Faculty : DR.S.SRIDHAR, Director-RVCT, R.V.College of Engineering , Bangalore -560059
COURSE OUTCOME
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CO1: Understand , Demonstrate and experiment simple Cloud Applications
CO2: Identify resource allocation, scheduling algorithms. and Implement Map-Reduce concept.
. CO3: Analyze virtual machines from available physical resources
CO4: Implement , Apply, Create , Setup a private cloud, Familiarize with Open Stack.
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Lesson Plan
Send email to drssridhar@yahoo.com to get the following :-
Teaching slides - 1
Teaching slides - 2
Teaching slides - 3
Teaching slides - 4
Teaching slides - 5
Teaching slides - 6
Teaching slides - 7
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Steps to carry out LAB EXPERIMENTS
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NOTE: Simulate using object oriented programming, any available cloud environment
(Eg; Amazon cloud) and VM ware for resource virtualization.
1. Create a Collaborative learning environment for a particular learning topic using Google Apps. Google
Drive, Google Docs and Google Slides must be used for hosting e-books, important articles and
presentations respectively. The instructor must use the Google Sheets to convey the timetable for different
events and for analyzing the scores for individual assignment submission.
Steps
Consider any particular domain of e-books , important articles and presentations
Make Slides for this domain using Google apps ( eg visit https://drsridhar.tripod.com/talk.htm)
and this will give learning environment
Faculty has to release timetable for different events, assignment scores etc.
as in the case of http://skrec.tripod.com/cloudcomputingcourse/
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2. Modeling and simulation Cloud computing environments, including Data Centers, Hosts and Cloudlets
and perform VM provisioning using CloudSim: Design a host with two CPU cores, which receives
request for hosting two VMs, such that each one requires two cores and plans to host four tasks units.
More specifically, tasks t1, t2, t3 and t4 to be hosted in VM1, while t5, t6, t7, and t8 to be hosted in VM2.
Implement space-shared allocation policy and time-shared allocation policy. Compare the results.
Steps
Create two clouds hosted in two VMs for eg. One in one domain and another in another domain
One cloud contains some tasks t1…i4 and another one contains t5…t8
Apply space share and time share policies.
If you deeply see my clouds created at https://drsridhar.tripod.com/talk.htm, many
such VMs are used for time share and space share policies
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3. Model a Cloud computing environment having Data center that had 100 hosts. The hosts are to be
modeled to have a CPU core (1000 MIPS), 2 GB of RAM and 1 TB of storage. Consider the workload
model for this evaluation included provisioning requests for 400 VMs, with each request demanding 1
CPU core (250 MIPS), 256 MB of RAM and 1 GB of storage. Each VM hosts a web-hosting application
service, whose CPU utilization distribution was generated according to the uniform distribution.
Each instance of a webhosting service required 150,000 MIPS or about 10 minutes to complete execution
assuming 100% utilization. Simulate Energy-conscious model for power consumption and power
management techniques such as Dynamic Voltage and Frequency Scaling (DVFS).
Initially, VMs are to be allocated according to requested parameters
(4 VMs on each host). The Cloud computing architecture
that is to be considered for studying energy conscious resource management techniques/policies included
a data center, CloudCoordinator, and Sensor component. The CloudCoordinator and Sensor perform their
usual roles. Via the attached Sensors (which are connected with every host), CloudCoordinator must
periodically monitor the performance status of active VMs such as load conditions, and processing share.
This real time information is to be passed to VMM, which can use it for performing appropriate resizing
of VMs and application of DVFS and soft scaling. CloudCoordinator continuously1 has to adapt
allocation of VMs by issuing VM migration commands and changing power states of nodes according to
its policy and current utilization of resources.
Steps
Please visit https://drsridhar.tripod.com/talk.htm which deals with many VMs
Generalize the same for 100 hosts for the considered metrics
This is just extension of previous Lab experiments in generalized manner
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4. Model and simulate the environment consisting of a data center with 10,000 hosts where each host was
modeled to have a single CPU core (1200MIPS), 4GB of RAM memory and 2TB of storage.
Consider the provisioning policy for VMs as space-shared, which allows one VM to be active in a host at a given
instance of time. Make a request from the end-user (through the Datacenter Broker) for creation and
instantiation of 50 VMs that had following constraints: 1024MB of physical memory, 1 CPU core and
1GB of storage. The application granularity was modeled to be composed of 300 task units, with each
task unit requiring 1,440,000 million instructions (20 minutes in the simulated hosts) to be executed on a
host. Minimal data transfer (300 KB) overhead can be considered for the task units (to and from the data
center). After the creation of VMs, task units were submitted in small groups of 50 (one for each VM) at
inter-arrival delay of 10 minutes.
Steps
This is again an exercise based on previous lab experiments but parameters are to be considered
in large scale After the creation of VMs , segmentation in small groups to be made , say 50 for
each VM with time delay interval of 10 mts
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5. Implement Map Reduce concept for
a. Strassen’s Matrix Multiplication for a huge matrix.
b. Computing the average number of citation index a researcher has according to age among some 1
billion journal articles. Consider a network of entities and relationships between them. It is required to
calculate a state of each entity on the basis of properties of the other entities in its neighborhood.
This state can represent a distance to other nodes, indication that there is a neighbor
with the certain properties, characteristic of neighborhood density and so on.
A network is stored as a set of nodes and each node contains a list of adjacent node IDs.
Mapper emits messages for each node using ID of the adjacent node as a key.
Reducer must re compute state and rewrite node with the new state. Implement this scenario.
Steps
Programming Logic has to be developed for this mathematical computation
Apply MapReduce concept using nodes, and networking concept to solve this problem
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Assignment 1
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Submission Date : 17th Sep. 2014
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1. Write notes on : Cloud computing, Cloud computing delivery models and
services, Ethical issues and Cloud vulnerabilities
2. What do you mean by : Cloud computing at Amazon, Cloud computing the
Google perspective and online services
3. Explain the following :- Open-source software platforms for private clouds,
Cloud storage diversity and vendor lock-in and ecological impact
4. What do you mean by Service level agreements, User experience and
software licensing.
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Assignment 2
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Submission Date : 27th Oct 2014
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1. Explain : Challenges of cloud computing, Architectural styles of cloud computing
2. Explain :: Coordination of multiple activities, Coordination based on a state machine model:
3. What do you mean by The Map Reduce programming model and Cloud for science and engineering
4. Give details on : High-performance computing on a cloud, Cloud computing for
Biology research and Social computing,
5. How digital content and cloud computing are related ?
6. Explain : Layering and virtualization, Virtual machine monitors, Virtual Machines
7. What do you understand by : Performance and Security Isolation,
8. How do you implement : Full virtualization and paravirtualization, Hardware support for
virtualization with some case study ?
9. What do you know about : Optimization of network virtualization, vBlades,
10. Explain :Performance comparison of virtual machines, The dark side of virtualization
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Assignment 3
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Submission Date : to be announced
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1. Explain : Policies and mechanisms for resource management, Application of control theory
to task scheduling on a cloud
2. Give details on : Stability of a two-level resource allocation architecture, Feedback control
based on dynamic thresholds, Coordination of specialized autonomic performance managers
3. Give utility-based model for cloud-based Web services
4. What do you mean by Resourcing bundling and Combinatorial auctions for cloud resources
5. Explain Scheduling algorithms for computing clouds, Fair queuing, Start-time fair queuing,
Borrowed virtual time
6. Give details on Cloud scheduling subject to deadlines, Scheduling Map Reduce applications
subject to deadlines, Resource management and dynamic scaling
7. What are Cloud security risks, The top concern for cloud users, Privacy and privacy impact assessment
8. What do you mean by : Operating system security, Virtual machine Security, Security of
virtualization and Security risks posed by shared images, Security risks posed by a management OS
9. Explain : A trusted virtual machine monitor, Amazon web services: EC2 instances, Connecting
clients to cloud instances through firewalls
10. What are Security rules for application and transport layer protocols in EC2, How to launch
an EC2 Linux instance and connect to it ?
11. How to use S3 in java, Cloud-based simulation of a distributed trust algorithm
12. What do you mean by : , A cloud service for adaptive data streaming, Cloud based
optimal FPGA synthesis
