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Insight driven resource management & scheduling
Future data center resource and workload managers – and their [distributed]schedulers – will require a new key integrate capability: analytics. Reason for this is the the pure scale and the complexity...
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Example 1: Intelligent Orchestration & Scheduling with Kubernetes
In the last blog I suggested that analytical capabilities need to move to the core of resource managers. This is very much needed for autonomous controlled large scale systems which figure out the...
View ArticleExample 2: Intelligent Orchestration & Scheduling with OpenLava
This is the second post in a series (the first post can be found here) about how to insert smarts into a resource manager. So let’s look how a job scheduler or distributed resource management system...
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Distributed systems: which cluster do I obey?
The topic of cluster formation in itself is not new. There are plenty of methods around to form cluster on the fly [1]. They mostly follow methods which make use of gossip protocols. Implementation can...
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Controlling a Mesos Framework
Note: This is purely for fun, and only representing early results. It is possible to combine more traditional scheduling and resource managers like OpenLava with DCOS like Mesos [1]. The basic...
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Q-Learning in python
There is a nice tutorial that explains how Q-Learning works here. The following python code implements the basic principals of Q-Learning: Let’s assume we have a state matrix defining how we can...
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Agent based bidding for merging graphs
There are multiple ways to merge two stitch two graphs together. Next to calculating all possible solutions or use evolutionarty algorithms bidding is a possible way. The nodes in the container, just...
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Tracing your functions
Note: this is mostly just for proof of concept – not necessarily something you want to do in a production system, but might be useful in a staging/test environment. Functions in a Serverless...
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Dancing links, algorithm X and the n-queens puzzle
Donald Knuth’s 24th annual Christmas lecture inspired me to implement algorithm X using dancing links myself. To demonstrate how this algorithms works, I choose the n-queens puzzle. This problem has...
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AI reasoning & planning
With the rise of faster compute hardware and acceleration technologies that drove Deep Learning, it is arguable that the AI winters are over. However Artificial Intelligence (AI) is not all about...
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AI planning algorithms in Rust
Artificial Intelligence (AI) is a hot topic, although it seams that the focus is on Neural Networks & Deep Learning on the software/algorithmic side, while GPUs/TPUs are the #1 topic for hardware....
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Write your functions in Rust – with Azure & Rocket
Rust is certainly one of the hot programming languages right now. Besides the cool feature set the language offers, companies – like Microsoft, Intel, Google, AWS, Facebook, etc. – embrace it:...
View ArticleIntent Driven Orchestration
So let’s start with a bolt statement: the introduction of Microservices/functions and Serverless deployment styles for cloud-native applications has triggered a need to shift the orchestration...
View ArticleYour personal AI LLM based agent
Trevor Noah made an interesting comment in one of hit recent podcast: You know, the new workforce becomes one where your GPT is almost your resume. Your GPT is almost more valuable than you are in a...
View ArticleSystem Effectiveness
In the world of distributed systems, performance has traditionally been the primary measure of success. Engineers have focused on optimizing for latency, throughput, reliability and scalability,...
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Power efficiency: throttling for Sustainability
The rise of AI applications has brought us soaring power demands and strains on the environment. Google reported a 13% emission increase thanks to AI, their footprint is huge, some power grids are...
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