================================================================================
                    NovaClustr Platform Technical Report
                         Version 4.2 - Internal Use
================================================================================

EXECUTIVE OVERVIEW
------------------
NovaClustr is a distributed computing platform designed for high-throughput
data processing across geographically dispersed data centers. Originally
developed in 2019 as an internal tool for batch analytics, it has since
evolved into a full-featured platform supporting real-time stream processing,
batch computation, and hybrid workloads. This report covers the five major
subsystems: Architecture, Networking, Storage, Security, and Monitoring.

The platform currently serves 14 internal teams and processes an average of
2.3 petabytes of data per day across 8 data center regions. Peak throughput
has reached 4.1 petabytes in a single 24-hour window during end-of-quarter
reporting cycles.


================================================================================
SECTION 1: ARCHITECTURE
================================================================================

1.1 Overview
The NovaClustr architecture follows a master-worker topology with automatic
failover. The control plane consists of 3 master nodes per region arranged
in a Raft consensus group. Worker nodes are organized into pools based on
their hardware profile and assigned workload type.

1.2 Node Types
There are four distinct node types in the NovaClustr architecture:

  - Controller Nodes: Manage cluster state, schedule jobs, and coordinate
    failover. Each region has exactly 3 controller nodes. Controllers
    require a minimum of 64 GB RAM and 16 CPU cores.

  - Compute Nodes: Execute data processing tasks. These are the most
    numerous nodes, with each region containing between 50 and 500 compute
    nodes depending on demand. Compute nodes are provisioned with 128 GB
    RAM and 32 CPU cores as the standard configuration.

  - Gateway Nodes: Handle external API requests and route them to the
    appropriate internal services. Each region has 5 gateway nodes behind
    a load balancer. Gateway nodes terminate TLS connections and perform
    request authentication.

  - Storage Nodes: Manage the distributed file system (see Section 3).
    Storage nodes are equipped with high-capacity NVMe drives, typically
    12 drives per node at 8 TB each, for a raw capacity of 96 TB per node.

1.3 Job Scheduling
The job scheduler uses a priority queue with 5 priority levels (P0 through
P4, where P0 is highest). Jobs are assigned to compute pools based on
resource requirements and data locality. The scheduler attempts to place
jobs on nodes that already have the required input data cached locally,
reducing network transfer overhead by an estimated 35%.

P0 jobs are reserved for critical production pipelines and have preemption
rights over P3 and P4 jobs. P1 and P2 jobs cannot preempt each other but
take priority in the queue over P3-P4. The average job scheduling latency
is 120 milliseconds for P0 jobs and 850 milliseconds for P4 jobs.

1.4 Fault Tolerance
NovaClustr employs a checkpoint-and-replay mechanism for fault tolerance.
Long-running jobs create checkpoints every 5 minutes by default (configurable
per job). When a node failure is detected, the job is restarted from the
most recent checkpoint on a different node. The detection timeout is 30
seconds, after which the controller marks the node as failed and begins
rescheduling its tasks.

In the event of a full region failure, workloads can be migrated to a
secondary region within 4 minutes using the cross-region failover protocol.
This protocol was tested during the 2024 DR exercise and successfully
migrated 1,247 active jobs with zero data loss.

1.5 Scaling
Auto-scaling is managed by the Capacity Controller subsystem. When the
average CPU utilization across a compute pool exceeds 75% for 10 consecutive
minutes, the Capacity Controller provisions additional nodes from the cloud
reserve pool. Scale-down occurs when utilization drops below 30% for 20
minutes. The minimum pool size is 10 nodes and the maximum is 500 nodes.


================================================================================
SECTION 2: NETWORKING
================================================================================

2.1 Overview
NovaClustr uses a multi-tier networking architecture optimized for both
east-west (intra-cluster) and north-south (external) traffic. All
inter-node communication uses gRPC over HTTP/2 with mutual TLS (mTLS).

2.2 Network Topology
Each data center region is connected via dedicated 100 Gbps fiber links
in a full mesh topology. Intra-region communication between nodes uses
25 Gbps Ethernet. The aggregate cross-region bandwidth capacity is
800 Gbps, though typical utilization averages 45% during normal operations.

2.3 Service Mesh
NovaClustr deploys a custom service mesh (NovaMesh) for service discovery,
load balancing, and traffic management. NovaMesh sidecar proxies are
deployed alongside every service instance. Key features include:

  - Automatic retry with exponential backoff (max 3 retries)
  - Circuit breaker pattern with a 50% error threshold
  - Request-level tracing with correlation IDs
  - Rate limiting per client at 10,000 requests per second

2.4 DNS and Service Discovery
Internal service discovery uses a custom DNS resolver integrated with
the cluster state store. DNS TTL is set to 15 seconds to allow rapid
failover. External DNS is managed through a commercial provider with
a 60-second TTL.

2.5 Data Transfer Optimization
For large data transfers between nodes, NovaClustr uses a custom
protocol called NovaBulk that operates over TCP with the following
optimizations:

  - Zero-copy data path using kernel bypass (DPDK)
  - Adaptive compression (LZ4 for low-latency, ZSTD for high-ratio)
  - Parallel transfer streams (up to 8 concurrent streams per transfer)
  - Transfer prioritization aligned with job priority levels

NovaBulk achieves 92% of theoretical link bandwidth in benchmarks,
compared to 67% for standard TCP file transfers.

2.6 Network Security
All network traffic is encrypted in transit using TLS 1.3. Certificate
rotation occurs every 90 days automatically. Network policies enforce
strict pod-to-pod communication rules, with a default-deny posture.
Only explicitly allowed service pairs can communicate.

2.7 Latency Requirements
The platform maintains strict latency SLAs:
  - Intra-region RPC calls: p99 < 5 ms
  - Cross-region RPC calls: p99 < 80 ms
  - Gateway to compute (job submission): p99 < 200 ms
  - DNS resolution: p99 < 2 ms


================================================================================
SECTION 3: STORAGE
================================================================================

3.1 Overview
NovaClustr's storage layer is built on NovaFS, a custom distributed file
system optimized for large sequential reads and writes. NovaFS provides
POSIX-like semantics with eventual consistency for metadata operations.

3.2 Data Replication
All data is replicated with a configurable replication factor. The default
is 3x replication within a region. For critical datasets, cross-region
replication can be enabled with 2x replication to a designated backup
region. The total storage capacity across all regions is 48 petabytes raw,
or approximately 16 petabytes usable after 3x replication.

3.3 File System Structure
NovaFS organizes data into namespaces, each of which can have independent
replication and retention policies. The standard namespaces are:

  - /hot     - Frequently accessed data, stored on NVMe (7-day retention)
  - /warm    - Moderately accessed data, stored on SSD (30-day retention)
  - /cold    - Archival data, stored on HDD (365-day retention)
  - /scratch - Temporary job data, automatically purged after 24 hours

3.4 Data Tiering
An automated tiering engine moves data between storage tiers based on
access patterns. Data that has not been accessed for 7 days is moved
from /hot to /warm. After 30 days of no access, data moves to /cold.
The tiering engine processes approximately 500 TB of data movements
per day.

3.5 Caching
A distributed caching layer (NovaCache) sits between compute nodes and
NovaFS. NovaCache uses a combination of local NVMe cache (2 TB per
compute node) and a shared memory cache cluster (10 nodes, 512 GB RAM
each). Cache hit rates average 78% for repeated workloads.

3.6 Backup and Recovery
Full backups are taken weekly on Sunday at 02:00 UTC. Incremental backups
run daily at 02:00 UTC. The backup window is 6 hours maximum. Recovery
point objective (RPO) is 24 hours for standard data and 1 hour for
critical datasets using continuous journaling. Recovery time objective
(RTO) is 4 hours for a full region restore.

3.7 Data Integrity
NovaFS uses SHA-256 checksums for all stored blocks. Background scrubbing
runs continuously, verifying approximately 2% of all blocks per day.
Corrupted blocks are automatically repaired from replicas. The measured
silent corruption rate is 0.0003% per year.


================================================================================
SECTION 4: SECURITY
================================================================================

4.1 Overview
NovaClustr implements a defense-in-depth security model with multiple
layers of protection. The security architecture is reviewed annually
by an external audit firm.

4.2 Authentication
All user and service authentication uses OAuth 2.0 with JWT tokens.
Token lifetime is 1 hour for interactive users and 24 hours for service
accounts. Multi-factor authentication (MFA) is mandatory for all human
users accessing the control plane. Service-to-service authentication
uses mutual TLS certificates issued by an internal PKI.

4.3 Authorization
Role-based access control (RBAC) is implemented with 4 predefined roles:

  - Admin: Full access to all cluster operations and configuration
  - Operator: Can manage jobs, view logs, and access monitoring dashboards
  - Developer: Can submit and manage their own jobs, read shared datasets
  - Viewer: Read-only access to dashboards and job status

Custom roles can be created by combining fine-grained permissions from a
catalog of 127 individual permission entries.

4.4 Data Encryption
Data at rest is encrypted using AES-256-GCM. Encryption keys are managed
by an internal KMS (Key Management Service) backed by hardware security
modules (HSMs). Key rotation occurs every 180 days. In-transit encryption
uses TLS 1.3 as described in the Networking section.

4.5 Audit Logging
All administrative actions and data access events are logged to an
immutable audit log. Audit logs are retained for 7 years in compliance
with regulatory requirements. The audit system processes approximately
50 million events per day. Log integrity is ensured through hash chaining.

4.6 Vulnerability Management
Automated vulnerability scanning runs weekly against all cluster nodes.
Critical vulnerabilities (CVSS >= 9.0) must be patched within 72 hours.
High vulnerabilities (CVSS >= 7.0) must be patched within 14 days.
The platform maintains a 99.2% patch compliance rate.

4.7 Network Segmentation
The cluster is segmented into 4 security zones:
  - Public Zone: Gateway nodes only
  - Application Zone: Compute nodes and service mesh
  - Data Zone: Storage nodes and databases
  - Management Zone: Controller nodes and monitoring

Traffic between zones is filtered by stateful firewalls with explicit
allow rules. No direct path exists from the Public Zone to the Data Zone.

4.8 Incident Response
The security team maintains a 24/7 on-call rotation. Mean time to
acknowledge (MTTA) for security incidents is 15 minutes. Mean time to
resolve (MTTR) for critical incidents is 4 hours. A quarterly tabletop
exercise is conducted to test incident response procedures.


================================================================================
SECTION 5: MONITORING
================================================================================

5.1 Overview
NovaClustr uses a comprehensive observability stack covering metrics,
logs, and traces. The monitoring infrastructure is deployed independently
from the main cluster to ensure availability during outages.

5.2 Metrics Collection
Metrics are collected using a pull-based model at 15-second intervals.
Each node exposes approximately 2,000 individual metrics. The total
metric ingestion rate across the platform is 18 million data points
per second. Metrics are stored in a time-series database with the
following retention:
  - Raw resolution (15s): 7 days
  - 1-minute aggregates: 30 days
  - 5-minute aggregates: 1 year
  - 1-hour aggregates: 5 years

5.3 Alerting
The alerting system supports multi-level alerts:
  - P1 (Critical): Pages on-call engineer, auto-creates incident ticket
  - P2 (Warning): Sends notification to team channel
  - P3 (Info): Logged for review during business hours

Alert deduplication prevents notification storms. The current alert
suppression window is 5 minutes for identical alerts. There are currently
342 active alert rules across the platform.

5.4 Log Aggregation
All application and system logs are shipped to a centralized log
aggregation service. Log ingestion rate averages 2.1 TB per day.
Full-text search is available on logs up to 30 days old. Older logs
are archived to cold storage in compressed format.

5.5 Distributed Tracing
End-to-end request tracing is implemented using OpenTelemetry. Traces
are sampled at a rate of 1% for normal traffic and 100% for error
paths. Trace data is retained for 7 days. The tracing system handles
approximately 500,000 spans per second.

5.6 Dashboards
The monitoring team maintains 45 operational dashboards covering:
  - Cluster health and capacity
  - Job throughput and latency
  - Storage utilization and I/O
  - Network traffic and errors
  - Security events and compliance

Custom dashboards can be created by any user with Operator or higher role.

5.7 Capacity Planning
Historical metrics feed into a capacity planning model that forecasts
resource needs 90 days ahead. The model uses linear regression on
trailing 180-day data with seasonal adjustment. Current forecasts
predict that storage capacity will need to increase by 25% within
the next 6 months, and compute capacity by 15%.

5.8 SLA Reporting
Monthly SLA reports are generated automatically. Key SLA targets:
  - Platform availability: 99.95% (measured: 99.97% trailing 12 months)
  - Job completion rate: 99.9% (measured: 99.92%)
  - Data durability: 99.999999% (11 nines target)
  - API response time p99: < 500 ms (measured: 312 ms)

5.9 On-Call Process
The on-call rotation covers 3 teams:
  - Platform team: infrastructure and cluster operations
  - Data team: storage and pipeline issues
  - Security team: security incidents and compliance

Each team has a primary and secondary on-call engineer. Escalation
from primary to secondary occurs after 15 minutes of non-acknowledgment.

================================================================================
END OF REPORT
================================================================================


================================================================================
APPENDIX A: ARCHITECTURE DEEP DIVE
================================================================================

A.1 Controller Node Internals
Each controller node runs the following core services:
  - Scheduler Service: Manages the priority queue and job placement
  - State Store: Maintains cluster state using an embedded key-value store
  - Health Monitor: Tracks node liveness via heartbeat mechanism
  - API Server: Exposes the control plane API for job submission and management

The state store uses a write-ahead log (WAL) for durability. WAL segments
are rotated every 64 MB or every 5 minutes, whichever comes first. State
snapshots are taken every 15 minutes to enable fast recovery. A full state
restore from snapshot takes approximately 45 seconds.

A.2 Compute Node Lifecycle
Compute nodes go through the following states:
  1. Provisioning: Node is being set up (avg 3 minutes)
  2. Initializing: Node registers with controller and downloads configuration
  3. Ready: Node is available for job assignment
  4. Busy: Node is executing one or more tasks
  5. Draining: Node is finishing current tasks before shutdown
  6. Terminated: Node has been removed from the cluster

Nodes in Draining state are not assigned new tasks but continue executing
current work. The drain timeout is 30 minutes, after which tasks are
forcefully migrated to other nodes.

A.3 Job Execution Model
Each job consists of one or more stages. Stages can be arranged in a
directed acyclic graph (DAG) to express dependencies. The executor
supports the following stage types:
  - Map: Parallel processing of input partitions
  - Reduce: Aggregation of intermediate results
  - Sort: Distributed sort across partitions
  - Join: Equi-join between two datasets
  - Filter: Predicate-based row filtering

A typical analytics job contains 3-7 stages. The largest observed job
had 47 stages processing 12 TB of input data across 200 compute nodes,
completing in 23 minutes.


================================================================================
APPENDIX B: NETWORKING PROTOCOLS
================================================================================

B.1 NovaBulk Protocol Details
The NovaBulk protocol uses a custom frame format:

  +---+---+---+---+---+---+---+---+
  | Magic (2B) | Version (1B) | Flags (1B) |
  +---+---+---+---+---+---+---+---+
  | Stream ID (4B)                 |
  +---+---+---+---+---+---+---+---+
  | Payload Length (4B)            |
  +---+---+---+---+---+---+---+---+
  | Checksum (4B)                  |
  +---+---+---+---+---+---+---+---+
  | Payload (variable)             |
  +---+---+---+---+---+---+---+---+

Maximum payload size per frame is 1 MB. Larger payloads are automatically
fragmented. Flow control uses a credit-based system where the receiver
grants credits in units of 256 KB.

B.2 Connection Pooling
Each node maintains a connection pool to every other node it communicates
with. Pool parameters:
  - Minimum connections per peer: 2
  - Maximum connections per peer: 16
  - Idle timeout: 300 seconds
  - Connection establishment timeout: 5 seconds
  - Maximum pending requests per connection: 128

B.3 Traffic Shaping
NovaClustr implements traffic shaping to prevent any single job from
monopolizing network bandwidth. Each job is allocated a bandwidth quota
based on its priority level:
  - P0: No limit (best effort maximum)
  - P1: 40% of available bandwidth
  - P2: 25% of available bandwidth
  - P3: 15% of available bandwidth
  - P4: 10% of available bandwidth

Unused bandwidth is redistributed proportionally among active jobs.


================================================================================
APPENDIX C: STORAGE INTERNALS
================================================================================

C.1 Block Layout
NovaFS uses a fixed block size of 128 MB for data blocks. Metadata blocks
are 4 KB. Each data block is stored with its SHA-256 checksum and a
generation counter for conflict resolution.

C.2 Write Path
The write path follows these steps:
  1. Client sends write request to the primary replica
  2. Primary writes to its local WAL
  3. Primary forwards the write to secondary replicas
  4. When all replicas acknowledge, primary responds to client
  5. Background compaction merges WAL entries into data blocks

Write latency p50 is 8 ms for intra-region writes and 45 ms for
cross-region writes. Write throughput per storage node is approximately
500 MB/s sustained.

C.3 Read Path
Reads are served from the nearest replica by default. If the nearest
replica is unavailable, the client library automatically fails over
to the next closest replica. Read-after-write consistency is guaranteed
within the same region but not across regions (cross-region reads may
see stale data for up to 5 seconds).

C.4 Garbage Collection
Deleted data is not immediately reclaimed. A garbage collection process
runs daily at 04:00 UTC, scanning for blocks with no live references.
Reclaimed space typically amounts to 2-5% of total capacity per week.


================================================================================
APPENDIX D: SECURITY INCIDENT LOG (SUMMARY)
================================================================================

D.1 Incidents in the Past 12 Months
  - 2025-01: Unauthorized access attempt via expired service account.
    Detected by audit system within 3 minutes. No data exposure.
  - 2025-04: Misconfigured firewall rule allowed unintended cross-zone
    traffic for 47 minutes. Corrected by automated policy enforcement.
  - 2025-07: Vulnerability in third-party library (CVSS 8.1). Patched
    within 48 hours across all nodes.
  - 2025-10: Phishing attempt targeting 3 engineers. Blocked by MFA.
    No credentials compromised.

D.2 Security Metrics Summary
  - Total security events analyzed: 183 million
  - True positive incidents: 4
  - False positive rate: 0.02%
  - Mean time to detect: 8 minutes
  - Mean time to contain: 35 minutes
  - Zero data breaches in trailing 24 months


================================================================================
APPENDIX E: MONITORING RUNBOOKS
================================================================================

E.1 High CPU Alert Response
When a P1 CPU alert fires (>90% utilization for 5+ minutes):
  1. Check if auto-scaling is functioning (verify Capacity Controller logs)
  2. Identify the top resource-consuming jobs
  3. Determine if the spike is expected (e.g., end-of-quarter processing)
  4. If unexpected, consider preempting low-priority jobs
  5. If scaling is stuck, manually provision nodes from the reserve pool

E.2 Storage Capacity Alert Response
When storage utilization exceeds 80%:
  1. Check garbage collection status (last run, reclaimed space)
  2. Review data tiering backlog
  3. Identify largest namespaces and their growth rates
  4. Consider accelerating cold tier migration
  5. If above 90%, initiate emergency capacity expansion

E.3 Network Partition Response
When a network partition is detected between regions:
  1. Verify physical link status with the network operations center
  2. Check for BGP route changes
  3. Assess impact on active jobs (cross-region dependencies)
  4. Enable partition-tolerant mode if not already active
  5. Begin failover procedures for affected workloads

E.4 Failed Job Investigation
When the job failure rate exceeds 1% over a 1-hour window:
  1. Check for common error patterns in job logs
  2. Verify node health across compute pools
  3. Check for storage I/O errors or latency spikes
  4. Review recent configuration changes
  5. Escalate to the data team if pipeline-specific

================================================================================
END OF APPENDICES
================================================================================
