Adaptive Scheduling in Dynamic Environments

The AI-RAN Taking Shape I'm thrilled to announce our latest research contribution that fundamentally transforms how we design, deploy, and test key functionalities of cellular networks. Our new paper "ALLSTaR - Automated LLM-Driven Scheduler Generation and Testing for Intent-Based RAN" represents three major industry firsts: ⚡ First-Ever Automated Scheduler Generation: We've developed LLM agents that automatically convert research papers into functional code, generating 18 different scheduling algorithms directly from academic literature using OCR and AI. No more months of manual implementation in ns-3 or Matlab! Automatically generated schedulers are automatically deployed in a live network as dApps through a CI/CD pipeline - without the need to change a single line of code in the gNodeB implementation (CU or DU);  ⚡ Intent-Based Scheduling: Network operators can now express high-level requirements in natural language ("prioritize users with bursty traffic") and ALLSTaR automatically translates these into optimized scheduling policies according to operator’s intent. ⚡ World's First O-RAN Compliant AI-RAN Testbed: All validation conducted on X5G with AutoRAN, production-grade, multi-vendor 5G infrastructure with GPU acceleration, AI-for-RAN and AI-and-RAN capabilities, demonstrating real-world viability at scale. This work also introduces a methodological paradigm shift: instead of implementing one algorithm at a time, we can now systematically evaluate a vast body of scheduling literature in production-like environments. We're moving from manual, months-long integration processes to automated, intent-driven networks that adapt in real-time. This is the Open RAN and the AI-RAN vision - and a pathway toward 6G that builds on our national strengths and open ecosystem. Full paper: https://lnkd.in/eTNWPNRR Open6G www.open6g.us #ORAN #AIRan #OpenRAN #5G #WirelessResearch #AI #MachineLearning #Telecommunications #Research Our brilliant team: Maxime Elkael Michele Polese Reshma Prasad Stefano Maxenti Office of the Under Secretary of Defense for Research and Engineering NSF AI-EDGE Institute National Telecommunications and Information Administration (NTIA) Qualcomm

Designing an AI System That Doesn’t Collapse Under Latency Spikes A single user query passes through multiple stages — tokenization → batching → GPU scheduling → model execution → post-processing → response assembly. Now picture this: A few heavy prompts take 5× longer than average. Your batching layer waits to fill the “perfect batch.” Meanwhile, the queue grows. Requests start timing out. Retries stack up. That’s when you realize: You’re not running out of compute. You’re running out of control. Here’s how you design for resilience instead of collapse 👇 1️⃣ Bounded Queues Never let latency scale linearly with load. Bound your input queues and shed load proactively — either by dropping excess requests or serving degraded responses. Unbounded queues are silent killers — they delay backpressure, causing cascading timeouts. Think of it like circuit breakers for inference — graceful denial is better than system-wide collapse. 2️⃣ Adaptive Batching Static batch sizes look great in benchmarks and terrible in production. Instead, make batch sizes dynamic — continuously tuned based on GPU occupancy, queue length, and recent tail latency percentiles (P95/P99). At low load, batch small for lower latency. At high load, batch large for throughput — but with strict timeouts. The goal is elasticity without unpredictability. 3️⃣ Token-Aware Scheduling Batching by request count is naive. In LLM workloads, token length determines cost. A single 10,000-token prompt can stall 15 smaller ones if batched together. Token-aware schedulers measure total token budget per batch and allocate GPU time accordingly. This ensures fairness and consistent latency curves even under mixed workloads. 4️⃣ Partial Caching Most engineers cache final model outputs. That helps little. What actually saves time is pre- and post-compute caching — tokenized inputs, embeddings, and prompt templates. These are deterministic and cheap to reuse, shaving milliseconds off critical paths. Combine that with vector cache lookups to skip redundant reasoning altogether. 5️⃣ Deadline-First Scheduling In multi-tenant inference systems, not all requests are equal. Prioritize requests based on expected completion deadlines instead of FIFO order. This minimizes tail latency and improves QoS across traffic tiers. It’s the same principle airlines use — business class boards first, but everyone still gets there. This is where systems engineering meets AI infrastructure. Because LLM inference at scale isn’t just about throughput — it’s about temporal predictability. Inside my Advanced System Design Cohort, we go deep into these challenges — how to design AI systems that don’t just scale, but stay stable under load. If you’ve been leading distributed systems or AI infra and want to sharpen your architectural depth, there’s a link to a form in the comments — apply, and we’ll check if you’re a great fit.

The Flexible Job Shop Scheduling Problem (FJSP) represents a critical advancement in industrial optimization, extending the classical Job Shop Scheduling Problem (JSSP) by introducing a dual-decision layer. While JSSP requires determining the sequence of operations on pre-assigned machines, FJSP adds the complexity of 'machine assignment', where each operation can be processed by any machine from a compatible set. This flexibility is essential for modern smart manufacturing, as it allows production systems to adapt to machine breakdowns and varying workloads, directly impacting operational efficiency and resource utilization in high-stakes environments. Historically, FJSP has been tackled using traditional exact methods like Integer Programming and meta-heuristics such as Genetic Algorithms (GA) or Taboo Search. More recently, Deep Reinforcement Learning (DRL) has emerged as a dominant approach, utilizing GNNs and Transformers to learn scheduling policies that can generate solutions in real-time. These neural net based methods treat the scheduling environment as a dynamic graph or sequence, attempting to map complex shop floor states to optimal dispatching rules. Despite their potential, current automated solvers face significant bottlenecks. The primary challenge lies in the 'curse of dimensionality' and sequence length. As the number of jobs and machines increases, the scheduling sequence grows quadratically, causing standard Transformers to suffer from extreme computational overhead due to their O(L^2) complexity. Furthermore, GNN-based methods often struggle to capture long-range dependencies between operations scheduled far apart in time, leading to sub-optimal machine assignments and increased makespan. To address the shortcomings highlighted above, the authors of [1] introduce M-CA (Mamba-CrossAttention), a novel architecture that replaces the standard self-attention mechanism with Selective State Space Modeling (Mamba). Mamba offers linear scaling O(L) with respect to sequence length, allowing the model to process much larger scheduling horizons efficiently. The M-CA framework specifically utilizes a 'Mamba-based Encoder' to capture global temporal dependencies and a 'Cross-Attention Decoder' to focus on the immediate machine-operation compatibility. This hybrid approach is superior because it maintains the high-fidelity global context of the entire factory state while drastically reducing the memory footprint and inference time required by traditional Transformers. Experiments show M-CA consistently outperforms state-of-the-art DRL baselines, Transformer-based models, and traditional heuristics across problem scales, achieving lower makespans and up to 5× faster inference. Mamba’s superior 'forgetting and remembering' mechanism drives scalability and robust performance by filtering out irrelevant scheduling noise to focus on critical constraints. The link to the paper [1] is posted in the comments.

Practice Standard for Scheduling – Third Edition The PMI Practice Standard for Scheduling (3rd Edition) provides a comprehensive framework for creating and maintaining effective project schedules. Core Concepts: ~ Distinguishes between schedule models (the planning tool), schedule instances (snapshots at specific points), and schedule presentations (outputs for stakeholders) ~ Emphasizes that scheduling is more than just setting dates—it's about creating a dynamic model that responds predictably to changes What's New in the Third Edition: ~ Expanded coverage of Agile and adaptive approaches (Scrum, Kanban) ~ Introduction to emerging trends: location-based scheduling, BIM (Building Information Modeling), lean scheduling ~ Enhanced guidance on earned schedule as a monitoring technique ~ Forensic schedule analysis for understanding variances Key Methodologies Covered: ~ Critical Path Method (CPM) - the foundation ~ Critical Chain - resource-focused approach with buffer management ~ Rolling Wave Planning - progressive elaboration ~ Monte Carlo simulation for risk analysis Essential Components: The standard identifies 111 scheduling components divided into: ~ 36 core-required components (mandatory for any schedule) ~ Conditional components for resource-loaded schedules, EVM integration, and risk management ~ Optional components for enhanced sophistication Practical Value: ~ Provides a conformance index to assess schedule quality ~ Emphasizes good practices: no open-ended activities, minimal constraints, proper logical relationships ~ Stresses that proper scheduling enables better communication, risk identification, and project control Bottom Line: Whether you're using predictive, adaptive, or hybrid approaches, this standard offers the blueprint for creating schedules that serve as true project management tools—not just compliance documents.

In industrial manufacturing, variability is the rule rather than the exception. Fluctuating demand, equipment breakdowns, supply chain disruptions, and labor shortages all contribute to the complexity of running a production facility. For plant managers, the challenge lies in maintaining optimal production levels in this unpredictable environment. To resolve these challenges, many manufacturers opt for Advanced Planning and Scheduling (APS) systems. However, APS systems reach their full potential only when they are digitally integrated with ERP and MES Systems. Here’s why. 𝐂𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐢𝐧𝐠 𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬:  Complex products often need multiple workstations and precisely timed specialist labor. Without a connected APS, coordinating resource availability and schedules becomes inefficient. 𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐌𝐢𝐱𝐞𝐬 𝐚𝐧𝐝 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐂𝐨𝐧𝐬𝐭𝐫𝐚𝐢𝐧𝐭𝐬:  Without an integrated APS, real-time handling of specialized conditions (allergens, dietary rules, and strict standards) falters, causing scheduling clashes, compliance risks, and unplanned changeovers. 𝐋𝐢𝐦𝐢𝐭𝐞𝐝 𝐓𝐢𝐦𝐞 𝐟𝐨𝐫 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠: Without real-time shop-floor data, such as equipment failures or production speed changes, planners can’t adjust schedules proactively. Managers must react instead, reducing efficiency and causing avoidable downtime. 𝐇𝐞𝐢𝐠𝐡𝐭𝐞𝐧𝐞𝐝 𝐀𝐝𝐦𝐢𝐧𝐢𝐬𝐭𝐫𝐚𝐭𝐢𝐯𝐞 𝐁𝐮𝐫𝐝𝐞𝐧: Manual spreadsheet juggling wastes time, invites errors, and spreads outdated data, impairing procurement, inventory, and production decisions. A UNS functions as a unified data model where Planning, Scheduling, and Execution systems continuously events. 𝐇𝐨𝐰 𝐢𝐭 𝐖𝐨𝐫𝐤𝐬 𝐢𝐧 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞? The APS system subscribes to ERP order data on the UNS. Whenever new or updated order events come in, it uses this real-time information to optimize the production sequence. Once the APS has recalculated the optimal schedule, it publishes an event back to the UNS. The MES then executes orders according to this schedule. A shared data framework keeps a continuous feedback loop, giving everyone up-to-date information for timely, informed decisions, leading to improved outcomes: → Rapid Scheduling Updates  → Quicker Response to Expedited Orders, Changes, and Capacity Requests → Enhanced Communication Across Departments Instead of waiting on manual updates or departmental handoffs, the system instantly reads the event from the UNS, recalculates the schedule, and publishes a new plan. This rapid response capability helps maintain on-time delivery and adapt to shifting priorities without costly delays.

Smart PPS (Production Planning and Scduling) : Redefining the Role of the Planner in Manufacturing - QeMFG Every manufacturing shopfloor has one silent warrior- the Planner. Balancing customer demands, production constraints, machine capacities, and supplier dependencies is no small feat. Yet, too often, planners find themselves stuck in Excel sheets, chasing updates, and firefighting issues rather than truly planning. This is exactly where Smart Production Planning & Scheduling (Smart PPS) transforms the game. 👉 From Firefighting to Foresight Smart PPS shifts planners from reactive problem solvers to strategic decision-makers. By digitizing and automating the core planning process, it ensures that production is not just scheduled, but intelligently orchestrated. 👉 What Planners Gain with Smart PPS Real-Time Visibility A unified dashboard highlights machine status, material availability, and workforce allocation - giving planners complete control at a glance. No more running around the shopfloor to gather updates. Dynamic Rescheduling Sudden changes—machine breakdowns, urgent customer orders, or material delays—are handled instantly with auto-rescheduling. Planners can adapt without disruption. Seamless ERP & IoT Integration Sales orders flow directly from ERP, and IoT-enabled machines send live production data. This keeps planning aligned with reality, not assumptions. Scenario Simulations “What if” analysis allows planners to evaluate multiple options before committing. Whether it’s adding a shift, re-prioritizing an order, or balancing supplier delays, decisions are powered by data - not guesswork. Cross-Functional Collaboration Procurement, Quality, and Shopfloor Supervisors all work on the same updated schedule, reducing miscommunication and rework. The Results Speak for Themselves 👉 Improved on-time delivery 👉 machine utilization 👉 Reduced idle time and bottlenecks Less stress for planners, more focus on strategy A stronger link between planning and Why It Matters When planners succeed, the entire shopfloor succeeds. And when the shopfloor runs smoothly, businesses not only meet deadlines - they win customer trust and unlock new growth opportunities. At QeMFG, our vision with Smart PPS is simple: empower the planner, elevate the production ecosystem, and create a future-ready manufacturing floor. 👉 Curious to see how Smart PPS can transform your planning process? Let’s connect. #SmartPPS #Manufacturing #Engineering #ProductionPlanning #ShopfloorExcellence #ERP #Industry40 #SmartManufacturing #QeMFG

🔷 Day 14: Reinforcement Learning in 5G Resource Allocation Optimizing spectrum, power, and scheduling through AI that learns from the network itself. 📌 Why Reinforcement Learning (RL) in 5G? Unlike supervised models that rely on labeled data, RL uses trial-and-error — learning from its environment through feedback (rewards). 5G resource allocation is dynamic and context-aware — RL fits perfectly. 📌 Key Resource Challenges in 5G NR Scheduling PRBs under ultra-low latency constraints Power control in dense small cell environments Mobility and handover management Interference-aware resource reuse Slice-specific QoS assurance 📌 How RL Solves These Agent: The network function (e.g., scheduler, SMO, RIC) State: Network KPIs like CQI, buffer size, UE mobility, demand Action: Allocate PRBs, select MCS, adjust transmit power Reward: Higher throughput, lower latency, reduced packet drop Over time, the RL agent learns to take optimal actions to maximize overall network performance. 📌 Practical Use Cases We Covered Dynamic PRB scheduling in congested cells Beam selection based on prior user movement patterns RAN slicing with real-time policy enforcement Intelligent power allocation to balance SINR across users 📌 What Makes RL Ideal for 5G? Operates in real-time environments Learns from unpredictable user behavior Scales across multi-agent setups (e.g., CU-DU split) Adapts to dynamic interference and load patterns 📘 Technical References ITU-T Y.3173 – Framework for ML in future networks O-RAN WG2 – Near-RT RIC AI Training & Inference 3GPP TR 38.891 – Study on AI/ML for 5G NR #5G #AIin5G #ReinforcementLearning #RANOptimization #5GNR #O_RAN #TelecomAI #NitinGupta #Day14 #ResourceAllocation #RIC #SON #5GTraining #WhatsAppLearning

Project Management and Planning method in environments with unstable or volatile economies is one that emphasizes adaptability, resilience, and risk management rather than rigid long-term predictions.key principles as below: - Agility & Flexibility – plans must adapt quickly to changing condition.so the monthly updating and adjusting reschedule based on Critical Chain Method and controlling resource also TIA method to assess the potential effects of unforeseen events or changes on a project's timeline is recommended. - Incremental Delivery – deliver value in small steps to reduce exposure, same as work package control. - Scenario & Contingency Planning – prepare multiple financial/resource scenarios in the Work program and budget as contingency for RISKs. - Strong Risk Management – monitor inflation, currency fluctuations, and supply-chain risks and update risk registry in line with schedule updating in short time like monthly. Means the best approach is Agile + Rolling-Wave Planning + Scenario-Based Risk Management. This ensures flexibility, short feedback cycles, and preparedness for multiple futures while still maintaining financial discipline. # project management # unstable economy #TIA#CCM#waterfall

Rethinking Operational Efficiency: Moving Beyond Rigid Schedules As CEOs and business leaders, we often rely on schedules—shifts, service rollouts, and predefined resource allocations—to manage our operations. While this approach provides structure, it inherently introduces inefficiencies that blow budgets & frustrate customers. Consider a grocery store with 12 aisles but only 3 open during peak hours, with long wait times and unhappy customers; or a convenience store with one register open and workers everywhere doing who knows what. How about a restroom cleaned twice a day in an airport area with minimal foot traffic, wasting labor on tasks that aren’t needed. These are clear examples of over- or under-utilization that impact both the bottom line and customer experience. The reality is, customer demand isn't static. It fluctuates throughout the day and week, with many factors affecting it -- yet many companies continue to operate on fixed schedules that can't adapt in real-time. Schedule-based operations based on snapshot survey responses are simply guesses that will almost always be wrong. Imagine a different approach—one where companies sense and analyze demand in real time, then dynamically allocate resources accordingly. This isn’t just a futuristic concept; it’s a practical strategy that can save hundreds of thousands of dollars annually. Our clients are leading the way and beating their competition with this approach today. Consider the ROI - if a business can reduce unnecessary staffing by just 20%, that’s a potential saving of tens of thousands of dollars per location each year— and hundreds of thousands overall. These are funds that can be reinvested into improving service quality, technology, or expansion. Beyond cost savings, pivoting from scheduled operations to demand-driven management enhances customer satisfaction, reduces wait times, and builds brand loyalty. The key is to harness real-time data—feedback, demand signals, environmental factors, and operational processes —and adapt accordingly. As leaders, it's time to rethink our operational models for a more efficient, customer-centric future. Let's move beyond the schedule and embrace sensing and adapting on the fly. Let me know other examples of under- or over-staffing that have frustrated you - I'd love to hear them!!! #OperationalEfficiency #CustomerExperience #SmartResources #BusinessInnovation FeedbackNow