6.1 Resource allocation and scheduling

Resource allocation and scheduling are crucial aspects of operating system management. They determine how system resources like CPU time, memory, and I/O devices are distributed among competing processes. Effective allocation strategies aim to maximize efficiency, fairness, and overall system performance.

CPU scheduling algorithms play a key role in resource allocation. From simple methods like First-Come-First-Served to more complex approaches like Multilevel Feedback Queue, these algorithms determine which processes run when. The choice of algorithm significantly impacts system responsiveness, throughput, and user experience.

Resource Allocation in Operating Systems

Principles and Process of Resource Allocation

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• Resource allocation assigns available resources to competing processes maximizing efficiency and utilization
• Operating systems manage primary resources
  • CPU time
  • Memory space
  • I/O devices
  • File storage
• Resource allocation problem decides which process gets resources, when, and for how long while ensuring system stability and preventing deadlocks
• Key principles guide resource allocation
  • Fairness
  • Efficiency
  • Priority
  • Avoidance of starvation and thrashing
• Resource allocation strategies can be static (fixed at compile-time) or dynamic (adjusted at runtime based on system state)

Resource Allocation Algorithms and Techniques

• Common resource allocation algorithms include
  • First-Come-First-Served (FCFS)
  • Round Robin (RR)
  • Priority Scheduling
  • Multi-level Queue Scheduling
• Advanced resource allocation techniques may involve
  • Machine learning algorithms optimize resource distribution based on historical data
  • Predictive models adjust allocation based on current system load
  • Adaptive algorithms dynamically adjust resource allocation based on changing system conditions
• Specialized allocation strategies address specific scenarios
  • Real-time systems use algorithms like Rate Monotonic or Earliest Deadline First to meet timing constraints
  • Distributed systems employ load balancing techniques to optimize resource utilization across multiple nodes

CPU Scheduling Algorithms

Fundamental CPU Scheduling Algorithms

• CPU scheduling determines which ready process executes next on the CPU
• First-Come-First-Served (FCFS) scheduling
  • Executes processes in arrival order
  • Simple implementation
  • Can lead to convoy effect (short processes wait behind long ones)
• Shortest Job First (SJF) scheduling
  • Prioritizes processes with shortest execution time
  • Optimizes average waiting time
  • May cause starvation of longer processes
• Round Robin (RR) scheduling
  • Allocates fixed time quantum to each process in circular order
  • Ensures fairness
  • May increase context switch overhead with small time quanta

Advanced CPU Scheduling Algorithms

• Priority Scheduling
  • Assigns priorities to processes
  • Executes highest priority process first
  • Can lead to priority inversion or starvation of low-priority processes
• Multilevel Queue Scheduling
  • Divides ready queue into separate queues with different priorities
  • Allows flexible scheduling policies
  • May cause inter-queue unfairness
• Multilevel Feedback Queue Scheduling
  • Allows processes to move between queues based on behavior
  • Balances responsiveness and throughput
  • Adapts to changing process characteristics
• Real-time scheduling algorithms
  • Rate Monotonic assigns higher priority to tasks with shorter periods
  • Earliest Deadline First prioritizes tasks with nearest deadlines
  • Designed to meet strict timing constraints in real-time systems

Performance Impact of Resource Allocation

Performance Metrics and Evaluation

• Key performance metrics for evaluating resource allocation strategies
  • Throughput (number of processes completed per unit time)
  • Turnaround time (time from process submission to completion)
  • Waiting time (time spent in ready queue)
  • Response time (time from submission to first response)
  • CPU utilization (percentage of time CPU is busy)
• Choice of resource allocation strategy significantly affects
  • System performance
  • User experience
  • Overall efficiency
• Evaluation techniques for resource allocation strategies
  • Simulation models system behavior under different allocation policies
  • Analytical modeling uses mathematical analysis to predict performance
  • Benchmarking measures real-world performance with standardized workloads

Factors Influencing Performance

• Preemptive vs. non-preemptive scheduling impacts
  • System's ability to respond to high-priority tasks
  • Fairness of resource distribution
• Context switching overhead affects performance
  • Especially significant for algorithms with frequent switches (Round Robin)
  • Includes time to save and restore process state
• Load balancing in multi-processor or distributed systems
  • Influences effectiveness of resource allocation strategies
  • Aims to evenly distribute workload across available resources
• Real-world workload characteristics affect allocation strategy effectiveness
  • I/O-bound processes benefit from different scheduling than CPU-bound processes
  • Mix of short and long jobs impacts algorithm choice

Fairness vs Efficiency of Resource Allocation

Balancing Fairness and Efficiency

• Fairness in resource allocation ensures equitable distribution among competing processes
• Efficiency aims to maximize overall system throughput and minimize resource idle time
• Trade-off between fairness and efficiency is key in designing resource allocation policies
• Critical issues in fair allocation
  • Starvation occurs when a process is indefinitely denied necessary resources
  • Priority inversion happens when a high-priority task is indirectly preempted by a lower-priority task
• Proportional share scheduling algorithms
  • Allocate resources in proportion to assigned shares
  • Balance fairness with configurable prioritization
  • Examples include Weighted Fair Queueing (WFQ) and Completely Fair Scheduler (CFS)

Evaluation and Adaptive Strategies

• Adaptive scheduling policies dynamically adjust based on
  • System state
  • Process behavior
  • Can improve both fairness and efficiency over static policies
• Evaluation metrics for fairness
  • Jain's fairness index measures equality of resource allocation
  • Coefficient of variation of completion times across processes
• Techniques to improve both fairness and efficiency
  • Dynamic priority adjustment based on waiting time or resource usage
  • Hybrid scheduling algorithms combining multiple strategies
  • Feedback mechanisms to adjust allocation based on system performance
• Considerations for specific environments
  • Cloud computing environments may use elastic resource allocation
  • Mobile devices balance performance with power efficiency