Cloud SUTs: Characteristics, Metrics & Potential Workloads
Cloud SUT proposal
OSGcloud group
Objective
•
To fill in the Research the group about the
thinking within the OSG working group
•
To solicit new ideas/proposals
Definition of cloud (NIST)
“Cloud computing is a model for enabling
convenient, on-demand network access to a
shared pool of configurable computing resources
that can be rapidly provisioned and released with
minimal management effort or service provider
interaction. The model promotes availability and
includes five essential characteristics, three
service models and four deployment models.”
Characteristics
•
Cloud Characteristics from the NIST list
–
On-demand self service
–
Broad network access
–
Resource pooling
–
Elasticity
–
Measured service
•
Characteristic components separated by layer (By osgcloud group)
–
Client
•
Standard client
•
Network connectivity
•
Access
•
Security
•
identity
–
Server and Storage
•
Processing power
•
Memory
•
Storage
•
Network
•
Power consumption
•
Performance measurement
–
Internet
•
Speed
•
Reliability
•
Availability
Metrics
•
Elasticity
–
Provisioning Interval
–
Agility
•
Durability
•
Response time
•
Throughput
•
Reliability
•
Power
•
Price
Potential Workloads/Use cases (some
workloads listed)
•
Data Analytics
Expert Search
Clustering
Customer Segmentation
•
Data Warehousing
Pipelines bring in data feed and clean and transform it (example: logs from web servers)
Iterative processing : one very large data set is maintained (often in the form of a graph).
•
Business OLTP
•
•
Memory Cloud
–
Key-value pair databases
•
Social Networking
–
Web2.0 based application
–
Write/read workload
–
Memory cloud
–
Search engine
Issues with defining the SUT
•
Cloud offerings may be different.
•
Inability on the user’s side to find out what
exact hardware is being used and how much is
being used.
•
Migration of hardware from one platform to
another.
•
Temporal aspect.
Multiple ways of classifying Cloud SUTs
•
Virtualized, non-virtualized, mixed
Whether the cloud offering is virtualized, non-virtualized, or mixed.
Non-virtualized clouds can also be elastic (think grid computing)
•
IaaS, PaaS, SaaS
Whether the cloud offering is IaaS, PaaS, SaaS, or a mixture of these
(e.g., Azure). It is easier to compare providers that do not mix
service offerings.
•
white box vs. black box
Whether benchmark and measurements will measure (1) and (2) as
a white box (from hardware to the software stack) or as a black box
(what customer purchases).
•
private, public, or hybrid, and whether it is solely for enterprises.
Cloud Offering categories
•
Cloud as a black box
–
No control over what is the exact hardware/software,
whether it changes with time etc.
–
User gets a description/QOS guarantees of what
he/she is paying for.
–
Unique identifier tied to the offering.
•
Cloud as a white-box
–
Full control and knowledge of part numbers and
software.
–
Description available in terms of exact hardware and
software.
Who is interested in what + Issues
Hardware vendors selling systems to Cloud
vendors – White-box approach
End customer trying to buy a Cloud service –
Black box approach
Resources to quantify
•
IAAS
–
Compute resource
–
Memory Resource (capacity/bandwidth)
–
Network capacity
–
Disk/Storage Capacity
•
PAAS
–
Hardware may not be described by vendor
–
Possible to measure as in IAAS
•
SAAS
–
Hardware not described
–
Is it important to know about the hardware resources?
Proposal to Describe a Cloud based
SUT
•
Users would describe SUT based on what they pay for.
Yet, there is a need for other engineering details.
•
Use micro-measurement tools to measure capacities of
various elements.
•
The micro-benchmarks should be easy and quick to run
and provide numbers describing different types of
resources.
•
This would then create a common denominator for
comparisons between systems; irrespective of the
specific aspect being measured in a given benchmark.
•
Characterizations must include temporal variations in
resources as well as limitations if there are any
Why is this needed?
•
SUT description necessary for reproducibility
as well as comparisons
•
Having a common denominator approach on
engineering components would help with
comparisons of known systems with black
boxes on the cloud.
Examples/suggestions
•
Use of virus detection scans to determine
memory capacity
•
Use of Transcoders between media formats to
measure cpu capacity.
•
SPECcpu programs to measure cpu capacity.
Questions to the Research group
•
Can we work on putting together a standard
set of such tools?
•
Is there any accepted set of similar tools that
people are using currently?
•
Other thoughts/discussions
Delve into the world of Cloud SUTs with a focus on understanding the diverse characteristics, key metrics, and potential workloads/use cases. Uncover the challenges of defining SUTs in cloud offerings and explore various classifications such as virtualized, non-virtualized, and mixed environments. Gain insights into the NIST definition of cloud computing and the importance of soliciting new ideas/proposals within OSG working groups.
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Presentation Transcript
Cloud SUT proposal OSGcloud group
Objective To fill in the Research the group about the thinking within the OSG working group To solicit new ideas/proposals
Definition of cloud (NIST) Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. The model promotes availability and includes five essential characteristics, service models and four deployment models. three
Characteristics Cloud Characteristics from the NIST list On-demand self service Broad network access Resource pooling Elasticity Measured service Characteristic components separated by layer (By osgcloud group) Client Standard client Network connectivity Access Security identity Server and Storage Processing power Memory Storage Network Power consumption Performance measurement Internet Speed Reliability Availability
Metrics Elasticity Provisioning Interval Agility Durability Response time Throughput Reliability Power Price
Potential Workloads/Use cases (some workloads listed) Data Analytics Expert Search Clustering Customer Segmentation Data Warehousing Pipelines bring in data feed and clean and transform it (example: logs from web servers) Iterative processing : one very large data set is maintained (often in the form of a graph). Business OLTP Mail Memory Cloud Key-value pair databases Social Networking Web2.0 based application Write/read workload Memory cloud Search engine
Issues with defining the SUT Cloud offerings may be different. Inability on the user s side to find out what exact hardware is being used and how much is being used. Migration of hardware from one platform to another. Temporal aspect.
Multiple ways of classifying Cloud SUTs Virtualized, non-virtualized, mixed Whether the cloud offering is virtualized, non-virtualized, or mixed. Non-virtualized clouds can also be elastic (think grid computing) IaaS, PaaS, SaaS Whether the cloud offering is IaaS, PaaS, SaaS, or a mixture of these (e.g., Azure). It is easier to compare providers that do not mix service offerings. white box vs. black box Whether benchmark and measurements will measure (1) and (2) as a white box (from hardware to the software stack) or as a black box (what customer purchases). private, public, or hybrid, and whether it is solely for enterprises.
Cloud Offering categories Cloud as a black box No control over what is the exact hardware/software, whether it changes with time etc. User gets a description/QOS guarantees of what he/she is paying for. Unique identifier tied to the offering. Cloud as a white-box Full control and knowledge of part numbers and software. Description available in terms of exact hardware and software.
Who is interested in what + Issues Hardware vendors selling systems to Cloud vendors White-box approach End customer trying to buy a Cloud service Black box approach
Resources to quantify IAAS Compute resource Memory Resource (capacity/bandwidth) Network capacity Disk/Storage Capacity PAAS Hardware may not be described by vendor Possible to measure as in IAAS SAAS Hardware not described Is it important to know about the hardware resources?
Proposal to Describe a Cloud based SUT Users would describe SUT based on what they pay for. Yet, there is a need for other engineering details. Use micro-measurement tools to measure capacities of various elements. The micro-benchmarks should be easy and quick to run and provide numbers describing different types of resources. This would then create a common denominator for comparisons between systems; irrespective of the specific aspect being measured in a given benchmark. Characterizations must include temporal variations in resources as well as limitations if there are any
Why is this needed? SUT description necessary for reproducibility as well as comparisons Having a common denominator approach on engineering components would help with comparisons of known systems with black boxes on the cloud.
Examples/suggestions Use of virus detection scans to determine memory capacity Use of Transcoders between media formats to measure cpu capacity. SPECcpu programs to measure cpu capacity.
Questions to the Research group Can we work on putting together a standard set of such tools? Is there any accepted set of similar tools that people are using currently? Other thoughts/discussions
