The document discusses solutions to problems from the textbook "Real Time System" by Jane W. S. Liu. It provides solutions to sample problems from chapters 3 through 6 of the textbook on its blog. For each chapter, it lists the question and problem number and provides a link to the blog post with the written solution. The blog aims to help students who are working through the textbook problems by providing solutions to compare their own work against.
Schedulability of Rate Monotonic Algorithm using Improved Time Demand Analysi...IJECEIAES
Real-Time Monotonic algorithm (RMA) is a widely used static priority scheduling algo- rithm. For application of RMA at various systems, it is essential to determine the system’s feasibility first. The various existing algorithms perform the analysis by reducing the scheduling points in a given task set. In this paper we propose a schedubility test algorithm, which reduces the number of tasks to be analyzed instead of reducing the scheduling points of a given task. This significantly reduces the number of iterations taken to compute feasibility. This algorithm can be used along with the existing algorithms to effectively reduce the high complexities encountered in processing large task sets. We also extend our algorithm to multiprocessor environment and compare number of iterations with different number of processors. This paper then compares the proposed algorithm with existing algorithm. The expected results show that the proposed algorithm performs better than the existing algorithms.
Senthilkanth,MCA..
The following ppt's full topic covers Operating System for BSc CS, BCA, MSc CS, MCA students..
1.Introduction
2.OS Structures
3.Process
4.Threads
5.CPU Scheduling
6.Process Synchronization
7.Dead Locks
8.Memory Management
9.Virtual Memory
10.File system Interface
11.File system implementation
12.Mass Storage System
13.IO Systems
14.Protection
15.Security
16.Distributed System Structure
17.Distributed File System
18.Distributed Co Ordination
19.Real Time System
20.Multimedia Systems
21.Linux
22.Windows
About real time system task scheduling basic concepts.It deals with task, instance,data sharing and their types.It also covers various important terminologies regarding scheduling algorithms.
SWARM INTELLIGENCE SCHEDULING OF SOFT REAL-TIME TASKS IN HETEROGENEOUS MULTIP...ecij
In this paper, a hybrid swarm intelligence algorithm (named VNABCSA) is presented for the scheduling of non-preemptive soft real-time tasks in heterogeneous multiprocessor platforms. The method is based on a combination of artificial bee colony and simulated annealing algorithms. The multi-objective function of the VNABCSA algorithm is defined to minimize the total tardiness of all tasks, total number of utilized processors, total completion time, total waiting time for all tasks, and total waiting time for all processors. We introduce a hybrid variable neighborhood search strategy to improve the convergence speed of the algorithm. Simulation results demonstrate the efficiency of the proposed methodology as compared with the
existing scheduling algorithms.
STATISTICAL APPROACH TO DETERMINE MOST EFFICIENT VALUE FOR TIME QUANTUM IN RO...ijcsit
Scheduling various processes is one of the most fundamental functions of the operating system. In that
context one of the most common scheduling algorithms used in most operating systems is the Round Robin
method in which, the ready processes waiting in the ready queue, take control of the processor for a short
period of time known as the time quantum (or time slice) circularly. Here we discuss the use of statistics
and develop a mathematical model to determine the most efficient value for time quantum. This is strictly
theoretical as we do not know the values of times for the various processes beforehand. However the
proposed approach is compared with the recent developed algorithms to this regard to determine the
efficiency of the proposed algorithm.
(Slides) Task scheduling algorithm for multicore processor system for minimiz...Naoki Shibata
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
Sara Afshar: Scheduling and Resource Sharing in Multiprocessor Real-Time Systemsknowdiff
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.
Survey Results Age Of Unbounded Data June 03 10nhaque
Enterprises today can generate, collect and consider more data than ever before. New types of data can provide insight into previously opaque processes and motivations, but prodigious quantities of data present opportunity, as well as complexity and distraction. nGenera Insight’s 2010 Leading in an Age of Unbounded Data survey garnered responses from over 70 major organizations, including many global corporations, to provide a cross-industry pulse of the state of enterprise data.
Kernel Recipes 2016 - Wo needs a real-time operating system (not you!)Anne Nicolas
A Real-Time Operating System (RTOS) is used for mission critical projects that require a deterministic response time for external stimuli. Many times project managers request an RTOS even though they don’t fully understand what exactly an RTOS gives them. And many times, a normal OS would do just fine, and perhaps even better, as an RTOS sacrifices throughput for determinism.
This talk will discuss exactly what an RTOS is and what it is good for. What types of requirements that require having an RTOS will be explained, as well as requirements where not having on RTOS would be more appropriate. You do not need to be a programmer to enjoy this talk. It will be discussed at a very high level but still contain enough information for a seasoned developer to get something out of it too.
Steven Rostedt, Red Hat
Schedulability of Rate Monotonic Algorithm using Improved Time Demand Analysi...IJECEIAES
Real-Time Monotonic algorithm (RMA) is a widely used static priority scheduling algo- rithm. For application of RMA at various systems, it is essential to determine the system’s feasibility first. The various existing algorithms perform the analysis by reducing the scheduling points in a given task set. In this paper we propose a schedubility test algorithm, which reduces the number of tasks to be analyzed instead of reducing the scheduling points of a given task. This significantly reduces the number of iterations taken to compute feasibility. This algorithm can be used along with the existing algorithms to effectively reduce the high complexities encountered in processing large task sets. We also extend our algorithm to multiprocessor environment and compare number of iterations with different number of processors. This paper then compares the proposed algorithm with existing algorithm. The expected results show that the proposed algorithm performs better than the existing algorithms.
Senthilkanth,MCA..
The following ppt's full topic covers Operating System for BSc CS, BCA, MSc CS, MCA students..
1.Introduction
2.OS Structures
3.Process
4.Threads
5.CPU Scheduling
6.Process Synchronization
7.Dead Locks
8.Memory Management
9.Virtual Memory
10.File system Interface
11.File system implementation
12.Mass Storage System
13.IO Systems
14.Protection
15.Security
16.Distributed System Structure
17.Distributed File System
18.Distributed Co Ordination
19.Real Time System
20.Multimedia Systems
21.Linux
22.Windows
About real time system task scheduling basic concepts.It deals with task, instance,data sharing and their types.It also covers various important terminologies regarding scheduling algorithms.
SWARM INTELLIGENCE SCHEDULING OF SOFT REAL-TIME TASKS IN HETEROGENEOUS MULTIP...ecij
In this paper, a hybrid swarm intelligence algorithm (named VNABCSA) is presented for the scheduling of non-preemptive soft real-time tasks in heterogeneous multiprocessor platforms. The method is based on a combination of artificial bee colony and simulated annealing algorithms. The multi-objective function of the VNABCSA algorithm is defined to minimize the total tardiness of all tasks, total number of utilized processors, total completion time, total waiting time for all tasks, and total waiting time for all processors. We introduce a hybrid variable neighborhood search strategy to improve the convergence speed of the algorithm. Simulation results demonstrate the efficiency of the proposed methodology as compared with the
existing scheduling algorithms.
STATISTICAL APPROACH TO DETERMINE MOST EFFICIENT VALUE FOR TIME QUANTUM IN RO...ijcsit
Scheduling various processes is one of the most fundamental functions of the operating system. In that
context one of the most common scheduling algorithms used in most operating systems is the Round Robin
method in which, the ready processes waiting in the ready queue, take control of the processor for a short
period of time known as the time quantum (or time slice) circularly. Here we discuss the use of statistics
and develop a mathematical model to determine the most efficient value for time quantum. This is strictly
theoretical as we do not know the values of times for the various processes beforehand. However the
proposed approach is compared with the recent developed algorithms to this regard to determine the
efficiency of the proposed algorithm.
(Slides) Task scheduling algorithm for multicore processor system for minimiz...Naoki Shibata
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
Sara Afshar: Scheduling and Resource Sharing in Multiprocessor Real-Time Systemsknowdiff
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.
Survey Results Age Of Unbounded Data June 03 10nhaque
Enterprises today can generate, collect and consider more data than ever before. New types of data can provide insight into previously opaque processes and motivations, but prodigious quantities of data present opportunity, as well as complexity and distraction. nGenera Insight’s 2010 Leading in an Age of Unbounded Data survey garnered responses from over 70 major organizations, including many global corporations, to provide a cross-industry pulse of the state of enterprise data.
Kernel Recipes 2016 - Wo needs a real-time operating system (not you!)Anne Nicolas
A Real-Time Operating System (RTOS) is used for mission critical projects that require a deterministic response time for external stimuli. Many times project managers request an RTOS even though they don’t fully understand what exactly an RTOS gives them. And many times, a normal OS would do just fine, and perhaps even better, as an RTOS sacrifices throughput for determinism.
This talk will discuss exactly what an RTOS is and what it is good for. What types of requirements that require having an RTOS will be explained, as well as requirements where not having on RTOS would be more appropriate. You do not need to be a programmer to enjoy this talk. It will be discussed at a very high level but still contain enough information for a seasoned developer to get something out of it too.
Steven Rostedt, Red Hat
Efficient Dynamic Scheduling Algorithm for Real-Time MultiCore Systems iosrjce
Imprecise computation model is used in dynamic scheduling algorithm having heuristic function to
schedule task sets. A task is characterized by ready time, worst case computation time, deadline and resource
requirements. A task failing to meet its deadline and resource requirements on time is split into mandatory part
and optional part. These sub-tasks of a task can execute concurrently on multiple cores, thus achieving
parallelization provided by the multi-core system. Mandatory part produces acceptable results while optional
part refines the result further. To study the effectiveness of proposed scheduling algorithm, extensive simulation
studies have been carried out. Performance of proposed scheduling algorithm is compared with myopic and
improved myopic scheduling algorithm. The simulation studies shows that schedulability of task split myopic
algorithm is always higher than myopic and improved myopic algorithm.
Temporally Extended Actions For Reinforcement Learning Based Schedulers IJSCAI Journal
Temporally extended actions have been proved to enhance the performance of reinforcement learning
agents. The broader framework of ‘Options’ gives us a flexible way of representing such extended course of
action in Markov decision processes. In this work we try to adapt options framework to model an operating
system scheduler, which is expected not to allow processor stay idle if there is any process ready or waiting
for its execution. A process is allowed to utilize CPU resources for a fixed quantum of time (timeslice) and
subsequent context switch leads to considerable overhead. In this work we try to utilize the historical
performances of a scheduler and try to reduce the number of redundant context switches. We propose a
machine-learning module, based on temporally extended reinforcement-learning agent, to predict a better
performing timeslice. We measure the importance of states, in option framework, by evaluating the impact
of their absence and propose an algorithm to identify such checkpoint states. We present empirical
evaluation of our approach in a maze-world navigation and their implications on "adaptive timeslice
parameter" show efficient throughput time.
TEMPORALLY EXTENDED ACTIONS FOR REINFORCEMENT LEARNING BASED SCHEDULERSijscai
Temporally extended actions have been proved to enhance the performance of reinforcement learning agents. The broader framework of ‘Options’ gives us a flexible way of representing such extended course of action in Markov decision processes. In this work we try to adapt options framework to model an operating system scheduler, which is expected not to allow processor stay idle if there is any process ready or waiting for its execution. A process is allowed to utilize CPU resources for a fixed quantum of time (timeslice) and subsequent context switch leads to considerable overhead. In this work we try to utilize the historical performances of a scheduler and try to reduce the number of redundant context switches. We propose a machine-learning module, based on temporally extended reinforcement-learning agent, to predict a better performing timeslice. We measure the importance of states, in option framework, by evaluating the impact of their absence and propose an algorithm to identify such checkpoint states. We present empirical evaluation of our approach in a maze-world navigation and their implications on "adaptive time slice parameter" show efficient throughput time.
TEMPORALLY EXTENDED ACTIONS FOR REINFORCEMENT LEARNING BASED SCHEDULERSijscai
Temporally extended actions have been proved to enhance the performance of reinforcement learning
agents. The broader framework of ‘Options’ gives us a flexible way of representing such extended course of
action in Markov decision processes. In this work we try to adapt options framework to model an operating
system scheduler, which is expected not to allow processor stay idle if there is any process ready or waiting
for its execution. A process is allowed to utilize CPU resources for a fixed quantum of time (timeslice) and
subsequent context switch leads to considerable overhead. In this work we try to utilize the historical
performances of a scheduler and try to reduce the number of redundant context switches. We propose a
machine-learning module, based on temporally extended reinforcement-learning agent, to predict a better
performing timeslice. We measure the importance of states, in option framework, by evaluating the impact
of their absence and propose an algorithm to identify such checkpoint states. We present empirical
evaluation of our approach in a maze-world navigation and their implications on "adaptive timeslice
parameter" show efficient throughput time.
Multiprocessor scheduling of dependent tasks to minimize makespan and reliabi...ijfcstjournal
Algorithms developed for scheduling applications on heterogeneous multiprocessor system focus on a
single objective such as execution time, cost or total data transmission time. However, if more than one
objective (e.g. execution cost and time, which may be in conflict) are considered, then the problem becomes
more challenging. This project is proposed to develop a multiobjective scheduling algorithm using
Evolutionary techniques for scheduling a set of dependent tasks on available resources in a multiprocessor
environment which will minimize the makespan and reliability cost. A Non-dominated sorting Genetic
Algorithm-II procedure has been developed to get the pareto- optimal solutions. NSGA-II is a Elitist
Evolutionary algorithm, and it takes the initial parental solution without any changes, in all iteration to
eliminate the problem of loss of some pareto-optimal solutions.NSGA-II uses crowding distance concept to
create a diversity of the solutions.
Temporal workload analysis and its application to power aware schedulingijesajournal
Power
-
aware scheduling reduces CPU energy consumption in hard real
-
time systems through dynamic
voltage scaling(DVS). The basic idea of power
-
aware scheduling
is to find slacks available to tasks and
reduce CPU‟s frequency or lower its voltage using the found slacks. In this paper, we introduce temporal
workload of a system which specifies how much busy its CPU is to complete the tasks at current time.
Analyzin
g temporal workload provides a sufficient condition of schedulability of preemptive early
-
deadline
first scheduling and an effective method to identify and distribute slacks generated by early completed
tasks. The simulation results show that proposed algo
rithm reduces the energy consumption by 10
-
70%
over the existing algorithm and its algorithm complexity is O(n). So, practical on
-
line scheduler could be
devised using the proposed algorithm.
Temporal workload analysis and its application to power aware schedulingijesajournal
Power-aware scheduling reduces CPU energy consumption in hard real-time systems through dynamic voltage scaling(DVS). The basic idea of power-aware scheduling is to find slacks available to tasks and reduce CPU‟s frequency or lower its voltage using the found slacks. In this paper, we introduce temporal workload of a system which specifies how much busy its CPU is to complete the tasks at current time. Analyzing temporal workload provides a sufficient condition of schedulability of preemptive early-deadline first scheduling and an effective method to identify and distribute slacks generated by early completed tasks. The simulation results show that proposed algorithm reduces the energy consumption by 10-70% over the existing algorithm and its algorithm complexity is O(n). So, practical on-line scheduler could be devised using the proposed algorithm.
(Paper) Task scheduling algorithm for multicore processor system for minimiz...Naoki Shibata
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
SMP4: Thread Scheduler
======================
INSTRUCTIONS
============
1. OVERVIEW
===========
In this MP, you will write a user-mode thread scheduler. The basic purpose
of a scheduler is to multiplex use of the computer across several threads
of execution. This MP deals with two different scheduling policies: FIFO
and Round Robin. You will implement both, for use in a simple cooperative
multi-threading system. Along the way, you'll also learn about implementing
object-oriented constructs in low-level procedural languages like C.
This assignment consists of implementing the core functionality of the
scheduler (Step 4) and answering 10 questions (Step 5). Code for
Step 4 goes in sched_impl.c and sched_impl.h.
2. THEORY OF OPERATION
======================
The given code in the MP defines the skeleton of a scheduler together with a
parameterized dummy workload. The idea is when you run the MP, you specify
a scheduling policy, scheduler queue size, some number of worker threads to
create, and, optionally, the number of iterations for which the worker
threads should run. The basic code that parses command line arguments and
creates these worker threads is provided in the MP, but you must implement
the core synchronization and scheduling operations.
As provided, the MP only includes the "dummy" scheduling algorithm, which
doesn't even try to do anything. You can run it like this:
make
./scheduler -dummy 0 N # where N is some number of worker threads
All threads run right away regardless of the queue size (even zero!), and
are scheduled by the operating system. The goal of this MP is to create
scheduler implementations which are a bit more controlled and predictable.
For example, once you have completed the MP, the following should work:
./scheduler -fifo 1 2 3
Main: running 2 workers on 1 queue_size for 3 iterations
Main: detaching worker thread 3075984304
Main: detaching worker thread 3065494448
Main: waiting for scheduler 3086474160
Thread 3075984304: in scheduler queue
Thread 3075984304: loop 0
Thread 3075984304: loop 1
Thread 3075984304: loop 2
Thread 3075984304: exiting
Thread 3065494448: in scheduler queue
Thread 3065494448: loop 0
Thread 3065494448: loop 1
Thread 3065494448: loop 2
Thread 3065494448: exiting
Scheduler: done!
The command line options used above specify:
-fifo Use FIFO scheduling policy
1 One thread can be in the scheduler queue at a time
2 Create 2 worker threads
3 Each thread runs for 3 time slices
Here's another example:
./scheduler -rr 10 2 3
Main: running 2 workers on 10 queue_size for 3 iterations
Main: detaching worker thread 3075828656
Main: detaching worker thread 3065338800
Main: waiting for scheduler 3086318512
Thread 3075828656: in scheduler queue
Thread 3065338800: in scheduler queue
Thread 3075828656: loop 0
Thread 3065338800: loop 0
Thread 3075828656: loop 1
T.
This a fake scientific article generated by a computer program. It is the parody of science and a perfect example of the problem of our age: the achievement without actual knowledge and effort.
Assignment 02 Process State SimulationCSci 430 Introduction to.docxcargillfilberto
Assignment 02: Process State Simulation
CSci 430: Introduction to Operating Systems Fall 2020
In this assignment we will simulate a three-state process model (ready, running and blocked) and a simple process control block structure as introduced in Chapter 3 of our textbook. This simulation will utilize a ready queue and a list of blocked processes. We will simulate processes being created, deleted, timing out because they exceed their time quantum, and becoming blocked and unblocked because of (simulated) I/O events.
Questions
How does round robin scheduling work?
How does an operating system manage processes, move them between ready, running and blocked states, and
determine which process is scheduled next?
What is the purpose of the process control block? How does the PCB help an operating system manage and
keep track of processes?
Objectives
• Explore the Process state models from an implementation point of view.
• Practice using basic queue data types and implementing in C.
• Use C/C++ data structures to implement a process control block and round robin scheduling queues. • Learn about Process switching and multiprogramming concepts.
• Practice using STL queues and list data structures.
Introduction
In this assignment you will simulate a three-state process model (ready, running and blocked) and a simple list of processes, like the process control block structure as discussed in Chapter 3. Your program will read input and directives from a file. The input describes events that occur to the processes running in the simulation. These are the full set of events that can happen to and about processes in this simulation:
In addition to these events, there are 2 other implicit events that need to occur before and after every simulated event 1
listed above.
Action dispatch
timeout
Description
Before processing each event, if the CPU is currently idle, try and dispatch a process from the ready queue. If the ready queue is not empty, we will remove the process from the head of the ready queue and allocate it the CPU to run for 1 system time slice quantum.
After processing each event, we need to test if the running process has exceeded its time slice quantum yet. If a process is currently allocated to the CPU and running, check how long it has been run on its current dispatch. If it has exceeded its time slice quantum, the process should be timed out. It will be put back into a ready state, and will be pushed back to the end of the system ready queue.
The input file used for system tests and simulations will be a list of events that occur in the system, in the order they are to occur. For example, the first system test file looks like this:
----- process-events-01.sim -------- new
cpu
cpu
cpu
new
cpu
cpu
cpu
cpu
block 83
cpu
cpu
unblock 83
cpu
cpu
done
cpu
cpu
cpu
cpu ----------------------------------
The simulation you are developing is a model of process management and scheduling as described in.
SMP4: Thread Scheduler (PART 1)
======================
INSTRUCTIONS
============
1. OVERVIEW
===========
In this MP, you will write a user-mode thread scheduler. The basic purpose
of a scheduler is to multiplex use of the computer across several threads
of execution. This MP deals with two different scheduling policies: FIFO
and Round Robin. You will implement both, for use in a simple cooperative
multi-threading system. Along the way, you'll also learn about implementing
object-oriented constructs in low-level procedural languages like C.
This assignment consists of implementing the core functionality of the
scheduler (Step 4) and answering 10 questions (Step 5). Code for
Step 4 goes in sched_impl.c and sched_impl.h.
2. THEORY OF OPERATION
======================
The given code in the MP defines the skeleton of a scheduler together with a
parameterized dummy workload. The idea is when you run the MP, you specify
a scheduling policy, scheduler queue size, some number of worker threads to
create, and, optionally, the number of iterations for which the worker
threads should run. The basic code that parses command line arguments and
creates these worker threads is provided in the MP, but you must implement
the core synchronization and scheduling operations.
As provided, the MP only includes the "dummy" scheduling algorithm, which
doesn't even try to do anything. You can run it like this:
make
./scheduler -dummy 0 N # where N is some number of worker threads
All threads run right away regardless of the queue size (even zero!), and
are scheduled by the operating system. The goal of this MP is to create
scheduler implementations which are a bit more controlled and predictable.
For example, once you have completed the MP, the following should work:
./scheduler -fifo 1 2 3
Main: running 2 workers on 1 queue_size for 3 iterations
Main: detaching worker thread 3075984304
Main: detaching worker thread 3065494448
Main: waiting for scheduler 3086474160
Thread 3075984304: in scheduler queue
Thread 3075984304: loop 0
Thread 3075984304: loop 1
Thread 3075984304: loop 2
Thread 3075984304: exiting
Thread 3065494448: in scheduler queue
Thread 3065494448: loop 0
Thread 3065494448: loop 1
Thread 3065494448: loop 2
Thread 3065494448: exiting
Scheduler: done!
The command line options used above specify:
-fifo Use FIFO scheduling policy
1 One thread can be in the scheduler queue at a time
2 Create 2 worker threads
3 Each thread runs for 3 time slices
Here's another example:
./scheduler -rr 10 2 3
Main: running 2 workers on 10 queue_size for 3 iterations
Main: detaching worker thread 3075828656
Main: detaching worker thread 3065338800
Main: waiting for scheduler 3086318512
Thread 3075828656: in scheduler queue
Thread 3065338800: in scheduler queue
Thread 3075828656: loop 0
Thread 3065338800: loop 0
Thread 3075828656: lo.
GENERATIVE SCHEDULING OF EFFECTIVE MULTITASKING WORKLOADS FOR BIG-DATA ANALYT...IAEME Publication
Scheduling of dynamic and multitasking workloads for big-data analytics is a challenging issue, as it requires a significant amount of parameter sweeping and iterations. Therefore, real-time scheduling becomes essential to increase the throughput of many-task computing. The difficulty lies in obtaining a series of optimal yet responsive schedules. In dynamic scenarios, such as virtual clusters in cloud, scheduling must be processed fast enough to keep pace with the unpredictable fluctuations in the workloads to optimize the overall system performance. In this paper, ordinal optimization using rough models and fast simulation is introduced to obtain suboptimal solutions in a much shorter timeframe.
Similar to Solution manual real time system bt jane w s liu solution manual (20)
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
Solution manual real time system bt jane w s liu solution manual
1. Solution Manual for Real Time System by Jane W. S. Liu
Real Time System by Jane W. S. Liu (Pearson), the book builds on the student's background
in Operating System, Embedded System. It covers techniques for scheduling, resource access
control, and validation that are, or are likely to be, widely used in real time computing and
communication systems. Each algorithm, protocol, or mechanism is defined by pseudo code or
simple rules that can serve as a starting point of implementation. With few exceptions, each
scheduling algorithm is accompanied that your application will meet its real time requirement
when scheduling according to the algorithm.
Here, in next successive posts, I am going to post solutions for the same Text-book (Real Time
System by Jane W. S. Liu). If you find any difficulty or wants to suggest anything, feel free to
comment...:)
Link: http://targetiesnow.blogspot.in/p/solution-manual-for.html
Real Time System by Jane W. S. Liu Chapter 3.1 Solution
Q. 3.1: Because sporadic jobs may have varying release times and execution times, the
periodic task model may be too inaccurate and can lead to undue under utilization of the processor
even when the inter release times of jobs are bounded from below and their executions are
bounded from above. As an example, suppose we have a stream of sporadic jobs whose inter
release times are uniformly distributed from 9 to 11. Their execution times are uniformly
distributed from 1 to 3.
a. What are the parameters of the periodic task if we were to use such a task to model the stream?
Solution:
liu.html
http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-s-
2. Real Time System by Jane W. S. Liu Chapter 3.2 Solution
Q.3.2: Consider the real-time program described by the psuedo code below. Names of jobs are
in italic.
At 9 AM, start: have breakfast and go to office;
At 10 AM,
if there is class,
teach;
Else, help students;
When teach or help is done, eat_lunch;
Until 2 PM, sleep;
If there is a seminar,
If topic is interesting,
listen;
Else, read;
Else
write in office;
When seminar is over, attend social hour;
discuss;
jog;
Solution http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-sliu_29.html
Real Time System by Jane W. S. Liu Chapter 3.3 Solution
Q.3.3:
job_1 | job_2 denotes a pipe: The result produced by job_1 is incrementally consumed
by job_2. (As an example, suppose that job_2 reads and displays one character at a time as each
handwritten character is recognized and placed in a buffer by job_1.) Draw a precedence
constraint graph to represent this producer-consumer relation between the jobs.
Solution http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-sliu_8405.html?spref=fb
Real Time System by Jane W. S. Liu Chapter 3.4 Solution
Q.3.4: Draw a task graph to represent the flight control system described in Figure 1-3.
a) Assume the producers and consumers do not explicitly synchronize (i.e., each consumer uses the
latest result generated by each of its producers but does not wait for the completion of the
producer.)
3. Solution http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-sliu_3066.html
Real Time System by Jane W. S. Liu Chapter 4.1 Solution
Q.4.1: The feasible interval of each job in the precedence graph in figure 4P-1 is given next to
its name. The execution time of all jobs are equal to 1.
a) Find the effective release times and deadlines of the jobs in the precendence graph in Figure 4P1.
Solution http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-sliu_9175.html
Real Time System by Jane W. S. Liu Chapter 4.2 Solution
Q.4.2: The execution times of the jobs in the precedence graph in figure 4P-2 are all equal to
1, and their release times are identical. Give a non preemptive optimal schedule that minimizes
the completion time of all jobs on three processors. Describe briefly the algorithm you used to find
the schedule.
Solution http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-sliu_4082.html
4. Real Time System by Jane W. S. Liu Chapter 4.4 Solution
Q.4.4: Consider a system that has five periodic tasks, A, B, C, D, and E, and three processors
P1, P2, P3. The periods of A, B, and C are 2 and their execution times are equal to 1. The periods of
D and E are 8 and their execution times are 6. The phase of every task is 0, that is, the first job of
the task is released at time 0. The relative deadline of every task is equal to its period.
a) Show that if the tasks are scheduled dynamically on three processors according to the LST
algorithm, some jobs in the system cannot meet their deadlines.
Sol:
Solution http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-sliu_1862.html
Real Time System by Jane W. S. Liu Chapter 4.5 Solution
Q.4.5: A system contains nine non-preemptable jobs named Ji, for i = 1, 2, ..., 9. Their
execution times are 3, 2, 2, 2, 2, 4, 4, 4, 4, and 9, respectively, their release times are equal to 0,
and their deadlines are 12. J1 is the immediate predecessor of J9, and J4 is the immediate
predecessor of J5, J6, J7, and J8. There are no other precedence constraints. For all the jobs, J i has
a higher priority than Jk if i < k.
a) Draw the precedence graph of the jobs.
Solution http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-sliu_6447.html
Real Time System by Jane W. S. Liu Chapter 4.7 Solution
Q.4.7: Consider the set of jobs in Figure 4-3. Suppose that the jobs have identical execution
time. What maximum execution time can the jobs have and still can be feasible scheduling on one
processor? Explain your answer.
Solution http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-sliu_3971.html
Real Time System by Jane W. S. Liu Chapter 5.1(a)(b)
Solution
5. Q.5.1: Each of the following systems of periodic tasks is scheduled and executed according to
a cyclic schedule. For each system, choose an appropriate frame size. Preemptions are allowed,
but the number of preemption should be kept small.
a) (6, 1), (10, 2), and (18, 2)
Solution http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-sliu_5855.html
Real Time System by Jane W. S. Liu Chapter 5.1(c)(d)
Solution
Q5.1: Each of the following systems of periodic tasks is scheduled and executed according to a
cyclic schedule. For each system, choose an appropriate frame size. Preemptions are allowed, but
the number of preemptions should be kept small.
c) (4, 0.5), (5, 1.0), (10, 2), and (24, 9)
Solution http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-sliu_3069.html
Real Time System by Jane W. S. Liu Chapter 5.1(e)(f)
Solution
Q.5.1: Each of the following systems of periodic tasks is scheduled and executed according to
a cyclic schedule. For each system, choose an appropriate frame size. Preemptions are allowed,
but the number of preemptions should be kept small.
e) (5, 0.1), (7, 1.0), (12, 6), and (45, 9)
Solution http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-sliu_793.html
Real Time System by Jane W. S. Liu Chapter 5.2 Solution
Q.5.2: A system uses the cyclic EDF algorithm to schedule sporadic jobs. The cyclic schedule of
periodic tasks in the system uses a frame size of 5, and a major cycle containts 6 frames. Supose
that the initial amounts of slack time in the frames are 1, 0.5, 0.5, 0.5, 1, and 1.
a.
Suppose that a sporadic job S(23, 1) arrives in frame 1, sporadic jobs S 2(16, 0.8)
and S3(20, 0.5) arrive in frame 2. In which frame are the accepted sporadic jobs scheduled?
Solution
liu_2408.html
http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-s-
6. Real Time System by Jane W. S. Liu Chapter 5.3 Solution
Q.5.3: Draw a network flow graph that we can use to find a preemptive cyclic schedule of the
periodic tasks
T1 = (3,7,1); T2 = (4,1); T3 = (6,2.4,8).
Solution
http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-s-
liu_4437.html
Real Time System by Jane W. S. Liu Chapter 5.4 Solution
Q.5.4:
T1 = (5,1);
A system contains the following periodic tasks:
T2 = (7,1,9);
T3 = (10,3) and T4 = (35,7).
If the frame size constraint (5-1) is ignored, what are the possible frame sizes ?
Solution
http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-s-
liu_8734.html
Real Time System by Jane W. S. Liu Chapter 6.4 Solution
Q.6.4: A system T contains for periodic tasks, (8, 1), (15, 3), (20, 4), and (22, 6). Its total
utilization is 0.80. Construct the initial segment in the time interval (0, 50) of a rate-monotonic
schedule of the system.
Solution http://targetiesnow.blogspot.in/2013/10/real-time-system-by-jane-w-sliu_1931.html
Real Time System by Jane W. S. Liu Chapter 6.5 Solution
Q.6.5: Which of the following systems of periodic tasks are schedulable by the rate-monotonic
algorithm? By the earliest-deadline-first algorithm? Explain your answer.
a.
T = {(8, 3), (9, 3), (15, 3)}
Solution http://targetiesnow.blogspot.in/2013/11/real-time-system-by-jane-w-sliu_6564.html
7. Real Time System by Jane W. S. Liu Chapter 6.8 Solution
Q.6.8: a) Use the time demand analysis method to show that the rate-monotonic algorithm
will produce a feasible schedule of the tasks (6,1), (8,2) and (15,6).
Solution http://targetiesnow.blogspot.in/2013/11/real-time-system-by-jane-w-sliu_6444.html
