Fair Share Scheduling Guide for System Admins

Fair share scheduling is a crucial resource management technique for system administrators. Here's what you need to know:

• Balances computer resources among users and jobs
• Considers current and past usage to allocate resources
• Prevents resource hogging and ensures equitable access

Key benefits:

• Improves system efficiency
• Reduces conflicts over resources
• Adapts to changing workloads

How it works:

1. Assigns resource shares to users or groups
2. Tracks recent usage history
3. Adjusts allocations based on usage patterns

Setting up fair share scheduling:

• Varies by system (Linux, HP-UX, AIX, Solaris)
• Requires careful configuration and monitoring
• Best practices include regular review and adjustment

Common pitfalls:

• Over-allocating resources
• Ignoring system overhead
• Misunderstanding shares vs. actual usage

Advanced techniques:

• Adjusting decay time windows
• Using compute reservations
• Combining with other scheduling types

Fair share scheduling is especially important for:

• Multi-user systems
• Cloud computing environments
• High-performance computing clusters

By mastering fair share scheduling, admins can optimize resource use, improve user satisfaction, and boost overall system performance.

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Basics of fair share scheduling

Fair share scheduling divides computer resources among users and jobs. It's like cutting a pie so everyone gets their slice, even if some are hungrier than others.

Main goals and ideas

Fair share scheduling balances workloads over time. It's not just about current resource use, but also recent usage.

It aims to:

• Give everyone a fair shot at resources
• Prevent resource hogging
• Balance short-term and long-term use

How CPU shares are given out

Fair share scheduling doesn't split CPU time equally. It uses a formula to determine resource allocation:

1. Check each user or group's intended share
2. Look at recent usage
3. Give more to those who've used less

For example, with four users (A, B, C, D) each due 25% CPU time, if B's been a heavy user lately, they might get less than 25% for a while so others can catch up.

How it's different from other scheduling

Fair share scheduling is unique because it considers more than just current usage. Here's a comparison:

Scheduling Type	How It Works	Main Focus
Round-Robin	Each process gets a turn	Current fairness
Priority-Based	High-priority tasks first	Task importance
Fair Share	Balances use over time	Long-term fairness

It's like a smart traffic cop who remembers who's been on the road a lot lately, not just who's driving now.

"The fair-share scheduler selects jobs dynamically to ensure no instance monopolizes the QPU, prioritizing groups that have used the least amount of their allocation."

This approach helps in:

• Multi-user systems
• Cloud computing
• High-performance computing

Setting up fair share scheduling

Here's how to set up fair share scheduling on different systems and avoid common pitfalls:

Setup steps for different systems

1. Linux with systemd

Enable the CPU controller in the user.slice cgroup. Then adjust cpu.weight values to set priorities.

2. HP-UX

Install HP's Process Resource Manager (PRM). Define resource pools and assign weights.

3. AIX

Set up IBM's Workload Manager (WLM). Create classes for users or processes.

4. Solaris

Configure Sun's System Resource Manager (SRM). Specify resource shares for groups.

Best ways to configure

To get the most out of fair share scheduling:

• Know your workloads before allocating shares
• Use reporting tools to track resource use
• Balance shares carefully (don't overdo it for important workloads)
• Choose between caps (limits) or guarantees

Common mistakes and how to avoid them

Mistake	How to Avoid
Over-allocating resources	Review and adjust shares regularly
Ignoring system overhead	Save resources for system processes
Not monitoring	Use built-in reporting tools often
Misunderstanding shares vs. utilization	Remember: shares ≠ actual usage

"Using fair share scheduling means organizing your system's work. This helps you understand workloads before you start tweaking performance."

Advanced fair share scheduling methods

Adjusting resource allocation

Want to boost your system's performance? Try these fair share scheduling tweaks:

• Tweak the weightFactor: Give certain share identifiers more juice. Set "blue" to 0.5, and it'll get double the resources of "yellow" at 1.0.
• Stretch the share decay time window: Got big differences in job counts or run times? A longer window can even things out.
• Use computeReservation: Set aside some capacity for inactive shares. It's like keeping a VIP lane open for urgent jobs.

Mixing with other scheduling types

Blend fair share with other scheduling flavors for better results:

Type	What you get
Real-time	Critical tasks get priority, but fairness stays intact
Time-sharing	Balances quick responses and fair resource sharing
Lottery	Adds a dash of randomness to prevent job starvation

Adjusting for different workloads

Different work needs different rules. Here's how to adapt:

1. GPU-heavy workloads

For Kubernetes with GPUs:

• Share GPU resources fairly, no matter when pods are created or their priority.
• Let new pods bump off long-running ones to keep things fair.

2. Mixed CPU and I/O workloads

• Check your data: Which tasks are hogging the CPU?
• Tweak your policies based on what you find.

3. Asynchronous GPU computation

• Use dynamic quotas based on estimated kernel burst time.
• Try a token-based time-sharing scheduler for managing GPU resources across PODs.

"Gemini's scheduler uses event-driven monitoring to collect GPU use data. This helps it adjust time slices for PODs running machine learning workloads on the fly."

Checking and managing fair share scheduling

Tools and commands for tracking

Want to keep an eye on your fair share scheduling? Here's what you need:

Use top -b -n 1 for a quick CPU and memory usage snapshot across users.

For a detailed CPU breakdown, try this:

top -b -n 1 | awk 'BEGIN{OFS="\t"} NR>7{sum[$2]+=$9; t+=$9;} END{print "user","%CPU"; for(u in sum){print u,sum[u]}print "total",t}'

Need memory usage too? Use:

top -b -n 1 | awk 'BEGIN{OFS="\t"} NR>7{sum[$2]+=$9; t+=$9; mem[$2]+=$10; tm+=$10} END{print "user","%CPU","%MEM"; for(u in sum){print u"\t"sum[u]"\t"mem[u];} print "total",t,tm}'

Understanding performance data

When you look at your fair share scheduling data, focus on:

1. CPU share distribution
2. Resource monopolization
3. Utilization vs. shares

Fixing common problems

Spotted an issue? Try these:

1. Adjust weightFactors for uneven resource use
2. Set minimum CPU shares to prevent starvation
3. Use Linux cgroups for better CPU performance balancing

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Fair share scheduling in virtual environments

Virtual environments make fair share scheduling tricky. Here's how to tackle it for VMs, containers, and cloud setups.

VMs and containers

The Linux kernel's Completely Fair Scheduler (CFS) doesn't cut it for multicore systems with VMs and containers.

A virtual runtime-based task migration algorithm can help:

• It caps the max virtual runtime difference among tasks
• In tests, it hit a 50.53 time unit max virtual runtime difference
• Only added 0.14% more run-time overhead than CFS

For containers, use Kubernetes' Resource Quota to stop resource hogging. Set CPU and memory limits per namespace.

Multi-user systems

Multi-user setups often hit performance snags due to workload spikes. Here's what to do:

1. Use Windows System Resource Manager (WSRM) for CPU control

2. Set up WSRM:

• Open WSRM.msc
• Find "Weighted_Remote_Sessions" policy
• Group users by priority (premium, standard, basic)
• Save and set as managing policy

3. Keep an eye on resources with tools like Prometheus

Cloud and mixed setups

For cloud and hybrid environments:

1. Right-size resources: Set limits based on actual needs. Example:

Payment service:
Requests: 100m CPU, 100Mi memory
Limits: 200m CPU, 200Mi memory

2. Use autoscaling: In Kubernetes, Horizontal Pod Autoscaling can scale up pods when demand spikes.

3. Monitor constantly: Use Prometheus to track resource use and app performance.

4. Fair share policies: In AWS Batch, adjust the weightFactor to prioritize workloads.

5. Reserve resources: Use computeReservation in AWS Batch for urgent jobs.

Real examples of fair share scheduling

Successful setups

AWS Batch users have seen great results with fair share scheduling. Here's how:

One company split resources evenly between two share IDs: yellow and blue. They submitted 500 yellow jobs, then added blue jobs. The scheduler immediately favored blue jobs, showing fair allocation at work.

Another customer tweaked their setup:

• They set blue's weightFactor to 0.5
• Result? Blue jobs got double the resources of yellow
• This meant blue jobs finished faster, even though submitted later

These changes let the company prioritize urgent work while keeping things fair.

Lessons from big setups

Large-scale use reveals key insights:

1. Balancing act

A major university allocated "fair shares" of CPU time over a semester. This stopped resource hogging before project deadlines.

2. Group scheduling works

Linux's Completely Fair Scheduler (CFS) proved great for HTTP servers. It shared virtual runtime across task groups, ensuring fair allocation between the server and its tasks.

3. Flexibility matters

AWS Batch users learned to use shareDecayInSeconds. This helps fine-tune how the scheduler calculates usage over time.

Performance before and after

Real-world data shows the impact:

Metric	Before	After	Change
Resource use	60%	85%	+25%
Urgent task completion	2 hours	45 mins	-62.5%
Slowdown complaints	20/week	3/week	-85%

A retail company saw these gains after setting up fair share policies on AWS Batch. They created three shareIdentifiers with 25% capacity each, allowing any workload to use up to 50% when needed.

Even The Gap saw benefits from fair scheduling in stores:

• Store productivity went up
• Sales revenue increased
• Labor costs went down

This shows fair scheduling isn't just for IT - it can boost real-world business too.

What's next for fair share scheduling

AI's impact on scheduling

AI is shaking up fair share scheduling. It's using machine learning to crunch big data and spot resource usage patterns. This helps admins make smarter calls on how to dish out CPU time and other resources.

What can AI do? It can:

• Guess when usage will spike
• Figure out the best way to split up shares
• Flag weird resource use

This means scheduling can be way more flexible and responsive.

Making fair share better

Fair share scheduling is set to level up in a few key areas:

1. On-the-fly tweaks

Right now, most systems use fixed share setups. But future schedulers might change shares in real-time based on:

• What's running
• Time of day
• How busy the system is

2. Juggling multiple resources

Most fair share systems are all about CPU time. But next-gen schedulers will likely balance more:

Resource	Why it matters
CPU	Boosts overall speed
Memory	Less swapping and paging
I/O	Faster disk and network
GPU	Better AI/ML workload spread

3. Playing nice with the cloud

As more stuff moves to the cloud, fair share scheduling needs to work across:

• Different cloud providers
• Mixed on-site and cloud setups
• Container-based systems

Gearing up for the future

To get ready for what's coming, system admins should:

1. Keep an eye on AI and machine learning in IT ops
2. Try out new scheduling tricks in test environments
3. Gather detailed info on resource use to feed future AI schedulers
4. Get to know how fair share works in cloud and container setups

"Fair share scheduling is evolving to handle complex, ever-changing computing environments", says Dr. Jane Chen from MIT. "Admins who roll with these changes will be pros at maximizing resource use down the road."

Wrap-up: Getting good at fair share scheduling

Fair share scheduling is crucial for managing IT resources. It gives users and jobs equal CPU time, unlike first-come, first-served methods. The system dishes out CPU shares based on rules, not queue order.

Here's the deal:

• Fair share policies balance resource use
• The scheduler checks current and recent job usage
• Higher usage now means less resources later

To level up your fair share scheduling:

1. Review and adjust often

Keep an eye on workloads and user needs. Change things up when needed.

2. Use monitoring tools

Track performance data to spot where you can do better.

3. Tweak key settings

Setting	What it does	How to adjust
shareDecaySeconds	Sets usage history window	Increase for big job differences
weightFactor	Gives preference to certain shares	Adjust based on needs
computeReservation	Holds capacity for urgent jobs	Set small for efficiency

4. Learn from others

Check out what works in similar setups.

Why it matters for IT:

• Stops resource fights
• Gives everyone a fair shot
• Boosts system efficiency

"Fair share scheduling isn't one-size-fits-all. It needs careful design for special jobs, like those using GPUs or lots of memory", says a Czech National Grid Infrastructure MetaCentrum researcher.

Get good at this, and you'll:

• Cut down on wasted time and delays
• Keep customers happy
• Help your org grow

Remember: Fair share scheduling never stops. Be ready to solve problems and keep tweaking as your org changes.

FAQs

What is fair share scheduling?

Fair share scheduling divides CPU resources equally among users or groups, not processes. It's all about making sure everyone gets their fair slice of the CPU pie in multi-user setups.

Here's the gist:

• Balances resources between users or groups
• Adapts to workload changes on the fly
• Stops resource hogs in their tracks

Let's break it down:

Scenario	CPU Distribution
4 users, 1 process each	25% CPU time per user
User B adds 2nd process	User B still gets 25% total (12.5% per process)
New user joins	20% for each of the 5 users

This is different from process-based scheduling, where each process might get an equal share, no matter who started it.

How does it handle different user groups?

Fair share scheduling can work on multiple levels:

1. Among groups

2. Among users within groups

3. Among processes for each user

This layered approach allows for smarter resource allocation, especially in complex setups with varying priorities.

How's it different from other scheduling methods?

Unlike first-come, first-served or round-robin, fair share looks at past usage. If you've been hogging the CPU, your processes might take a back seat for a bit to even things out.

Any downsides?

Fair share scheduling isn't a one-size-fits-all solution. It might not be the best fit for:

• Real-time systems with tight deadlines
• Massive-scale systems needing more complex scheduling

How do I set it up?

It depends on your system. Linux users have the Completely Fair Scheduler (CFS) built right into the kernel. Other systems might need some tweaking or extra tools.

Always check your system's docs for the nitty-gritty on setting up and fine-tuning fair share scheduling.

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