Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progress 3]
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The document details Atakan Aral's PhD thesis on modeling and optimizing resource allocation in cloud computing, focusing on cache placement for mobile cloud environments. It discusses various topics including problem modeling, proposed solutions, and the contributions of the thesis, alongside a time plan for the research process. The thesis aims to develop a distributed and context-aware algorithm to improve data access latency and reduce costs in mobile cloud computing.
![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
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Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progress 3]
- 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 a3 a4 e1 b3 b2 b1 e4 e2 e3 f3 d1 f1 f2 d2 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 c d f e a1 a2 a3 a4 e1 b3 b2 b1 e4 e2 e3 f3 d1 f1 f2 d2 c1 c2 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 e a1 a2 a3 a4 e1 b3 b2 b1 e4 e2 e3 f3 d1 f1 f2 d2 c1 c2 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 e a1 a2 a3 a4 e1 b3 b2 b1 e4 e2 e3 f3 d1 f1 f2 d2 c1 c2 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 d f e a1 a2 a3 a4 e1 b3 b2 b1 e4 e2 e3 f3 d1 f1 f2 d2 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 c d f e a1 a2 a3 a4 e1 b3 b2 b1 e4 e2 e3 f3 d1 f1 f2 d2 c1 c2 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 a3 a4 e1 b3 b2 b1 e4 e2 e3 f3 d1 f1 f2 d2 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 b3 b2 b1 e4 e2 e3 f3 d1 f1 f2 d2 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 e a1 a2 a3 a4 e1 b3 b2 b1 e4 e2 e3 f3 d1 f1 f2 d2 c1 c2 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