Predictable Data Centers

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1 Predictable Data Centers Thomas Karagiannis Hitesh Ballani, Paolo Costa, Fahad Dogar, Keon Jang, Greg O Shea, Eno Thereska, and Ant Rowstron Systems & Networking Microsoft Research, Cambridge

2 Predictable Data Centers Goal: Enable predictable application performance in multi-tenant data centers Multi-tenant data center is a data center with multiple (possibly competing) tenants Private data centers Run by organizations like Facebook, Microsoft, Google, etc Tenants: Product groups and applications Cloud data centers Unpredictability: a key hindrance to cloud adoption Amazon EC2, Microsoft Azure, Rackspace, etc. Tenants: Users renting virtual machines

3 Dimensions of unpredictability Performance No throughput or latency guarantees Private data centers: SLA violations, starvation Public data centers: Impossible to provide SLAs Costs Absence of performance guarantees implies unpredictable costs Location-dependent costs Fairness Payment not always translates to performance differentiation

4 Dimensions of unpredictability Performance No throughput or latency guarantees Private data centers: SLA violations, starvation Public data centers: Impossible to provide SLAs Costs Root-cause: Shared resources Absence of performance guarantees implies unpredictable costs (network & storage) Location-dependent costs Fairness Payment not always translates to performance differentiation

5 Performance Unpredictability - Public cloud Ken Ken Map Reduce Data analytics on an isolated cluster Job Enterprise Results Data analytics in the public cloud Map Reduce Results Job Azure data center Completion Time 4 hours Completion Time 4-8 hours

6 Performance Unpredictability - Public cloud Ken Ken Map Reduce Data analytics on an isolated cluster Job Enterprise Results Data analytics in the public cloud Map Reduce Results Job Azure data center Completion Time 4 hours Completion Time 4-8 hours Variable tenant costs Expected cost (based on 4 hour completion time) = $100 Actual cost = $

7 VM cost / unit time Cost unpredictability: Public cloud Today s Price CPU-bound jobs Job Cost = $ k N T (e.g.., k= $0.085 /hour) k Bandwidth (B) Network-bound jobs Job Cost = $ k N T but T = L, hence.. B Job Cost = $ k N L B Simple and intuitive Cost depends on B (high variability) No incentive for the provider

8 Towards a predictable cloud Performance Guarantee network throughput Virtual Network Abstractions [Oktopus, SIGCOMM 11] Request <N, B> N VMs. Each VM can send and receive at B Mbps Pricing Dominant resource pricing [HotNets 11] Change the cloud model! Job-based pricing [Bazaar, SOCC 12]

9 Bazaar Enables predictable performance and cost Tenant Job Request Perf/Cost constraints Bazaar Resources Required VMs and network Provider Job Cost Resource Utilization Today s pricing: Resource-based Bazaar enables job-based pricing!

10 Dimensions of unpredictability Performance No throughput or latency guarantees Private data centers: SLA violations, starvation Public data centers: Impossible to provide SLAs Costs Absence of performance guarantees implies unpredictable costs Location-dependent costs Fairness Payment not always translates to performance differentiation

11 SLA violations: User-facing online services Request Response Application SLAs 200 ms 100 ms 45 ms Aggregator 5ms 5ms 5ms 25ms 35ms 25ms Agg. Agg. Agg. 4ms 4ms 30ms 4ms 4ms 22ms 15ms 25ms Worker Worker Worker Worker Cascading SLAs SLAs for components at each level of the hierarchy Network SLAs Deadlines on communications between components Flow Deadlines A flow is useful if and only if it satisfies its deadline Today s transport protocols: Deadline agnostic and strive for fairness

12 Flows Flows Case for unfair sharing: Flow f1, 20ms f1 Limitations of Fair Sharing Status Quo Deadline aware X f1 Flow f2, 40ms f Time Flows Flows get f1 bandwidth f2 get in a accordance fair share of to bandwidth their deadlines Flow Deadline f1 misses awareness its deadline ensures (incomplete both flows response satisfy deadlines to user) f Time

13 Flows Flows Limitations of Fair Sharing Case for flow quenching: 6 flows with 30ms deadline 30 Time X X X X X X 30 Time With Insufficient deadline awareness, bandwidth one to satisfy flow can all deadlines be quenched With All fair other share, flows all make flows their miss the deadline deadline (partial (empty response)

14 Predictability in private data centers Deadline-driven flow scheduling D 3 [SIGCOMM 11] Prioritize flows based on deadlines Expose flow deadlines to the network Explicit rate control Task aware Data Centers From flow level metrics to application level benefits

15 How to allocate network resources? Fair Sharing Assign equal amount of bandwidth to all flows QoS aware allocation Two Broad Approaches Meet flow deadlines [D3, Sigcomm 11] Minimize flow completion times [PDQ, Sigcomm 12] Common Theme: Focus on flow level metrics Do they translate into app level benefits?

16 Task Oriented Applications Typical DC apps perform tasks Unit of work that can be linked to a waiting user Examples Answering a user s search query Generating a user s wall From the network s perspective, tasks generate rich workflows

17 Flow vs. Task aware optimizations Goal: Minimize Task Completion Times Task 1 Task 2 Shortest Flow First (SFF) Link A 3 6 Task Aware Link A 3 6 Link B 3 6 Link B 6 3 time time

18 Dimensions of unpredictability Performance No throughput or latency guarantees Private data centers: SLA violations, poor performance Public data centers: Impossible to provide SLAs Costs Absence of performance guarantees implies unpredictable costs Location-dependent costs Fairness Payment not always translates to performance differentiation

19 Two types of cloud traffic Intra-tenant Among a tenant s VMs Inter-tenant Tenant-tenant traffic Tenant provided cloud services storage, DB, identity management, etc Significant fraction: 10-35% of DC traffic Q: How to share DC network?

20 State of the art P and Q are paying same amount p 1000Mbps r1 r2 Allocation P (Mbps) Q (Mbps) Per flow Seawall FairCloud q r3 r4 Q gets larger network share despite same payment! Q: Whose payment should dictate the flow bandwidth?

21 Hadrian: Network sharing in the cloud [NSDI 13] What semantics should we provide to tenants? Virtual network abstraction How to allocate bandwidth? Ensure upper-bound proportionality: Network share proportional to payment Based on minimum of payment of communicating tenants How to place virtual machines? Greedy heuristic that guarantees min bandwidth

22 Summary Unpredictability Key hindrance to cloud adoption Root cause: Shared resources Several challenges: performance, cost, fairness

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