2026-09-22
Optimizing Data Centre Infrastructure for the AI Era
The growth of AI and other compute-intensive applications is driving higher infrastructure densities and placing new demands on power, cooling, networking and physical space. At the same time, data centre environments are becoming increasingly heterogeneous, combining different generations of servers, accelerators, storage systems, networking technologies, cooling architectures and distributed facilities.
These developments are changing the nature of data centre planning.
This paper outlines how Arctos Labs ECO can be applied to optimizing data centres to better meet business objectives.
2026-04-17
Elastic and Smarter use of Infrastructure – service delivery for digital demand & response
In today’s dynamic world of IT, it is significantly more challenging to achieve cost-efficient, high-performing infrastructure operations that support the needs of the applications running on them. In short, aligning response to demand while taking constraints at any point in time becomes challenging every day. This paper explores two complementary mechanisms to address these circumstances: – Elastic and Smart use of infrastructure. – Infrastructure capacity analysis. The paper outlines the joint effort of Arctos Labs ECO decision optimization and partner Lytn with its prediction technology in achieving an overarching layer of smart governance for dynamic demand–response.
2025-10-09
The Orchestration Blueprint for Scalable Private Networks
Mobile Private Networks (MPNs) offer a strategic opportunity for Communication Service Providers (CSPs) to support enterprise digitalization. It is expected that MPN spend will reach US$ 21 billion by 2030. However, CSPs face a dual challenge: first, they must scale deployments efficiently to serve a growing number of enterprise customers without driving up operational complexity or cost. Second, they need to meet diverse, often stringent customer-specific requirements, such as ultra-low latency, heightened security, high availability, and industry-specific compliance. Balancing these demands requires more than just traditional deployment methods — it calls for a flexible, intelligent automation strategy that can optimize resource usage, adapt to varying network conditions, and deliver tailored solutions at scale — all while meeting performance, latency, and security constraints and maintaining operational agility. Read this White Paper to understand how Arctos Labs has partnered with FNT Software and Inmanta to enable such an Orchestration Blueprint
2024-12-13
Solution brief: Multi-cloud Kubernetes Optimization from Taikun and Arctos Labs
The rising adoption of Kubernetes across multi-cloud environments spanning from hyperscalers to edge and private clouds unlocks a lot of flexibility, but also opens the door to unnecessary spending or low quality of service. Used right (optimally), multi-cloud technologies, such as the leading solution from Taikun, Cloudworks, are very powerful. Through this solution, Taikun and Arctos Labs add a delivery optimization feature to Taikun’s portfolio of Taikun that makes it possible to deliver Kubernetes-based clusters and applications with less effort and at lower costs.
2024-06-07
Policy Management & Optimization for The Edge and Multi-Cloud
With a more diverse set of services and infrastructure, the need for powerful tools to tailor and control those services increases. This paper introduces the recently added Open Policy Agent-based capabilities into our portfolio of Edge Cloud Optimization technologies. The paper outlines the concept of rule-based policies as well as optimization policies and how Arctos Labs is applying those and has extended the technologies to make them more powerful. The paper describes how Policy Management & Optimization are applied in the process of orchestrating services.
2023-12-15
Solution brief: Intelligent orchestration of mobile private networks
Mobile private networks (MPNs) are increasingly seen as an enabler for enterprise digitalization. Therefore, MPNs represent a huge business opportunity for communication service providers (CSPs). However, CSPs face the challenge of:
- achieving economies of scale and cost efficiency, while….
- still requiring flexibility to tailor MPN deployments to customer-specific requirements, such as QoS and security.
Together with Inmanta, Arctos Labs has built an advanced and intelligent automation solution to achieve the desired efficiency and flexibility at scale. This enables CSPs to fully automate the optimal deployment of individual MPN components and applications from multiple vendors across on-prem, telco cloud, and public cloud. With this solution, we are not just automating repetitive tasks for component deployment; we are adding substantial intelligence into where to place those components
2023-06-21
Towards a more efficient and easier to use edge computing
Edge computing is increasingly applied. Initial use cases tend to be static with respect to which applications should be placed in which edge locations. When additional and more complex use cases emerge, and cloud/telco providers increase their presence with more locations, there needs to be more advanced orchestration capabilities to harvest potential performance gains, minimize costs, reduce their CO2 footprint, or improve resilience by intelligently placing application components across the edge-to-cloud continuum. This paper argues that the way forward is to introduce an intent-based concept that centres around the application and its required infrastructure characteristics, complemented by cost metrics and a placement engine that can match such intents to the specific infrastructure that is available.
2023-04-03
Orchestration and optimization in industrial IoT (Internet of things)
This paper outlines how multi-domain orchestration and optimization capabilities can contribute to increased availability and reliability of industrial IoT systems by making operational capabilities more autonomous. By adding intelligence and optimization into software deployment and failure remediation on a holistic and multi-domain layer, orchestration and optimization capabilities will enable operational teams to be more responsive and achieve higher availability and reliability of their industrial IoT systems. These capabilities further help enterprises to manage their set of digital information technology (IT) / operational technology (OT) assets, explore more data-driven operations, and achieve a higher level of integration of IT and OT systems towards Industry 4.0 visions.
2023-03-07
Dynamic placement optimization in edge computing scenarios can enable significant capex and opex savings, new research indicates
Edge computing typically reduces the amount of data sent upwards in networks, thereby contributing to less energy being consumed. There is, however, a risk that smaller, distributed, and heterogeneous edge infrastructure will come with lower and varying utilization, leading to lower efficiency. To better understand these topics, Arctos Labs, Wind River, and RISE (Research Institutes of Sweden) have conducted research with the aim of better understanding: Where is the most resource-efficient location to place a specific workload at a particular time in a dynamic edge-to-cloud continuum with a fleet of edge locations?
2022-05-31
On the relation between distance and latency
With the arrival of edge computing, more and more applications are relying on a tight latency budget and, more importantly, on shorter latencies. This does not mean that all application components need to be as close as possible, but rather that there is a relation between latency and application experience/functionality or similar characteristics. This paper contains an examination of the relation between geographical distance and latency. As one can imagine, for shorter distances, the latency variation becomes greater as it becomes more dependent on the detours needed to travel through the physical data network infrastructure topology. But at what distance does it start to matter? Read the Paper to find out
2020-09-02
Going green @ the edge: Cost modelling of Edge compute
Data is increasingly fuelling the world to become increasingly digital, through technologies related to video, 5G, artificial intelligence, VR/AR, etc being explored. To support all these new services, edge compute is promoted. Edge compute introduces cost issues that need to be considered as input for business decisions to build or use a distributed set of clouds. Economy-of-scale aspects may make smaller data centres less efficient. It is also well known that greenhouse gas emissions from data centres are significant and on the rise, whereby efficiency becomes increasingly important. Also, the cost of data transport is already high and is contributing to double-digit figures of global electricity consumption. Those figures can only be expected to increase as the world becomes more dependent on data. These two trends raise the question of whether it is more efficient to move around data or whether less data transfer would justify a potentially less efficient data centre closer to the user.
2019-10-23
Placement of Workloads in Edge & Cloud Networks
Edge compute promises to deliver cloud computing capabilities for tomorrow’s applications and an exploding amount of data. Networks with edge compute are complex, and the risk of underutilization and unprofitability is prominent. This is because of the delicate balance between proximity to users to achieve performance vs the scale to enable stable statistical utilization. This paper introduces Arctos Labs placement technology to enable workload placement optimization that can continuously assess the optimal distribution of workload components across an edge-to-central cloud continuum in a closed-loop concept.