As the volume and velocity of data in the cloud is increasing, the geographical distribution of where it is produced, processed and consumed is also gaining more significance. It is getting less feasible to move data to a distant data center for processing and move output again to the consumer location. Several promising approaches including Fog Computing, Mobile Cloud Computing, Cloudlets and Nano Data Centers are instead suggesting to bring processing entities to the edge of the cloud network to reduce latency. One issue we have identified in this scenario regarding resource management is the optimal selection of processing entity count and location. Since processing entities may also be communicating among themselves and possibly with a centralized data storage, we suggest that a hierarchical caching mechanism for the distant data will increase computation performance. We formulated this problem in the third period of the thesis and planned to further study during the fourth period.

1. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Modeling and Optimization of Resource Allocation in Cloud
PhD Thesis Progress – Third Report
Atakan Aral
Thesis Advisor: Asst. Prof. Dr. Tolga Ovatman
Istanbul Technical University – Department of Computer Engineering
January 7, 2016
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

2. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Outline
1 Introduction
Contribution to the Thesis
Time Plan
2 Summary of the Previous Work
3 Literature Review
4 Cache Placement for Mobile Cloud Computing
Problem Modeling
Proposed Solution
5 Conclusion
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

3. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Contribution to the Thesis
Time Plan
Outline
1 Introduction
Contribution to the Thesis
Time Plan
2 Summary of the Previous Work
3 Literature Review
4 Cache Placement for Mobile Cloud Computing
Problem Modeling
Proposed Solution
5 Conclusion
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

4. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Contribution to the Thesis
Time Plan
Journal Submission
Submitted to Future Generation Computer Systems, ELSEVIER (IF: 2.786)
SI: "Middleware Services for Heterogeneous Distributed Computing"
First Decision Date: Nov 15, 2015 (Under review as of Jan 06, 2016)
Also presented in IEEE 8th International Conference on Cloud Computing,
CLOUD 2015
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

5. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Contribution to the Thesis
Time Plan
Literature Review and Problem Modeling
Areas of interest:
Mobile Cloud Computing
Fog Computing
Cloudlets, Nanodatacenters
Self- and Context-aware Resource Management
Optimal Placement of Data Object Caches onto the Cloudlets
A distributed and context-aware algorithm
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

6. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Contribution to the Thesis
Time Plan
Outline
1 Introduction
Contribution to the Thesis
Time Plan
2 Summary of the Previous Work
3 Literature Review
4 Cache Placement for Mobile Cloud Computing
Problem Modeling
Proposed Solution
5 Conclusion
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

7. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Contribution to the Thesis
Time Plan
Gantt Chart
2015
7 8 9 10 11 12
TBM Evaluation
Manuscript Preparation
Journal Submission
Literature Review
Problem Modeling
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

8. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Outline
1 Introduction
Contribution to the Thesis
Time Plan
2 Summary of the Previous Work
3 Literature Review
4 Cache Placement for Mobile Cloud Computing
Problem Modeling
Proposed Solution
5 Conclusion
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

9. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Topology Based Mapping (TBM)
Main Idea
Map VM Clusters onto the federated cloud infrastructure based on their topology.
Decreases deployment latency (by placing VMs close to the broker)
Decreases communication latency (by placing connected VMs to the
neighbour data centers)
Shortens execution time and increases throughput
Reduces resource costs (by balancing load and avoiding overload in any DC)
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

10. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
UML Activity Diagram
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

11. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Excluded Points
Geo-distributed user access
Virtual Machine or Data Replication
User mobility
Virtual Machine Migration
Topology Based Matching is a semi-centralized algorithm
Complete utilization, capacity and topology information of the data centers
and the network is available at all peers.
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

12. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Outline
1 Introduction
Contribution to the Thesis
Time Plan
2 Summary of the Previous Work
3 Literature Review
4 Cache Placement for Mobile Cloud Computing
Problem Modeling
Proposed Solution
5 Conclusion
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

13. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Mobile Cloud Computing
1 Computation is carried out in the cloud and the mobile device acts a thin
client.
Mobile elements are resource-poor relative to static elements.
Mobile elements are more prone to loss, destruction, and subversion than static
elements.
Mobile elements must operate under a much broader range of networking
conditions.
2 Nearby mobile devices form a cloud to assist each other in computation
intensive tasks.
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

14. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Nano Data Centers
Small computation entities provided by ISPs on gateways/modems.
Managed in a P2P architecture by the ISP.
Main motivation is to reduce data center energy consumption.
Reuse already committed baseline power
Avoid cooling costs
Reduce network energy consumption
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

15. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Fog Computing
Main motivation is to leverage Internet of Things
Applications that require very low latency
Geo-distributed applications
Fast mobile applications (vehicle, rail)
Large-scale distributed control systems
Computation can be on high-end servers, edge routers, access points, set-top
boxes, vehicles, sensors, mobile phones
Cooperation between edge and core
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

16. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Cloudlets
"Data center in a box"
Provided and owned by local businesses (e.g. coffee shops, offices)
Allows code offloading using Virtual Machines
Fall back to distant cloud or own resources of the mobile device
LAN latency and bandwidth
Stores only cached data
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

17. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
Outline
1 Introduction
Contribution to the Thesis
Time Plan
2 Summary of the Previous Work
3 Literature Review
4 Cache Placement for Mobile Cloud Computing
Problem Modeling
Proposed Solution
5 Conclusion
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

18. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
Motivation
As the volume and velocity of the data in cloud is increasing, geographical
distribution of where it is produced, processed and consumed is also gaining
more significance
Mobile cloud computing offers a solution for the low-latency access to
high-capacity computing resources.
However, data is still mostly central and it is not feasible to replicate it in large
number of geo-distributed locations.
Due to economical factors
Due to the limited storage capacity of the edge entities
To keep it consistent and available for analysis
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

19. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
Definition
Create caches of data objects on data centers and edge entities
Decide the number and location of the caches based on:
Magnitude of user access
Locations of user access
Cloud storage pricing
In an attempt to reduce:
Data access latency
Storage cost
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

20. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
Issues and Requirements
Cost-Latency Tradeoff
Customer preference for the level of aggression should be considered.
Complete topology information is no longer feasible
A distributed solution is necessary.
User access is dynamic and mobile
The solution must also be context-aware.
Edge entities have limited storage capacity
Constraints must be respected.
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

21. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
Outline
1 Introduction
Contribution to the Thesis
Time Plan
2 Summary of the Previous Work
3 Literature Review
4 Cache Placement for Mobile Cloud Computing
Problem Modeling
Proposed Solution
5 Conclusion
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

22. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
Centralized Solutions
k-Medians Given a node set V with pairwise distance function d and service
demands s(vj), ∀vj ∈ V, select up to k nodes to act as medians so as
to minimize the service cost C(V, s, k).
C(V, s, k) =
∀vj ∈V
s(vj)d(vj, m(vj))
Facility location Given a node set V with pairwise distance function d and service
demands s(vj), ∀vj ∈ V and facility costs f(vj), ∀vj ∈ V, select a set of
nodes F to act as facilities so as to minimize the joint cost C(V, s, f)
of acquiring the facilities and servicing the demand.
C(V, s, f) =
∀vj ∈F
f(vj) +
∀vj ∈V
s(vj)d(vj, m(vj))
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

23. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
Distributed Solution
Replication algorithm for the central storage:
1 Create a cache for a data object in one of the neighbours.
Replication algorithm in the cache locations:
1 Migrate the cache to one the neighbours.
2 Duplicate the cache to one the neighbours.
3 Remove the cache.
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

24. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
Sample Scenario
a
b
c
d
f
e
a1
a2
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c1 c2
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

25. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
ITERATION 1d: User demand locations
a
b
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f
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Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

26. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
ITERATION 1d: User demand received from c and f
a
b
c
d
f
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Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

27. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
ITERATION 1d: Cache creation decision
a
b
c
d
f
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a1
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Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

28. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
ITERATION 2f: Migration decision
a
b
c
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c1 c2
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

29. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
ITERATION 2c: Duplication decision
a
b
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Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

30. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
ITERATION 3e: Migration decision
a
b
c
d
f
e
a1
a2
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d1
f1
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c1 c2
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

31. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
ITERATION 3a: Migration decision
a
b
c
d
f
e
a1
a2
a3
a4
e1
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b2
b1
e4
e2
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f3
d1
f1
f2
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c1 c2
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

32. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
ITERATION 3c: Removal decision
a
b
c
d
f
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Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

33. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
Inputs
Demand for each data object i from each neighbour j: Dij
Average latency for each data object i from each neighbour j: Lij
Latency from each node k to each neighbour j: Njk
Cost of storing each data object i at each neighbour and current location j: Cij
User provided level of aggression: A
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

34. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
Operation conditions
Create a cache of object i at neighbour j iff:
LijDijA > Cij
Remove the cache of the object i at k iff:
∀j
(LijDijA) < Cik
Duplicate the cache of the object i from k to l iff:
LilDilA > Cil ∧
∀j=l
(LijDijA) > Cik
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

35. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
Operation conditions
Migrate the cache of the object i from k to l iff:
∀j
(LijDijA) −
∀j=l
(Lij + Nkl)DijA + (Lil − Nkl)DilA > Cil − Cik
A special case where ∃!j[Dij > 0]:
NklDilA > Cil − Cik
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

36. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
Possible Problems and Solutions
Multiple migrations/duplications are feasible
Prefer the option with the greatest benefit
Both migration and removal as feasible
Prefer migration
A costly node blocks the migration path
Dynamic aggression level
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

37. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Problem Modeling
Proposed Solution
Contribution
There exists distributed VM replication methods
The whole entity is replicated which is not feasible for big data.
There also exists distributed data storage methods
In our model data is still stored centrally while caches are distributed.
As far as we are aware, all other studies apply a centralized approach.
Not feasible in the case of mobile cloud computing where the topology is too
large and dynamic.
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

38. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Outline
1 Introduction
Contribution to the Thesis
Time Plan
2 Summary of the Previous Work
3 Literature Review
4 Cache Placement for Mobile Cloud Computing
Problem Modeling
Proposed Solution
5 Conclusion
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

39. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Publications
Aral, A. and Ovatman, T. (2014). Improving resource utilization in cloud
environments using application placement heuristics. In Proceedings of the
4th International Conference on Cloud Computing and Services Science
(CLOSER), pages 527–534.
Aral, A. and Ovatman, T. (2015). Subgraph matching for resource allocation in
the federated cloud environment. In Proceedings of 8th IEEE International
Conference on Cloud Computing (IEEE CLOUD), pages 1033–1036.
Aral, A. and Ovatman, T. (2016). Network-Aware Embedding of Virtual
Machine Clusters onto Federated Cloud Infrastructure. (Submitted to FGCS
on 15-September-2015, under review as of 06-January-2016)
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

40. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Summary
Journal Submission
Literature Review
Problem Modeling
Cache Placement for Mobile Cloud Computing
Distributed Context-Aware Algorithm
To reduce latency
To decrease costs
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

41. Introduction
Summary of the Previous Work
Literature Review
Cache Placement for Mobile Cloud Computing
Conclusion
Thank you for your time.
Atakan Aral Modeling and Optimization of Resource Allocation in Cloud