1. Addressing Data Compatibility on Programmable Network Platforms Ada Gavrilovska, Karsten Schwan College of Computing Georgia Tech

2. distributed networked infrastructures advanced network services: – transformation for interoperability with external partners/heterogeneous clients – data integration from multiple sources – distribution to/specialization for multiple sinks service quality guarantees

3. Need for Efficient Data Exchanges Data Exchanges: –across distributed components, from heterogeneous sources to variety of clients –discrepancies among the data representations used by sources, clients, or intermediate application components (e.g., due to natural mismatches or due to dynamic component evolution) –requirements to route, combine, or otherwise manipulate data as it is being transferred –efficiency == perceived service quality, honoring performance guarantees (SLAs…) expressed in the context of the application

4. Network-near service execution Existing middleware-level approaches enable ‘horizontally’ flexible service deployment solutions: –on nodes along data path Our approach: enable ‘vertical’’ flexibility by permitting applications to push service execution closer to the network Our assumption: nodes have multiple execution contexts –smart, programmable NICs –dedicated/specialized cores for communication –attached network processors (Intel IXP NPs)

5. ‘In-transit’ Data Transformations by default, deliver data to/from application components on general purpose processing context –kernel or application (OS-bypass capabilities) enable execution of middleware- /application-level processing actions jointly with communications –use metadata to describe application data, processing actions & requirements, platform state… –offload computational CPU, enable direct data placement of needed data, benefit from specialized hardware… configure paths dynamically –application needs, context capabilities…

6. Enabling In-Transit Data Morphing Represent application-level data –reliable UDP in NP –application meta-data for format description Classification –flexible classification based on tag consisting of network and application-level fields Handlers –stream handlers – computational unit applied to application data, can be executed jointly with fast path at well-defined application points –may be chained -> handler chains Operations –support individual data manipulations as well as merging and splitting Reconfiguration –modify data path through platform, parameters, or deploy new codes

7. Execution environment

8. Meeting application-level quality every 3s deliver complete, or at least partial updates –if latency increases, drop immediately remainder of data item –e.g., data reformatting… under heavy loads, maintain acceptable service-levles for critical data/customers only –discard all other data, deploy specialized `handlers’ for critical data –e.g., filter + downsample… deploy service to processing context better suited for its execution –service implementation and performance profile differs based on context/resources available –e.g., multicast…

9. plus IXP2850 as alternate switch Physical Testbed

10. Handler chaining: feasibility and complexity caterer blocked seats Rule Chains Throughput (Mbps)

11. Benefit from specialized hardware In transit data morphing –merging data from multiple clients, distributing subsets of it, reformatting… –varied merge criteria, performance dominated by merge operation, not occupancy of hash tables in our case Other services –data reformatting, multicast/mirroring, filtering… –`database-operands’

12. Benefit from appropriate service placement Performance advantage: –offload and overlap communication/computation –deploy specialized actions

13. Conclusions and future work Programmable networking platforms are suitable for efficient execution of higher-level services Select classes of services benefit for parallelism and specialized hardware components available Flexible reconfiguration needed to address dynamics in application interests and operating conditions Understanding of handler resource requirements, efficient monitoring of platform resource capability and compiler tools needed Currently integrating runtime environment underneath an existing event-based middleware system Considering future (heterogeneous) multicore platforms Other services – e.g., virtualization

14. www.cercs.gatech.edu/projects/npg

15. Query Performance

17. Scalable Data Distribution (contd.) Graphs…