Long-Term Resource Fairness

Transcription

1 Long-Term Resource Fairness Towards Economic Fairness on Pay-as-you-use Computing Systems Shanjiang Tang, u-sung Lee, ingsheng He, Haikun Liu School of Computer Engineering Nanyang Technological University

2 Pay-s-You-Use is Pervasive Charge users based on the amount of resources used over time (e.g., Hourly). dvantages Elasticity Flexibility Cost efficiency Pay-as-you-use is becoming common and popular. Supercomputing, Cloud Computing 2

3 Resource Utilization = User resource demands are heterogeneous. Users have different demands. user s demand is changing over time. Static provisioning/partitioning causes underutilization. Resource utilization is a critical problem in such pay-as-you-use environments. Providers waste resources ( waste investment and lose profit). Users waste money. Twitter s Cluster 3 One week data from Twitter production cluster [Delimitrou et. l. SPLOS 14]

4 To Share or Not To Share? Resource Sharing can improve resource utilization. llow underloaded users to release resources to other users. llow overloaded users to temporarily use more resources (from others). Reduce the idle resources at runtime. Resolve resource contention across users. What about fairness? If the fairness is not solved, resource sharing is unlikely to achieve in pay-as-you-use environments. 4

5 Pay-as-you-use Fairness: Resource-as-you-pay The total resources a user gained should be proportional to her payment. This is a Service-Level greement (SL). : : 60 $ 40 $ 60% 40% Resource Service Resource Service = Resources-per-time X service time 5

6 Fair Policy in Existing Systems State-of-the-art: Max-min fairness Select the user with the minimum allocation/share ratio every time. Consider the present requirement only (memoryless). Memoryless fairness has severe problems in pay-as-you-use environments, violating the following properties: Resource-as-you-pay fairness guarantee. Non-Trivial workload incentive and sharing incentive. Truthfulness (Users may get benefits by cheating). 8

7 Problems with MemoryLess Fairness Resource-as-you-pay Fairness Problem E.g.,, equally pay for total resource of 100 units. Current llocation at t1: New Demand Time t ccumulate Resource Usage: Unsatisfied Demand

8 Problems with MemoryLess Fairness Resource-as-you-pay Fairness Problem E.g.,, equally pay for total resource of 100 units. Current llocation at t2: New Demand Time t t ccumulate Resource Usage: Unsatisfied Demand

9 Problems with MemoryLess Fairness Resource-as-you-pay Fairness Problem E.g.,, equally pay for total resource of 100 units. Current llocation at t3: New Demand Time t t t ccumulated resource usage: Unsatisfied Demand

10 Problems with MemoryLess Fairness Resource-as-you-pay Fairness Problem E.g.,, equally pay for total resource of 100 units. Current llocation at t4: New Demand Time t t t t ccumulated resource usage: Unsatisfied Demand Existing Fair Policy fails to satisfy Resource-as-you-pay fairness!!! 12

11 MemoryLess Fairness Violates Sharing Incentives Non-trivial workload and sharing incentive Problem Yielding resources to others have no benefits. Suppose,, and C equally pay for total resource of 100 units. has 13 idle resource units. In that case, can be selfish, either idle or running trivial workloads. : : C: CPU s idle resource 13

12 Cheating User enefits on MemoryLess Fairness Truthfulness Problem Suppose,, C equally pay for a cluster of 100 units, with true demand to be 33, 21 and 80, respectively. Case 1: all are honest. Case 2: User cheats and claims the demand to be 40. : : C: : : C: s cheating gets benefits Case 1: is honest Case 2: is cheating 14

13 Our Work Challenges: can we find a fair sharing policy that satisfies the following properties? Resource-as-you-pay fairness Non-trivial workload and sharing incentives Truthfulness Our Solution: Long-Term Resource Fairness Ensure resource fairness over a period of time. With historical information considered. 15

14 Long-Term Resource Fairness asic Concept: Loan agreement (Lending w/o interests) When resources are not needed, users can lend the resources to others. When more resources are needed, others should give back. enefit others and user herself. 16

15 Long-Term Resource Fairness Satisfy Pay-as-you-use Fairness Current llocation at t1: 20 Lend Resources: 30 New Demand Time t ccumulated resource usage: Unsatisfied Demand

16 Long-Term Resource Fairness Satisfy Pay-as-you-use Fairness Current llocation at t2: Lend Resources: New Demand Time t t ccumulated resource usage: Unsatisfied Demand

17 Long-Term Resource Fairness Satisfy Pay-as-you-use Fairness Current llocation at t2: Lend Resources: New Demand Time t t t ccumulated resource usage: Unsatisfied Demand

18 Long-Term Resource Fairness Satisfy Pay-as-you-use Fairness Current llocation at t3: Lend Resources: New Demand Time t t t ccumulated resource usage: Unsatisfied Demand

19 Long-Term Resource Fairness Satisfy Pay-as-you-use Fairness Current llocation at t3: Lend Resources: New Demand Time t t t t ccumulated resource usage: Unsatisfied Demand

20 Long-Term Resource Fairness Satisfy Pay-as-you-use Fairness Current llocation at t4: Lend Resources: 0 0 New Demand Time t t t t ccumulated resource usage: Unsatisfied Demand 0 60 Long-Term Resource Fairness satisfy Resource-as-you-pay fairness. 22

21 Other Properties of Long-Term Resource Fairness Satisfy non-trivial workload and sharing incentives Running trivial workload can waste money. Not sharing idle resource can waste money. Users cannot get benefits by lying (strategy proof). Proof sketches are in the paper. 23

22 LTYRN Implement Long-Term Resource Fairness in YRN Extend memoryless max-min fairness to long-term maxmin fairness. dd a few components into resource manager Support full long-term and time window-based requirements. Currently support a single resource type (main memory). 24

23 LTYRN Design Quantum Updater (QU) Estimates task execution time. Updates the resource usage history periodically. Resource Controller (RC) Manages and updates resource for each queue. Resource llocator (R) Performs long-term resource allocation. Runs when there are pending tasks and idle resources. 25

24 Evaluation Hadoop Cluster 10 nodes, each with two Intel X5675 CPUs (6 cores per CPU with 3.07 GHz), 24G DDR3 memory, 56G hard disks. YRN-2.2.0, configured with 24G memory per node. Macro-benchmarks Synthetic Facebook Workload Purdue Workload HIVE/TPC-H Spark Detailed setups are in the paper. 26

25 Metrics Evaluation metrics Fairness degree for each user (>1 for sharing benefits; <1 for sharing loss) Resource-as-you-pay fairness pplication performance enchmark scenario The four macro benchmarks equally share the cluster. Each benchmark runs in a separate queue. Window size =1 day. 27

26 Sharing enefit/loss LTYRN enables sharing benefits for all applications. (a). YRN (b). LTYRN 29

27 Resource-as-you-pay Fairness Results LTYRN achieves resource-as-you-pay fairness. 30

28 Performance Results Sharing always achieves a better performance. Long-term fairness is comparable to memory-less fairness (max-min). 31

29 Conclusions Max-min resource fairness is memoryless and unsuitable for pay-as-you-use computing. We define long-term resource fairness that can satisfy the desirable properties. We develop LTYRN by integrating long-term resource fairness into YRN Homepage: 32

30 We are Hosting IEEE CloudCom 2014 in Singapore Deadline for paper submissions: July 31, 2014 Notification of Paper acceptance: September 2, 2014 Conference: December 15-18,

31 Thanks! Question? 35