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Introduction
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Modeling and Optimization of Resource Allocation in Cloud
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To decrease deployment delay (by placing VMs close t...
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UML Activity Diagram
Atakan Aral Modeling and Optimization of ...
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Subgraph Matching
Search space is all possible injective match...
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LAD Filtering
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LAD Filtering
D1 = D3 = D5 = D6 = A, B, C, D, E, F, G
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Load Modeling
Number of Clusters Based on the population densi...
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VM memory request Data center memory capa...
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Aral, A. and Ovatman, T. (2014). Improving resour...
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Additional constraints (jurisdiction...
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Thank you for your time.
Atakan Aral Modeling and Optimization...
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Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progress 2]

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In addition to software delivered as services over the Internet, hardware and software systems that make the delivery possible are referred as cloud computing. In cloud computing, resources such as CPU, memory, bandwidth and storage are treated as utilities that can scale up and down on demand. It also allows per-usage metering and billing of these resources. By means of cloud computing, cloud users can handle unexpected high demands without over-provisioning and they do not need to invest for abundant hardware resources initially. Cloud providers, on the other hand, have the opportunity of reallocating idle resources for other cloud users.

One general research challenge in cloud computing is the efficient allocation of cloud resources to users since cloud providers should satisfy quality of service (QoS) objectives while minimizing their operational cost. Up to 85 percent of computing capacity remains idle in distributed computing environments and this wastage is mainly due to poor optimization of job placement, parallelization and scheduling. We aim to model resource allocation problem in cloud systems, analyze and optimize it using graphs and formal behavioral models (e.g. finite automata).

The problem that we are currently interested, is to distribute virtual machines (VMs) to the datacenters (DCs) with geographical locations in such a way that network latency and infrastructure cost is minimized while WAN bandwidth and DC capacity limits are respected. We model latency as a function of DC load, inter-DC communication and proximity to user. Both VM requests and cloud infrastructure are represented by graphs where vertices correspond to machines (either physical or virtual) and edges correspond to network connections between them. Our approach employs weighted graph similarity and subgraph matching to suggest an efficient placement or the list of migrations to reach an efficient placement.

In the first six months period of the thesis project, we collected evaluation data, developed the simulation environment and defined the experimental setup which contains metrics and baseline methods. In addition, we developed the preliminary version of the resource selection algorithm. While, in the second period, we completed the algorithm design and tuning, carried out detailed, progressive evaluation on the suggested algorithm and documented our work.

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Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progress 2]

  1. 1. Introduction Algorithm Design Evaluation Results Conclusion Modeling and Optimization 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. 2. Introduction Algorithm Design Evaluation Results Conclusion 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
  3. 3. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contribution to 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. 4. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contribution to 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. 5. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contribution to 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. 6. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contribution to 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. 7. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contribution to 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. 8. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contribution to 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. 9. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contribution to 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. 10. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contribution to 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. 11. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contribution to 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. 12. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contribution to 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. 13. Introduction Algorithm Design Evaluation Results Conclusion Preliminary Information Contribution to 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. 14. Introduction Algorithm Design Evaluation Results Conclusion 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
  15. 15. Introduction Algorithm Design Evaluation Results Conclusion Objective To decrease deployment 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. 16. Introduction Algorithm Design Evaluation Results Conclusion UML Activity Diagram Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
  17. 17. Introduction Algorithm Design Evaluation Results Conclusion Subgraph Matching Search space 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. 18. Introduction Algorithm Design Evaluation Results Conclusion LAD Filtering Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
  19. 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. 20. Introduction Algorithm Design Evaluation Results Conclusion 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
  21. 21. Introduction Algorithm Design Evaluation Results Conclusion Bandwidth Modeling Bandwidth capacities 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. 22. Introduction Algorithm Design Evaluation Results Conclusion Bandwidth Modeling VM1 VM3VM2 VM1 VM2VM3 Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
  23. 23. Introduction Algorithm Design Evaluation Results Conclusion Cost Modeling 1 Fixed 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. 24. Introduction Algorithm Design Evaluation Results Conclusion Load Modeling Number of 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. 25. 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
  26. 26. Introduction Algorithm Design Evaluation Results Conclusion 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
  27. 27. Introduction Algorithm Design Evaluation Results Conclusion Performance Criteria and 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 ($) Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
  28. 28. Introduction Algorithm Design Evaluation Results Conclusion VM memory request 0,00 50,00 100,00 150,00 200,00 250,00 300,00 1 2 3 4 5 6 7 8 VMDeploymentLatency(Seconds) VM RAM ANF LBG RAN TBF LNF 0,0 0,5 1,0 1,5 2,0 2,5 3,0 1 2 3 4 5 6 7 8 VMCommunicationLatency(Seconds) VM RAM ANF LBG RAN TBF LNF Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
  29. 29. Introduction Algorithm Design Evaluation Results Conclusion VM memory request 0 50 100 150 200 250 300 350 400 1 2 3 4 5 6 7 8 TaskCompletionTime(Hours) VM RAM ANF LBG RAN TBF LNF 0 500 1000 1500 2000 2500 3000 1 2 3 4 5 6 7 8 Throughput(MIPS) VM RAM ANF LBG RAN TBF LNF Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
  30. 30. Introduction Algorithm Design Evaluation Results Conclusion VM memory request 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 RejectionRate(%) VM RAM ANF LBG RAN TBF LNF 0 10000 20000 30000 40000 50000 60000 1 2 3 4 5 6 7 8 Cost($) VM RAM ANF LBG RAN TBF LNF Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
  31. 31. Introduction Algorithm Design Evaluation Results Conclusion Link bandwidth request 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($) Link BW ANF LBG RAN TBF LNF Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
  32. 32. Introduction Algorithm Design Evaluation Results Conclusion Minimum number of requests 0 500 1000 1500 2000 2500 1 2 3 4 5 6 7 8 Throughput(MIPS) Number of Requests ANF LBG RAN TBF LNF Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
  33. 33. Introduction Algorithm Design Evaluation Results Conclusion VM network intensity 0 50 100 150 200 250 300 350 1 2 3 4 5 6 7 8 TaskCompletionTime(Hours) Network intensity ANF LBG RAN TBF LNF 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 1 2 3 4 5 6 7 8 Cost($) Network intensity ANF LBG RAN TBF LNF Atakan Aral Modeling and Optimization of Resource Allocation in Cloud
  34. 34. Introduction Algorithm Design Evaluation Results Conclusion 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
  35. 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. 36. Introduction Algorithm Design Evaluation Results Conclusion Planned Studies Algorithm Additional constraints (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. 37. Introduction Algorithm Design Evaluation Results Conclusion Thank you for your time. Atakan Aral Modeling and Optimization of Resource Allocation in Cloud

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