Managed Service Streaming (MSS) Overview

• Managed Service Streaming is a streaming architecture that abstracts data endpoints via platform-managed services, enhancing security and scalability.
• It employs automated ingress controllers and centralized DNS resolution to provision API-driven messaging clusters for HPC and federated facilities.
• MSS prioritizes operational simplicity and multi-tenant feasibility, trading off some throughput and latency compared to direct streaming methods.

Managed Service Streaming (MSS) is a streaming architecture paradigm in which data streams are transmitted and managed by facility- or platform-owned services, providing users with API-driven, abstracted access to streaming endpoints without direct exposure to IP addressing, manual network configuration, or low-level service deployment. MSS targets scalable, multi-user, cross-facility scenarios, particularly in advanced computing ecosystems where complex, secure, and rapid ingestion and egress of data are required for high-performance computing (HPC), AI workflows, and federated facilities (George et al., 28 Sep 2025). This approach is characterized by managed ingress controllers, platform-level routing, and service orchestration, contrasting with direct or proxy-based streaming solutions that require significant user-led configuration.

1. Architectural Principles and Data Flow

MSS architectures route data streams through a platform-managed network stack layered atop the user’s streaming application. Producers publish data to a facility-managed fully qualified domain name (FQDN), not directly to compute node IP addresses or ports. All incoming traffic is processed by an ingress controller or load balancer—external to the cluster hosting end services—which centrally terminates TLS connections, resolves FQDNs, and applies security rules. A platform route controller injects further indirection, mapping requests to designated streaming endpoints (e.g., pods running a message broker such as RabbitMQ) deployed on dedicated Data Streaming Nodes (DSNs), typically within a Kubernetes/OpenShift cluster managed by the facility platform (George et al., 28 Sep 2025).

Key architectural elements:

• Externally available stable FQDN for user access
• Facility-owned load balancer / ingress controller for network termination and routing
• Platform-managed DNS resolution and certificate management
• Automated provisioning of streaming backends (e.g., RabbitMQ clusters) via APIs
• All security, routing, scaling, and endpoint resolution handled by the facility platform, decoupled from user code and network configuration

Unlike Direct Streaming (DTS), which necessitates direct exposure of node ports and manual NAT/firewall configuration, or Proxied Streaming (PRS), which leverages user-managed overlay proxies (such as Stunnel or HAProxy), MSS fully abstracts network paths and enforces platform policy and scheduling across all data ingress and egress.

2. Implementation Details: DS2HPC and Containerized Platforms

MSS in production HPC and multi-facility environments is typically achieved via frameworks such as Data Streaming to HPC (DS2HPC), combined with container orchestration platforms like OpenShift. Users interact with the MSS substrate through standardized REST APIs, provisioning ephemeral, on-demand message broker clusters (e.g., a three-node RabbitMQ AMQPS service) by specifying job resource requirements programmatically:

curl -X POST "https://s3m.apps.olivine.ccs.ornl.gov/olcf/v1alpha/streaming/rabbitmq/provision_cluster" \ -H "Authorization: TOKEN" \ -H "Content-Type: application/json" \ -d '{"kind": "general", "name": "rabbitmq", "resourceSettings": {"cpus": 12, "ram-gbs": 32, "nodes": 3, "max-msg-size": 536870912}'

The resultant FQDN and AMQPS connection string are returned for immediate client use, integrating seamlessly into security policies enforced by the ingress.

The route controller inside OpenShift orchestrates name-based routing of external connections to backend pods, allowing for elasticity and isolation between users' workloads. The cluster operator can manage resource allocation, scaling, and routing without user intervention—a design that aligns with multi-user, multi-tenant requirements of modern managed service environments.

Note that while toolkits such as SciStream are crucial for building high-performance, memory-to-memory proxy paths in PRS, within MSS, all user traffic is expected to flow through the managed service overlay, with platform APIs handling provisioning and topology concerns.

3. Performance Evaluation: Throughput and Latency Characteristics

When evaluated on production-grade infrastructure at the Oak Ridge Leadership Computing Facility using synthetic benchmarks representative of AI-HPC workflow messaging patterns, MSS exhibits distinct performance dynamics compared to DTS and PRS (George et al., 28 Sep 2025):

Metric	DTS	PRS	MSS
Network Hops	1	2	2+
Deployment Complexity	High (manual)	Moderate	Low (managed service)
Throughput (msgs/sec)	Highest	Intermediate	Saturates at 4–8 consumers
RTT (latency)	Lowest	Comparable/DTS	Significantly higher at scale
Scalability (multi-user)	Limited	Better	Best in ease, with perf penalty

Quantitative findings:

• Under broadcast and work-sharing patterns, MSS throughput saturates at approximately 4–8 consumers, peaking well below DTS (MSS: ~256 msgs/sec; DTS: up to ~685 msgs/sec with large payloads).
• RTT in MSS is consistently higher, reaching up to 1.8 seconds (Dstream) and even 40 seconds (Lstream) under feedback-heavy and high-consumer-count workloads, representing a 6.9x overhead versus direct streaming under some scenarios.
• As consumer count increases, MSS experiences pronounced performance bottlenecks not present in DTS or well-optimized PRS configurations.

This performance profile is a direct consequence of the additional network hops (ingress to route controller to DSN pod), additional load-balancing indirection, and the uniform application of authentication and authorization.

4. Scalability, Administrative Simplicity, and Multi-User Feasibility

MSS is engineered for maximum deployment feasibility in multi-user, federated, or externally-facing facilities. Its most salient benefits include:

• Abstracting endpoint management with stable, public DNS-based connectivity
• Automated handling of all certificate management and TLS termination
• No requirement for user-defined firewall, NAT, or low-level port exposure rules
• API-based, programmatic service provisioning; rapid instantiation of isolated streaming endpoints on shared resources

From an operational perspective, MSS is thus highly attractive for environments where users have heterogeneous workloads, do not control facility firewalls, or where dynamic, ephemeral connection endpoints are needed by a multitude of clients with varying software stacks.

In trade-off, the facility operator assumes greater responsibility for securing, scaling, and optimizing the infrastructure. The platform must monitor and mitigate hot spots, manage connection state at scale, and resolve ingress/route mapping conflicts—challenges which grow with concurrent user volume.

5. Comparison with Alternative Architectures

MSS, DTS, and PRS exemplify distinct points in the design space for cross-facility streaming:

• DTS: Offers "minimal-hop" network paths, exposing endpoints directly for the lowest possible latency and highest throughput, but is infeasible in highly partitioned or policy-constrained environments due to complex requirements for port exposure, firewall/NAT traversal, and manual configuration.
• PRS: Employs intermediary, often user-managed proxies (e.g., SciStream + HAProxy) to tunnel traffic, balancing moderate administrative overhead with near-DTS performance for non-extreme scale in many patterns.
• MSS: Maximizes ease of use and deployment scalability at the expense of higher end-to-end latency and diminished throughput under heavy or feedback-dominated loads. Its architecture is particularly suited to large-scale, federated, policy-controlled settings where platform-managed ingress, automatic provisioning, and strong separation between users are necessary.

6. Operational and Research Implications

The deployment of MSS architectures represents a strategic choice prioritizing administrative simplicity, user isolation, and scalability in federated contexts over per-connection performance guarantees (George et al., 28 Sep 2025). Implications include:

• For workflows dominated by a modest number of consumers or those requiring strict platform policy enforcement, MSS is optimal.
• Scientific workflows, AI model feeds, or HPC data exchange patterns demanding maximum throughput at scale may exceed current MSS bottlenecks, prompting consideration of more direct or proxy-facilitated approaches, or necessitating investment in optimizing facility ingress and backend routing.
• MSS operational patterns align with trends in Platform-as-a-Service (PaaS) and serverless deployments, where user-facing endpoints are abstracted and lifecycle-managed.

A plausible implication is that further research will likely address how ingress and platform routing can be made more performant within MSS, possibly by parallel ingress scaling, direct data-plane optimization, or dynamic route controller strategies tailored to high-feedback and high-fan-in/fan-out workflows.

7. Summary of Core Trade-offs

Managed Service Streaming establishes a practical foundation for cross-facility, secure, and user-agnostic streaming, achieving administrative and policy goals at the expense of some raw network efficiency. Its architectural abstraction enables rapid, secure, and multi-tenant service delivery, but the additional protocol layers—while essential for operational scalability—introduce concrete throughput and latency penalties under conditions of high consumer concurrency or feedback-centric communication patterns. The suitability of MSS must be continually evaluated as workloads, user demands, and facility constraints evolve, with direct and proxy streaming architectures as alternatives for scenarios where absolute performance cannot be compromised (George et al., 28 Sep 2025).