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Cloud Computing > LV 5 > Flashcards

LV 5 Flashcards

1
Q

Why elastic workload?

A

Reduce over/under provisioning
Reduce const
Increase customer satisfaction

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2
Q

Scalability limit

A

Web: overhead with parallelisation, sequential part dominates the execution
Transaction based apps: shared resources (e.g database)

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3
Q

Scalability

A

Characteristic of an application to increase capacity with the amount of resources

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4
Q

Capacity of application depends on

A

Available resource capacities
Application design

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5
Q

Elasticity

A

Dynamic adaptation of capacity to change in workload
No shutdown/restart required

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6
Q

Capacity planning in cloud

A

Possible due to dynamic resource management and pay per use cost model
High elasticity

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7
Q

Vertical Scaling - Scaling up

A

Increase capacity of singe Service instance by increasing its resources
( increase cpu time percentage, clock frequency, more cores)
Advantage: no change in service required

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8
Q

Horizontal Scaling - Scaling out

A

Capacity increase if service by creating more instances ( copies of services, load balancer on top)

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9
Q

Vertical scaling

A

Advantages: easy to replace resource with more powerful
No application redesign
Disadvantages:
More powerful resource might be too expensive
Resource capacity is limited
Replacement of resource causes service interruption

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10
Q

Horizontal scaling: pros and cons

A

Pros: scaling through adding more resources
No requirement for more powerful hardware
Cons: increased amount of resources comes with more management overhead
Required distributed software architecture
Long term solution !

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11
Q

SLO

A

service level objective
Latency of requests
Failed request rate
Service availability

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12
Q

Auto scaling policy

A

Analyzer -> scheduler -> scaling actions -> executer -> cloud CMDs

Policy -> scheduler

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13
Q

Auto scaling approach: reactive

A

Detect under/overloaded service
Scale in/out or down/up according to policy

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14
Q

Autoscaling approaches: scheduled

A

Policy specifies scaling events
Apply scaling actions at appropriate time

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15
Q

Autoscaling approaches: predictive

A

Continuously predict future workload
If workload change, schedule scaling actions ahead in time
Goals: circumvent scaling latency, enable more time consuming scaling decisions

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16
Q

Resource centric auto scaler

A

Scaling actions modify resources
Services are implicitly adapted

17
Q

Service centric auto scalers

A

Scaling actions modify service instances
Resources are implicitly adapted

18
Q

AWS Reactive Autoscaling

A

Resource centric: scaling of VM

19
Q

AWS Scaling Policies

A

Target tracking scaling (specify target value, automatically adjust resources to meet target)
Simple scaling
Step scaling

20
Q

AWS Predictive Autoscaling

A

Determines proactively minimum of Autoscaling group

21
Q

Load balancing

A

Distributes requests among services
Scaling out: works only if all replicas are equally busy

22
Q

Load balancing: goals

A

Efficient utilization of set of resources
Increase availability
Reduce response time and failure rates

23
Q

Static vs dynamic load balancing

A

Static: no feedback from server
E.g round robin
Dynamic: feedback on the status

24
Q

Dynamic load balancing

A

Distributed: shifts work between different nodes
Cooperative: have the same goal (optimize memory workload)
Non-cooperate: different goals( optimize cpu workload)

Non-distributed:
Centralized: one central LB
Semi-centralized: nodes are partitioned and one LB responsible for partition

25
Q

Approaches for web apps

A

Round robin dns
DNS Delegation
Client-side random sampling
Server-side load balancing

26
Q

Classes of LB Algorithms

A

Class-aware: classification of requests
Content-aware: request content
Client-aware: packet source

27
Q

LB Algorithms

A

Round robin and weighted round robin
Least connection and weighted least connection
Resource based
Weighed response time

28
Q

AWS Elastic load balancing

A

Distributes upcoming traffic across the instances in the auto scaling group

Cloud Computing (14 decks)

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