In the evolving computing landscape, extending from centralized clouds to multi-cloud and edge, the need for adaptable observability is becoming increasingly critical. Traditional static monitoring approaches grapple with inefficient data transfer, limited scalability, heterogeneous environments, and rigid metric processing pipelines. This paper introduces a novel metric processing system, Programmable Metric Flows (PMF), which is rooted in the principle of dynamism. PMF is a first- of-its-kind, light-weight, SQL-based metric processor. It empowers optimization-driven transformations of metrics, tailored to evolving resource availability and application requirements. This paper demonstrates how PMF enables various transformations for dynamic and fine-grained metric collection. We also showcase the capability of PMF for dynamically tuning the frequency of metrics to reduce the WAN cost in edge environments. Our experiments show that PMF performs at par with state-of-the- art techniques in terms of metric processing capability, with 10X lesser resource utilization. We envision PMF to usher in an era of lightweight programmability for observability platforms. PMF is open-source and available at https://github.com/observ-vol-mgtIPMF

Multi-cloud deployments are set to change the very fabric of cloud computing, allowing applications to be deployed over multiple clusters and cloud providers, offering flexibility and geo-redundancy. Existing multi-cloud networking approaches either require tight coupling of clusters or separation of control and management for intra and inter cluster networking. Unlike compute and storage, networking for multi-cluster cannot be approached as an extension to single cluster operations. Multi -cloud necessitates that networking must be rethought in terms of the application and not the underlying networking solution, cluster type or cloud provider. We propose Application Networking Interface (ANI), an application centric, provider agnostic interface to control and manage networking solutions for single and multi-cluster contexts. ANI presents ex-citing opportunities for application networking and we envision that it will revamp the outlook towards multi-cloud networking.

Containerized Network Functions (CNFs) have seen recent increase in adoption due to the success of orchestration platforms and the natural flexibility of containers for deployment to heterogeneous, resource constrained environments like the edge. However, the weak isolation model of containers makes them susceptible to performance degradation due to interference from other containers. The interference manifests as stress on various resources like CPU, memory, cache, bandwidth of a CNF. While identifying the exact resource that is under stress is essential to employ the right remediation strategies, it is a challenging problem given the wide spectrum of applications, platforms and hardware, especially with the high performance requirements of CNFs with stringent SLAs. We present Sari, a first of a kind practical NF -agnostic framework leveraging temporal patterns in syscalls. Sari works for multi-service NFs and generalizes to a wide range of conditions. We leverage supervised time series classification and achieve performance upto 98.9% with minimal captures at runtime (starting from 25ms). Sari'sprediction error is 63.33% lower than any existing stress identification framework for VNFs and to the best of our knowledge, the first to address the problem for CNFs.

Observability Volume Management (OVM) presents a lightweight, automated processing system to help manage the large amounts of Observability data. The focus is on automating, analyzing, and making recommendations for volume management in multi-cloud, edge, and distributed systems.

In this paper, we introduce a new Java-based parallel GPGPU simulator, GpuTejas. GpuTejas is a fast trace driven simulator, which uses relaxed synchronization, and non-blocking data structures to derive its speedups. Secondly, it introduces a novel scheduling and partitioning scheme for parallelizing a GPU simulator. We evaluate the performance of our simulator with a set of Rodinia benchmarks. We demonstrate a mean speedup of 17.33x with 64 threads over sequential execution, and a speedup of 429X over the widely used simulator GPGPU-Sim. We validated our timing and simulation model by comparing our results with a native system (NVIDIA Tesla M2070). As compared to the sequential version of GpuTejas, the parallel version has an error limited to <;7.67% for our suite of benchmarks, which is similar to the numbers reported by competing parallel simulators.