towards economic fairness for big data processing in pay-as-you-go cloud computing nanyang...
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Towards Economic Fairness for Big Data Processing in Pay-as-you-go Cloud Computing
Nanyang Technological University
Shanjiang Tang, Bu-Sung Lee, Bingsheng He
School of Computer Engineering
Nanyang Technological University
23/4/18
Pay-as-You-Go Cloud Computing is Pervasive and Popular• Charge users based on the amount of resources
used over time (e.g., Hourly).• Advantages
– Elasticity– Flexibility– Cost efficiency
• Big data processing on the cloud– E.g., product recommendation, user’s behavior analysis, log analysis– Processing Frameworks: MapReduce, Spark.
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Twitter’s Cluster
Twitter’s Cluster
One week data from Twitter production cluster [Delimitrou et. Al. ASPLOS’14]
Resource Utilization =
• User resource demands are heterogeneous.– Users have different demands.– A 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.
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• Resource Sharing can improve resource utilization.– Allow underloaded users to release
resources to other users.– Allow overloaded users to temporarily
use more resources (from others). Reduce the idle resources at runtime.
• What about fairness?– If the fairness is not solved, resource
sharing is unlikely to achieve in pay-as-you-use environments.
To Share or Not To Share?
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Pay-as-you-go Fairness: Resource-as-you-pay• The total resources a user gained should be
proportional to her payment.
• This is a Service-Level Agreement (SLA).
60 $
40 $40 $
A:A:
B:B:
60% 40%40%
Resource ServiceResource Service
AA BB
Resource Service = Resources-per-time X service timeResource Service = Resources-per-time X service time
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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.– Cost-efficient workload incentive. (Users should be better
not to submit dirty workload)– Truthfulness (Users should not get benefits by cheating).
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Problems with MemoryLess Fairness
• Resource-as-you-pay Fairness Problem– E.g., A, B equally pay for total resource of 100 units.
Time
New Demand
A B
t1 20 100
AA BB
Accumulate Resource
Usage:
Accumulate Resource
Usage:
20
8080
2020 8080
Unsatisfied DemandUnsatisfied Demand
AA BB00 2020
7
Current Allocation at t1:
Current Allocation at t1:
Nanyang Technological University23/4/18
Problems with MemoryLess Fairness
• Resource-as-you-pay Fairness Problem– E.g., A, B equally pay for total resource of 100 units.
Time
New Demand
A B
t1 20 100
t2 40 60
AA BB
Accumulate Resource
Usage:
Accumulate Resource
Usage:
40
6060
6060 140140
AA BB
Unsatisfied DemandUnsatisfied Demand
00 2020
8
Current Allocation at t2:
Current Allocation at t2:
Nanyang Technological University23/4/18
Problems with MemoryLess Fairness
• Resource-as-you-pay Fairness Problem– E.g., A, B equally pay for total resource of 100 units.
Time
New Demand
A B
t1 20 100
t2 40 60
t3 80 50
AA BB
Accumulated resource usage:
Accumulated resource usage:
50
5050
110110 190190
AA BB
Unsatisfied DemandUnsatisfied Demand
3030 2020
9
Current Allocation at t3:
Current Allocation at t3:
Nanyang Technological University23/4/18
Problems with MemoryLess Fairness
• Resource-as-you-pay Fairness Problem– E.g., A, B equally pay for total resource of 100 units.
Time
New Demand
A B
t1 20 100
t2 40 60
t3 80 50
t4 60 50
AA BB
Accumulated resource usage:
Accumulated resource usage:
50
5050
160160 240240
AA BB
Unsatisfied DemandUnsatisfied Demand
4040 2020
Existing Fair Policy fails to satisfy Resource-as-you-pay fairness!!!Existing Fair Policy fails to satisfy Resource-as-you-pay fairness!!!10
Current Allocation at t4:
Current Allocation at t4:
Nanyang Technological University23/4/18
Other Problems for MemoryLess Fairness• Cost-inefficient workload incentive Problem
– Selfish users might possess unneeded resources by submitting dirty workloads.
• Untruthfulness Problem– Cheating users can get benefit under memoryless policy.
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Proof sketches are in the paper.
Our Work
• Challenges: can we find a fair sharing policy that satisfies the following properties?– Resource-as-you-pay fairness– Cost-efficient workload incentives– Truthfulness
• Our Solution: Long-Term Resource Fairness– Ensure resource fairness over a period of time. – With historical information considered.
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Long-Term Resource Fairness
• Basic 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. Benefit others and user herself.
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Long-Term Resource Fairness
• Satisfy Pay-as-you-use Fairness
Time
New Demand
A B
t1 20 100
AA BB
Accumulated resource usage:
Accumulated resource usage:
20
8080
2020 8080
Unsatisfied DemandUnsatisfied Demand
AA BB00 2020
14
Current Allocation at t1:
Current Allocation at t1:
AA
BB
Lend Resources:
Lend Resources:
3030
-30-30
Nanyang Technological University23/4/18
Long-Term Resource Fairness
• Satisfy Pay-as-you-use Fairness
Time
New Demand
A B
t1 20 100
t2 40 60
AA BB
Accumulated resource usage:
Accumulated resource usage:
40
6060
6060 140140
AA BB
Unsatisfied DemandUnsatisfied Demand
00 2020
15
Current Allocation at t2:
Current Allocation at t2:
AA
BB
Lend Resources:
Lend Resources:
4040
-40-40
Nanyang Technological University23/4/18
Long-Term Resource Fairness
• Satisfy Pay-as-you-use Fairness
Time
New Demand
A B
t1 20 100
t2 40 60
AA BB
Accumulated resource usage:
Accumulated resource usage:
40
6060
6060 140140
AA BB
Unsatisfied DemandUnsatisfied Demand
00 2020
16
Current Allocation at t2:
Current Allocation at t2:
AA
BB
Lend Resources:
Lend Resources:
4040
-40-40 t3 80 50
Nanyang Technological University23/4/18
Long-Term Resource Fairness
• Satisfy Pay-as-you-use Fairness
Time
New Demand
A B
t1 20 100
t2 40 60
t3 80 50
AA BB
Accumulated resource usage:
Accumulated resource usage:
80
2020
140140 160160
AA BB
Unsatisfied DemandUnsatisfied Demand
00 5050
17
Current Allocation at t3:
Current Allocation at t3:
AA
BB
Lend Resources:
Lend Resources:
1010
-10-10
Nanyang Technological University23/4/18
Long-Term Resource Fairness
• Satisfy Pay-as-you-use Fairness
Time
New Demand
A B
t1 20 100
t2 40 60
t3 80 50
AA BB
Accumulated resource usage:
Accumulated resource usage:
80
2020
140140 160160
AA BB
Unsatisfied DemandUnsatisfied Demand
00 5050
18
Current Allocation at t3:
Current Allocation at t3:
AA
BB
Lend Resources:
Lend Resources:
1010
-10-10
t4 60 50
Nanyang Technological University23/4/18
Long-Term Resource Fairness
• Satisfy Pay-as-you-use Fairness
Time
New Demand
A B
t1 20 100
t2 40 60
t3 80 50
t4 60 50
AA BB
Accumulated resource usage:
Accumulated resource usage:
60
4040
200200 200200
AA BB
Unsatisfied DemandUnsatisfied Demand
00 6060
Long-Term Resource Fairness satisfy Resource-as-you-pay fairness.Long-Term Resource Fairness satisfy Resource-as-you-pay fairness.19
Current Allocation at t4:
Current Allocation at t4:
AA
BB
Lend Resources:
Lend Resources:
00
00
Nanyang Technological University23/4/18
Properties of Long-Term Resource Fairness• Satisfy resource-as-you-pay fairness
• Satisfy cost-efficient workload incentives– Running dirty/cost-inefficient workload can waste
money.
• Users cannot get benefits by lying (strategy-proof).
– A robust policy should avoid cheating users get benefit. Otherwise, nobody is willing to share resources.
20
Proof sketches are in the paper.
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LTYARN
• Implement Long-Term Resource Fairness in YARN– Extend memoryless max-min fairness to long-term max-
min fairness.– Add a few components into resource manager• Support full long-term and time window-based
requirements.• Currently support a single resource type (main
memory).• The experimental Results demonstrates the
effectiveness of our approach.
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Detailed design and experimental results are in the paper.
Future Work Plan
• Consider Multi-resource fairness– Different types of resources, e.g., <memory, cpu>.
• Move to Heterogeneous pricing plans and instance types– On-demand price plan, reserved price plan– t2.micro, t2.small, t2.medium, and m3.large
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