure to consider the broader suite of features and capabilities available with cloud load-balancing services. In particular, features can include support for a single front-end IP address, support for automatic workload scaling, and integration with other cloud services, such as monitoring and alerting.

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What are the different types of cloud load balancing? - TechTarget

In the thick of the pandemic,TechHQcovered the story of theunsung heroesof the tech world data center workers.

Stringent lockdown measures have impacted the daily workflow of various businesses, and only key essential workers are given the green light to offices and other facilities.

Ambiguity arises when contract data center workers are not given the same pass for movement. Yet these operators and contractors are the front liners when it comes to maintaining and keeping data centers running.

In this light, the significant role of data centers is highlighted more than ever, and some trends are spotted to take shape due to the unique challenges brought upon by the pandemic.

To understand the impact of COVID-19 and remote working on the evolution of the data centers,TechHQinterviewed Lenovo DCGs APAC Director for Software Defined Infrastructure, Kumara Raghavan.

In the past, data center administrators would have to schedule downtime during the weekends and be on standby to do upgrades and updates when the power users were using the mainstream applications. Raghavan added those were the norms by and large, especially if you have a quality hyper converged infrastructure and software combination.

In todays climate, recurring themes Raghavan noted were automation and self-managing tech in data centers that minimize the reliance of human workers being physically present all the time.

We have administrators who do their firmware updates while doing their shopping its a one click upgrade.

Thats just an example of the various levels of automation that has crept into the data centers, he said.

Besides that, for companies with hybrid cloud, they are able to preset the provisioning for servers and applications when a spike occurs. Citing it as a result of automation and scripting, Raghavan shared that this is the reality for companies with a high degree of resilience due to the increasing level of automation baked into their data centers.

Furthermore, Raghavan shared that companies are focused on balancing the workload of data servers by diversifying their traffic through multiple data centers across specific geographical regions. In light of the pandemic, another consideration for companies would be to fulfill the demand of a rise in number of employees working from home.

These may be some of the general trends that are observed, but the decision and evolving role of data center management are dependent on a companys available resources, budgets, purposes, and essentially, experience with the cloud and data centers.

Raghavan explained there are two kinds of behaviors that are driving the way companies manage their data centers in the current environment.

For companies that are still at the early stages of digital transformation, they are most likely to pivot as they face the drastic surge in work from home capacity requirements.

The heightened demand includes the need for extra bandwidth, more compute power or storage but without change to existing budgets. Hence, companies have to rethink how to best cater to these growing needs without completely blowing out on their cash reserves.

Once, they have planned their own transformation, I think its easy for them to move capacity and that is one of the facilities that come from the principle of the hybrid cloud, Raghavan shared.

As for companies that didnt have the full capacity or are inadequately prepared for upheaval times of the pandemic, opening up capability on public clouds seems a viable solution. Raghavan noted companies aiming for want an immediate fix to their problems and are looking to public cloud.

Despite the spike of interest in public cloud as a temporary relief, Raghavan said we dont see this as a permanent move.

I think the underlying concerns of why everything has not shifted to public cloud, contrary to some expectations, havent really gone away, he said.

Some of the considerations impeding mass migration to public cloud relate to the control of data and costs. In the short term, a monthly bill, as opposed to a single capital expenditure, may be a more feasible option for companies but in the long run, the cost proves to be quite expensive.

Raghavan also pointed out the aspect of latency in public cloud as a factor to the trend not taking off, especially in todays environment that there is so much demand on the network.

In sum, we see some temporary increase in the public cloud utilization, but in reality, customers are now thinking through accelerating their digital transformation [] and we see that they have better management of the workflow.

In essence, the management of data centers is noted to be influenced by emerging technologies such as the cloud, edge, and artificial intelligence (AI).

Raghavan shared that the AI tools related to common management will gain prominence.

Besides that, edge devices have the potential to help companies monitor and manage different zones of data centers. For instance, companies can change the settings of data center cooling based on the volume of workload, potentially saving huge amounts of costs spend on cooling and driving efficiency. The key is to try and reduce the dependency on humans when it comes to intervening and that happens within typical servers, said Raghavan.

Adding to the mix, self-managing technologies and predictive technologies will be major players in the space. Cutting-edge technologies will be able to monitor a range of parameters of drives. For instance, algorithms can track the performance of disks and predict with a fair amount of accuracy in the malfunction of it. By doing so, data center administrators are alerted of possible failures and are able to act in a swift manner, minimizing disruption.

In sum, the utilization of AI tools in data centers may not be scaled completely and widely in the discussed level but it does indicate the direction data center management is heading.

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How data centers will become automated and self-reliant - TechHQ

Portworx, the California container storage startup, today issued a so-called momentum release, boasting of customer and revenue growth.

As usual with US startups, the company does not mention actual figures but by any reckoning it is a small fish in the data storage world annual revenues according to this possibly out-of-date estimate are $14m. However, Portworxs bullishness is an indicator that the container storage market could shaping up into serious money.

Portworx today said it had more than 145 customers, including 54 Global 2000 or government accounts. It reports 136 per cent growth in revenue year over year in Q1 2020, and 92 per cent revenue growth from Q4 2019. Thirteen sales were over $100,000, up from five sales over $100,000 in Q1 2019.

A 2019 Portworx survey showed 87 per cent of respondents said that they used container technologies, compared with 80 per cent in 2018 and 55 per cent in 2017.

Almost Ninety per cent of enterprises are already running container technology and more than half the containers they run are stateful, according to the 451 Research survey: Voice of the Enterprise DevOps, Security, AI/ML, and Cloud Native 2020. And those customers need storage.

Portworx emerged from stealth in 2015 and has bagged $55.5m funding over three rounds. Its software runs on commodity servers and aggregates their storage into a virtual SAN providing scale-out block storage. It provides storage from this pool for containers, at container granularity, and with a global namespace. File and object storage are on its roadmap.

Portworxs pitch is that the storage supplied to containers through an orchestration layer like Kubernetes should be containerised itself and also enterprise class, with features like security, data protection, backup and recovery, disaster recovery, SLA management, and compliance.

It says traditional enterprise storage, with the features, is suited to virtual server environments but not cloud-native ones, even if they Kubernetes CSI plug-ins. Storage provision for containers has to be supplied at the speed and scale of container instantiation, deployment and removal. Portworx claims that only cloud-native storage, its cloud-native storage, can meet this need not legacy SANs.

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Portworx upbeat on container storage revenues Blocks and Files - Blocks and Files