Data centers consist of various users with multiple roles and differentiated levels of access. Tenant execution flows can be of different priorities based on the role of the tenant and the nature of the process. Traditionally enterprise network optimizations are made at each specific layer, from the physical layer to the application layer. However, a cross-layer optimization of cloud networks would utilize the data available to each of the layers in a more efficient manner. This paper proposes an approach and architecture for differentiated quality of service (QoS). By employing a selective redundancy in a controlled manner, end-to-end delivery is guaranteed for priority tenant application flows despite congestion. The architecture, in a higher level, focuses on exploiting the global knowledge of the underlying network readily available to the Software-Defined Networking (SDN) controller to cater the requirements of the tenant applications. QoS is guaranteed to the critical tenant flows in multi-tenant clouds by cross-layer enhancements across the network and application layers.

1. Selective Redundancy in Network-as-a-Service:
Differentiated QoS in Multi-Tenant Clouds
Pradeeban Kathiravelu, Lu´ıs Veiga
INESC-ID Lisboa
Instituto Superior T´ecnico, Universidade de Lisboa
Lisbon, Portugal
11th
International Workshop on
Enterprise Integration, Interoperability and Networking (EI2N 2016)
26th
October 2016, Rhodes, Greece.
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2. Introduction
Introduction
Cloud data centers consist of various tenants with multiple roles.
Differentiated Quality of Service (QoS) in multi-tenant clouds.
Service Level Agreements (SLA).
Different priorities among tenant processes.
Network is shared among the tenants.
End-to-end delivery guarantee despite congestion for critical flows.
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3. Introduction
Software-Defined Networking (SDN) for Clouds
Cross-layer optimization of clouds with SDN.
Centralized control plane of the network-as-a-service.
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4. Introduction
Middleboxes in the cloud networks
Middleboxes - hardware and software.
Device that manipulates network traffic, other than packet forwarding.
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5. Introduction
Motivation
How to offer differentiated QoS and SLA in multi-tenant networks?
Application-level user preferences and system policies.
Performance guarantees at the network-level.
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6. Introduction
Motivation
How to offer differentiated QoS and SLA in multi-tenant networks?
Application-level user preferences and system policies.
Performance guarantees at the network-level.
More potential in having them both!
SDN, Middleboxes, . . .
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7. Introduction
Goals
How to offer differentiated QoS and SLA in multi-tenant networks?
Leverage SDN to offer a selective partial redundancy in network flows.
FlowTags - Software middlebox to tag the flows with contextual
information.
Application-level preferences to the network control plane as tags.
Dynamic flow routing modifications based on the tags.
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8. Solution Architecture
SMART
An SDN Middlebox Architecture for Reliable Transfers.
An architectural enhancement for network flows allocation, routing,
and control.
Timely delivery of priority flows by dynamically diverting them to a
less congested path.
Cloning subflows of higher priority flows.
An adaptive approach in cloning and diverting of the flows.
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9. Solution Architecture
Contributions
A cross-layer architecture ensuring differentiated QoS.
A context-aware appraoch in load balancing the network.
servers supporting multihoming, connected topologies, . . .
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10. Solution Architecture
SMART Approach
Divert and clone subflows by setting breakpoints in the flows in their
route to avert congestion.
Trade-off of minimal redundancy to ensure the SLA of priority flows.
Adaptive execution with contextual information on the network.
Leverage FlowTags middlebox
to pass application-level system and user preferences to the network.
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11. Solution Architecture
SMART Enhancements
When to break and when to merge?
Clone destination.
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12. Solution Architecture
SMART Deployment
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13. SMART Workflow
SMART Workflow
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14. SMART Workflow
I: Tag Generation for Priority Flows
Tag generation query and response.
between the hosts and the FlowTags
controller.
A centralized controller for
FlowTags.
Tag the flows at the origin.
FlowTagger software middlebox.
A generator of the tags.
Invoked by the host application layer.
Similar to the FlowTags-capable
middleboxes for NATs.
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15. SMART Workflow
II: Regular routing till the tags are violated
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16. SMART Workflow
II: Regular routing till the tags are violated
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17. SMART Workflow
III: When a threshold is met
Controller is triggered through OpenFlow API.
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18. SMART Workflow
III: When a threshold is met
Controller is triggered through OpenFlow API.
A series of control flows inside the control plane.
Modify flow entries in the relevant switches.
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19. SMART Workflow
SMART Control Flows: Rules Manager
A software middlebox in the control plane.
Consumes the tags from the packet.
Similar to FlowTags-capable firewalls.
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20. SMART Workflow
Rules Manager Tags Consumption
Interprets the tags
as input to the SMART Enhancer
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21. SMART Workflow
SMART Enhancer
Core of the SMART architecture.
Gets the input to the enhancement algorithms.
Decides the flow modifications.
Breakpoint node.
Brekpoint packet.
Clone/divert decisions.
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22. Implementation
Prototype Implementation
Developed in Oracle Java 1.8.0.
OpenDaylight Beryllium as the core SDN controller.
Enhancer and the Rules Manager middlebox as controller extensions.
Developed as OSGi bundles.
Deployed into Apache Karaf runtime of OpenDaylight.
FlowTags middlebox controller deployed along the SDN controller.
Originally a POX extension.
Network nodes and flows emulated with Mininet.
Larger scale cloud deployments simulated.
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23. Evaluation
Evaluation Strategy
Data center network with 1024 nodes and leaf-spine topology.
Path lengths of more than two-hops.
Up to 100,000 of short flows.
Flow completion time < 1 s.
A few non-priority elephant flows.
SLA → maximum permitted flow completion time for priority flows
Uniformly randomized congestion.
hitting a few uplinks of nodes concurrently.
overwhelming amount of flows through the same nodes and links.
Benchmark: SMART enhancements over base routing algorithms.
Performance (SLA awareness), redundancy, and overhead.
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24. Evaluation
SMART Adaptive Clone/Replicate with Shortest-Path
Replicate the subsequent flows once a previous flow was cloned.
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25. Evaluation
SMART Adaptive Clone/Replicate with ECMP
Repeat the experiment with Equal-cost multi-path routing.
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26. Conclusion
Related Work
Multipath TCP (MPTCP) uses the available multiple paths between
the nodes concurrently to route the flows across the nodes.
Performance, bandwidth utilization, and congestion control
through a distributed load balancing.
ProgNET leverages WS-Agreement and SDN for SLA-aware cloud.
pFabric for deadline-constrained data flows with minimal completion
time.
QJump linux traffic control module for latency-sensitive applications.
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27. Conclusion
Conclusion
Conclusions
SMART leverages redundancy in the flows as a mean to improve the
SLA of the priority flows.
Opens an interesting research question leveraging SDN, middleboxes,
and redundancy.
Cross-layer optimizations through tagging the flows.
For differentiated QoS.
Future Work
Implementation of SMART on a real data center network.
Evaluate against the identified related work quantitatively.
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28. Conclusion
Conclusion
Conclusions
SMART leverages redundancy in the flows as a mean to improve the
SLA of the priority flows.
Opens an interesting research question leveraging SDN, middleboxes,
and redundancy.
Cross-layer optimizations through tagging the flows.
For differentiated QoS.
Future Work
Implementation of SMART on a real data center network.
Evaluate against the identified related work quantitatively.
Thank you!
Questions?
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