Download as PDF, PPTX
![Introduction Algorithm Design Evaluation Results Conclusion Evaluation Variables VM memory request Data center memory capacity is 64x and each VM requires memory allocation between 1x and 8x Link bandwidth request Available bandwidth in each link is 80y and bandwidth allocation to/from other VMs is between 1y and 8y. Minimum number of requests Average number of requests from the least populated location is in the range [2, 16] depending on this variable. VM network intensity Ratio of local computation and inter-VM communication is between 3 and 1/3. Atakan Aral Modeling and Optimization of Resource Allocation in Cloud](https://image.slidesharecdn.com/progress2-150812102524-lva1-app6892/75/Modeling-and-Optimization-of-Resource-Allocation-in-Cloud-PhD-Thesis-Progress-2-25-2048.jpg)
Uploaded byAtakanAral
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progress 2]
This document summarizes Atakan Aral's PhD thesis progress report on modeling and optimizing resource allocation in cloud computing. The report outlines Aral's contributions, including developing a topology-based matching algorithm for distributed VM placement and evaluating it against baseline methods. Evaluation covers factors like bandwidth, costs, loads, and optimization criteria including deployment time, communication latency, throughput, and rejection rates. Future work is planned to enhance the algorithm and evaluation.
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progress 2]
- 1. Introduction Algorithm Design Evaluation Results Conclusion Modeling andOptimization of Resource Allocation in Cloud PhD Thesis Progress – Second Report Atakan Aral Thesis Advisor: Asst. Prof. Dr. Tolga Ovatman Istanbul Technical University – Department of Computer Engineering June 22, 2015 Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 2. Introduction Algorithm Design Evaluation Results Conclusion Outline 1 Introduction PreliminaryInformation Contribution to the Thesis Time Plan 2 Algorithm Design 3 Evaluation 4 Results 5 Conclusion Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 3. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contributionto the Thesis Time Plan Outline 1 Introduction Preliminary Information Contribution to the Thesis Time Plan 2 Algorithm Design 3 Evaluation 4 Results 5 Conclusion Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 4. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contributionto the Thesis Time Plan Outline 1 Introduction Preliminary Information Contribution to the Thesis Time Plan 2 Algorithm Design 3 Evaluation 4 Results 5 Conclusion Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 5. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contributionto the Thesis Time Plan Federated Cloud Definition Mechanisms and policies for scaling hosted services across multiple, geographically distributed data centers and dynamically coordinating load distribution among these data centers. Aims an open and online cloud economy in which providers: operate as parts of a market driven resource leasing federation; can dynamically partner with each other to create a seemingly infinite pool of IT resources. While users of cloud infrastructure: avoid vendor lock-in and can easily hybridize their private data center; can scale VMs across multiple IaaS providers in different geo-locations. Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 6. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contributionto the Thesis Time Plan Distributed VMs Opportunities: Available mechanisms and policies such as Federated Cloud; Very high speed inter-DC communication technologies such as optical fiber; Programming models that minimize size of data flow between nodes such as MapReduce Advantages: fault tolerance vendor independence closer proximity to user base cost benefits Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 7. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contributionto the Thesis Time Plan Distributed VMs VM Placement Risks: Cooperating VMs on distant DCs; VMs far away from their user base; VMs placed without considering different pricing strategies of vendors Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 8. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contributionto the Thesis Time Plan Outline 1 Introduction Preliminary Information Contribution to the Thesis Time Plan 2 Algorithm Design 3 Evaluation 4 Results 5 Conclusion Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 9. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contributionto the Thesis Time Plan Algorithm Design and Implementation Suggested Topology Based Matching (TBM) algorithm employs a graph theoretical approach in combination with some heuristics. Incremental development Re-evaluation after each new improvement to compare against baselines to detect bottlenecks and other problems Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 10. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contributionto the Thesis Time Plan Evaluation Bandwidth modeling Cost modeling Load modeling Evaluation variables 7 baseline methods, 12 performance criteria, 4 variables Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 11. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contributionto the Thesis Time Plan Documentation A conference paper to be presented, another journal paper being written Application for TUBITAK 1002 - Short Term R&D Funding Program Batch evaluation process which generates and logs results and charts for each run Revision control and documentation (https://github.com/atary/RalloCloud/) Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 12. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contributionto the Thesis Time Plan Outline 1 Introduction Preliminary Information Contribution to the Thesis Time Plan 2 Algorithm Design 3 Evaluation 4 Results 5 Conclusion Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 13. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contributionto the Thesis Time Plan Gantt Chart 2015 1 2 3 4 5 6 Algorithm Design Implementation Evaluation Modification Documentation Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 14. Introduction Algorithm Design Evaluation Results Conclusion Outline 1 Introduction PreliminaryInformation Contribution to the Thesis Time Plan 2 Algorithm Design 3 Evaluation 4 Results 5 Conclusion Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 15. Introduction Algorithm Design Evaluation Results Conclusion Objective To decreasedeployment delay (by placing VMs close to the broker) To decrease communication delay (by placing connected VMs to the neighbour data centers) To reduce resource costs (by balancing load and avoiding overload in any DC) Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 16. Introduction Algorithm Design Evaluation Results Conclusion UML ActivityDiagram Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 17. Introduction Algorithm Design Evaluation Results Conclusion Subgraph Matching Searchspace is all possible injective matchings from the set of pattern nodes to the set of target nodes. Systematically explore the search space: Start from an empty matching Extend the partial matching by matching a non matched pattern node to a non matched target node Backtrack if some edges are not matched Repeat until all pattern nodes are matched (success) or all matchings are already explored (fail). Filters are necessary to reduce the search space by pruning branches that do not contain solutions. Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 18. Introduction Algorithm Design Evaluation Results Conclusion LAD Filtering AtakanAral Modeling and Optimization of Resource Allocation in Cloud
- 19. Introduction Algorithm Design Evaluation Results Conclusion LAD Filtering D1= D3 = D5 = D6 = A, B, C, D, E, F, G D2 = D4 = A, B, D Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 20. Introduction Algorithm Design Evaluation Results Conclusion Outline 1 Introduction PreliminaryInformation Contribution to the Thesis Time Plan 2 Algorithm Design 3 Evaluation 4 Results 5 Conclusion Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 21. Introduction Algorithm Design Evaluation Results Conclusion Bandwidth Modeling Bandwidthcapacities are modeled not in links but in DCs. When a link is utilized, same amount of bandwidth is reduced from the DCs in both sides of the link. More generally, bandwidth capacities of all the nodes that are on the shortest path are utilized. Bandwidth request between two VMs is nonbifurcated. (No path-splitting) Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 22. Introduction Algorithm Design Evaluation Results Conclusion Bandwidth Modeling VM1VM3VM2 VM1 VM2VM3 Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 23. Introduction Algorithm Design Evaluation Results Conclusion Cost Modeling 1Fixed pricing based on memory, bandwidth and duration. 2 Dynamic pricing via Yield management Increase the price of the resource that is running low in a DC Cost = minCost + (maxCost − minCost) ∗ Util Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 24. Introduction Algorithm Design Evaluation Results Conclusion Load Modeling Numberof Clusters Based on the population density around each location. Range: 1:16 Number of VMs Based on Poisson distribution: λ = 3 Cluster Topologies Either linear or complete Arrival Times Uniform random in the range [0, 50) Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 25. Introduction Algorithm Design Evaluation Results Conclusion Evaluation Variables VMmemory request Data center memory capacity is 64x and each VM requires memory allocation between 1x and 8x Link bandwidth request Available bandwidth in each link is 80y and bandwidth allocation to/from other VMs is between 1y and 8y. Minimum number of requests Average number of requests from the least populated location is in the range [2, 16] depending on this variable. VM network intensity Ratio of local computation and inter-VM communication is between 3 and 1/3. Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 26. Introduction Algorithm Design Evaluation Results Conclusion Outline 1 Introduction PreliminaryInformation Contribution to the Thesis Time Plan 2 Algorithm Design 3 Evaluation 4 Results 5 Conclusion Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 27. Introduction Algorithm Design Evaluation Results Conclusion Performance Criteriaand Baseline Heuristics Arbitrary Next-fit (ANF) Load Balancing (LBG) Random Choice (RAN) Latency based Next-fit (LNF) VM Deployment Latency (Seconds) VM Communication Latency (Seconds) Task Completion Time (Hours) Throughput (MIPS) Rejection Rate (%) Cost ($) Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 28. Introduction Algorithm Design Evaluation Results Conclusion VM memoryrequest 0,00 50,00 100,00 150,00 200,00 250,00 300,00 1 2 3 4 5 6 7 8 VMDeploymentLatency(Seconds) VM RAM ANF LBG RAN TBF LNF 0,0 0,5 1,0 1,5 2,0 2,5 3,0 1 2 3 4 5 6 7 8 VMCommunicationLatency(Seconds) VM RAM ANF LBG RAN TBF LNF Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 29. Introduction Algorithm Design Evaluation Results Conclusion VM memoryrequest 0 50 100 150 200 250 300 350 400 1 2 3 4 5 6 7 8 TaskCompletionTime(Hours) VM RAM ANF LBG RAN TBF LNF 0 500 1000 1500 2000 2500 3000 1 2 3 4 5 6 7 8 Throughput(MIPS) VM RAM ANF LBG RAN TBF LNF Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 30. Introduction Algorithm Design Evaluation Results Conclusion VM memoryrequest 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 RejectionRate(%) VM RAM ANF LBG RAN TBF LNF 0 10000 20000 30000 40000 50000 60000 1 2 3 4 5 6 7 8 Cost($) VM RAM ANF LBG RAN TBF LNF Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 31. Introduction Algorithm Design Evaluation Results Conclusion Link bandwidthrequest 0,0 0,5 1,0 1,5 2,0 2,5 1 2 3 4 5 6 7 8 VMCommunicationLatency(Seconds) Link BW ANF LBG RAN TBF LNF 0 10000 20000 30000 40000 50000 60000 1 2 3 4 5 6 7 8 Cost($) Link BW ANF LBG RAN TBF LNF Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 32. Introduction Algorithm Design Evaluation Results Conclusion Minimum numberof requests 0 500 1000 1500 2000 2500 1 2 3 4 5 6 7 8 Throughput(MIPS) Number of Requests ANF LBG RAN TBF LNF Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 33. Introduction Algorithm Design Evaluation Results Conclusion VM networkintensity 0 50 100 150 200 250 300 350 1 2 3 4 5 6 7 8 TaskCompletionTime(Hours) Network intensity ANF LBG RAN TBF LNF 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 1 2 3 4 5 6 7 8 Cost($) Network intensity ANF LBG RAN TBF LNF Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 34. Introduction Algorithm Design Evaluation Results Conclusion Outline 1 Introduction PreliminaryInformation Contribution to the Thesis Time Plan 2 Algorithm Design 3 Evaluation 4 Results 5 Conclusion Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 35. Introduction Algorithm Design Evaluation Results 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). (to appear) Aral, A. and Ovatman, T. (2015). Graph theoretical allocation of map reduce clusters in federated cloud. (for journal submission) Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 36. Introduction Algorithm Design Evaluation Results Conclusion Planned Studies Algorithm Additionalconstraints (jurisdiction, partially known topology) Vertical scaling support Hybrid cloud support Homeomorphism Connected components Evaluation Significance study Evaluation with topology improvements Multi-objective optimization Dynamic heuristic selection, meta-heuristics Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
- 37. Introduction Algorithm Design Evaluation Results Conclusion Thank youfor your time. Atakan Aral Modeling and Optimization of Resource Allocation in Cloud