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Subramaniam Kalambur

Publications and source records attributed to Subramaniam Kalambur.

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MiSeRTrace: Kernel-level Request Tracing for Microservice Visibility

With the evolution of microservice applications, the underlying architectures have become increasingly complex compared to their monolith counterparts. This mainly brings in the challenge of observability. By providing a deeper understanding into the functioning of distributed applications, observability enables improving the performance of the system by obtaining a view of the bottlenecks in the implementation. The observability provided by currently existing tools that perform dynamic tracing on distributed applications is limited to the user-space and requires the application to be instrumented to track request flows. In this paper, we present a new open-source framework MiSeRTrace that can trace the end-to-end path of requests entering a microservice application at the kernel space without requiring instrumentation or modification of the application. Observability at the comprehensiveness of the kernel space allows breaking down of various steps in activities such as network transfers and IO tasks, thus enabling root cause based performance analysis and accurate identification of hotspots. MiSeRTrace supports tracing user-enabled kernel events provided by frameworks such as bpftrace or ftrace and isolates kernel activity associated with each application request with minimal overheads. We then demonstrate the working of the solution with results on a benchmark microservice application.

cs.DC

Megha: Decentralized Global Fair Scheduling for Federated Clusters

Increasing scale and heterogeneity in data centers have led to the development of federated clusters such as KubeFed, Hydra, and Pigeon, that federate individual data center clusters. In our work, we introduce Megha, a novel decentralized resource management framework for such federated clusters. Megha employs flexible logical partitioning of clusters to distribute its scheduling load, ensuring that the requirements of the workload are satisfied with very low scheduling overheads. It uses a distributed global scheduler that does not rely on a centralized data store but, instead, works with eventual consistency, unlike other schedulers that use a tiered architecture or rely on centralized databases. Our experiments with Megha show that it can schedule tasks taking into account fairness and placement constraints with low resource allocation times - in the order of tens of milliseconds.

cs.DC