按照要求，在 AWS
上部署云服务。
Summary
You need to create software to help determine a set of allocations such that
each allocation specifies which tasks are allocated to run on a particular
processor. These allocations are based on data from within a configuration
file.
Your software will use the following configuration files stored on an Azure
blob.
For each configuration file, the ideal goal is to find one or more
allocations:
- that consume a minimum amount of energy
- where the overall runtime of each allocation does not exceed a program duration
- where processor RAM constraints are satisfied
There might be one or more allocations that meet these constraints. Also,
there might be no allocation that meets these constraints because the program
duration is too small for all combinations of task allocations.
Application
As your software might not be able to find the ideal allocation within several
years, it must attempt to obtain a set of allocations within 5 minutes, where
these allocations have the smallest energy consumption that you can find but
also satisfy the time and memory constraints. That is, after 5 minutes, your
software must:
- stop searching for other allocations,
- display the low energy consuming allocations that your program found,
- display the runtimes and amount of energy consumed next to each allocation, and
- display the maximum of amount of processor RAM required by the tasks allocated to that processor, and the amount of RAM available to that processor.
Your client software will directly read a configuration file that is stored on
Azure. That is, your software must open the file using its URL. Do not
download these configuration files, and open them on a local drive.
You will design and implement one or more WCF Services to compute a set of
allocations based on the imported configuration data. Each WCF Service will
run on an AWS virtual machine. This service will be invoked by your client
software. This client software will provide/pass configuration data to this
service, and this service will return a set of allocations.
In order to use more compute resources (AWS VMs) to more quickly determine a
set of allocations, you will use several AWS virtual machines, AMIs, auto
scaling groups, a load balancer, and other AWS resources. Each of these VMs
will have your WCF Service installed.
As your software will send several asynchronous requests to your WCF Services,
it will receive responses from several WCF Services. Each response should
contain a set of allocations. For example: - WCF Service 1 returns a set of 8 allocations, where each allocation consumes 100.
- WCF Service 2 returns a set of 4 allocations, where each allocation consumes 150.
- WCF Service 3 returns a set of 12 allocations, where each allocation consumes 90.
- WCF Service 4 returns a set of 5 allocations, where each allocation consumes 123.
Your software needs to determine which response contains allocations that
consume the smallest amount of energy. For example, the set of 12 allocations
(in the above list) consume the smallest amount of energy.
AWS Cloud Architecture
The cloud architecture that you will implement is depicted in Figure 1.
In general, client requests are directly sent to the application load
balancer. Each request is sent to one of the Microsoft IIS web servers on a
VM, and finally to one of your WCF
Services. A response will be delivered to the client.
This architecture will use three different types of VM: t2.nano, t2.micro, and
t2.small.
This architecture will use three auto scaling groups. Each auto scaling group:
- uses only one type of VM, and not the same as another group
- commences with one VM
- launches one VM when CPU utilisation exceeds 70%
- does not exceed 4 VMs
- terminates one VM when CPU utilisation is less than 30%
- retains at least 1 VM
Other main AWS resources required for this assessment task are depicted in
Figure 1.
Submission
Please submit 2 items.
- A ZIP file of your Visual Studio files and folders. See Assessment Task 2
Submission.pdf for details. - An mp4 video about your cloud system. This video should present AWS resources used in your cloud system, and it should demonstrate your cloud system working.
