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.

In recent years, with the maturation of container orchestration platforms like Kubernetes, containers are now becoming the default way to deploy cloud-native applications, designed as microservices, on public and private clouds. These trends have also spread to the field of Telecommunications, boosted by the onset of 5G. Network functions processing millions of packets per second, earlier run as proprietary physical boxes, are now being realized as disaggregated container based microservices (CNFs) running on commodity clusters managed by orchestrators, like Kubernetes, on Telco clouds. While container orchestrators have evolved to meet the needs of enterprise applications, Telco workloads still remain a second class citizen, as the orchestrator is presently unaware of the networking needs of CNFs and cannot guarantee QoS of network intensive functions. In this work, we examine orchestration of network sensitive functions and identify the key networking requirements of containerized Telco workloads from the orchestration platform. We design and propose NACO - Network Aware Container Orchestration, a minimal, cloud-native and scalable extension to the Kubernetes platform to address these requirements and provide first class lifecycle management of CNFs used in Telco workloads. We implement a prototype of the system and demonstrate that we can achieve network aware container orchestration with minimal operation times.

We present Move2Kube, a replatforming framework that automates the transformation of the deployment specification and development pipeline of an application from a non-Kubernetes platform to a Kubernetes-based one, minimizing changes to the application's functional implementation and architecture. Our contributions include: (1) a standardized intermediate representation to which diverse application deployment artifacts could be translated, (2) an extension framework for adding support for new source platforms, and target artifacts while allowing customization as per organizational standards. We provide initial evidence of its effectiveness in terms of effort reduction, and highlight the current research challenges and future lines of work. Move2Kube is being developed as an open source community project and it is available at https://move2kube.konveyor.io/

In this paper, we design and implement the first-ever decentralized replicated relational database with blockchain properties that we term blockchain relational database . We highlight several similarities between features provided by blockchain platforms and a replicated relational database, although they are conceptually different, primarily in their trust model. Motivated by this, we leverage the rich features, decades of research and optimization, and available tooling in relational databases to build a blockchain relational database. We consider a permissioned blockchain model of known, but mutually distrustful organizations each operating their own database instance that are replicas of one another. The replicas execute transactions independently and engage in decentralized consensus to determine the commit order for transactions. We design two approaches, the first where the commit order for transactions is agreed upon prior to executing them, and the second where transactions are executed without prior knowledge of the commit order while the ordering happens in parallel. We leverage serializable snapshot isolation (SSI) to guarantee that the replicas across nodes remain consistent and respect the ordering determined by consensus, and devise a new variant of SSI based on block height for the latter approach. We implement our system on PostgreSQL and present detailed performance experiments analyzing both approaches.

YouTube has emerged as the largest player among video streaming services, serving video content for users using DASH. Research studies on various aspects of YouTube, especially its streaming service, abound in the literature. However, these works study YouTube streaming from the periphery, and report results based on their understanding of general DASH recommendations. In this study, we explore in depth YouTube's implementation of the DASH client. We identify important parameters in YouTube's rate adaptation algorithm, and study their roles. In a departure from existing literature, we observe that YouTube opportunistically adapts segment length, in addition to quality level, in response to bandwidth fluctuations. We report that this scheme results in a much lower average data wastage ratio (0.82x10-6), than reported earlier. We also propose an analytical model, augmented with a machine learning based classifier (with average accuracy of 85.75%), to predict data consumption for a playback session in advance.