Feedback queuing models for time shared systems
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This is a presentation created to facilitate a research paper discussion on 'Feedback queuing models for time shared systems' for a final year undergraduate course. This includes a summary of the concepts presented with the paper, excluding their statistical proofs.
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Feedback Queueing Models for Time Shared Systems
This presentation was prepared by Ishara Amarasekera and Prabath Weerasinghe based on the paper, Feedback Queueing Models for Time Shared Systems by Edward G. Coffman and Leonard Kleinrock.
This presentation contains a summary of the content provided in this research paper and was presented as a paper discussion for the course, Advanced Operating Systems in Computer Science.
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This document discusses scheduling in distributed systems. It covers:
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2) Scheduling dependent tasks modeled as directed acyclic graphs (DAGs) using techniques like critical path on a processor (CPOP) and heterogeneous earliest finish time (HEFT).
3) The need for scheduling algorithms to adapt to dynamic grid environments where tasks may have dependencies on shared files and network transfer times vary.
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The document contains solutions to chapter 9 homework problems involving scheduling algorithms. Problem 9.16 asks to calculate turnaround times and average turnaround time for 5 batch jobs (A-E) using different scheduling algorithms: (a) round robin with 1 minute time quantum, (b) priority scheduling based on predefined priorities, (c) FCFS, (d) shortest job next. For each algorithm, the turnaround times for each job and the average are provided.
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Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
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2) QPS works in two phases - an offline phase that generates execution partitions and an online phase that manages task activation and scheduling according to EDF rules.
3) The algorithm aims to achieve low preemption and migration overhead while providing flexibility to adapt to system load variations through a master/slave server abstraction model.
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Feedback Queueing Models for Time Shared Systems
This presentation was prepared by Ishara Amarasekera and Prabath Weerasinghe based on the paper, Feedback Queueing Models for Time Shared Systems by Edward G. Coffman and Leonard Kleinrock.
This presentation contains a summary of the content provided in this research paper and was presented as a paper discussion for the course, Advanced Operating Systems in Computer Science.
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Distributed systems scheduling
This document discusses scheduling in distributed systems. It covers:
1) Common scheduling techniques like min-min, max-min, and sufferage for scheduling independent tasks on dedicated systems.
2) Scheduling dependent tasks modeled as directed acyclic graphs (DAGs) using techniques like critical path on a processor (CPOP) and heterogeneous earliest finish time (HEFT).
3) The need for scheduling algorithms to adapt to dynamic grid environments where tasks may have dependencies on shared files and network transfer times vary.
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The document contains solutions to chapter 9 homework problems involving scheduling algorithms. Problem 9.16 asks to calculate turnaround times and average turnaround time for 5 batch jobs (A-E) using different scheduling algorithms: (a) round robin with 1 minute time quantum, (b) priority scheduling based on predefined priorities, (c) FCFS, (d) shortest job next. For each algorithm, the turnaround times for each job and the average are provided.
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Replication in Distributed Systems
Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
Quasi Partitioned Scheduling
1) The document discusses the Quasi-Partitioned (QPS) scheduling algorithm for optimal and adaptive real-time scheduling on multiprocessors. QPS uses a quasi-partitioned approach that combines partitioned and global scheduling.
2) QPS works in two phases - an offline phase that generates execution partitions and an online phase that manages task activation and scheduling according to EDF rules.
3) The algorithm aims to achieve low preemption and migration overhead while providing flexibility to adapt to system load variations through a master/slave server abstraction model.
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The document discusses different models of communication between concurrent processes:
1) Shared memory model uses a shared variable or port for communication and requires explicit synchronization methods. Data can be persistent in registers or non-persistent passing through wires.
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The document describes a problem involving scheduling of processes with I/O and processor usage over multiple time periods, where each process spends half the time period doing I/O and half using the processor, and defines metrics like turnaround time and throughput to compute for different numbers of simultaneous jobs under two scheduling models. It also involves possible process state transitions and determining which are possible or impossible, and suggests an intermediate scheduling policy that balances priority and performance when processes are in the Ready and Ready/Suspend states.
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. In this slide, queue theory is applied to enhance the service of a bank in lines. For this, firstly a queue model (M/M/C): (GD/∞/∞) is selected to find out efficiency of the servers, number of service facilities, when (days of the week) customers can typically be expected to arrive,amount of time customers has to spend to get the desired service, length of the queue, how much time the customers have to wait before the service starts, how much time the customers have to wait in the bank, human psychology (frustration). After that, the optimal number of counter is calculated to improve the operational efficiency. At last, we calculate the optimal service rate and service efficiency.
Queuing model
queuing system is determined by:
-Arrival characteristics
-Queue characteristics
-Service facility characteristics
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Sara Afshar: Scheduling and Resource Sharing in Multiprocessor Real-Time Systems
PhD Candidate,
Department of Computer science
Mälardalen University
Time: Tuesday, Dec. 30, 2014, 11:30 a.m.
Location: Computer Engineering Department, Urmia University
Abstract:
The processor is the brain of a computer system. Usually, one or more programs run on a processor where each program is typically responsible for performing a particular task or function of the system. The performance of all the tasks together results in the system functionality. In many computer systems, it is not only enough that all tasks deliver correct output, but it is also crucial that these activities are delivered in a proper time. This type of systems that have timing requirements are known as real-time systems. A scheduler is responsible for scheduling all tasks on the processor, i.e., it dictates which task to run and when to run to ensure that all tasks are carried out on time. Typically, such tasks/programs need to use the computer system’s hardware and software resources to perform their calculation. Examples of such type of resources that are shared among programs are I/O devices, buffers and memories. Technology that is used for the management of shared resources is known as resource sharing synchronization protocol.
In recent years, a shift from single-processor platforms to multiprocessor platforms has become inevitable due to availability of processor chips and requirements on increased performance. Scheduling and resource sharing protocols have been well studied for uniprocessor systems. However, in the context of multiprocessors, still such techniques are not fully mature. The shift towards multi-core technology has revealed the demand for real-time scheduling algorithms along with synchronization protocols to support real-time applications on multiprocessors, both with and without dependencies.
In this talk, we first have an introduction to real-time embedded systems. Next, we look at scheduling and resource sharing policies in uniprocessor platforms. Further, we discuss the extension of scheduling and resource sharing policies for multiprocessor platforms and present the recent challenges arisen in this context.
Biography:
Sara Afshar is a PhD student at Mälardalen University. She has received her B.Sc. degree in Electrical Engineering from Tabriz University, Iran in 2002. She worked at different engineering companies until 2009. In the year 2010 she started her M.Sc. in Embedded Systems at Mälardalen University. She obtained her Master degree in 2012 and at the same year she started her PhD studies in Mälardalen University. Currently she is working on the topic of resource sharing in multiprocessor systems. She is part of the Complex Real-Time Embedded Systems group at Mälardalen University.
PROCESS.pptx
A process represents a program in execution. It progresses sequentially through different states from start to termination. A process has sections for stack, heap, text, and data in memory. Shortest Job First (SJF) scheduling allocates the CPU to the process with the shortest estimated run time remaining. It aims to minimize average waiting times but requires knowing future process durations. First Come First Served (FCFS) scheduling handles processes in the order they arrive without preemption, but is prone to the convoy effect where short jobs wait behind long ones.
Queuing Theory
Queuing Theory is a chapter in Computer Networking which is the course of Computer Science Engineering.
Planificacion
Here are some key metrics that schedulers should optimize for in different contexts:
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The right scheduling priorities depend
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Operating system 30 preemptive scheduling
In non-preemptive scheduling once a process has been allocated the CPU it runs uninterrupted until it finishes execution.
On the other hand, in preemptive schedule algorithms, the running process may be interrupted by a higher priority process in between its execution.
Whenever a process gets into ready state or the currently running finishes execution, the priority of the ready state process is checked against that of the running process.
If the priority of the ready process is more it is allowed to be allocated to the CPU.
Therefore, in these schemes, the CPU is allocated to the process with the highest priority all the time.
This gives rise to frequent context switching, which can become very costly in terms of CPU time wasted in switching. In the following sections we will explore some of such scheduling algorithms
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UNIPROCESS SCHEDULING.pptx
FCFS (First Come First Serve) is a non-preemptive scheduling algorithm where processes are executed in the order of their arrival. It uses a first-in first-out queue and is simple to implement. However, it can result in longer waiting times, especially for I/O bound processes. It also favors CPU-intensive processes as they can monopolize the CPU once started. The document then provides an example of FCFS scheduling along with the calculation of turnaround time and waiting time for each process.
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ch_scheduling (1).ppt
CPU scheduling determines which process will be assigned to the CPU for execution. There are several types of scheduling algorithms:
First-come, first-served (FCFS) assigns processes in the order they arrive without preemption. Shortest-job-first (SJF) selects the process with the shortest estimated run time, but may result in starvation of longer processes. Priority scheduling assigns priorities to processes and selects the highest priority process, but low priority processes risk starvation.
21 process scheduling alogorithm
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Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Feedback queuing models for time shared systems
- 1. Feedback Queuing Models for Time- Shared Systems (Paper Discussion) -Cited by 93 related articles- EDWARD G. COFFMAN Princeton University, Princeton, New Jersey AND LEONARD KLEINROCK University of California, Los Angeles, California Published in 1968 This presentation is a summary of the paper content, that is used to provide the foundation of the paper discussion
- 2. Eefficiently serve the user queue • Main Concern : Extending the analysis on time shared processor operations • Main assumption : User’s service time is a not known priori
- 3. 2. Time-Sharing Models A. Round – Robin B. Processor-shared model C. Multiple level FB model D. Multiple level FB model with priorities
- 4. A. Round – Robin
- 5. Assumptions • Preemptive resume • No swap time upper bounds on system performance • inter- arrival time distribution - A (t) • The service requirements of arriving units -B(r)
- 6. Markov Assumptions 1. Input process has a discrete time parameter t = nq, n is distributed according to the geometric distribution. Then, Mean inter-arrival period = q/1-€ sec Mean arrival rate = 1-€ /q per sec Similarly, Mean servicing time = q/1-£ sec Where q is the time quantum(the basic time interval) , 1-€ - probability of arrival of a new unit 1-£ - probability of receiving service
- 7. Markov Assumptions (Ctd.) 2. Both A(t) and B(r) follows Poisson process exponentially distributed
- 8. Assumption at the End of Time Interval • Late arrival – Eject the unit in service • Allow to join end of queue – Instantly new unit arrive (under probability) • Early arrival – Vice versa
- 9. B. Processor-shared Models • Round-robin system in which q 0 • All units in the system receive service concurrently • No waiting time in queue • Program speed = 1/k the speed from processor alone speed if k-1 processes running
- 10. Generalization priority processor- shared model • q !=0 member of p priority group goes in a queue • q 0 reduced to a processor shared model
- 11. C. Multiple level FB model (FBN) • N th level is quantum controlled , FCFS • Lower level unit comes N th level unit is preempted after the quantum in progress • q 0 implies in the limit a FCFS • FB1 FCFS Possible Starvation at last level??
- 12. D. Multiple level FB model with priorities • Assign external priorities to arriving units • Within a group FCFS • Arrival queue level low in the front of queue A proposed step : 1. Different quantum size for different levels 2. Different mean service time for different priority units
- 13. 4. Shortest-Job-First Model • Service the unit with shortest service time • No preemption at new arrival Possible starvation for long service required units?? A proposed step : 1. Improvements to get the information on total service time required by the unit at arrival
- 14. 5. Examples and Discussion • RR, FBN, SJF favor short service time • RR implicit discrimination on past service • FBN explicitly based on past service We can have a discussion comparing the presented models
- 15. Compare FB and RR • Shorter service requirement shorter wait than in FCFS for both FB and RR • RR is better for long service requirements • FB1 and FB 7 comparison
- 16. RR waiting times FB waiting times • Waiting time increase without a change in the number of levels as q increase • What more can we observe?
- 17. Summary • Superior treatment given certain units inferior treatment to some other units • Paper provides system designers with several options, presenting the behavior of each model
- 18. Thank You! All the diagrams are from the research paper itself and from the internet. I am grateful to all those resources.
Editor's Notes
- Earlier has mentioned that low priority queues are considered only when higher priorities are empty. Input traffic is separated into P priority groups. But no information on how it is done
- The shorter waiting times at FB1 is in the expense of long waiting times for longer service units