Adaptive Scheduling with QoS Satisfaction in Hybrid Cloud Environment 研究生：李羿慷 指導老師：張玉山 老師

Outline 1.Introduction 2.Related Work 3.Problem Definition and Formulation 4.Adaptive Scheduling Algorithm with QoS Satisfaction 5.Experiments and Discussion 6.Conclusions and Future Work 2

1.Introduction 2.Related Work 3.Problem Definition and Formulation 4.Adaptive Scheduling Algorithm with QoS Satisfaction 5.Experiments and Discussion 6.Conclusions and Future Work 3

1. Introduction Cloud computing – Huge data store and highly parallel computing – Cloud services: SaaS, PaaS, IaaS Private cloud – Control and security issue – One-time purchase and long term maintain Public cloud – Flexible, scalable – Pay-per-use 4

Introduction (cont.) Cloud environment workload status – Ex: Yahoo! Video 5

Introduction (cont.) Hybrid Cloud – Combine Private and Public cloud – Private cloud Regular workload Constant maintenance cost – Public cloud Transit overloading Pay-per-use (cost issue) 6

Introduction (cont.) Cloud services – Efficiency – Reliability – Cost Quality of Service (QoS) – Response time ↓ – Payment ↓ 7

Introduction (cont.) Hybrid Cloud – Guarantee user QoS demand Workload dispatching – Private Cloud Maximize utilization Minimize execution time – Public Cloud Minimize cost expense 8

Introduction (cont.) To improve QoS satisfaction in hybrid cloud – We propose: Adaptive Scheduling with QoS Satisfaction in Hybrid Cloud Environment 9

MMKP Mapping the QoS satisfaction and cost function into a MMKP – MMKP (Multi-dimension Multi-choice Knapsack Problem) – MMKP is proved as NP-complete – Maximal utilization – Minimal cost value – QoS deadline constraint 10

Introduction (cont.) We may solve our problem by finding a near optimal heuristic solutions in polynomial time Using dynamic programming finding a heuristic solution (near optimal) – Solving complex problem into smaller sub- problems Using CloudSim to evaluate the experiment 11

1.Introduction 2.Related Work 3.Problem Definition and Formulation 4.Adaptive Scheduling Algorithm with QoS Satisfaction 5.Experiments and Discussion 6.Conclusions and Future Work 12

2. Related Work FIFO: first come first serve – Most common – Drawback: Convoy Effect Fair (Facebook), Capacity (Yahoo) Scheduling – Solve multi-user problem in FIFO – Ensure every task has approximately equal computational resource/time 13

Related Work (cont.) Intelligent Workload Factoring for A Hybrid Cloud Computing Model – Split the workload into two parts – Base load and trespassing load (privately-owned data center and public cloud service) – Reduce data cache/replication overhead – But not support real-time QoS constraint computing 14

Related Work (cont.) GA-Based Task Scheduler for the Cloud Computing Systems – Genetic Algorithm based for task level scheduling in Hadoop MapReduce – Achieve better load balancing – GA for making the optimal decision – Not for Hybrid Cloud – Not supporting QoS constraint 15

Related Work (cont.) Cost-Minimizing Scheduling of Workflows on a Cloud of Memory Managed Multicore Machines – Service-oriented architecture framework – Cost function, maps values of workflow tardiness to corresponding cost function value – To minimize the sum of cost function values for all workflows – Cost function not for user-aspect design – Not supporting Hybrid Cloud 16

1.Introduction 2.Related Work 3.Problem Definition and Formulation 4.Adaptive Scheduling Algorithm with QoS Satisfaction 5.Experiments and Discussion 6.Conclusions and Future Work 17

3. Problem Definition and Formulation 3.1 Resource Slot Definition 3.2 Request Job Definition 3.3 Problem Formulation 18

3.1 Resource Slot Definition Private resource slot – One node (machine) in private cloud can generate more slots – Base on virtual machine infrastructure – One slot require one CPU resource ability – Basic unit for handling request task 19

Resource Slot Definition (cont.) 20 Example of Private Slots

Resource Slot Definition (cont.) Resource Slots has different computing ability, depends on CPU speed, memory…etc – Unit: Million Instruction Per Second Private Cloud set data replications between resource slots 21

Resource Slot Definition (cont.) Public Resource Slot – Resource from charging public cloud provider – Based on different instance type – Unify charging policy by Computing price Storage price Data transfer price 22

Resource Slot Definition (cont.) 23 Example of Public Slots

3.2 Request Job Definition Target applications – Internet-based applications – Focus on data sets on certain kinds of distributable, parallel problems – Ex: image and video rendering codes and highly parallel data analysis codes – Each application has a completion deadline 24

25 Example of Request Jobs

3.3 Problem Formulation For guarantee QoS demand deadline constraint – Maximize private slot utilization – Minimize task execution time – Minimize cost value 26

Definition Deadline constraint: – For Job J i = {V i1 ~ V in }, and deadline D i – Code size SC ij for task V ij – For private slot PrR k and computing ability Prμ k, k = 1 to m 27

Definition (cont.) Budget control – For Job J i = {V i1 ~ V in }, and cost budget M i – Code size SC ij for task V ij – Information data size SD ij for task V ij – For public slot PuR q and computing price x q – For public slot PuR q and storage price y q k = 1 to m 28

Definition (cont.) Estimated Finish Time (Est) – For task V ij on private slot PrR k – Code size SC ij for V ij – Computing ability Prμ k for PrR k 29

Definition (cont.) Estimated Execution Time (EEt) – For task V ij on private slot PrR k – Code size SC ij for V ij – Computing ability Prμ k for PrR k Data Transmission Time (Dtt) – For task V ij and code size SC ij – Network bandwidth NB – Disk speed DS k on resource slot k 30,

Definition (cont.) Cost Function (CostF) – Code size SC ij and information data size SD ij for task V ij – Computing price x k, storage price y k, data transfer in price dti k and data transfer out price dti o for public resource slot PuR k 31

MMKP Mapping our mathematical formulate problems into MMKP (NP-complete) – MMKP (Multi-dimension Multi-choice Knapsack Problem) – Maximal utilization – Minimal cost value – QoS deadline constraint We may solve our problem by finding a near optimal heuristic solutions in polynomial time 32

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1.Introduction 2.Related Work 3.Problem Definition and Formulation 4.Adaptive Scheduling Algorithm with QoS Satisfaction 5.Experiments and Discussion 6.Conclusions and Future Work 34

4. Adaptive Scheduling Algorithm with QoS Satisfaction 4.1 Resource Needed Weight 4.2 Execution Time Estimation with Task on Different Slots 4.3 Dynamic Programming for Dispatching to Candidate Slots 4.4 Dispatch Selection from Slot Queue 4.5 Dynamic Programming for Minimal Cost on Public Slot 35

4.1 Resource Needed Weight If multi Jobs arrive in the pool at the same time, they’ll share the resource by the % of Resource Needed Weight W i Differ from Fair Scheduling, guarantee the resource amount base on code size and deadline – W x :W y :W z ->Slot distributed rate, for Job x, y, z 36

Resource Needed Weight (cont.) 37 Fair SchedulingAsQ

4.2 Execution Time Estimation with Task on Different Slots Collect private cloud resources’ current status and information – Remain code size – Computation ability Calculate estimated finish time (Est) Can find out when the slot will be available 38

Example of Est 39

Execution Time Estimation with Task on Different Slots (cont.) Calculate estimated execution time (EEt) of current tasks on every private resource from 1 to k – Estimated Execution time – Data Transfer Time If, Dtt=0 Else if, 40

Example of EEt 41

Execution Time Estimation with Task on Different Slots (cont.) By having Est and EEt, the slots which were able to finish the task before the deadline can be selected The slots which can reach the QoS (deadline) will be collect in a candidate set 42 Example of Est + EEt

Example of Overloading Dispatch 43

4.3 Dynamic Programming for Dispatching to Candidate Slots The optimal scheduling has been mapping to MMKP Using dynamic programming to solve the NP- complete problem Finding the minimal runtime of every tasks and slots – Data location, computation ability, network bandwidth…etc, will effect the total runtime 44

Example of Scheduling Job 2 45

Dynamic Programming for Dispatching to Candidate Slots (cont.) Dynamic programming will make the decision with minimal execution time of all The less execution time we take, the more task we can serve on the same private cloud resources with same operation cost More on private, less on charging public 46

4.4 Dispatch Selection from Slot Queue When transit overloading or strict deadline – Private slots can not handle in QoS demand Need to dispatch into charging public slots Examining the possibility of task in queue with dispatching into public slots – Data transmission time 47

Example of Job 3 Arrive 48

Example of Examining Dtt in Queue 49

4.5 Dynamic Programming for Minimal Cost on Public Slot Trying to minimize the cost in renting public resource slots Cost function with knapsack problem can be solve by dynamic programming Find out the minimal cost and reach the QoS deadline 50

Example of Minimum Cost Selection 51

Algorithm of [Execution Time Estimation with Task on Different Slots] & [Dynamic Programming for Dispatching to Candidate Slots] 52

Algorithm of [Dispatch Selection from Slot Queue] 53

Algorithm of [Dynamic Programming for Minimal Cost on Public Slot] 54

1.Introduction 2.Related Work 3.Problem Definition and Formulation 4.Adaptive Scheduling Algorithm with QoS Satisfaction 5.Experiments and Discussion 6.Conclusions and Future Work 55

5. Experiments and Discussion CloudSim Support for modeling and simulation with customizable policies for resources scheduler on Cloud computing 56 Slot experiment setup image size5 GB RAM512 MB BW1,000 CPU Number1 computing ability[10, 50] MIPS Task experiment setup file size[200, 400] MB output size[20, 40] MB code size[400, 1000] MI

5.1 Measurement of AsQ, FIFO and Fair – Latency measurement – QoS satisfaction rate measurement – Cost analysis 5.2 Measurement of AsQ and COSHIC – Cost analysis – Latency measurement – QSR spending time measurement – Normalized Violated Quality Value measurement 57

5.1 Latency Measurement Latency measurement – No deadline limit – Private resource only – 5, 10, 20, 50 tasks – Waiting time – Execution time – Finish time 58

Task Waiting Time Measurement 59

Task Execution Time Measurement 60

Task Finish Time Measurement 61

Measurement of AsQ, FIFO and Fair (cont.) QoS satisfaction rate measurement – Percentage of complete in time tasks of all – QSR = k/n n=total task number, k=task number which response before deadline, 0 ≦ k ≦ n – 20, 50, 70 tasks – Private slots only – Deadline: loose → strict 62

QSR Measurement (20 tasks) 63

QSR Measurement (50 tasks) 64

QSR Measurement (70 tasks) 65

Measurement of AsQ, FIFO and Fair (cont.) QSR – Cost measurement – 50, 70 tasks – Using public slots – Paying more for higher QSR 66 Public Cloud Slots Computing ability (MIPS) Computing price ($/MI) Storage price ($/MB) 100.1[0.01, 0.05] 200.2[0.01, 0.05] 500.5[0.01, 0.05]

QSR – Cost Measurement (50 tasks) 67

QSR – Cost Measurement (70 tasks) 68

Measurement of AsQ, FIFO and Fair (cont.) Cost analysis – 20, 50, 70 tasks – Deadline: loose → strict 69

Cost Analysis (20 tasks) 70

Cost Analysis (50 tasks) 71

Cost Analysis (70 tasks) 72

5.2 Measurement of AsQ and COSHIC Compare with “Cost-optimal Scheduling in Hybrid IaaS Clouds” – Linear programming formulation – Assume that applications are CPU and network intensive – Scheduling applications in the public cloud, in terms of cost minimization 73

Cost Analysis 74 Cost 22.7% as AsQ

Task Execution Time Measurement 75 Time spend 10.7%

Task Finish Time Measurement 76 Time spend 16.8%

QSR Spending Time Measurement 77 COSHIC spend 4.9 times than AsQ

NVQV Measurement Normalized Violated Quality Value For normalized the performance between execution time and cost value 78

NVQV Measurement (cont.) 79

Comparison with other Scheduling Algorithm 80

1.Introduction 2.Related Work 3.Problem Definition and Formulation 4.Adaptive Scheduling Algorithm with QoS Satisfaction 5.Experiments and Discussion 6.Conclusions and Future Work 81

6. Conclusions and Future Work We propose Adaptive Scheduling Algorithm with QoS Satisfaction Satisfy user QoS demand Near optimal resource allocation – Better resource utilization Lower cost spend for service provider 82

Finding suitable workload on private cloud with better tradeoff between operation cost and computing efficiency Reliability Implement on a real cloud environment 83

End 84