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Cloud Computing Architecture > L8 - Cluster Management > Flashcards

L8 - Cluster Management Flashcards

1
Q

What is resource allocation?

A

how much CPU/DRAM/disk/net to allocate to each app

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2
Q

What is resource assignment?

A

What should run on which physical nodes?

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3
Q

What is private resource allocation? What is its other name?

A

each app receives a private, static set of resources

static partitioning

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4
Q

Advantages of static partitioning?

A
  1. simplicity
  2. performance isolation
  3. allows specialised HW (e.g. not everyone needs a GPU)
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5
Q

Disadvantages of static partitioning?

A
  1. low utilisation
  2. hard to solve failures
  3. hard to maintain

about 2&3: not clear how to migrate a machine

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6
Q

What 3 properties do we want the scheduler to fulfil in case of shared resource assignment?

A
  1. Fairness
  2. Efficient resource usage
  3. Isolation
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7
Q

List the algorithm from the lecture for shared resource assignment.

A
  1. Fair queueing (extends 1) (for a single resource)
  2. Weighted max-min fair queueing (extends 2)
  3. Dominant resource fairness
  4. Token bucket
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8
Q

What does work conserving mean? Which property implies that?

A

Resources should not remain idle while there are users whose demand is not fully satisfied.

This is implied by “Efficient resource usage”.

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9
Q

Why do we want work conserving schedulers?

A

It keeps resources well-utilised.

It maximises overall throughput across different users.

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10
Q

Name a strategy that is not work conserving.

A

time division multiplexing

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11
Q

What are the different notions of fairness?

A
  1. Max-min fairness
  2. Dominant resource fairness
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12
Q

What are the properties of max-min fairness?

A

share guarantee: each user gets at least 1/n of the unless their demand is less

strategy-proof: users are not better off by asking for more than they need

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13
Q

What does DRF try to achieve?

A

identify the dominant resource share of each user and maximise the minimum dominant share across all users

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14
Q

What is the drawback of DRF?

A

not work conserving

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15
Q

What is the issue with max-min fairness?

A

With max-min fairness, a user’s allocation depends on the demands of other users that are sharing the resource. –> no performance predictability

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16
Q

What is the goal of token buckets?

A

guarantee a baseline bandwidth, but also allow bounded bursts

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17
Q

How does the token bucket idea work?

A

Control traffic by delaying requests until they accumulate sufficient tokens.

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18
Q

What does resource assignment try to optimise?

A
  1. performance
  2. resource utilisation
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19
Q

Explain the first step of resource assignment.

A

Filter machines that satisfy hard constraints

e.g., VM may need a machine with a GPU

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20
Q

Explain the second step of resource assignment.

A

Rank candidate nodes to find machine that best
satisfies soft constraints

e.g., best-fit to avoid resource fragmentation

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21
Q

List different methods for cluster management system architecture.

A
  1. centralised
  2. distributed
  3. hierarchical e.g. two-level
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22
Q

Next questions are about Borg. First, what is Borg?

A

Google’s centralised cluster manager

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23
Q

What does Borgmaster do?

A

It is the main scheduler.
It polls Borglets every few seconds

extra: 5 replicas

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24
Q

What does Borglet do?

A

Manages and monitors tasks and resources on machines it is responsible for.

extra: 10k heterogenous machines per Borglet

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25
Q

What strategies does Borg deploy to achieve high utilisation?

A
  1. admission control
  2. efficient task-packing
  3. over-commitment
  4. machine sharing
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26
Q

What is Kubernetes?

A

Cluster management for containerised applications;

  • manage complexity of container lifecycle and allocating/setting up hardware resources for the containers.
  • like an OS for your cloud cluster
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27
Q

List container orchestration primitives!

A
  1. Resource scaling
  2. Resource allocation
  3. Load balancing
  4. Lifecycle and health
  5. Naming and discovery
  6. Storage volumes
  7. Logging and monitoring
  8. Debugging and introspection
  9. Identity and authorization
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28
Q

Resource scaling

A

make sets of containers bigger or smaller

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29
Q

Resource allocation

A

decide where my containers should run

30
Q

Load balancing

A

distribute traffic across a set of containers

31
Q

Lifecycle and health

A

keep my containers running despite failures

32
Q

Naming and discovery

A

find where my containers are now

33
Q

Storage volumes

A

provide data to containers

34
Q

Logging and monitoring

A

track what’s happening with my containers

35
Q

Debugging and introspection

A

enter or attach to containers

36
Q

Identity and authorization

A

control who can do things to my containers

37
Q

What do the Kubernetes containers do?

A

Handle package dependencies

38
Q

What is a pod?

A

A pod is the unit of scheduling and migration in Kubernetes.

a bunch of containers with same properties

39
Q

List those properties!

A
  1. Lifecycle: live together, die together
  2. Network: same IP address, same routes, iptables
  3. Storage volumes: can share data
  4. Intended to run a common task
40
Q

Kubernetes service?

A

A group of pods that work together

extra: provides load balancing among pod replicas

41
Q

How do you control pod placement in Kubernetes?

A

use labels and selectors

42
Q

How do you keep N pods running?

A

use ReplicaSets: layer on top of Pod API that
ensures N copies of a pod are running

43
Q

What does the Horizontal Pod Autoscaler do?

A

automatically scale pods as needed
- based on CPU utilisation (or custom metrics)
- can set user-defined min/max bounds

44
Q

What is a potential problem with relying only on CPU utilization as a scaling metric?

A

good for compute bound apps but maybe I/O is the bottleneck

45
Q

What other metrics would you consider for auto-scaling besides CPU utilization?

A
  1. memory capacity
  2. memory BW
  3. network BW
46
Q

What properties does resource isolation try to achieve?

A
  1. Applications must not be able to affect each other’s performance
  2. Repeated runs of the same application should see similar behaviour
47
Q

What are the resource allocation mechanisms in Kubernetes?

A

Request: How much of a resource (CPU, RAM) the container is asking to use, with a strong guarantee of availability

Limit: Max amount of a resource the container can access

48
Q

Does the scheduler overcommit to requests?

A

No.

49
Q

List 3 Kubernetes Quality of Service classes.

A
  1. Guaranteed: highest protection
  2. Burstable: medium protection
  3. Best effort: lowest protection
50
Q

Relation of request and limit for Guaranteed class?

A

request > 0 && limit == request

51
Q

Relation of request and limit for Burstable class?

A

request > 0 && limit > request

52
Q

Relation of request and limit for Best effort class?

A

request == 0

53
Q

What are the advantages of centralised design?

A

can make globally optimal decisions

54
Q

What are the drawbacks of centralised design?

A

scalability: hard to enforce consistency

55
Q

Name 2 two-level cluster managers

A

Mesos and YARN

56
Q

How does Mesos work?

A

Lecture on 05.04
Min: 3.5

57
Q

List two distributed cluster management algorithms.

A

Omega and Sparrow

58
Q

List two new challenges serverless brings to the cluster management besides resource allocation and assignment.

A
  1. resource scaling: How many containers (“slots”) to keep warm for a function?
  2. request routing: To which node and “slot” do we send a particular invocation?
59
Q

What does Quasar try to solve?

A

Over-provisioning

60
Q

How does Quasar solve over-provisioning?

A

Don’t ask users for allocation request/resource demand.

They don’t really know it anyway.

61
Q

What do the users specify in this case? (Quasar)

A

performance goals

62
Q

What does the cluster manager do in this case? (Quasar)

A

profiles applications and dynamically adjusts resource allocations

63
Q

How does the cluster manager understand resource/performance tradeoffs? (Quasar)

A

It combines the following:

  1. Small signal from a short run of a new application
  2. Large signal from previously run applications
64
Q

What does the cluster manager do at the end? (still Quasar)

A

For each new application, it needs to recommend a resource allocation and assignment.

65
Q

How does one build a recommender system?

A

collaborative filtering

66
Q

What is collaborative filtering?

A

Predict preferences of new users given preferences of other users SVD and PQ reconstruction.

67
Q

What needs to be considered to recommend resource allocations to applications? (4)

A
  1. scale-out
  2. scale-up
  3. HW heterogeneity
  4. Interference
68
Q

What does scale out mean?

A

Use 4 nodes or a single node?

69
Q

What does scale up mean?

A

Use a 8-core VM or a single core VM?

70
Q

List 3 steps of Quasar’s functionality.

A

Step 1: short profiling runs produce initial performance data.

Step 2: collaborative filtering techniques fill in missing data

Step 3: Greedy scheduler uses output to find the number and type of resources that maximise utilisation and performance.

71
Q

To summarise, what are the challenges of using shared clusters?

A
  1. Resource allocation: how many resources should an app get?
  2. Resource assignment: which specific resources does an app get?
  3. Variability: within an app (different phases), within datasets, and load

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