Last Updated: December 2025
Total Features: 26 (22 Ares + 4 Polaris)
Completed: 14/26
- Feature Categories
- Ares Features (22)
- Polaris Features (4)
- Implementation Roadmap
- Feature Dependencies
ARES (22 features)
├── Core Scheduling Engine (6) - Job placement, fairness, priorities
├── Control Plane Design (6) - Global coordination, federation
├── Resource Awareness (4) - GPU, NUMA, network topology
├── Job Execution & Reliability (5) - Lifecycle, retries, exactly-once
└── Observability & Security (5) - Metrics, dashboards, RBAC
POLARIS (4 features) - Observability platform for Ares
├── Global Metrics Pipeline
├── Unified Dashboard & Tracing
├── Cluster & Job Health Dashboard
└── Audit Logging & Policy Validation
These features handle the core job scheduling logic - where jobs run, how resources are allocated, and prioritization.
| # | Feature | Description | Status |
|---|---|---|---|
| 1 | Multi-Cluster Scheduling | Schedule workloads across multiple K8s clusters | Done |
| 2 | Multi-Region Federation | Global scheduling across data centers and regions | Done |
| 3 | Fair Resource Allocation | DRF / weighted fair queuing to prevent monopolization | Future |
| 4 | Topology-Aware Scheduling | Co-locate by network / rack / zone proximity | Done |
| 5 | Priority & Preemption | High-priority jobs preempt lower-priority jobs | Future |
| 6 | Exactly-Once Execution | Distributed leases + fencing tokens | Done |
| # | Feature | Description | Status |
|---|---|---|---|
| 7 | Global Control Plane | Central orchestrator coordinating per-cluster schedulers | Done |
| 8 | Cluster Autonomy | Local failover when global plane unreachable | Future |
| 9 | Dynamic Cluster Registration | Clusters join/leave dynamically without downtime | Done |
| 10 | Health & Heartbeat | Health synchronization across clusters | Done |
| 11 | Eventual Consistency | CRDTs or Raft for critical state | Future |
| 12 | API Gateway | Unified global API endpoint for job submission | Done |
| # | Feature | Description | Status |
|---|---|---|---|
| 13 | GPU-Aware Scheduling | GPU count, model, NVLink topology, NUMA affinity | Done |
| 14 | NUMA Awareness | Minimize cross-NUMA memory penalties | Future |
| 15 | Network Bandwidth Aware | Network latency / bandwidth in scheduling | Future |
| 16 | Heterogeneous Hardware | CPUs, GPUs, TPUs, FPGAs, custom accelerators | Future |
| # | Feature | Description | Status |
|---|---|---|---|
| 17 | Job Lifecycle Management | Pending → Running → Succeeded / Failed | Done |
| 18 | Idempotent Submission | Client-side deduplication using request IDs | Done |
| 19 | Distributed Locking | Lease-based coordination for exactly-once | Done |
| 20 | Checkpointing | Persist mid-job state for fault recovery | Future |
| 21 | Backoff & Retry Policy | Exponential backoff with jitter for failed jobs | Future |
| # | Feature | Description | Status |
|---|---|---|---|
| 22 | Global Metrics Pipeline | Prometheus / OpenTelemetry metrics federation | Future |
| 23 | Unified Dashboard | Grafana dashboards and distributed tracing | Future |
| 24 | Health Dashboard | Real-time job latency, failure rates, utilization | Future |
| 25 | RBAC + Tenant Isolation | Namespace / tenant quota and access control | Future |
| 26 | Audit Logging | Central logging and policy validation | Future |
Summary: 12 implemented features · 14 planned enhancements
Purpose: Schedule workloads across multiple Kubernetes clusters
Complexity: High
Status: 🚧 In Progress (Phase 2)
Dependencies: #7 (Global Control Plane)
Problem:
Single cluster: Limited to 100 GPUs
Multi-cluster: Scale to 1000+ GPUs across regions
Design:
- Global job queue (etcd)
- Per-cluster resource tracking
- Cross-cluster placement scoring
- Network latency awareness
Implementation:
type ClusterState struct {
ClusterID string
AvailableGPUs int
Latency time.Duration
Load float64
}
func PlaceJobAcrossClusters(job Job, clusters []ClusterState) string {
// Score each cluster
scores := make(map[string]float64)
for _, cluster := range clusters {
score := calculateClusterScore(job, cluster)
scores[cluster.ClusterID] = score
}
return chooseBestCluster(scores)
}Metrics:
- Cross-cluster placement decisions/sec
- Average job latency per cluster
- Cluster load distribution (fairness)
Purpose: Global scheduling decisions spanning geographic regions
Complexity: High
Status: ⏳ Planned (Phase 2)
Dependencies: #1, #7, #8, #9
Problem:
US-WEST-1: 200 GPUs (high load)
US-EAST-1: 150 GPUs (low load)
EU-WEST-1: 100 GPUs (idle)
User in US wants to run job → Should schedule in EU?
- Pro: Faster start (idle capacity)
- Con: Higher latency, data transfer costs
Design:
- Global coordinator (Raft consensus)
- Region-aware placement
- Data gravity considerations
- Cost optimization
Challenges:
- Network latency between regions (50-150ms)
- Data locality (datasets may be region-locked)
- Regulatory compliance (data residency laws)
Purpose: Prevent resource starvation using DRF (Dominant Resource Fairness)
Complexity: Medium
Status: ✅ Complete (MVP)
Dependencies: None
Problem:
Tenant A: Submits 1000 jobs (starves everyone)
Tenant B: Submits 10 jobs (gets nothing)
Tenant C: Submits 5 jobs (gets nothing)
UNFAIR!
Solution - Dominant Resource Fairness (DRF):
Each tenant gets fair share of their "dominant resource"
Tenant A: Dominant = CPUs (uses 80% CPU, 20% GPU)
Tenant B: Dominant = GPUs (uses 30% CPU, 90% GPU)
Allocate to B first (GPU is scarce), then A
Implementation:
type TenantUsage struct {
TenantID string
CPUShare float64 // 0.0 - 1.0
GPUShare float64 // 0.0 - 1.0
DominantShare float64 // max(CPUShare, GPUShare)
}
func ScheduleWithDRF(tenants []TenantUsage, job Job) string {
// Sort by dominant share (ascending)
sort.Slice(tenants, func(i, j int) bool {
return tenants[i].DominantShare < tenants[j].DominantShare
})
// Allocate to tenant with lowest dominant share
return tenants[0].TenantID
}Guarantees:
- Share guarantee: Each tenant gets ≥ 1/n of resources (n = # tenants)
- Strategy-proof: No incentive to lie about requirements
- Envy-free: No tenant prefers another's allocation
Purpose: Co-locate workloads by network/rack/zone proximity + GPU topology
Complexity: Medium-High
Status: ✅ Complete (MVP - basic)
Dependencies: #13 (GPU-Aware Scheduling)
Problem:
Job needs 8 GPUs
Bad placement:
GPU-0, GPU-4, GPU-9, GPU-13, GPU-18, GPU-22, GPU-27, GPU-31
→ Spread across 4 nodes, high cross-node traffic
Good placement:
GPU-0, GPU-1, GPU-2, GPU-3, GPU-4, GPU-5, GPU-6, GPU-7
→ All on one node, NVLink communication
Design:
Topology Hierarchy:
Region → Zone → Rack → Node → GPU
Scoring:
Same node: +100 points
Same rack: +50 points
Same zone: +20 points
Same region: +5 points
Different region: -50 points
Implementation:
type TopologyScore struct {
SameNode int
SameRack int
SameZone int
CrossRegion int
}
func ScorePlacement(gpus []GPU) float64 {
score := 0.0
for i := 0; i < len(gpus)-1; i++ {
for j := i+1; j < len(gpus); j++ {
if gpus[i].Node == gpus[j].Node {
score += 100
} else if gpus[i].Rack == gpus[j].Rack {
score += 50
} else if gpus[i].Zone == gpus[j].Zone {
score += 20
}
// etc.
}
}
return score
}Purpose: Allow high-priority workloads to preempt lower-priority jobs
Complexity: Medium
Status: ✅ Complete (MVP)
Dependencies: #17 (Job Lifecycle Management)
Problem:
Production ML inference: CRITICAL (needs GPUs NOW)
Research experiment: LOW (can wait)
If all GPUs busy with research → Inference waits → BAD
Solution - Priority Levels:
Priority 0: CRITICAL (production inference, urgent)
Priority 1: HIGH (important training jobs)
Priority 2: NORMAL (standard workloads)
Priority 3: LOW (research, batch jobs)
Priority 4: BEST_EFFORT (can be killed anytime)
Preemption Logic:
func CanPreempt(running Job, incoming Job) bool {
// Can only preempt if priority gap ≥ 2
priorityGap := running.Priority - incoming.Priority
return priorityGap >= 2
}
func Preempt(victim Job) {
// Graceful shutdown
victim.SendSIGTERM()
// Wait 30 seconds
time.Sleep(30 * time.Second)
// Force kill if still running
if victim.IsRunning() {
victim.SendSIGKILL()
}
// Checkpoint state (if supported)
victim.Checkpoint()
}Purpose: Guarantee no duplicate execution under retries/failures
Complexity: High
Status: ✅ Complete (MVP)
Dependencies: #18, #19
See: EXACTLY_ONCE.md for full design
Three-Layer Defense:
Layer 1: Request Deduplication (Redis)
Same RequestID → Same JobID
Layer 2: Distributed Leasing (etcd)
Only one worker holds lease
TTL-based expiration
Layer 3: Fencing Tokens (etcd)
Monotonic counters prevent zombies
Atomic compare-and-swap
Guaranteed Properties:
- ✅ No duplicates (job never executes >1 time)
- ✅ No lost executions (result persists)
- ✅ Convergence (recovers from any failure)
These features handle global coordination, cluster management, and control plane architecture.
Purpose: Central orchestrator coordinating per-cluster schedulers
Complexity: High
Status: ⏳ Planned (Phase 2)
Dependencies: None (foundational)
Design:
┌─────────────────┐
│ Global Control │
│ Plane │
└────────┬────────┘
│
┌─────────────────┼─────────────────┐
│ │ │
↓ ↓ ↓
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Cluster 1 │ │ Cluster 2 │ │ Cluster 3 │
│ Scheduler │ │ Scheduler │ │ Scheduler │
└─────────────┘ └─────────────┘ └─────────────┘
Responsibilities:
- Accept job submissions (single entry point)
- Route jobs to appropriate cluster
- Track cluster health
- Global resource accounting
- SLA enforcement
Implementation:
type GlobalScheduler struct {
clusters map[string]*ClusterState
jobQueue *JobQueue
// Raft consensus for HA
raft *raft.Raft
}
func (g *GlobalScheduler) SubmitJob(job Job) error {
// 1. Validate job
// 2. Choose cluster
cluster := g.chooseCluster(job)
// 3. Route to cluster scheduler
return cluster.ScheduleJob(job)
}Purpose: Local failover control when global plane unreachable
Complexity: Medium
Status: ⏳ Planned (Phase 2)
Dependencies: #7
Problem:
Global control plane crashes
↓
Clusters can't schedule new jobs
↓
Complete outage (BAD!)
Solution - Autonomous Mode:
1. Cluster detects global plane unreachable (heartbeat timeout)
2. Cluster switches to AUTONOMOUS mode
3. Cluster schedules jobs locally (with local queue)
4. When global plane returns:
- Reconcile state
- Resume normal mode
Implementation:
type ClusterMode int
const (
FEDERATED ClusterMode = 0 // Normal: global control
AUTONOMOUS ClusterMode = 1 // Failover: local control
)
func (c *ClusterScheduler) HeartbeatLoop() {
for {
if !c.global.IsReachable() {
c.mode = AUTONOMOUS
log.Warn("Global plane unreachable, switching to autonomous mode")
} else if c.mode == AUTONOMOUS {
c.mode = FEDERATED
c.reconcileState()
}
time.Sleep(10 * time.Second)
}
}Purpose: Clusters join/leave federation dynamically
Complexity: Medium
Status: ⏳ Planned (Phase 3)
Dependencies: #7, #8
Design:
New cluster comes online:
1. Cluster announces to global plane (etcd discovery)
2. Global plane validates cluster (health check)
3. Cluster added to routing table
4. Start receiving job assignments
Cluster goes offline:
1. Heartbeat timeout detected
2. Global plane marks cluster OFFLINE
3. Drain existing jobs (graceful shutdown)
4. Remove from routing table
Purpose: Gossip-based or Raft-based health synchronization
Complexity: Medium
Status: ⏳ Planned (Phase 3)
Dependencies: #7, #9
Options:
Option A: Gossip Protocol (like Consul)
Pros: Decentralized, scales to 1000s of nodes
Cons: Eventually consistent, complex debugging
Option B: Raft Consensus (like etcd)
Pros: Strong consistency, simpler reasoning
Cons: Limited to ~100 nodes, single leader bottleneck
Likely choice: Raft (simpler, good enough for <100 clusters)
Purpose: CRDTs or Raft for critical state
Complexity: High
Status: ⏳ Planned (Phase 3)
Dependencies: #7, #10
Design Decision:
Metadata (cluster health, topology):
→ Eventual consistency OK (CRDTs)
→ Faster, scales better
Critical state (job assignments, leases):
→ Strong consistency required (Raft)
→ Prevents split-brain
Purpose: Unified global API for job submission
Complexity: High
Status: ⏳ Planned (Phase 3)
Dependencies: #7
Design:
RESTful API:
POST /api/v1/jobs → Submit job
GET /api/v1/jobs/:id → Get job status
DELETE /api/v1/jobs/:id → Cancel job
GET /api/v1/clusters → List clusters
GET /api/v1/clusters/:id → Get cluster state
These features handle intelligent resource placement based on hardware topology.
Purpose: Match GPU count, model, NVLink, NUMA affinity
Complexity: Medium-High
Status: ✅ Complete (MVP - basic)
Dependencies: None
See: GPU_TOPOLOGY.md for full design
Key Capabilities:
- Detect GPU count, model, memory
- Parse NVLink topology matrix
- Score placements (NVLink +50, PCIe -30)
- Choose optimal GPU subset
Impact: 3-5x training speedup
Purpose: Minimize cross-NUMA penalties
Complexity: Medium-High
Status: ⏳ Planned (Phase 2)
Dependencies: #13
Problem:
NUMA Node 0: CPU 0-15, GPU 0-3, Memory 0-63GB
NUMA Node 1: CPU 16-31, GPU 4-7, Memory 64-127GB
Job allocated: GPU-0, CPU-20, Memory-70GB
→ GPU on Node 0
→ CPU on Node 1
→ Memory on Node 1
→ Cross-NUMA traffic (slow!)
Solution:
Allocate resources from SAME NUMA node:
GPU-0 → CPU 0-15 → Memory 0-63GB
Result: Local memory access, faster
Purpose: Factor latency/bandwidth into scheduling score
Complexity: Medium-High
Status: ⏳ Planned (Phase 3)
Dependencies: #4
Design:
Network Topology:
10 GbE switch: Latency=0.5ms, BW=10Gbps
40 GbE switch: Latency=0.3ms, BW=40Gbps
100 GbE switch: Latency=0.1ms, BW=100Gbps
Job requires high BW between nodes:
→ Prefer 100 GbE connected nodes
Purpose: Support CPUs, GPUs, TPUs, FPGAs, custom accelerators
Complexity: Medium
Status: ⏳ Planned (Phase 2)
Dependencies: #13
Design:
type Accelerator interface {
Type() string // "GPU", "TPU", "FPGA"
Model() string // "V100", "TPUv4", "Xilinx"
Memory() int64 // Bytes
Supports(job Job) bool
}
func ScheduleHeterogeneous(job Job, resources []Accelerator) Accelerator {
candidates := filterByRequirements(job, resources)
return chooseBest(candidates)
}These features handle job lifecycle, retries, and execution guarantees.
Purpose: State machine for job states (Pending → Running → Succeeded/Failed)
Complexity: Low
Status: ✅ Complete (MVP)
Dependencies: None
State Machine:
┌─────────┐
│ PENDING │
└────┬────┘
│ (worker acquires lease)
↓
┌─────────┐
│ RUNNING │
└────┬────┘
│
┌──────┴──────┐
│ │
↓ ↓
┌──────────┐ ┌─────────┐
│ SUCCEEDED│ │ FAILED │
└──────────┘ └────┬────┘
│ (retry if < maxRetries)
↓
┌──────────┐
│ RETRYING │
└────┬─────┘
│ (backoff delay)
↓
┌─────────┐
│ PENDING │
└─────────┘
Purpose: Client-side deduplication with request IDs
Complexity: Medium
Status: ⏳ Planned (Phase 2)
Dependencies: None
Design:
Client generates RequestID (UUID)
Client submits job with RequestID
Scheduler checks Redis: "Did I see this RequestID before?"
- YES: Return existing JobID (no-op)
- NO: Create new job, cache RequestID → JobID mapping
Guarantees:
- Same RequestID always returns same JobID
- No duplicate jobs created
Purpose: Lease-based coordination for exactly-once
Complexity: Medium
Status: ✅ Complete (MVP - implicit in #6)
Dependencies: None
Design:
type Lease struct {
JobID string
WorkerID string
FencingToken int64
TTL int64 // seconds
AcquiredAt time.Time
}
func AcquireJobLease(jobID string, workerID string, ttl int) (*Lease, error) {
// Atomic compare-and-swap
lease := &Lease{
JobID: jobID,
WorkerID: workerID,
FencingToken: getNextToken(), // Monotonic counter
TTL: ttl,
}
// Only succeeds if no existing lease
success := etcd.CompareAndSwap("/leases/" + jobID, nil, lease)
if !success {
return nil, errors.New("lease already held")
}
return lease, nil
}Purpose: Mid-job state persistence for fault recovery
Complexity: High
Status: ⏳ Planned (Phase 3)
Dependencies: #17, #21
Design:
Long-running job (10 hours):
Checkpoint every 30 minutes
Save model weights, optimizer state to S3
Job crashes at hour 5:
Resume from last checkpoint (hour 4.5)
Lost work: 30 minutes (not 5 hours!)
Purpose: Exponential backoff with jitter, SLA-aware
Complexity: Medium
Status: ✅ Complete (MVP)
Dependencies: #17
Formula:
backoff = min(initialBackoff * multiplier^attempt, maxBackoff)
backoff += random(-25%, +25%) // Jitter
Example:
Attempt 1: 1s + jitter → 1.2s
Attempt 2: 2s + jitter → 1.8s
Attempt 3: 4s + jitter → 4.3s
Attempt 4: 8s + jitter → 7.6s
Prevents: Thundering herd, retry storms
These features provide visibility, monitoring, and access control.
Purpose: Prometheus + OpenTelemetry federation
Complexity: Medium
Status: ✅ Complete (MVP)
Dependencies: None
Architecture:
Worker → Prometheus scrape
↓
Push to global aggregator
↓
Long-term storage (Thanos/Cortex)
↓
Query via Grafana
Metrics Exposed:
ares_jobs_total{state="running|succeeded|failed"}ares_gpu_utilization{gpu_id="0"}ares_scheduling_latency_secondsares_lease_acquisitions_total
Purpose: Grafana + Jaeger/Tempo for traces
Complexity: Low-Medium
Status: ✅ Complete (MVP)
Dependencies: #22
Features:
- Real-time job queue view
- GPU utilization heatmap
- Cluster health overview
- Distributed traces (OpenTelemetry)
Purpose: Real-time view of job latency, failure rate, utilization
Complexity: Low-Medium
Status: ✅ Complete (MVP)
Dependencies: #22, #23
Panels:
- Jobs/sec (last 5 minutes)
- Failure rate (%)
- P50/P95/P99 scheduling latency
- GPU utilization by cluster
- Retry rate
Purpose: Namespace/tenant-level quota and policy enforcement
Complexity: Medium
Status: ⏳ Planned (Phase 2)
Dependencies: #3
Design:
Tenant A:
- Max GPUs: 10
- Max jobs: 100
- Priority: NORMAL
Tenant B:
- Max GPUs: 50
- Max jobs: 500
- Priority: HIGH
Enforce quotas at submission time
Purpose: All job/API actions logged and validated centrally
Complexity: Medium
Status: ⏳ Planned (Phase 3)
Dependencies: #12
Design:
Audit log entry:
{
"timestamp": "2025-01-15T10:30:00Z",
"action": "SUBMIT_JOB",
"user": "alice@nyu.edu",
"job_id": "abc-123",
"cluster": "us-west-1",
"status": "SUCCESS"
}
Polaris is the observability platform purpose-built for Ares. It extends Ares' basic metrics (#22-24) with advanced SLA tracking, anomaly detection, and operational insights.
Status: ✅ Complete (basic), 🚧 Enhanced version in progress
Dependencies: Ares #22
Polaris Enhancements:
- Federated Prometheus across all clusters
- Long-term storage (Thanos/Cortex)
- Cross-cluster metric aggregation
- Custom SLA metrics
Design:
┌──────────┐ ┌──────────┐ ┌──────────┐
│Cluster 1 │ │Cluster 2 │ │Cluster 3 │
│Prometheus│ │Prometheus│ │Prometheus│
└─────┬────┘ └─────┬────┘ └─────┬────┘
│ │ │
└──────────────┼──────────────┘
↓
┌────────────────┐
│ Polaris Global │
│ Aggregator │
└────────┬───────┘
↓
┌────────────────┐
│ Thanos Storage │
│ (Long-term) │
└────────────────┘
Metrics:
- Standard Ares metrics (jobs, latency, GPU utilization)
- NEW: Cross-cluster correlation
- NEW: SLA compliance rate
- NEW: Cost per job (compute time × GPU cost)
Status: ✅ Complete (basic), 🚧 Enhanced version in progress
Dependencies: Ares #22, #23
Polaris Enhancements:
- Distributed tracing across clusters (Jaeger/Tempo)
- End-to-end job latency visualization
- Anomaly detection (ML-based)
- Cost dashboards
Example Trace:
Job abc-123 end-to-end latency: 145ms
Span 1: Client submit → 5ms
Span 2: Global scheduler route → 10ms
Span 3: Cluster-1 queue → 20ms
Span 4: GPU placement scoring → 15ms
Span 5: Worker lease acquire → 50ms
Span 6: Job execution → 40ms
Span 7: Result commit → 5ms
Status: ✅ Complete (basic), 🚧 Enhanced version in progress
Dependencies: Ares #22-24
Polaris Enhancements:
-
SLA Compliance Tracking
- Jobs meeting SLA deadline (%)
- P95/P99 latency vs SLA target
- SLA violations by cluster
-
Anomaly Detection
- Sudden failure rate spike (alert)
- GPU utilization drop (alert)
- Scheduling latency outliers
-
Predictive Insights
- "Cluster-2 will hit capacity in 2 hours"
- "Job queue backlog growing, scale up?"
Dashboard Panels:
┌─────────────────────────────────────────────┐
│ SLA Compliance (Last 24h) │
│ ███████████████████████░░░░ 92% (target: 95%)│
└─────────────────────────────────────────────┘
┌─────────────────────────────────────────────┐
│ Cluster Health Heatmap │
│ Cluster-1: ████████████████ 95% healthy │
│ Cluster-2: ██████████░░░░░░ 60% healthy ⚠️ │
│ Cluster-3: ████████████████ 100% healthy ✅ │
└─────────────────────────────────────────────┘
Status: ⏳ Planned (Phase 3)
Dependencies: Ares #12, #26
Polaris Enhancements:
- Centralized audit log aggregation
- Policy violation detection
- Security event correlation
- Compliance reporting
Example Policy:
policy:
name: "GPU usage quota"
rule: "tenant.gpu_hours < tenant.quota"
action: "REJECT_JOB"
alert: "Email admin + Slack #alerts"Completed Features (7):
- #3: Fair Resource Allocation
- #4: Topology-Aware Scheduling (basic)
- #5: Priority & Preemption
- #6: Exactly-Once Job Execution
- #13: GPU-Aware Scheduling (basic)
- #17: Job Lifecycle Management
- #21: Backoff & Retry Policy
In Progress (2):
- #22: Global Metrics Pipeline (basic)
- #23: Unified Dashboard (basic)
Timeline: 36 days, sustainable pace
Features to Add (7):
- #1: Multi-Cluster Scheduling (basic)
- #7: Global Control Plane
- #8: Cluster Autonomy
- #14: NUMA Awareness
- #16: Heterogeneous Hardware
- #18: Idempotent Job Submission
- #25: RBAC + Tenant Isolation
Focus: Production-grade reliability + observability
Timeline: 8 weeks while interviewing
Features to Add (9):
- #2: Multi-Region Federation
- #9: Dynamic Cluster Registration
- #10: Health & Heartbeat Propagation
- #11: Eventual Consistency
- #12: API Gateway
- #15: Network Bandwidth Awareness
- #20: Checkpointing & Recovery
- #24: Enhanced Polaris Dashboard
- #26: Audit Logging
Focus: Scale, federation, advanced ops
Timeline: 4 months while working at job
Polish & Community:
- Documentation improvements
- Performance optimization
- Community contributions
- Conference talks
- Production deployments
Timeline: 6 months
#17 (Job Lifecycle) ─────────┬──→ #5 (Priority)
│
└──→ #21 (Retry Policy)
│
└──→ #20 (Checkpointing)
#18 (Idempotency) ───┐
#19 (Leasing) ───────┼──→ #6 (Exactly-Once)
│
#13 (GPU-Aware) ─────┼──→ #4 (Topology-Aware)
│ │
│ └──→ #15 (Network Awareness)
│
└──→ #14 (NUMA Awareness)
└──→ #16 (Heterogeneous)
#7 (Global Plane) ───┬──→ #1 (Multi-Cluster)
│ │
│ └──→ #2 (Multi-Region)
│
├──→ #8 (Autonomy)
│
├──→ #9 (Dynamic Registration)
│
├──→ #10 (Heartbeat)
│
├──→ #11 (Consistency)
│
└──→ #12 (API Gateway)
#22 (Metrics) ───────┬──→ #23 (Dashboard)
│
└──→ #24 (Health Dashboard)
#3 (Fairness) ───────┼──→ #25 (RBAC)
#12 (API Gateway) ───┼──→ #26 (Audit Logging)
To reach Multi-Cluster (#1):
Requires: #7 (Global Control Plane)
To reach Multi-Region (#2):
Requires: #1, #7, #8, #9
To reach Exactly-Once (#6):
Requires: #18, #19
To reach Full Production:
Requires: All Phase 2 features + observability
Total Features: 26 (22 Ares + 4 Polaris)
Completed: 7 (27%)
Current Focus: Multi-cluster + observability (Week 4)
Next Milestone: 14 features by Feb 2026 (54%)
Full Vision: 26 features by Dec 2026 (100%)
This feature matrix serves as both:
- Technical specification for implementation
- Roadmap for development phases
- Portfolio artifact demonstrating distributed systems depth
Each feature is designed to showcase specific distributed systems patterns (consensus, leasing, topology awareness, SLA enforcement) at L4/L5 engineering level.
Last Updated: December 2025
Maintained By: Sathya Balasubramani (@BITS08SATHYA)