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Google Taps Nobl9 to Improve Services Reliability
Tag:
CAP theorem
The Limits of Eventual Consistency
ROELBOB
|
April 16, 2021
|
CAP theorem
,
distributed databases
,
humor
,
parody
,
satire
,
tech humor
...
CAP Theorem No Match for Mainframes
Bob Reselman
|
July 30, 2019
|
big data
,
CAP theorem
,
data management
,
devops
,
mainframe technology
,
REST APIs
One of the biggest problems in modern distributed computing is data management. These days data are spread out all over the place. As a result, getting the data you need, exactly when ...
Cloud Workload Resilience
PulseMeter
Step
1
of
8
12%
How do you define cloud resiliency for cloud workloads? (Select 3)
(Required)
The ability to recover from failures, high loads and cyberattacks.
Continue servicing workload requests during the recovery of failed components or services.
Implementing security measures to protect cloud workloads from cybersecurity threats like DDoS.
Maintaining uninterrupted service in the face of software, infrastructure and network failures or disruptions.
Designing cloud workloads to adapt to sudden changes in demand automatically.
Regularly performing backups to prevent data loss and enable quick recovery in case of failures.
Designing cloud workloads so they can more easily move between cloud locations and service providers.
Using geographically distributed or redundant cloud data centers.
Resiliency is just another marketing buzzword.
In what ways does cloud-native architecture improve the resiliency of workloads deployed in the cloud? (Rank order)
Smaller, self-contained microservices fail independently without impacting overall availability.
Containerized software provides isolation and consistency, making it easier to scale and recover from failure.
Stateless design patterns increase scalability and can fail independently without impacting other parts of cloud applications.
Serverless design pattern allows events to initiate the operation of the discrete functions as needed.
Cloud-native architecture significantly influences the resiliency of cloud-deployed applications.
Cloud-native architecture provides limited or no resiliency improvement.
How important is improving the resiliency of cloud workloads for your organization in 2024? (Select 1)
(Required)
The number-one priority
In the top three priorities
In the top 10 priorities
Important but not a priority
Not important
Which of the following do you use to improve the resiliency of cloud workloads? (Select all that apply)
(Required)
Containers
Microservices
Container orchestration
Serverless
Multiple cloud providers
Infrastructure-as-code (IaC)
Data distribution or replication
Observability tools based on OpenTelemetry
Failure testing
What factors in cloud-native (microservices) workloads increase resiliency? (Rank order)
Distribute workloads
Portable workloads across multiple cloud providers
Move some workloads to the edge
Kubernetes clusters for failover and load distribution
Stateless software design
Increased security posture
Setting meaningful and achievable resiliency goals
What are the most significant challenges to improving the resiliency of cloud workloads or cloud-native applications? (Select all that apply)
(Required)
Lack of skills and knowledge
Legacy or existing architecture of applications
Large amount of technical debt
Multiple or conflicting definitions of resiliency
Inability to gain executive support
Ability to test resiliency without impacting service levels
Insufficient funding
Other
How much of your cloud workload is cloud-native today? (Select 1)
(Required)
100% - all cloud-native
Greater than 50%
25-50%
Under 25%
0% - No cloud-native
What is your business or organization's size (# employees)? (Select 1)
(Required)
1 - 100
101 - 500
501 - 1000
1001 - 5000
Over 5000
Δ
