This document presents a genetic algorithm approach for process scheduling in distributed operating systems. It aims to minimize total execution time, maximize processor utilization, and balance load across processors. The algorithm represents each schedule as a chromosome and uses genetic operators like selection, crossover and mutation to evolve better schedules over generations. Experimental results show the proposed genetic algorithm can optimize multiple scheduling objectives simultaneously in distributed systems.
The document describes a major project report on a cloud-based intrusion detection system using a backpropagation neural network based on particle swarm optimization. It discusses cloud computing concepts, characteristics, service models, and security threats. The proposed methodology uses particle swarm optimization to optimize training data sets for a backpropagation neural network intrusion detection system. Soft computing techniques like artificial neural networks, fuzzy logic, genetic algorithms, and particle swarm optimization are applied. The objectives are to design an intrusion detection system and evaluate its performance on test data sets.
Genetic Algorithm for task scheduling in Cloud Computing EnvironmentSwapnil Shahade
This document proposes a modified genetic algorithm to schedule tasks in cloud computing environments. It begins with an introduction and background on cloud computing and task scheduling. It then describes the standard genetic algorithm approach and introduces the modified genetic algorithm which uses Longest Cloudlet to Fastest Processor and Smallest Cloudlet to Fastest Processor scheduling algorithms to generate the initial population. The implementation and results show that the modified genetic algorithm reduces makespan and cost compared to the standard genetic algorithm.
The document discusses task scheduling algorithms in grid computing. It introduces the Min-Min and Max-Min algorithms. Min-Min schedules tasks based on minimum completion time, selecting the task-resource pair with the lowest expected completion first. Max-Min selects the task with the maximum minimum expected completion time across all resources first. The document hypothesizes that Min-Min performs better with heavier tasks while Max-Min is better with lighter tasks. It outlines a simulation to test this, modeling tasks, resources, and scheduling algorithms to calculate makespan and evaluate the hypothesis.
This document provides a summary of a student's seminar paper on resource scheduling algorithms. The paper discusses the need for resource scheduling algorithms in cloud computing environments. It then describes several types of algorithms commonly used for resource scheduling, including genetic algorithms, bee algorithms, ant colony algorithms, workflow algorithms, and load balancing algorithms. For each algorithm type, it provides a brief introduction, overview of the basic steps or concepts, and some examples of applications where the algorithm has been used. The paper was submitted by a student named Shilpa Damor to fulfill requirements for a degree in information technology.
A Review on Scheduling in Cloud Computingijujournal
Cloud computing is the requirement based on clients that this computing which provides software,
infrastructure and platform as a service as per pay for use norm. The scheduling main goal is to achieve
the accuracy and correctness on task completion. The scheduling in cloud environment which enables the
various cloud services to help framework implementation. Thus the far reaching way of different type of
scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling
and grid scheduling. The survey gives an elaborate idea about grid, cloud, workflow scheduling to
minimize the energy cost, efficiency and throughput of the system.
TASK SCHEDULING USING AMALGAMATION OF MET HEURISTICS SWARM OPTIMIZATION ALGOR...Journal For Research
Cloud Computing is the latest networking technology and also popular archetype for hosting the application and delivering of services over the network. The foremost technology of the cloud computing is virtualization which enables of building the applications, dynamically sharing of resources and providing diverse services to the cloud users. With virtualization, a service provider can guarantee Quality of Service to the user at the same time as achieving higher server consumption and energy competence. One of the most important challenges in the cloud computing environment is the VM placemnt and task scheduling problem. This paper focus on Metaheuristic Swarm Optimisation Algorithms(MSOA) deals with the problem of VM placement and Task scheduling in cloud environment. The MSOA is a simple parallel algorithm that can be applied in different ways to resolve the task scheduling problems. The proposed algorithm is considered an amalgamation of the SO algorithm and the Cuckoo search (CS) algorithm; called MSOACS. The proposed algorithm is evaluated using Cloudsim Simulator. The results proves the reduction of the makespan and increase the utilization ratio of the proposed MSOACS algorithm compared with SOA algorithms and Randomised Allocation Allocation (RA).
task scheduling in cloud datacentre using genetic algorithmSwathi Rampur
Task scheduling and resource provisioning is the core and challenging issues in cloud environment. Processes running in the cloud environment will race for available resources in order to complete their tasks with the minimum execution time; it is clear that we need an efficient scheduling technique for mapping between processes running and available resources. In this research paper, we are presented a non-traditional optimization technique, which mimics the process of evolution and based on the mechanics of natural selection and natural genetics called Genetic algorithm (GA), which minimizes the execution time and in turn reduces computation cost. We had done comparison with Round Robin algorithm and used CloudSim toolkit for our tests, results shows that Meta heuristic GA gives better performance than other scheduling algorithm.
This document provides an overview of task scheduling algorithms for load balancing in cloud computing. It begins with introductions to cloud computing and load balancing. It then surveys several existing task scheduling algorithms, including Min-Min, Max-Min, Resource Awareness Scheduling Algorithm, QoS Guided Min-Min, and others. It discusses the goals, workings, results and problems of each algorithm. It identifies the need for an optimized task scheduling algorithm. It also discusses tools like CloudSim that can be used to simulate scheduling algorithms and evaluate performance.
The document describes a major project report on a cloud-based intrusion detection system using a backpropagation neural network based on particle swarm optimization. It discusses cloud computing concepts, characteristics, service models, and security threats. The proposed methodology uses particle swarm optimization to optimize training data sets for a backpropagation neural network intrusion detection system. Soft computing techniques like artificial neural networks, fuzzy logic, genetic algorithms, and particle swarm optimization are applied. The objectives are to design an intrusion detection system and evaluate its performance on test data sets.
Genetic Algorithm for task scheduling in Cloud Computing EnvironmentSwapnil Shahade
This document proposes a modified genetic algorithm to schedule tasks in cloud computing environments. It begins with an introduction and background on cloud computing and task scheduling. It then describes the standard genetic algorithm approach and introduces the modified genetic algorithm which uses Longest Cloudlet to Fastest Processor and Smallest Cloudlet to Fastest Processor scheduling algorithms to generate the initial population. The implementation and results show that the modified genetic algorithm reduces makespan and cost compared to the standard genetic algorithm.
The document discusses task scheduling algorithms in grid computing. It introduces the Min-Min and Max-Min algorithms. Min-Min schedules tasks based on minimum completion time, selecting the task-resource pair with the lowest expected completion first. Max-Min selects the task with the maximum minimum expected completion time across all resources first. The document hypothesizes that Min-Min performs better with heavier tasks while Max-Min is better with lighter tasks. It outlines a simulation to test this, modeling tasks, resources, and scheduling algorithms to calculate makespan and evaluate the hypothesis.
This document provides a summary of a student's seminar paper on resource scheduling algorithms. The paper discusses the need for resource scheduling algorithms in cloud computing environments. It then describes several types of algorithms commonly used for resource scheduling, including genetic algorithms, bee algorithms, ant colony algorithms, workflow algorithms, and load balancing algorithms. For each algorithm type, it provides a brief introduction, overview of the basic steps or concepts, and some examples of applications where the algorithm has been used. The paper was submitted by a student named Shilpa Damor to fulfill requirements for a degree in information technology.
A Review on Scheduling in Cloud Computingijujournal
Cloud computing is the requirement based on clients that this computing which provides software,
infrastructure and platform as a service as per pay for use norm. The scheduling main goal is to achieve
the accuracy and correctness on task completion. The scheduling in cloud environment which enables the
various cloud services to help framework implementation. Thus the far reaching way of different type of
scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling
and grid scheduling. The survey gives an elaborate idea about grid, cloud, workflow scheduling to
minimize the energy cost, efficiency and throughput of the system.
TASK SCHEDULING USING AMALGAMATION OF MET HEURISTICS SWARM OPTIMIZATION ALGOR...Journal For Research
Cloud Computing is the latest networking technology and also popular archetype for hosting the application and delivering of services over the network. The foremost technology of the cloud computing is virtualization which enables of building the applications, dynamically sharing of resources and providing diverse services to the cloud users. With virtualization, a service provider can guarantee Quality of Service to the user at the same time as achieving higher server consumption and energy competence. One of the most important challenges in the cloud computing environment is the VM placemnt and task scheduling problem. This paper focus on Metaheuristic Swarm Optimisation Algorithms(MSOA) deals with the problem of VM placement and Task scheduling in cloud environment. The MSOA is a simple parallel algorithm that can be applied in different ways to resolve the task scheduling problems. The proposed algorithm is considered an amalgamation of the SO algorithm and the Cuckoo search (CS) algorithm; called MSOACS. The proposed algorithm is evaluated using Cloudsim Simulator. The results proves the reduction of the makespan and increase the utilization ratio of the proposed MSOACS algorithm compared with SOA algorithms and Randomised Allocation Allocation (RA).
task scheduling in cloud datacentre using genetic algorithmSwathi Rampur
Task scheduling and resource provisioning is the core and challenging issues in cloud environment. Processes running in the cloud environment will race for available resources in order to complete their tasks with the minimum execution time; it is clear that we need an efficient scheduling technique for mapping between processes running and available resources. In this research paper, we are presented a non-traditional optimization technique, which mimics the process of evolution and based on the mechanics of natural selection and natural genetics called Genetic algorithm (GA), which minimizes the execution time and in turn reduces computation cost. We had done comparison with Round Robin algorithm and used CloudSim toolkit for our tests, results shows that Meta heuristic GA gives better performance than other scheduling algorithm.
This document provides an overview of task scheduling algorithms for load balancing in cloud computing. It begins with introductions to cloud computing and load balancing. It then surveys several existing task scheduling algorithms, including Min-Min, Max-Min, Resource Awareness Scheduling Algorithm, QoS Guided Min-Min, and others. It discusses the goals, workings, results and problems of each algorithm. It identifies the need for an optimized task scheduling algorithm. It also discusses tools like CloudSim that can be used to simulate scheduling algorithms and evaluate performance.
Task Scheduling using Tabu Search algorithm in Cloud Computing Environment us...AzarulIkhwan
1. The document proposes using Tabu Search algorithm for task scheduling in cloud computing environments using CloudSim simulator. It aims to maximize throughput and minimize turnaround time compared to traditional algorithms like FCFS.
2. The methodology section describes how CloudSim simulator works and the components involved in task scheduling. It also provides an overview of how the Tabu Search algorithm guides the search process to avoid getting stuck at local optima.
3. The expected result is that Tabu Search algorithm will provide higher throughput and lower turnaround times for cloud tasks compared to FCFS, as Tabu Search is designed to escape local optima and find better solutions.
The document discusses using a genetic algorithm to schedule tasks in a cloud computing environment. It aims to minimize task execution time and reduce computational costs compared to the traditional Round Robin scheduling algorithm. The proposed genetic algorithm mimics natural selection and genetics to evolve optimal task schedules. It was tested using the CloudSim simulation toolkit and results showed the genetic algorithm provided better performance than Round Robin scheduling.
Energy-aware Task Scheduling using Ant-colony Optimization in cloudLinda J
The document proposes an energy-aware task scheduling algorithm using ant colony optimization for cloud computing. It aims to minimize energy consumption in datacenters by scheduling tasks efficiently across virtual machines and physical hosts. The algorithm uses concepts from ant colony optimization to probabilistically determine good task-to-resource allocations. The results show that the proposed approach reduces energy consumption by 22% compared to a first-come, first-served scheduling approach.
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...IRJET Journal
This document proposes a scheduling algorithm for allocating resources in cloud computing based on the Project Evaluation and Review Technique (PERT). It aims to address issues like starvation of lower priority tasks. The algorithm models task allocation as a directed acyclic graph and uses PERT to schedule critical and non-critical tasks, prioritizing higher priority tasks. The algorithm is evaluated against other scheduling methods and shows improvements in reducing completion time and optimizing resource allocation for all tasks.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document summarizes an adaptive checkpointing and replication strategy to tolerate faults in computational grids. It proposes maintaining a balance between the overheads of replication and checkpointing. Tasks are replicated on up to three resources based on each resource's probability of permanent failure. Checkpoints are taken adaptively based on the probability of recoverable failure. If a resource fails permanently, the task resumes from the last checkpoint. If a failure is recoverable, the task resumes on the same resource. This strategy aims to minimize resource wastage from replication while utilizing different resource speeds.
An efficient approach for load balancing using dynamic ab algorithm in cloud ...bhavikpooja
This document outlines a proposed approach for efficient load balancing using a dynamic Ant-Bee algorithm in cloud computing. It discusses limitations of existing ant colony and bee colony algorithms for load balancing. The author aims to develop a new AB algorithm approach that combines aspects of ant colony optimization and bee colony algorithms to improve load balancing optimization and overcome issues like slow convergence and tendency to stagnate in ant colony algorithms. The proposed approach would leverage both the dynamic path finding of ants and pheromone updating of bees for more effective load balancing in cloud environments.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
This document summarizes a research paper that proposes a new algorithm called KD-Tree approach for efficient virtual machine (VM) allocation in cloud computing environments. The algorithm aims to minimize the response time for allocating VMs to user requests. It does this by adopting a KD-Tree data structure to index physical host machines, allowing the scheduler to quickly find the host that can accommodate a new VM request with the minimum latency in O(Log n) time. The proposed approach is evaluated through simulations using the CloudSim toolkit and is shown to outperform an existing linear scheduling strategy (LSTR) algorithm in terms of reducing VM allocation times.
This document proposes a new Ranking Chaos Optimization (RCO) algorithm to solve the dual scheduling problem of cloud services and computing resources (DS-CSCR) in private clouds. It introduces the DS-CSCR concept and models the characteristics of cloud services and computing resources. The RCO algorithm uses ranking selection, individual chaos, and dynamic heuristic operators. Experimental results show RCO has better searching ability, time complexity, and stability compared to other algorithms for solving DS-CSCR. Future work is needed to study additional quality of service properties and improve RCO for other optimization problems.
(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.
This document proposes a new task scheduling algorithm called Dynamic Heterogeneous Shortest Job First (DHSJF) for heterogeneous cloud computing systems. DHSJF aims to improve performance metrics like reduced makespan and low energy consumption by considering the heterogeneity of resources and workloads. It discusses existing scheduling algorithms like Round Robin, First Come First Serve and their limitations. The proposed DHSJF algorithm prioritizes tasks with the shortest estimated completion time to optimize resource utilization and improve overall performance of the cloud computing system. Simulation results show that DHSJF provides better results for metrics like average waiting time and turnaround time as compared to Round Robin and First Come First Serve scheduling algorithms.
Cloud computing is a mix of distributed, grid and parallel processing. It is as of late in pattern on account of the
benefits it gives. It gives a pool of resources which are shared among different clients. Alongside its expanding request, it endures
with a few issues. A standout amongst the most vital and testing issue of cloud computing is load balancing. Load balancing
essentially intends to adjust the load similarly among a few hubs so hub is over-burden, under loaded or sitting inactive. Till date
there are numerous calculations proposed to deal with load balancing yet none of them has been demonstrated as productive one.
In this paper a load balancing algorithm is proposed utilizing rule of genetic algorithm. Fitness of assignments is ascertained and
on the premise of fitness load balancing is done. In this algorithm priority is appointed to the wellness computed in like manner
the chromosome with most noteworthy fitness is doled out least priority. Fitness here stands for the aggregate cost needs to
actualize an errand. Increasingly the cost more is the fitness. The entire simulation is performed on cloudsim 3.0 toolbox which is
JAVA based simulator.
Optimized Assignment of Independent Task for Improving Resources Performance ...ijgca
Grid computing has emerged from category of distributed and parallel computing where the
heterogeneous resources from different network are used simultaneously to solve a particular problem that
need huge amount of resources. Potential of Grid computing depends on my issues such as security of
resources, heterogeneity of resources, fault tolerance & resource discovery and job scheduling. Scheduling
is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing
resources and is an NP-complete problem. To achieve the promising potential of grid computing, an
effective and efficient job scheduling algorithm is proposed, which will optimized two important criteria to
improve the performance of resources i.e. makespan time & resource utilization. With this, we have
classified various tasks scheduling heuristic in grid on the basis of their characteristics.
In the era of big data, even though we have large infrastructure, storage data varies in size,
formats, variety, volume and several platforms such as hadoop, cloud since we have problem associated
with an application how to process the data which is varying in size and format. Data varying in
application and resources available during run time is called dynamic workflow. Using large
infrastructure and huge amount of resources for the analysis of data is time consuming and waste of
resources, it’s better to use scheduling algorithm to analyse the given data set, for efficient execution of
data set without time consuming and evaluate which scheduling algorithm is best and suitable for the
given data set. We evaluate with different data set understand which is the most suitable algorithm for
analysis of data being efficient execution of data set and store the data after analysis
Max Min Fair Scheduling Algorithm using In Grid Scheduling with Load Balancing IJORCS
This paper shows the importance of fair scheduling in grid environment such that all the tasks get equal amount of time for their execution such that it will not lead to starvation. The load balancing of the available resources in the computational grid is another important factor. This paper considers uniform load to be given to the resources. In order to achieve this, load balancing is applied after scheduling the jobs. It also considers the Execution Cost and Bandwidth Cost for the algorithms used here because in a grid environment, the resources are geographically distributed. The implementation of this approach the proposed algorithm reaches optimal solution and minimizes the make span as well as the execution cost and bandwidth cost.
This document proposes a fair scheduling algorithm with dynamic load balancing for grid computing. It begins by introducing grid computing and the need for efficient load balancing algorithms to distribute tasks. It then describes dynamic load balancing approaches, including information, triggering, resource type, location, and selection policies. The proposed algorithm uses a fair scheduling approach that assigns tasks to processors based on their estimated fair completion times to ensure tasks receive equal shares of computing resources. It also includes a dynamic load balancing component that migrates tasks between processors to maintain balanced loads across all resources. Simulation results demonstrated the algorithm achieved balanced loads across processors and reduced overall task completion times.
Configuration Optimization for Big Data SoftwarePooyan Jamshidi
The document discusses configuration optimization for big data software using an approach developed in the DICE project funded by the European Union's Horizon 2020 program. It describes optimizing configurations for Apache Storm and Cassandra to significantly reduce configuration time. Experiments showed large performance variations between configurations and that default settings often performed poorly compared to optimized settings. Tuning on one version did not guarantee good performance on other versions, but transferring more observations from other versions improved performance, though with diminishing returns due to increased optimization costs.
STUDY ON PROJECT MANAGEMENT THROUGH GENETIC ALGORITHMAvay Minni
This document describes using a genetic algorithm to solve resource constrained project scheduling problems. It begins with an introduction explaining that planning and scheduling projects involves managing many possible solutions and resource allocations. It then provides sections on genetic algorithms, the basic genetic algorithm process, and why genetic algorithms are suitable for this type of optimization problem. The document outlines the general formulation of resource constrained project scheduling as a linear programming problem and provides an example problem scenario. It includes flowcharts and discusses implementing the proposed genetic algorithm solution methodology.
This document provides an overview of genetic algorithms. It begins with an introduction to genetic algorithms, noting they were developed in the 1970s, inspired by Darwinian evolution. It then describes key features of genetic algorithms, including that they maintain a population of solutions, use reproduction, mutation and crossover to create new populations, and favor fitter solutions. The document discusses various methods for population selection, including roulette wheel selection, rank selection and tournament selection. It also covers the anatomy of a genetic algorithm and provides a simple example to maximize x2 to demonstrate the genetic algorithm process.
Survival of the Fittest: Using Genetic Algorithm for Data Mining OptimizationOr Levi
Presented at the eBay Inc Data Conference 2013:
“Survival of the Fittest: Using Genetic Algorithm for Data Mining Optimization”
Showed a Genetic Algorithm based method to optimize cluster analysis and developed a demo, applying this algorithm, for grouping similar items on eBay into a catalog of unique products.
Airline scheduling and pricing using a genetic algorithmAlan Walker
Some of my earlier research work on "clean sheet" scheduling for airlines. AIrline timetables are generally created by hand, and then mathematical models are used to optimize and tweak them. This was the first published work in the industry for diong the whole process in one system. Combines a number of different airline operations research models into a single framework.
When the AA / Sabre Research Group was created, one of the goals was a clean-sheet scheduling system. Bob Crandall told my boss that there no way it could be done for even three aircraft - so this is what I built.
This document provides an overview of genetic algorithms. It discusses how genetic algorithms are inspired by natural evolution and use techniques like selection, crossover, and mutation to arrive at optimal solutions. The document covers the history of genetic algorithms, how they work, examples of using genetic algorithms to optimize problems, and their applications in fields like electromagnetism. Genetic algorithms provide a way to find optimal solutions to complex problems by simulating the natural evolutionary process of reproduction, mutation, and selection of offspring.
Task Scheduling using Tabu Search algorithm in Cloud Computing Environment us...AzarulIkhwan
1. The document proposes using Tabu Search algorithm for task scheduling in cloud computing environments using CloudSim simulator. It aims to maximize throughput and minimize turnaround time compared to traditional algorithms like FCFS.
2. The methodology section describes how CloudSim simulator works and the components involved in task scheduling. It also provides an overview of how the Tabu Search algorithm guides the search process to avoid getting stuck at local optima.
3. The expected result is that Tabu Search algorithm will provide higher throughput and lower turnaround times for cloud tasks compared to FCFS, as Tabu Search is designed to escape local optima and find better solutions.
The document discusses using a genetic algorithm to schedule tasks in a cloud computing environment. It aims to minimize task execution time and reduce computational costs compared to the traditional Round Robin scheduling algorithm. The proposed genetic algorithm mimics natural selection and genetics to evolve optimal task schedules. It was tested using the CloudSim simulation toolkit and results showed the genetic algorithm provided better performance than Round Robin scheduling.
Energy-aware Task Scheduling using Ant-colony Optimization in cloudLinda J
The document proposes an energy-aware task scheduling algorithm using ant colony optimization for cloud computing. It aims to minimize energy consumption in datacenters by scheduling tasks efficiently across virtual machines and physical hosts. The algorithm uses concepts from ant colony optimization to probabilistically determine good task-to-resource allocations. The results show that the proposed approach reduces energy consumption by 22% compared to a first-come, first-served scheduling approach.
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...IRJET Journal
This document proposes a scheduling algorithm for allocating resources in cloud computing based on the Project Evaluation and Review Technique (PERT). It aims to address issues like starvation of lower priority tasks. The algorithm models task allocation as a directed acyclic graph and uses PERT to schedule critical and non-critical tasks, prioritizing higher priority tasks. The algorithm is evaluated against other scheduling methods and shows improvements in reducing completion time and optimizing resource allocation for all tasks.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document summarizes an adaptive checkpointing and replication strategy to tolerate faults in computational grids. It proposes maintaining a balance between the overheads of replication and checkpointing. Tasks are replicated on up to three resources based on each resource's probability of permanent failure. Checkpoints are taken adaptively based on the probability of recoverable failure. If a resource fails permanently, the task resumes from the last checkpoint. If a failure is recoverable, the task resumes on the same resource. This strategy aims to minimize resource wastage from replication while utilizing different resource speeds.
An efficient approach for load balancing using dynamic ab algorithm in cloud ...bhavikpooja
This document outlines a proposed approach for efficient load balancing using a dynamic Ant-Bee algorithm in cloud computing. It discusses limitations of existing ant colony and bee colony algorithms for load balancing. The author aims to develop a new AB algorithm approach that combines aspects of ant colony optimization and bee colony algorithms to improve load balancing optimization and overcome issues like slow convergence and tendency to stagnate in ant colony algorithms. The proposed approach would leverage both the dynamic path finding of ants and pheromone updating of bees for more effective load balancing in cloud environments.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
This document summarizes a research paper that proposes a new algorithm called KD-Tree approach for efficient virtual machine (VM) allocation in cloud computing environments. The algorithm aims to minimize the response time for allocating VMs to user requests. It does this by adopting a KD-Tree data structure to index physical host machines, allowing the scheduler to quickly find the host that can accommodate a new VM request with the minimum latency in O(Log n) time. The proposed approach is evaluated through simulations using the CloudSim toolkit and is shown to outperform an existing linear scheduling strategy (LSTR) algorithm in terms of reducing VM allocation times.
This document proposes a new Ranking Chaos Optimization (RCO) algorithm to solve the dual scheduling problem of cloud services and computing resources (DS-CSCR) in private clouds. It introduces the DS-CSCR concept and models the characteristics of cloud services and computing resources. The RCO algorithm uses ranking selection, individual chaos, and dynamic heuristic operators. Experimental results show RCO has better searching ability, time complexity, and stability compared to other algorithms for solving DS-CSCR. Future work is needed to study additional quality of service properties and improve RCO for other optimization problems.
(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.
This document proposes a new task scheduling algorithm called Dynamic Heterogeneous Shortest Job First (DHSJF) for heterogeneous cloud computing systems. DHSJF aims to improve performance metrics like reduced makespan and low energy consumption by considering the heterogeneity of resources and workloads. It discusses existing scheduling algorithms like Round Robin, First Come First Serve and their limitations. The proposed DHSJF algorithm prioritizes tasks with the shortest estimated completion time to optimize resource utilization and improve overall performance of the cloud computing system. Simulation results show that DHSJF provides better results for metrics like average waiting time and turnaround time as compared to Round Robin and First Come First Serve scheduling algorithms.
Cloud computing is a mix of distributed, grid and parallel processing. It is as of late in pattern on account of the
benefits it gives. It gives a pool of resources which are shared among different clients. Alongside its expanding request, it endures
with a few issues. A standout amongst the most vital and testing issue of cloud computing is load balancing. Load balancing
essentially intends to adjust the load similarly among a few hubs so hub is over-burden, under loaded or sitting inactive. Till date
there are numerous calculations proposed to deal with load balancing yet none of them has been demonstrated as productive one.
In this paper a load balancing algorithm is proposed utilizing rule of genetic algorithm. Fitness of assignments is ascertained and
on the premise of fitness load balancing is done. In this algorithm priority is appointed to the wellness computed in like manner
the chromosome with most noteworthy fitness is doled out least priority. Fitness here stands for the aggregate cost needs to
actualize an errand. Increasingly the cost more is the fitness. The entire simulation is performed on cloudsim 3.0 toolbox which is
JAVA based simulator.
Optimized Assignment of Independent Task for Improving Resources Performance ...ijgca
Grid computing has emerged from category of distributed and parallel computing where the
heterogeneous resources from different network are used simultaneously to solve a particular problem that
need huge amount of resources. Potential of Grid computing depends on my issues such as security of
resources, heterogeneity of resources, fault tolerance & resource discovery and job scheduling. Scheduling
is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing
resources and is an NP-complete problem. To achieve the promising potential of grid computing, an
effective and efficient job scheduling algorithm is proposed, which will optimized two important criteria to
improve the performance of resources i.e. makespan time & resource utilization. With this, we have
classified various tasks scheduling heuristic in grid on the basis of their characteristics.
In the era of big data, even though we have large infrastructure, storage data varies in size,
formats, variety, volume and several platforms such as hadoop, cloud since we have problem associated
with an application how to process the data which is varying in size and format. Data varying in
application and resources available during run time is called dynamic workflow. Using large
infrastructure and huge amount of resources for the analysis of data is time consuming and waste of
resources, it’s better to use scheduling algorithm to analyse the given data set, for efficient execution of
data set without time consuming and evaluate which scheduling algorithm is best and suitable for the
given data set. We evaluate with different data set understand which is the most suitable algorithm for
analysis of data being efficient execution of data set and store the data after analysis
Max Min Fair Scheduling Algorithm using In Grid Scheduling with Load Balancing IJORCS
This paper shows the importance of fair scheduling in grid environment such that all the tasks get equal amount of time for their execution such that it will not lead to starvation. The load balancing of the available resources in the computational grid is another important factor. This paper considers uniform load to be given to the resources. In order to achieve this, load balancing is applied after scheduling the jobs. It also considers the Execution Cost and Bandwidth Cost for the algorithms used here because in a grid environment, the resources are geographically distributed. The implementation of this approach the proposed algorithm reaches optimal solution and minimizes the make span as well as the execution cost and bandwidth cost.
This document proposes a fair scheduling algorithm with dynamic load balancing for grid computing. It begins by introducing grid computing and the need for efficient load balancing algorithms to distribute tasks. It then describes dynamic load balancing approaches, including information, triggering, resource type, location, and selection policies. The proposed algorithm uses a fair scheduling approach that assigns tasks to processors based on their estimated fair completion times to ensure tasks receive equal shares of computing resources. It also includes a dynamic load balancing component that migrates tasks between processors to maintain balanced loads across all resources. Simulation results demonstrated the algorithm achieved balanced loads across processors and reduced overall task completion times.
Configuration Optimization for Big Data SoftwarePooyan Jamshidi
The document discusses configuration optimization for big data software using an approach developed in the DICE project funded by the European Union's Horizon 2020 program. It describes optimizing configurations for Apache Storm and Cassandra to significantly reduce configuration time. Experiments showed large performance variations between configurations and that default settings often performed poorly compared to optimized settings. Tuning on one version did not guarantee good performance on other versions, but transferring more observations from other versions improved performance, though with diminishing returns due to increased optimization costs.
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genetic algorithm based music recommender systemneha pevekar
The goal of a recommender
system is to generate meaningful recommendations to
a collection of users for items or products that might
interest them.
Many of the largest e-commerce websites are already
using recommender systems to help their customers
find products to purchase or download.
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Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
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manner. Such systems share the algorithm and processing unit as computing resources which
leads to highly inter process communications capabilities. The main part of the proposed
algorithm is resource management unit which performs task partitioning and co-scheduling .In
this paper, we present a technique for integrated task partitioning and co-scheduling on the
privately owned network. We focus on real-time and non preemptive systems. A large variety of
experiments have been conducted on the proposed algorithm using synthetic and real tasks.
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optimal scheduling and strong link between partitioning, scheduling and communication. Some
important models for task partitioning are also discussed in the paper. We target the algorithm
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A report on designing a model for improving CPU Scheduling by using Machine L...MuskanRath1
Disclaimer: Please let me know in case some of the portions of the article match your research. I would include the link to your research in the description section of my article.
Description:
The main concern of our paper describes that we are proposing a model for a uniprocessor system for improving CPU scheduling. Our model is implemented at low-level language or assembly language and LINUX is used for the implementation of the model as it is an open-source environment and its kernel is editable.
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Scheduling Using Multi Objective Genetic Algorithmiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
1. The document discusses using a multi-objective genetic algorithm (MOGA) for static, non-preemptive scheduling of tasks on homogeneous multiprocessor systems. The goal is to minimize job completion time.
2. A genetic algorithm is proposed that determines suitable task priorities to find sub-optimal scheduling solutions. Genetic algorithms mimic natural selection to evolve better solutions over multiple generations.
3. The document outlines the genetic algorithm process of selection, crossover and mutation to evolve scheduling solutions, and evaluates solutions based on metrics like makespan and speedup.
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This document discusses analytical modeling of parallel systems. It begins by outlining topics like sources of overhead in parallel programs, performance metrics, and scalability. It then discusses basics of analytical modeling, noting that parallel runtime depends on input size, number of processors, and machine communication parameters. Several performance measures are introduced, like wall clock time and speedup. Sources of overhead like idling, excess computation, and communication are described. Metrics like parallel time, total overhead, speedup, and efficiency are formally defined. The impact of non-cost optimality and ways to build granularity are discussed. Finally, scaling characteristics and isoefficiency as a metric of scalability are covered.
This is a presentation for Chapter 7 Distributed system management
Book: DISTRIBUTED COMPUTING , Sunita Mahajan & Seema Shah
Prepared by Students of Computer Science, Ain Shams University - Cairo - Egypt
This document discusses parallel matrix multiplication algorithms on the Parallel Random Access Machine (PRAM) model. It describes algorithms that multiply matrices using different numbers of processors, from n3 processors down to n2 processors. The time complexity is O(log n) in all cases, while the processor and work complexities vary based on the number of processors. Block matrix multiplication is also introduced as a more efficient approach for shared memory machines by improving data locality.
This document presents a genetic algorithm approach for scheduling jobs with burst times and priorities to find a schedule that is near or equal to the optimal shortest job first (SJF) schedule. It discusses related work on using genetic algorithms for scheduling problems. The proposed algorithm uses a genetic algorithm to generate priorities that are assigned to jobs to find a schedule with a total turnaround time close to the SJF schedule. Experimental results show that the genetic algorithm approach produces solutions very close to SJF and better than a priority-based algorithm in terms of total turnaround time.
This document presents a task allocation model for balancing resource utilization in a multiprocessor environment. It discusses partitioning a task into modules and allocating the modules to processors to minimize execution time. The model aims to minimize total execution cost while balancing the load across processors and minimizing inter-task communication costs. It presents the mathematical modeling and development of an algorithm to allocate m modules of a task to n processors. The algorithm considers execution costs, communication costs, and task sizes to determine the optimal allocation that balances utilization across processors. An example application of the model to a system with 3 processors and 9 task modules is provided.
The document provides an introduction to data structures and algorithms analysis. It discusses that a program consists of organizing data in a structure and a sequence of steps or algorithm to solve a problem. A data structure is how data is organized in memory and an algorithm is the step-by-step process. It describes abstraction as focusing on relevant problem properties to define entities called abstract data types that specify what can be stored and operations performed. Algorithms transform data structures from one state to another and are analyzed based on their time and space complexity.
This document describes an efficient dynamic scheduling algorithm for real-time multi-core systems. It proposes a task split myopic scheduling algorithm that exploits parallelism in tasks to meet deadlines. The algorithm splits tasks that cannot meet their deadline into mandatory and optional sub-tasks, which can then execute concurrently on multiple cores. Simulation studies show the proposed algorithm has higher schedulability than myopic and improved myopic algorithms. The algorithm aims to better utilize executing cores to increase the probability of tasks meeting their deadlines while maintaining a low computational complexity of O(kn), the same as myopic algorithms.
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.
An Algorithm for Optimized Cost in a Distributed Computing SystemIRJET Journal
This document summarizes an algorithm for optimized cost allocation in a distributed computing system. The algorithm considers a set of tasks that need to be assigned to processors across multiple phases. It calculates execution costs, residing costs, communication costs, and reallocation costs to determine the optimal allocation that minimizes overall system costs. The algorithm is demonstrated on a sample problem involving 4 tasks to be allocated across 2 processors over 5 phases. Cost matrices are provided and the algorithm partitions the problem into subproblems to determine the lowest cost allocation for each phase and overall.
The document discusses load balancing algorithms for cluster computing environments. It proposes a fully centralized and partially distributed algorithm (FCPDA) that dynamically maps jobs to communicators (groups of processors) to improve response time and performance. The algorithm allows a communicator to take on additional jobs if it completes its initial job early. This approach aims to better balance the workload compared to other algorithms and reduce overall job completion time.
This summary provides the key details about a proposed load balancing algorithm in 3 sentences:
The document proposes a fully centralized and partially distributed load balancing algorithm that dynamically distributes tasks from a master processor to slave processors organized into communicators. The master processor monitors the workload and response time of each communicator to dynamically map additional tasks as communicators complete their work, improving resource utilization and response time. The algorithm forms a matrix to track the workload and response time of each communicator for different task types to aid the master processor in optimally balancing the load over time.
This document discusses different models for multiprocessor real-time scheduling, including identical, uniform, and unrelated processor models. It also covers global, partitioned, and semi-partitioned scheduling models. Global scheduling allows jobs to migrate to any processor, while partitioned scheduling assigns each task to a dedicated processor. Semi-partitioned scheduling uses both partitioning and reservations to allow some migration. The document outlines advantages and disadvantages of each approach, and describes concepts like scheduling anomalies, bin-packing problems, demand-bound functions, and schedulability tests involved in multiprocessor real-time scheduling.
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Genetic Algorithm for Process Scheduling
1. Genetic Algorithm
For
Process Scheduling
In
Distributed Operating System
Adhokshaj Mishra
Department of Computer Science and Engineering,
University Institute of Engineering and Technology,
CSJM University
Kanpur, INDIA
Email: adhokshajmishra@indidigilabs.in
Ankur Verma
Department of Computer Science and Engineering,
University Institute of Engineering and Technology,
CSJM University
Kanpur, INDIA
Email: ankurverma@indidigilabs.in
2. Abstract
The problem of process scheduling in distributed system is one of
the important and challenging area of research in computer
engineering. Scheduling in distributed operating system has an
important role in overall system performance. Process scheduling in
distributed system can be defined as allocating processes to
processor so that total execution time will be minimized, utilization
of processors will be maximized and load balancing will be
maximized. The scheduling in distributed system is known as NP-
Complete problem even in the best conditions, and methods based
on heuristic search have been proposed to obtain optimal and
suboptimal solutions. Genetic algorithm is one of the widely used
techniques for constrain optimization. Genetic algorithm is basically
search algorithm based on natural selection and natural genetics. In
this paper, using the power of genetic algorithms, we solve this
problem considering load balancing efficiently.
Keywords: Genetic Algorithm, Distributed Systems, Load Balancing
1. Introduction
The computational complicated process cannot be executed on the
computing machine in an accepted interval time. Therefore, they
must be divided into small sub-process. The sub-process can be
executed either in the expensive multiprocessor or in the
distributed system. Distributed system is preferred due to better
ratio of cost per performance. Scheduling in distributed operating
systems is a critical factor in overall system performance. Process
scheduling in a distributed operating system can be stated as
allocating processes to processors so that total execution time will
3. be minimized, utilization of processors will be maximized, and load
balancing will be maximized. Process scheduling in distributed
system is done in two phases: in first phase processes are
distributed on computers and in second processes execution order
on each processor must be determined.
The methods used to solve scheduling problem in distributed
computing system can be classified into three categories graph
theory based approaches, mathematical models based methods and
heuristic techniques.
Heuristic algorithm can be classified into three categories iterative
improvement algorithms, the probabilistic optimization algorithms
and constructive heuristics. Heuristic can obtain sub optimal
solution in ordinary situations and optimal solution in particulars.
The first phase of process scheduling in a distributed system is
process distribution on computer. The critical aspects of this phase
are load balancing. Recently created processes may be overloaded
heavily while the others are under loaded or idle. The main
objectives of load balancing are to speared load on processors
equally, maximizing processors utilization and minimizing total
execution time.
The second phase of process scheduling in distributed computing
system is process execution ordering on each processor. Genetic
algorithm used for this phase. Genetic algorithm is guided random
search method which mimics the principles of evolution and natural
genetics. Genetic algorithms search optimal solution from entire
solution space. They often can obtain reasonable solution in all
situations. Nevertheless, their main drawback is to spend much time
for schedule. Hence, we propose a modified genetic algorithm to
overcome from drawback through this paper.
4. In this paper using the power of genetic algorithms we solve this
problem. Process distribution on different processor done based on
processors load. The proposed algorithm maps each schedule with a
chromosome that shows the execution order of all existing process
on processors. The fittest chromosomes are selected to reproduce
offspring: chromosomes which their corresponding schedules have
less total execution time, better load balance and processor
utilization. We assume that the distributed system processes are
non uniform and non-preemptive, that is the processors may be
different and a processor completes current process before
executing a new one the load balancing mechanism used in this
paper only schedule process without process migration.
2. Preliminaries
2.1 System and Process Model
The system used for simulation is loosely coupled non-uniform
system, all tasks are non-pre-emptive and no process migration is
assumed. The process scheduling problem considered in this paper
is based on the deterministic model. A distributed system with m
processors, m>1 should be modeled as follows:
P= {p1, p2, p3...pm} is the set of processors in the distributed system.
Each processor can only execute one process at each moment; a
processor completes current process before executing a new one,
and a process cannot be moved to another processor during
execution. R is an m × m matrix, where the element ruv 1≤ u, v ≤ m of
R, is the communication delay rate between Pu and Pv. H is an m × m
matrix, where the element huv 1≤ u, v ≤m of H, is the time required to
transmit a unit of data from Pu and Pv. It is obvious that huu=0 and
ruu=0.
5. T= {t1, t2, t3…tm} is the set of processes to execute. A is an n x m
matrix, where the element aij 1 ≤i ≤n, 1 ≤ j ≤ m of A, is the execution
time of process ti on processor pj. In homogeneous distributed
systems the execution time of an individual process on all
processors is equal, that means: 1 ≤i ≤ n; ai1 = ai2 = ai3 = … = aim. D is
a linear matrix, where the element di 1 ≤ i ≤ n of D, is the data
volume for process ti to be transmitted, when the process ti is to be
executed on a remote processor.
F is a linear matrix, where the element fi 1 ≤ i ≤ n of F is the target
processor that is selected for the process ti to be executed on. C is a
linear matrix, where the element ci 1 ≤ i ≤ n of C, is the processor
that the process ti is presented on just now.
The problem of process scheduling is to assign for each process
tia processor fiP so that the total execution time is minimized,
utilization of processors is maximized, and load balancing will be
maximized. In such systems, there are finite numbers of processes,
each having a process number and an execution time and placed in a
process pool from which processes are assigned to processors. The
main objective is to find a schedule with minimum cost. The
following definitions are also needed:
Definition 1
The processor load for each processor is the sum of processes
execution times allocated to that processor. However, as the
processors may not always be idle when a chromosome (schedule)
is evaluated, the current existing load on individual processor must
also be taken into account. Therefore:
������������ .������������ ������������������������������������������������������ ������������ .������������ ������������������ ������������������������������������������������
������������������������������������������������������ ������������ ������������������������������������������������������ ������������
������������������������������������������������������ ������ ������������������������������������������������������ ������
������������������������ ������������ = ������������ ,������ + ������=1 ������������,������ …….. (1)
������ =1
6. Definition 2
The length or maxspan of schedule T is the maximal finishing time of
all the processes or maximum load. Also, communication cost (CC)
to spread recently created processes on processors must be
computed:
Maxspan(T) = max(Load(pi)) 1≤i≤ Number of processors …(2)
������������������ .������������ ������������������ ������������������������������������������������������
CC T = (rc i f i + hc i f i X di ) ……. (3)
������=1
Definition 3
The Processor utilization for each processor is obtained by dividing
the sum of processing times by maxspan, and the average of
processors utilization is obtained by dividing the sum of all
utilizations by number of processors:
������������������������ (������������ )
������(������������ ) = ������������������������������������������ ……….. (4)
������������ .������������ ������������������������������������������������������������
( ������=1 ������(������������ ))
������������������������ = ������������. ������������ ������������������������������������������������������������ …….. (5)
Definition 4
Number of Acceptable Processor Queues (NoAPQ): We must define
thresholds for light and heavy load on processors. If the processes
completion time of a processor (by adding the current system load
and those contributed by the new processes) is within the light and
7. heavy thresholds, this processor queue will be acceptable. If it is
above the heavy threshold or below the light-threshold, then it is
unacceptable, but what is important is average of number of
acceptable processors queues, which is achievable by:
������������������������������
������������������������������������������������ = ������������. ������������ ������������������������������������������������������������ …… (6)
Definition 5
A Queue associated with every processor, shows the processes that
processor has to execute. The execution order of processes on each
processor is based on queues.
The Proposed Genetic Algorithm
Genetic algorithms, as powerful and broadly applicable stochastic
search and optimization techniques, are the most widely known
types of evolutionary computation methods today. In general, a
genetic algorithm has five basic components as follows:
1. An encoding method that is a genetic representation
(genotype) of solutions to the program.
2. A way to create an initial population of individuals
(chromosomes).
3. An evaluation function, rating solutions in terms of their
fitness, and a selection mechanism.
4. The genetic operators (crossover and mutation) that alter the
genetic composition of offspring during reproduction.
5. Values for the parameters of genetic algorithm.
Genotype
8. In the GA-Based algorithms each chromosome corresponds to a
solution to the problem. The genetic representation of individuals is
called Genotype. In this paper a chromosome consists of an array of
n digits, where n is the number of processes. Indexes show process
numbers and a digit can take any one of the 1...m values, which
shows the processor that the process is assigned to. If more than
one process is assigned to the same processor, the left to-right order
determines their execution order on that processor.
Initial Population
As discussed before, the main objective of GA is to find a schedule
with optimal cost while load-balancing; processors utilization and
cost of communication are considered. We take into account all
objectives in following equation. The fitness function of a Schedule T
is defined as follows:
������ ������ ������������������������ ������ (������ ������ ������������������������������������������������ )
������������������������������������������(������) = ……. (7)
������ ������ ������������������������������������������ ������ ������ (������ ������ ������������(������))
Where 0 < α, β, γ, θ ≤ 1 are control parameters to control effect of
each part according to special cases and their default value is one.
This equation shows that a fitter solution (Schedule) has less
maxspan, less communication cost, higher processor utilization and
higher Average number of acceptable processor queues.
Selection
The selection process used here is based on spinning the roulette
wheel, which each chromosome in the population has a slot sized in
proportion to its fitness. Each time we require an offspring, a simple
spin of the weighted roulette wheel gives a parent chromosome. The
probability pi that a parent Ti will be selected is given by:
9. ������(������������ )
������������ = ������������������������������������������ ……. (8)
������ =1 ������(������������ )
Where F(Ti) is the fitness of chromosome Ti.
Crossover
Crossover is generally used to exchange portions between strings.
Crossover is not always affected; the invocation of the crossover
depends on the probability of the crossover Pc. We have
implemented two crossover operators. The GA uses one of them,
which is decided randomly.
Single-Point Crossover
This operator randomly selects a point, called Crossover point, on the
selected chromosomes, then swaps the bottom halves after
crossover point, including the gene at the crossover point and
generate two new chromosomes called children.
Proposed Crossover
This operator randomly selects points on the selected
chromosomes, then for each child non-selected genes are taken from
one parent and selected genes from the other.
Mutation
Mutation is used to change the genes in a chromosome. Mutation
replaces the value of a gene with a new value from defined domain
for that gene. Mutation is not always affected, the invocation of the
Mutation depend on the probability of the Mutation Pm. We have
implemented two mutation operators. The GA uses one of them,
which is decided randomly.
10. First Mutation Operator
This operator randomly selects two points on the selected
chromosome, and then generates a chromosome by swapping the
genes at the selected points.
Second Mutation Operator
The other approach is to check if any jobs could be swapped
between processors which would result in a lower make span. If we
want to test every possible swap, it would be computationally very
intensive, and in larger problems would take an unfeasible amount
of time. It also seems unreasonable to consider swapping processes
on processors which their load is significantly below the make span,
therefore we try to swap processes between overloaded and under
loaded processors. This concept can be implemented as follows:
1. First, select a processor, say pv, which has maximum finish
time.
2. Second, select a processor, say pu, which has minimum finish
time.
3. Third, try to transfer a process from pv to pu or swap a single
pair of processes between pv and pu that improves the make
span of both processors the most.
4. This process is repeated until no improvement is possible.
Replacement Strategy
With genetic operators (crossover, mutation) are applied on
selected patterns T1, T2 two new chromosomes T’ and T” are
generated. These chromosomes are added to new temporary
population. By repeating this operation, a new temporary
population with size of 2*POPSIZE is generated. After that fitter
11. chromosomes are selected from current population and new
temporary population, at last selected chromosomes made new
population and algorithm restarts.
Termination Condition
We can apply multiple choices for termination condition: Max
number of generation, algorithm convergence, and equal fitness for
fittest selected chromosomes in respective iterations.
The Structure of Proposed Genetic Algorithm
Our proposed GA-Based algorithm starts with a generation of
individuals. A certain fitness function is used to evaluate the fitness
of each individual. Good individuals survive after selection
according to the fitness of individuals. Then the survived individuals
reproduce offspring through crossover and mutation operators. This
process iterates until termination condition is satisfied. It is
Considerable to say that parameters such as pc, pm, POPSIZE,
NOGEN, α, β, γ and θ must be determined before GA is started. The
algorithm is as below:
Procedure GA-based algorithm
Begin
Initialize P (k): {Create an initial population}
Evaluation P (k): {evaluate all individuals in the population}
Repeat
For i=1 to 2*POPSIZE do
Select 2 chromosomes as parent1 and parent2 from
population
Child1 and Child2←Crossover (parent1, parent2);
Child1←Mutation (Child1);
Child2←Mutation (Child2);
Add (new temp population, Child1, Child2);
12. End For
Make (new population, new temp population, population);
Population = new Population;
While (not termination condition);
Select best chromosome in population as solution and return it;
End
Conclusions
Scheduling in distributed operating systems has a significant role in
overall system performance and throughput. The scheduling in
distributed systems is known as an NP-complete problem even in
the best conditions. We have presented and evaluated new GA-
Based method to solve this problem. This algorithm considers multi
objectives in its solution evaluation and solves the scheduling
problem in a way that simultaneously minimizes maxspan and
communication cost, and maximizes average processor utilization
and load-balance. Most existing approaches tend to focus on one of
the objectives. Experimental results prove that our proposed
algorithm tend to focus on all of the objectives simultaneously and
optimize them.
References
1. A Genetic Algorithm for Process Scheduling in Distributed
Operating Systems considering Load Balancing, by M. Nikravan
and M. H. Kashani
2. A Modified Genetic Algorithm for Process Scheduling in
Distributed System, by Vinay Harsora and Dr. Apurva Shah,
International Journal of Computer Applications, AIT – 2011