The document presents a hybrid swarm intelligence algorithm called VNABCSA for scheduling soft real-time tasks in heterogeneous multiprocessor systems. VNABCSA combines artificial bee colony and simulated annealing algorithms. It aims to minimize total tardiness, number of processors used, completion time, total waiting time of tasks and processors. The algorithm represents solutions as an ordering of tasks and assignment to processors. It uses artificial bee colony for global search and simulated annealing for local search to improve convergence. Simulation results show it performs better than existing scheduling algorithms.
Bounded ant colony algorithm for task Allocation on a network of homogeneous ...ijcsit
This document summarizes a research paper that proposes a bounded ant colony algorithm (BTS-ACO) for task scheduling on a network of homogeneous processors using a primary site. The algorithm uses an initial bound on each processor's load to control task allocation. It investigates scheduling tasks from a sorted list (SLoT) versus a random list (RLoT). Simulation results show that BTS-ACO with a sorted task list achieves better performance than a random list in terms of scheduling time, makespan, and load balancing.
The document describes a novel approach called Enhanced Ant Colony Optimization (EACO) for scheduling tasks in a grid computing environment. EACO aims to improve task scheduling by minimizing makespan time compared to existing algorithms like Modified Ant Colony Optimization, MAX-MIN, and Resource Aware Scheduling Algorithm. It does this by considering system and network performance in dynamic grids and selecting resources according to their availability. The document presents the procedures of EACO and the existing algorithms, experimental results showing EACO achieves lower makespan, and concludes EACO is effective for task scheduling in grids.
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.
Cell Charge Approximation for Accelerating Molecular Simulation on CUDA-Enabl...ijcax
Methods for Molecular Dynamics(MD) simulations are investigated. MD simulation is the widely used computer simulation approach to study the properties of molecular system. Force calculation in MD is computationally intensive. Paral-lel programming techniques can be applied to improve those calculations.
The major aim of this paper is to speed up the MD simulation calculations by/using General Purpose Graphics Processing Unit(GPU) computing paradigm, an efficient and economical way for parallel computing. For that we are proposing a method called cell charge approximation which treats the
electrostatic interactions in MD simulations.This method reduces the complexity of force calculations.
DYNAMIC TASK PARTITIONING MODEL IN PARALLEL COMPUTINGcscpconf
Parallel computing systems compose task partitioning strategies in a true multiprocessing
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.
Goal of computation model is to provide a realistic representation of the costs of programming
The results show the benefit of the task partitioning. The main characteristics of our method are
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
for task partitioning which improve the inter process communication between the tasks and use
the recourses of the system in the efficient manner. The proposed algorithm contributes the
inter-process communication cost minimization amongst the executing processes.
This document summarizes an academic research paper that analyzes an optimal N-policy for a Bernoulli feedback Mx/G/1 machining system with general setup times. The paper develops a mathematical model of the system using supplementary variable technique to obtain the probability generating function of the system queue size distribution and mean number of failed units. It also derives the Laplace-Stieltjes transform of the waiting time and evaluates the mean waiting time. Finally, it formulates the total operational cost function to determine the optimal value of N that minimizes costs.
This document discusses using a genetic algorithm to solve the job shop scheduling problem. It begins with an abstract that introduces using genetic algorithms for job shop scheduling. It then provides more details on the problem and discusses using genetic algorithms with modifications like generating the initial population using priority rules and incorporating critical block and disjunctive graph distance in the crossover and mutation operations. The document outlines the genetic algorithm approach with sections on chromosome representation and decoding, data structures, generating the initial population, crossover and mutation operations. The goal is to minimize the makespan value for job shop scheduling.
Parallelization of Graceful Labeling Using Open MPIJSRED
This document summarizes research on parallelizing the graceful graph labeling problem using OpenMP on multi-core processors. It introduces the concepts of parallelization, multi-core architecture, and OpenMP. An algorithm is designed to parallelize graceful labeling by distributing graph vertices across processor cores. Execution time and speedup are measured for graphs of increasing size, showing improved speedup and reduced time with parallelization. Results show consistent performance gains as graph size increases due to better utilization of the multi-core architecture.
Bounded ant colony algorithm for task Allocation on a network of homogeneous ...ijcsit
This document summarizes a research paper that proposes a bounded ant colony algorithm (BTS-ACO) for task scheduling on a network of homogeneous processors using a primary site. The algorithm uses an initial bound on each processor's load to control task allocation. It investigates scheduling tasks from a sorted list (SLoT) versus a random list (RLoT). Simulation results show that BTS-ACO with a sorted task list achieves better performance than a random list in terms of scheduling time, makespan, and load balancing.
The document describes a novel approach called Enhanced Ant Colony Optimization (EACO) for scheduling tasks in a grid computing environment. EACO aims to improve task scheduling by minimizing makespan time compared to existing algorithms like Modified Ant Colony Optimization, MAX-MIN, and Resource Aware Scheduling Algorithm. It does this by considering system and network performance in dynamic grids and selecting resources according to their availability. The document presents the procedures of EACO and the existing algorithms, experimental results showing EACO achieves lower makespan, and concludes EACO is effective for task scheduling in grids.
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.
Cell Charge Approximation for Accelerating Molecular Simulation on CUDA-Enabl...ijcax
Methods for Molecular Dynamics(MD) simulations are investigated. MD simulation is the widely used computer simulation approach to study the properties of molecular system. Force calculation in MD is computationally intensive. Paral-lel programming techniques can be applied to improve those calculations.
The major aim of this paper is to speed up the MD simulation calculations by/using General Purpose Graphics Processing Unit(GPU) computing paradigm, an efficient and economical way for parallel computing. For that we are proposing a method called cell charge approximation which treats the
electrostatic interactions in MD simulations.This method reduces the complexity of force calculations.
DYNAMIC TASK PARTITIONING MODEL IN PARALLEL COMPUTINGcscpconf
Parallel computing systems compose task partitioning strategies in a true multiprocessing
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.
Goal of computation model is to provide a realistic representation of the costs of programming
The results show the benefit of the task partitioning. The main characteristics of our method are
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
for task partitioning which improve the inter process communication between the tasks and use
the recourses of the system in the efficient manner. The proposed algorithm contributes the
inter-process communication cost minimization amongst the executing processes.
This document summarizes an academic research paper that analyzes an optimal N-policy for a Bernoulli feedback Mx/G/1 machining system with general setup times. The paper develops a mathematical model of the system using supplementary variable technique to obtain the probability generating function of the system queue size distribution and mean number of failed units. It also derives the Laplace-Stieltjes transform of the waiting time and evaluates the mean waiting time. Finally, it formulates the total operational cost function to determine the optimal value of N that minimizes costs.
This document discusses using a genetic algorithm to solve the job shop scheduling problem. It begins with an abstract that introduces using genetic algorithms for job shop scheduling. It then provides more details on the problem and discusses using genetic algorithms with modifications like generating the initial population using priority rules and incorporating critical block and disjunctive graph distance in the crossover and mutation operations. The document outlines the genetic algorithm approach with sections on chromosome representation and decoding, data structures, generating the initial population, crossover and mutation operations. The goal is to minimize the makespan value for job shop scheduling.
Parallelization of Graceful Labeling Using Open MPIJSRED
This document summarizes research on parallelizing the graceful graph labeling problem using OpenMP on multi-core processors. It introduces the concepts of parallelization, multi-core architecture, and OpenMP. An algorithm is designed to parallelize graceful labeling by distributing graph vertices across processor cores. Execution time and speedup are measured for graphs of increasing size, showing improved speedup and reduced time with parallelization. Results show consistent performance gains as graph size increases due to better utilization of the multi-core architecture.
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.
On the-joint-optimization-of-performance-and-power-consumption-in-data-centersCemal Ardil
The document summarizes research on jointly optimizing performance and power consumption in data centers. It models the process of mapping tasks in a data center onto machines as a multi-objective problem to minimize both energy consumption and response time (makespan), subject to deadline and architectural constraints. It proposes using a simple goal programming technique that guarantees Pareto optimal solutions with good convergence. Simulation results show the technique achieves superior performance compared to other approaches and is competitive with optimal solutions for small-scale problems.
Implementation and evaluation of novel scheduler of UC/OS (RTOS)Editor Jacotech
The document summarizes proposed modifications to the scheduler of the UC/OS real-time operating system (RTOS) to allow tasks with the same priority level to be queued. The proposed scheduler uses time-slicing to schedule multiple tasks at the same priority level. Amendments were made to task management structures and system calls related to scheduling and time management. Evaluation on a hardware board showed the modified scheduler added minimal overhead.
In today's world developers are faced with the problem of writing high-performing algorithms that scale efficiently across a range of multi-core processors. Traditional blocked algorithms need to be tuned to each processor, but the discovery of cache-oblivious algorithms give developers new tools to tackle this emerging challenge. In this talk you will learn about the external memory model, the cache-oblivious model, and how to use these tools to create faster, scalable algorithms.
Optimum capacity allocation of distributed generation units using parallel ps...eSAT Journals
Abstract This paper proposes the application of Parallel Particle Swarm Optimization (PPSO) technique to find the optimal sizing of multiple DG(Distributed Generation) units in the radial distribution network by reduction in real power losses and enhancement in voltage profile. Message passing interface (MPI) is used for the parallelization of PSO. The initial population of PSO algorithm has been divided between the processors at run time. The proposed technique is tested on standard 123-bus test system and the obtained results show that the simulation time is significantly reduced and is concluded that parallelization helps in enhancing the performance of basic PSO. The procedure has been implemented in an environment in which OpenDSS (Open Distribution System Simulator) is driven from MATLAB. An adaptive weight particle swarm optimization algorithm has been developed in MATLAB , parallelization is achieved using MATLABMPI and the unbalanced three-phase distribution load flow (DLF) has been performed using Electric Power Research Institute’s (EPRI) open source tool OpenDSS. Index Terms: Distributed Generation, Message Passing Interface, Optimal Placement, Parallel Particle Swarm Optimisation
This document summarizes a research paper that implemented Levenberg-Marquardt artificial neural network training using graphics processing unit (GPU) hardware acceleration. The key points are:
1) This appears to be the first description of implementing artificial neural networks using the Levenberg-Marquardt training method on a GPU.
2) The paper describes their approach for implementing the Levenberg-Marquardt algorithm on a GPU, which involves solving the matrix inversion operation that is typically computationally expensive.
3) Results show that training networks using the GPU implementation can be up to 10 times faster than using a CPU-only implementation on the same hardware.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Priority based round robin (PBRR) CPU scheduling algorithmIJECEIAES
This paper introduce a new approach for scheduling algorithms which aim to improve real time operating system CPU performance. This new approach of CPU Scheduling algorithm is based on the combination of round-robin (RR) and Priority based (PB) scheduling algorithms. This solution maintains the advantage of simple round robin scheduling algorithm, which is reducing starvation and integrates the advantage of priority scheduling. The proposed algorithm implements the concept of time quantum and assigning as well priority index to the processes. Existing round robin CPU scheduling algorithm cannot be dedicated to real time operating system due to their large waiting time, large response time, large turnaround time and less throughput. This new algorithm improves all the drawbacks of round robin CPU scheduling algorithm. In addition, this paper presents analysis comparing proposed algorithm with existing round robin scheduling algorithm focusing on average waiting time and average turnaround time.
IRJET- Latin Square Computation of Order-3 using Open CLIRJET Journal
This document discusses using OpenCL parallel programming to compute Latin squares of order 3 more efficiently than sequential algorithms. It proposes dividing the input matrix into sub-matrices that are processed concurrently by multiple processing elements in the GPU. This parallel approach reduces the computation time compared to performing the operations sequentially on the CPU. First, the input matrix is divided based on task or data parallelism. Then the sub-matrices are computed simultaneously by different processing elements. The results are combined and stored in GPU memory before being transferred to CPU memory and output. Implementing the Latin square computation with OpenCL exploits parallelism to improve efficiency over the traditional sequential approach.
ANALYSINBG THE MIGRATION PERIOD PARAMETER IN PARALLEL MULTI-SWARM PARTICLE SW...ijcsit
This document analyzes the effect of different migration periods on the Parallel Comprehensive Learning Particle Swarm Optimization (PCLPSO) algorithm. PCLPSO is a parallel multi-swarm algorithm based on Particle Swarm Optimization (PSO) and Comprehensive Learning PSO (CLPSO). It uses multiple swarms that work cooperatively and concurrently. The migration period determines how often each swarm shares its best solution with other swarms, and affects the algorithm's efficiency. The document tests PCLPSO on 14 benchmark optimization functions using different migration periods, to analyze their impact on the algorithm's performance.
This document presents a comparative study of two genetic algorithm-based task allocation models in distributed computing systems. It aims to minimize turnaround time, where the previous model aimed to maximize reliability. The models are implemented on two example cases, with the minimum turnaround time model finding an allocation with a turnaround of 14 units and slightly lower reliability than the maximum reliability model's allocation of 20 units. In conclusion, minimizing turnaround time leads to slightly reduced reliability compared to maximizing reliability.
Comparative Analysis of Job Scheduling for Grid Environment ............................................................1
Neeraj Pandey, Ashish Arya and Nitin Kumar Agrawal
Hackers Portfolio and its Impact on Society ........................................................................................1
Dr. Adnan Omar and Terrance Sanchez, M.S.
Ontology Based Multi-Viewed Approach for Requirements Engineering ..............................................1
R. Subha and S. Palaniswami
Modified Colonial Competitive Algorithm: An Approach for Graph Coloring Problem ..........................1
Hojjat Emami and Parvaneh Hasanzadeh
Security and Privacy in E-Passport Scheme using Authentication Protocols and Multiple Biometrics
Technology ........................................................................................................................................1
V. K. Narendira Kumar and B. Srinivasan
Comparative Study of WLAN, WPAN, WiMAX Technologies ................................................................1
Prof. Mangesh M. Ghonge and Prof. Suraj G. Gupta
A New Method for Web Development using Search Engine Optimization ............................................1
Chutisant Kerdvibulvech and Kittidech Impaiboon
A New Design to Improve the Security Aspects of RSA Cryptosystem ..................................................1
Sushma Pradhan and Birendra Kumar Sharma
A Hybrid Model of Multimodal Approach for Multiple Biometrics Recognition ...................................1
P. Prabhusundhar, V.K. Narendira Kumar and B. Srinivasan
Second Genetic algorithm and Job-shop scheduling presentationAccenture
This document describes a genetic algorithm approach for solving job shop scheduling problems. It proposes new crossover and mutation operators designed based on the characteristics of job shop problems. The crossover operator combines operation orders from different machines in the parents. The mutation operator permutes successive operations on the same machine if they are on the critical path, to potentially reduce makespan. Experimental results using the new operators show improved convergence speed over a simple genetic algorithm.
AN EFFICIENT HEURISTIC ALGORITHM FOR FLEXIBLE JOB SHOP SCHEDULING WITH MAINTE...mathsjournal
This document summarizes a heuristic algorithm for solving the flexible job shop scheduling problem with preventive maintenance constraints. The objectives are to minimize makespan, total workload, and maximum machine workload.
The algorithm uses a constructive procedure to sequentially assign each operation (job or maintenance) to machines based on a calculated total cost (TC) value. TC considers factors like processing time, workload balance, and maintenance window compliance. Multiple parameter settings are tested to generate initial solutions. Computational results on benchmark problems show the heuristic finds good quality solutions very quickly, making it suitable for practical applications.
This document presents algorithms for jointly scheduling periodic and aperiodic tasks in statically scheduled distributed real-time systems. The algorithms determine unused resources in a static schedule and use this information to incorporate aperiodic tasks while still meeting periodic task deadlines. The algorithms perform optimally for hard aperiodic tasks and have been shown to have high acceptance ratios for aperiodic tasks via simulation.
This document summarizes the key findings from analyzing workload traces collected from four large compute pools (A, B, C, D) at Intel over a one-month period. Millions of jobs were submitted to each pool. Most jobs required a single CPU core and under 8GB of memory, but some needed more resources. The pools varied in their number of jobs, users, machines, cores and burstiness of resource demands over time. Common heuristics for matching jobs to machines sometimes failed to optimally utilize resources, motivating the development of new scheduling approaches.
Fault-Tolerance Aware Multi Objective Scheduling Algorithm for Task Schedulin...csandit
Computational Grid (CG) creates a large heterogeneous and distributed paradigm to manage and execute the applications which are computationally intensive. In grid scheduling tasks are assigned to the proper processors in the grid system to for its execution by considering the execution policy and the optimization objectives. In this paper, makespan and the faulttolerance of the computational nodes of the grid which are the two important parameters for the task execution, are considered and tried to optimize it. As the grid scheduling is considered to be NP-Hard, so a meta-heuristics evolutionary based techniques are often used to find a solution for this. We have proposed a NSGA II for this purpose. The performance estimation ofthe proposed Fault tolerance Aware NSGA II (FTNSGA II) has been done by writing program in Matlab. The simulation results evaluates the performance of the all proposed algorithm and the results of proposed model is compared with existing model Min-Min and Max-Min algorithm which proves effectiveness of the model.
A NEW TOOL FOR LARGE SCALE POWER SYSTEM TRANSIENT SECURITY ASSESSMENTPower System Operation
This document proposes a new method for fast assessment of transient security power limits in large power systems using neural networks. It establishes a nonlinear mapping between transient energy margin and generator power at different fault clearing times and load levels using a self-organizing map. The transient security power limits of generators can then be estimated very quickly by inputting fault clearing time and load level data. Testing on a sample power system shows the proposed method can accurately estimate security limits without needing to calculate analytical sensitivities, providing faster results than traditional methods.
The document presents a novel hyper-heuristic scheduling algorithm called HHSA for cloud computing systems. HHSA aims to find better scheduling solutions than traditional rule-based algorithms by employing diversity and improvement detection operators to dynamically determine which low-level heuristic to use. The performance of HHSA is evaluated on CloudSim and Hadoop and shown to significantly reduce makespan compared to other algorithms.
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.
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
This document summarizes a research paper on power-aware scheduling for real-time embedded systems. It introduces the concept of temporal workload, which measures how busy a CPU is over time based on tasks' remaining execution times and deadlines. Analyzing temporal workload provides insights into the behaviors of earliest deadline first (EDF) scheduling. It is shown that temporal workload decreases monotonically under EDF and is not affected by task execution order. The paper then presents an algorithm for power-aware scheduling that utilizes temporal workload analysis to identify slack times when the CPU frequency can be reduced to save energy while still meeting tasks' deadlines. Simulation results show the algorithm reduces energy consumption by 10-70% compared to prior methods.
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.
On the-joint-optimization-of-performance-and-power-consumption-in-data-centersCemal Ardil
The document summarizes research on jointly optimizing performance and power consumption in data centers. It models the process of mapping tasks in a data center onto machines as a multi-objective problem to minimize both energy consumption and response time (makespan), subject to deadline and architectural constraints. It proposes using a simple goal programming technique that guarantees Pareto optimal solutions with good convergence. Simulation results show the technique achieves superior performance compared to other approaches and is competitive with optimal solutions for small-scale problems.
Implementation and evaluation of novel scheduler of UC/OS (RTOS)Editor Jacotech
The document summarizes proposed modifications to the scheduler of the UC/OS real-time operating system (RTOS) to allow tasks with the same priority level to be queued. The proposed scheduler uses time-slicing to schedule multiple tasks at the same priority level. Amendments were made to task management structures and system calls related to scheduling and time management. Evaluation on a hardware board showed the modified scheduler added minimal overhead.
In today's world developers are faced with the problem of writing high-performing algorithms that scale efficiently across a range of multi-core processors. Traditional blocked algorithms need to be tuned to each processor, but the discovery of cache-oblivious algorithms give developers new tools to tackle this emerging challenge. In this talk you will learn about the external memory model, the cache-oblivious model, and how to use these tools to create faster, scalable algorithms.
Optimum capacity allocation of distributed generation units using parallel ps...eSAT Journals
Abstract This paper proposes the application of Parallel Particle Swarm Optimization (PPSO) technique to find the optimal sizing of multiple DG(Distributed Generation) units in the radial distribution network by reduction in real power losses and enhancement in voltage profile. Message passing interface (MPI) is used for the parallelization of PSO. The initial population of PSO algorithm has been divided between the processors at run time. The proposed technique is tested on standard 123-bus test system and the obtained results show that the simulation time is significantly reduced and is concluded that parallelization helps in enhancing the performance of basic PSO. The procedure has been implemented in an environment in which OpenDSS (Open Distribution System Simulator) is driven from MATLAB. An adaptive weight particle swarm optimization algorithm has been developed in MATLAB , parallelization is achieved using MATLABMPI and the unbalanced three-phase distribution load flow (DLF) has been performed using Electric Power Research Institute’s (EPRI) open source tool OpenDSS. Index Terms: Distributed Generation, Message Passing Interface, Optimal Placement, Parallel Particle Swarm Optimisation
This document summarizes a research paper that implemented Levenberg-Marquardt artificial neural network training using graphics processing unit (GPU) hardware acceleration. The key points are:
1) This appears to be the first description of implementing artificial neural networks using the Levenberg-Marquardt training method on a GPU.
2) The paper describes their approach for implementing the Levenberg-Marquardt algorithm on a GPU, which involves solving the matrix inversion operation that is typically computationally expensive.
3) Results show that training networks using the GPU implementation can be up to 10 times faster than using a CPU-only implementation on the same hardware.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Priority based round robin (PBRR) CPU scheduling algorithmIJECEIAES
This paper introduce a new approach for scheduling algorithms which aim to improve real time operating system CPU performance. This new approach of CPU Scheduling algorithm is based on the combination of round-robin (RR) and Priority based (PB) scheduling algorithms. This solution maintains the advantage of simple round robin scheduling algorithm, which is reducing starvation and integrates the advantage of priority scheduling. The proposed algorithm implements the concept of time quantum and assigning as well priority index to the processes. Existing round robin CPU scheduling algorithm cannot be dedicated to real time operating system due to their large waiting time, large response time, large turnaround time and less throughput. This new algorithm improves all the drawbacks of round robin CPU scheduling algorithm. In addition, this paper presents analysis comparing proposed algorithm with existing round robin scheduling algorithm focusing on average waiting time and average turnaround time.
IRJET- Latin Square Computation of Order-3 using Open CLIRJET Journal
This document discusses using OpenCL parallel programming to compute Latin squares of order 3 more efficiently than sequential algorithms. It proposes dividing the input matrix into sub-matrices that are processed concurrently by multiple processing elements in the GPU. This parallel approach reduces the computation time compared to performing the operations sequentially on the CPU. First, the input matrix is divided based on task or data parallelism. Then the sub-matrices are computed simultaneously by different processing elements. The results are combined and stored in GPU memory before being transferred to CPU memory and output. Implementing the Latin square computation with OpenCL exploits parallelism to improve efficiency over the traditional sequential approach.
ANALYSINBG THE MIGRATION PERIOD PARAMETER IN PARALLEL MULTI-SWARM PARTICLE SW...ijcsit
This document analyzes the effect of different migration periods on the Parallel Comprehensive Learning Particle Swarm Optimization (PCLPSO) algorithm. PCLPSO is a parallel multi-swarm algorithm based on Particle Swarm Optimization (PSO) and Comprehensive Learning PSO (CLPSO). It uses multiple swarms that work cooperatively and concurrently. The migration period determines how often each swarm shares its best solution with other swarms, and affects the algorithm's efficiency. The document tests PCLPSO on 14 benchmark optimization functions using different migration periods, to analyze their impact on the algorithm's performance.
This document presents a comparative study of two genetic algorithm-based task allocation models in distributed computing systems. It aims to minimize turnaround time, where the previous model aimed to maximize reliability. The models are implemented on two example cases, with the minimum turnaround time model finding an allocation with a turnaround of 14 units and slightly lower reliability than the maximum reliability model's allocation of 20 units. In conclusion, minimizing turnaround time leads to slightly reduced reliability compared to maximizing reliability.
Comparative Analysis of Job Scheduling for Grid Environment ............................................................1
Neeraj Pandey, Ashish Arya and Nitin Kumar Agrawal
Hackers Portfolio and its Impact on Society ........................................................................................1
Dr. Adnan Omar and Terrance Sanchez, M.S.
Ontology Based Multi-Viewed Approach for Requirements Engineering ..............................................1
R. Subha and S. Palaniswami
Modified Colonial Competitive Algorithm: An Approach for Graph Coloring Problem ..........................1
Hojjat Emami and Parvaneh Hasanzadeh
Security and Privacy in E-Passport Scheme using Authentication Protocols and Multiple Biometrics
Technology ........................................................................................................................................1
V. K. Narendira Kumar and B. Srinivasan
Comparative Study of WLAN, WPAN, WiMAX Technologies ................................................................1
Prof. Mangesh M. Ghonge and Prof. Suraj G. Gupta
A New Method for Web Development using Search Engine Optimization ............................................1
Chutisant Kerdvibulvech and Kittidech Impaiboon
A New Design to Improve the Security Aspects of RSA Cryptosystem ..................................................1
Sushma Pradhan and Birendra Kumar Sharma
A Hybrid Model of Multimodal Approach for Multiple Biometrics Recognition ...................................1
P. Prabhusundhar, V.K. Narendira Kumar and B. Srinivasan
Second Genetic algorithm and Job-shop scheduling presentationAccenture
This document describes a genetic algorithm approach for solving job shop scheduling problems. It proposes new crossover and mutation operators designed based on the characteristics of job shop problems. The crossover operator combines operation orders from different machines in the parents. The mutation operator permutes successive operations on the same machine if they are on the critical path, to potentially reduce makespan. Experimental results using the new operators show improved convergence speed over a simple genetic algorithm.
AN EFFICIENT HEURISTIC ALGORITHM FOR FLEXIBLE JOB SHOP SCHEDULING WITH MAINTE...mathsjournal
This document summarizes a heuristic algorithm for solving the flexible job shop scheduling problem with preventive maintenance constraints. The objectives are to minimize makespan, total workload, and maximum machine workload.
The algorithm uses a constructive procedure to sequentially assign each operation (job or maintenance) to machines based on a calculated total cost (TC) value. TC considers factors like processing time, workload balance, and maintenance window compliance. Multiple parameter settings are tested to generate initial solutions. Computational results on benchmark problems show the heuristic finds good quality solutions very quickly, making it suitable for practical applications.
This document presents algorithms for jointly scheduling periodic and aperiodic tasks in statically scheduled distributed real-time systems. The algorithms determine unused resources in a static schedule and use this information to incorporate aperiodic tasks while still meeting periodic task deadlines. The algorithms perform optimally for hard aperiodic tasks and have been shown to have high acceptance ratios for aperiodic tasks via simulation.
This document summarizes the key findings from analyzing workload traces collected from four large compute pools (A, B, C, D) at Intel over a one-month period. Millions of jobs were submitted to each pool. Most jobs required a single CPU core and under 8GB of memory, but some needed more resources. The pools varied in their number of jobs, users, machines, cores and burstiness of resource demands over time. Common heuristics for matching jobs to machines sometimes failed to optimally utilize resources, motivating the development of new scheduling approaches.
Fault-Tolerance Aware Multi Objective Scheduling Algorithm for Task Schedulin...csandit
Computational Grid (CG) creates a large heterogeneous and distributed paradigm to manage and execute the applications which are computationally intensive. In grid scheduling tasks are assigned to the proper processors in the grid system to for its execution by considering the execution policy and the optimization objectives. In this paper, makespan and the faulttolerance of the computational nodes of the grid which are the two important parameters for the task execution, are considered and tried to optimize it. As the grid scheduling is considered to be NP-Hard, so a meta-heuristics evolutionary based techniques are often used to find a solution for this. We have proposed a NSGA II for this purpose. The performance estimation ofthe proposed Fault tolerance Aware NSGA II (FTNSGA II) has been done by writing program in Matlab. The simulation results evaluates the performance of the all proposed algorithm and the results of proposed model is compared with existing model Min-Min and Max-Min algorithm which proves effectiveness of the model.
A NEW TOOL FOR LARGE SCALE POWER SYSTEM TRANSIENT SECURITY ASSESSMENTPower System Operation
This document proposes a new method for fast assessment of transient security power limits in large power systems using neural networks. It establishes a nonlinear mapping between transient energy margin and generator power at different fault clearing times and load levels using a self-organizing map. The transient security power limits of generators can then be estimated very quickly by inputting fault clearing time and load level data. Testing on a sample power system shows the proposed method can accurately estimate security limits without needing to calculate analytical sensitivities, providing faster results than traditional methods.
The document presents a novel hyper-heuristic scheduling algorithm called HHSA for cloud computing systems. HHSA aims to find better scheduling solutions than traditional rule-based algorithms by employing diversity and improvement detection operators to dynamically determine which low-level heuristic to use. The performance of HHSA is evaluated on CloudSim and Hadoop and shown to significantly reduce makespan compared to other algorithms.
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.
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
This document summarizes a research paper on power-aware scheduling for real-time embedded systems. It introduces the concept of temporal workload, which measures how busy a CPU is over time based on tasks' remaining execution times and deadlines. Analyzing temporal workload provides insights into the behaviors of earliest deadline first (EDF) scheduling. It is shown that temporal workload decreases monotonically under EDF and is not affected by task execution order. The paper then presents an algorithm for power-aware scheduling that utilizes temporal workload analysis to identify slack times when the CPU frequency can be reduced to save energy while still meeting tasks' deadlines. Simulation results show the algorithm reduces energy consumption by 10-70% compared to prior methods.
(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.
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.
SCHEDULING DIFFERENT CUSTOMER ACTIVITIES WITH SENSING DEVICEijait
Most periodic tasks are assigned to processors using partition scheduling policy after checking feasibility conditions. A new approach is proposed for scheduling different activities with one periodic task within the system. In this paper, control strategies are identified for allocating different types of tasks (activities) to
individual computing elements like Smartphone or microphones. In our simulation model, each periodic task generates an aperiodic tasks are taken into consideration. Different sets of periodic tasks and aperiodic tasks are scheduled together. This new approach proves that when all different activities are
scheduled with one periodic tasks leads to better performance.
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.
Proposing a scheduling algorithm to balance the time and cost using a genetic...Editor IJCATR
This summary provides the key details from the document in 3 sentences:
The document proposes a genetic algorithm approach combined with a local search algorithm inspired by binary gravitational attraction to solve scheduling problems in grid computing. The algorithm aims to minimize task completion time and costs by optimizing resource selection and load balancing. Experimental results showed that the proposed algorithm achieved better optimization of time and costs and selection of resources compared to other algorithms.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Proposing a Scheduling Algorithm to Balance the Time and Energy Using an Impe...Editor IJCATR
Computational grids have become an appealing research area as they solve compute-intensive problems within the scientific
community and in industry. A grid computational power is aggregated from a huge set of distributed heterogeneous workers; hence, it
is becoming a mainstream technology for large-scale distributed resource sharing and system integration. Unfortunately, current grid
schedulers suffer from the haste problem, which is the schedule inability to successfully allocate all input tasks. Accordingly, some tasks
fail to complete execution as they are allocated to unsuitable workers. Others may not start execution as suitable workers are previously
allocated to other peers. This paper presents an imperialist competition algorithm (ICA) method to solve the grid scheduling problems.
The objective is to minimize the makespan and energy of the grid. Simulation results show that the grid scheduling problem can be
solved efficiently by the proposed method
Optimal Round Robin CPU Scheduling Algorithm using Manhattan Distance IJECEIAES
In Round Robin Scheduling the time quantum is fixed and then processes are scheduled such that no process get CPU time more than one time quantum in one go. The performance of Round robin CPU scheduling algorithm is entirely dependent on the time quantum selected. If time quantum is too large, the response time of the processes is too much which may not be tolerated in interactive environment. If time quantum is too small, it causes unnecessarily frequent context switch leading to more overheads resulting in less throughput. In this paper a method using Manhattan distance has been proposed that decides a quantum value. The computation of the time quantum value is done by the distance or difference between the highest burst time and lowest burst time. The experimental analysis also shows that this algorithm performs better than RR algorithm and by reducing number of context switches, reducing average waiting time and also the average turna round time.
Nitt paper A fuzzy multi-objective based un-related parallel Machine Schedu...Arudhra N
This paper addresses a multi-objective scheduling problem to minimize makespan, tardiness, load variation, flow time, and secondary resource constraints for an unrelated parallel machine scheduling problem. A multi-objective evolutionary algorithm called Fuzzy-Non-dominated Sorting Genetic Algorithm (FNSGA-II) is proposed to solve this computationally challenging problem. The performance of FNSGA-II is validated on randomly generated test problems and is found to perform reasonably well in terms of quality, computational time, diversity and spacing metrics. The paper formulates the scheduling problem as a fuzzy mixed-integer non-linear programming model and describes the implementation of FNSGA-II using evolutionary operators like crossover, mutation, and non-dom
Reinforcement learning based multi core scheduling (RLBMCS) for real time sys...IJECEIAES
This document summarizes a reinforcement learning based multi-core scheduling (RLBMCS) algorithm for real-time systems. The algorithm uses reinforcement learning to dynamically assign task priorities and place tasks in a multi-level feedback queue to schedule tasks across multiple processor cores. It aims to optimize metrics like CPU utilization, throughput, turnaround time, waiting time, response time and deadline meet ratio. Tasks can transition between four states - initial, objective degradation, objective progression, and objective stabilization - based on changes to a multi-objective optimization function. The scheduler acts as the agent and assigns tasks to queues/actions based on task and system states to maximize the optimization function over time.
DGBSA : A BATCH JOB SCHEDULINGALGORITHM WITH GA WITH REGARD TO THE THRESHOLD ...IJCSEA Journal
In this paper , we will provide a scheduler on batch jobs with GA regard to the threshold detector. In The algorithm proposed in this paper, we will provide the batch independent jobs with a new technique ,so we can optimize the schedule them. To do this, we use a threshold detector then among the selected jobs, processing resources can process batch jobs with priority. Also hierarchy of tasks in each batch, will be determined with using DGBSA algorithm. Now , with the regard to the works done by previous ,we can provide an algorithm that by adding specific parameters to fitness function of the previous algorithms ,develop a optimum fitness function that in the proposed algorithm has been used. According to assessment done on DGBSA algorithm, in compare with the similar algorithms, it has more performance. The effective parameters that used in the proposed algorithm can reduce the total wasting time in compare with previous algorithms. Also this algorithm can improve the previous problems in batch processing with a new technique.
A Review of Different Types of Schedulers Used In Energy ManagementIRJET Journal
This document reviews different types of schedulers used for energy management in embedded systems. It discusses dynamic voltage and frequency scaling (DVFS) which aims to reduce energy consumption by varying CPU frequency and voltage dynamically. Real-time DVFS ensures quality of service by developing task schedules while reducing energy via DVFS. The paper surveys various DVFS scheduling algorithms that utilize CPU idle time to change frequency/voltage or use other techniques to meet power requirements. These algorithms can be offline, using worst-case execution times to pre-schedule all tasks, or online, making decisions in real-time based on past task executions to improve scheduling.
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.
A Heterogeneous Static Hierarchical Expected Completion Time Based Scheduling...IRJET Journal
The document presents a new scheduling algorithm called Hierarchical Expected Completion Time based Scheduling (HECTS) for tasks on multiprocessor systems. HECTS has two phases: 1) It prioritizes tasks based on their level in the task graph and calculates an expected completion time value for each task. Tasks are sorted by completion time value. 2) It uses an insertion-based approach to assign tasks to processors, trying to find the best time slot between already scheduled tasks without violating dependencies. The algorithm is evaluated based on speedup, efficiency and schedule length, and compared to other list scheduling algorithms. Simulation results show HECTS improves performance metrics over existing approaches.
Proposing a New Job Scheduling Algorithm in Grid Environment Using a Combinat...Editor IJCATR
Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature of the resources.
The purpose of job scheduling in grid environment is to achieve high system throughput and minimize the execution time of applications.
The complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively.
To obtain a good and efficient method to solve scheduling problems in grid, a new area of research is implemented. In this paper, a job
scheduling algorithm is proposed to assign jobs to available resources in grid environment. The proposed algorithm is based on Ant
Colony Optimization (ACO) algorithm. This algorithm is combined with one of the best scheduling algorithm, Suffrage. This paper uses
the result of Suffrage in proposed ACO algorithm. The main contribution of this work is to minimize the makespan of a given set of
jobs. The experimental results show that the proposed algorithm can lead to significant performance in grid environment.
This document provides a comparative analysis of various grid-based scheduling algorithms. It discusses six different algorithms: Min-Min, Sufferage, Heterogeneous Earliest Finish Time (HEFT), Critical Path-On-a-Processor (CPOP), Reliability Aware Scheduling Algorithm with Duplication of HDC System (RASD), and Hierarchical Job Scheduling for Clusters of Workstations (HJS). It compares the algorithms based on parameters like response time, resource utilization, load balancing, and considers factors like architecture, environment, and dynamicity. The document concludes that grid scheduling is important for optimizing resource allocation in distributed, heterogeneous environments.
Comparative Analysis of Various Grid Based Scheduling Algorithmsiosrjce
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.
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GRID SIDE CONVERTER CONTROL IN DFIG BASED WIND SYSTEM USING ENHANCED HYSTERES...ecij
The document presents a novel control strategy using an Enhanced Hysteresis Controller (EHC) for the Grid Side Converter (GSC) of a DFIG-based wind energy system. The EHC improves upon standard hysteresis control by incorporating the DC link voltage as an input to the integrator, allowing for higher duty ratio linearity, larger fundamental GSC currents with less harmonics. Simulation results on a 15kW DFIG system show the EHC provides fast transient response for the GSC and regulates the DC link voltage with smooth GSC currents and power during grid disturbances like voltage dips. Comparisons to a system without GSC control show significant reductions in oscillations through use of the proposed EHC strategy.
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SWARM INTELLIGENCE SCHEDULING OF SOFT REAL-TIME TASKS IN HETEROGENEOUS MULTIPROCESSOR SYSTEMS
1. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 5, Number 1, March 2016
DOI : 10.14810/ecij.2016.5101 1
SWARM INTELLIGENCE SCHEDULING OF SOFT
REAL-TIME TASKS IN HETEROGENEOUS
MULTIPROCESSOR SYSTEMS
Hamideh Kazemi1
, Zeynab Molay Zahedi2
and Mohammad Shokouhifar*3
1
Department of Computer Engineering, Nobonyad High Education Institute, Sirjan, Iran
2
Department of Computer Engineering, Islamic Azad University,
Science and Research Branch, Shiraz, Iran
3
Department of Electrical Engineering, Shahid Beheshti University G.C., Tehran, Iran
ABSTRACT
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.
KEYWORDS
Multiprocessor Systems, Soft Real-time Tasks, Scheduling, Artificial Bee Colony, Simulated Annealing.
1. INTRODUCTION
Real-time tasks can be classified with respect to the timing constraints into two categories: hard
real-time tasks and soft real-time tasks [1]. In the case of hard real-time systems, e.g., patient
monitoring, the violation of timing constraints is not acceptable. On the other hand, slight
violation of timing constraints is not critical for the soft tasks, e.g., telephone switching and image
processing applications. Although usefulness of a soft task decreases over time after its deadline
expires, it does not cause dangerous damages [2].
Generally, rate monotonic (RM) and earliest deadline first (EDF) are applied for task scheduling
in hard real-time uni-processor systems [3]. These methods guarantee the optimality of the
achieved solution in the case of hard tasks. However, they have some drawbacks in hard tasks
with overloaded situations. The main objective in soft systems is to minimize the total tardiness
of tasks. Although rate regulating proportional share (RRPS) [4] and modified proportional share
(MPS) [5] have been proposed for the scheduling of soft real-time tasks, they are restricted only
for the uni-processor systems and cannot cope with overloads.
Task scheduling in multiprocessor platforms is more difficult than that in uni-processor ones. In
multiprocessor systems, the objective is not only to optimize an execution order of tasks, but also
to determine an specific processor for each task to be executed. In homogeneous systems, all
processors are identical, but the scheduling problem becomes more difficult for the heterogeneous
multiprocessor systems. The additional complexity appears from the fact that the execution time
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of each task is different upon the different processors. As an example, a mathematical formula
calculation may be executed much faster on a floating coprocessor than a digital image processor
[6].
Recently, metaheuristic algorithms have widely been applied for the scheduling problem [7-16].
Different genetic algorithms (GAs) have been proposed in [7-9], in which of them, only one
objective (e.g., total tardiness, total completion time, etc.) is considered. Moreover, some multi-
objective GAs have been introduced [10-13]. These methods were applied for general tasks
without time constraints. As a result, they cannot be used for the real-time task scheduling. A
hybrid evolutionary algorithm based on GA and simulated annealing (SA) has been utilized for
the soft real-time tasks [14], in which, the convergence speed of GA was enhanced by employing
the acceptance rule of SA during the population updating phase. The method was followed in [15]
with introducing new encoding and decoding schemes in GA. The objective function combines
the adaptive weight approach to utilize some information from the current population to adjust the
weights of the objective function [15]. Recently, we have proposed a swarm intelligence
algorithm based on artificial bee colony (ABC) for the soft real-time task scheduling [16]. The
objective was considered to minimize the total tardiness, total number of utilized processors, and
the completion time, simultaneously.
In this paper, a hybrid strategy based on ABC and SA (named VNABCSA algorithm) is used for
the scheduling of soft real-time tasks in heterogeneous multiprocessor systems. The objective of
the proposed scheduling algorithm is to simultaneously minimize total tardiness of all tasks, total
number of utilized processors, final completion time, total waiting time for all tasks, and total
waiting time for all processors. A hybrid variable neighborhood strategy is investigated to
enhance the neighborhood exploration mechanism of ABC and SA, aim at avoid trapping in local
minima points and improve the convergence speed of both ABC and SA.
The rest of the paper is organized as follows: In Section 2, the scheduling problem for soft real-
time tasks and the proposed multi-objective criterion is mathematically formulated. In Section 3,
the proposed hybrid VNABCSA scheduling algorithm is described. Simulation results and
comparison with the other scheduling algorithms are illustrated in Section 4. Finally, Section 5
provides the conclusion remarks and suggestions for future works.
2. SCHEDULING CRITERION
It is assumed that all tasks have timing constrained. The precedence relations among the tasks can
be considered from the task relations graph. The scheduling algorithm simultaneously minimizes
the total tardiness, the number of utilized processors, the completion time, the total waiting time
of all tasks, and the total waiting time of all utilized processors, under the following conditions:
• System is a heterogeneous multiprocessor system.
• All tasks are non-preemptive.
• Tasks have soft deadlines.
• Tasks have precedence relations among them.
• Each processor can process one task at a time.
• Each task can be processed on one processor.
• Execution time of each task on each processor is known.
• Deadline of each task is known.
Minimize:
1 1 2 2 3 3 4 4 5 5Objective Function w f w f w f w f w f= + + + + (1)
( )1
1 1
0,
N M
S m m
i i i i
i m
f max t c d x
= =
= + −
∑ ∑ (2)
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2
1 1
1,
M N
m
i
m i
f min x
= =
=
∑ ∑ (3)
{ }3
F
i
i
f max t= (4)
{ }4
1
0,
N
S E
i i
i
f max t t
=
= −∑ (5)
5
1 1
M N
m
i
m i
f w
= =
= ∑∑ (6)
S m
m i i i m
i m
i
t x if isthe firsttask on p
Z otherwis
w
e
τ
=
(7)
1 1
max 0 max( ), , ( )
M
m m m
j j j j
j
m
N
m S m S
i i i j i
i
c x xZ t x t preτ τ
==
= ∈
− +
∑ ∑ (8)
Subject to:
1
0
m im
i
if p is selected for
x
otherwise
τ
=
(9)
1
1
M
m
i
m
x
=
=∑ (10)
S E
i it t≥ (11)
1
M
F S m m
i i i i
m
t t c x
=
= + ∑ (12)
1
, ( )
M
E E m m
i j j j j i
m
t t c x preτ τ
=
≥ + ∈∑ (13)
( ) { }
( )
1
0
max
i
M
i E m m
j j j j
j
m
E
i
t
if pre
t c x pre
τ
τ τ
=
∈ ∅
=
+ ∈
∑
(14)
( )1
0
j i
ij
if pre
e
otherwise
τ τ ∈
=
(15)
In the above equations, the parameters and notations can be defined as follows:
N total number of tasks
M total number of processors
i,j task index, i,j=1,2,3,…,N
m processor index, m=1,2,3,…,M
s term index within the objective function, s=1,2,3,4,5
G=(T,E) task graph
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T={߬i} set of N tasks
P={pm} set of M processors
E={eij} matrix of directed edges among tasks
eij a binary parameter defining that ߬i or not
xi
m
a binary parameter defining that task ߬i is assigned on processor pm or not
ci
m
computation time of task ߬i on processor pm
di
deadline of task ߬i
ti
E
earliest possible start time of task ߬i
ti
S
real start time of task ߬i
ti
F
finish time of task ߬i
ti
L
latest possible start time of task ߬i to be executed without tardiness
wi
m
waiting time of processor pm due to task ߬i
pre(߬i) set of predecessor tasks of task ߬i
suc(߬i) set of successor tasks of task ߬i
Equation (1) is the proposed multi-objective function to be minimized. Equation (2) means to
minimize the total tardiness of all tasks. Total tardiness in Eq. (2) can be calculated using sum of
the tardiness of each task (see Fig. 1). Figure 1a shows the normal execution of task without
tardiness, where, ti
S
+ci
m
-di is smaller than or equal to 0, and thus there is no tardiness. Figure 1b
illustrates the execution of a task with tardiness, where, ti
S
+ci
m
-di is larger than 0. Therefore, the
tardiness can be simply calculated as ti
S
+ci
m
-di. Equation (3) means to minimize the number of
utilized processors which have at least one task on them. Equation (4) means to minimize the
completion time of the last utilized processor. Equation (5) means to minimize the total waiting
time for all tasks. Equation (6) means to minimize the total waiting time for all utilized
processors. The constraint conditions are shown from (9) to (15). Equation (10) means that each
task can be processed on one processor. Equation (11) means that the task can be started after its
earliest possible start time. Equation (14) calculates the earliest possible start time for ߬i which
can be defined by the maximum finishing time of all its predecessors.
Figure 1. Tardiness in task execution.
3. PROPOSED METHODOLOGY
ABC [17] has a good global exploration in the search space [18-20]. On the other hand, SA [21]
has very good local search strategy. Recently, hybrid strategies based on evolutionary algorithms
and SA have been proposed to gain with the advantages of the both global and local search
[14,22-23]. In this paper, a hybrid Variable Neighborhood method based on ABC and SA (named
VNABCSA algorithm) is proposed for the scheduling of soft real-time tasks in heterogeneous
multiprocessor systems. At first, ABC is utilized for global searching among the search space.
Then, SA is performed in order to search in the vicinity of the final solution of ABC. Overall
flowchart of VNABCSA scheduling algorithm can be seen in Fig. 2
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Figure 2. Overall flowchart of the VNABCSA scheduing algorithm.
3.1. Problem Representation
As mentioned above, in heterogeneous multiprocessor systems, the objective is not only to
optimize an execution order of tasks, but also to determine a processor for the each task to be
executed. More specifically, the first is an ordering problem, whereas the last is an assignment
problem. Recently, SA and ABC have been applied for a popular ordering problem (Traveling
Salesman Problem) [24-25], and a popular assignment problem (Multiple Knapsack Problem)
[26]. In this paper, a hybrid structure is applied to represent feasible solutions, which is
partitioned into two parts. The first defines overall order of tasks to be executed (ordering part),
and the last determines the processor numbers to which tasks are assigned (assignment part). The
length of each part is equal to the total number of all tasks. Therefore, the number of optimization
variables is 2×N, where N is the number of all tasks. It is worth noting that the precedence
relationship between tasks with respect to the given task graph should be satisfied within the
ordering part. Figure 3 illustrates the representation of a feasible solution for a dataset with 9
tasks and 3 processors.
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987654321
368591427Ordering Part :
232321312Assignment Part :
Figure 3. Representation of a feasible solution for a task graph with 9 tasks and 3 processors.
3.2. ABC Phase
In ABC algorithm, half of colony is the population of employed bees and the other half is related
to onlooker bees. At first, the feasible solutions are randomly explored by employed bees. For the
each solution, the ordering part is randomly constructed with respect to the precedence
relationships. On the other hand, any task ߬i could not be presented before the tasks within pre(߬i)
set. In order to achieve this issue, at first all tasks that have not any predecessor tasks are ordered
randomly and placed at the beginning of the ordering part. Then, the set of all successor tasks of
those tasks were ordered randomly after them. This process continues until all tasks would be
ordered. Moreover, in order to fill the assignment part, a processor is randomly selected for the
each task. The procedure of bee encoding and generation of initial population can be seen in Fig.
4.
Figure 4. An example for initial population generation procedure.
Whenever all bees construct their solutions, they come back into the hive and share the gathered
information about the quality of food sources with the other bees waiting in the hive. On the other
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hand, the quality of each solution is evaluated according to the corresponding objective function
value achieved by Eq. (1). In this way, the quality of k-th bee can be calculated as nekk=1/objk ,
where nekk and objk are the nectar value and the corresponding objective value for the k-th bee,
respectively.
In the every iteration of ABC, each bee constructs a new solution, aim at improve its position and
its food-source quality. Generally, population updating in ABC is done in three phases: employed
phase, onlooker phase, and scout phase. Each employed bee is moved onto her previously visited
environment to explore a new solution within the vicinity of the present one. If the nectar (fitness)
of the new solution is higher than the previous one, the bee forgets the previous solution and
memorizes the new one.
As mentioned above, the employed bee whose food source has been abandoned will become a
scout bee. It can be controlled by a parameter called limit. Then, the scout bee caries out random
searching the whole search space to discover a new solution. It is done like the random search
mechanism for construction of the initial population.
The more nectar the food source gathered by an employed bee, the larger probability for the
employed bee to be selected via onlookers. The probability of i-th employed bee to be selected is
calculated as follows:
( )1
( )i
i NE
jj
nec
P
nec
α
α
=
=
∑
(16)
where Pi is the probability of the i-th employed bee to be chosen by onlookers, NE is the
population size of employed bees, and α is a constant parameter that adjusts the selection type.
The larger α, the more probability of selecting the employed bee with more fitness.
As mentioned above, each onlooker bee selects an employed bee according to Eq. (16). Then, she
goes onto the food source area of the selected employed, in order to explore a new food source in
the vicinity area of the employed. It is worth noting that he proposed variable neighborhood
search mechanism is applied for the neighborhood search in both employed and onlooker phases.
3.3. SA Phase
In general, SA starts with a random initial solution. However, in the proposed hybrid VNABCSA
algorithm, the final global best solution found via ABC is used as the initial solution for SA. At
the each iteration, a new solution, Solutionnew
, is generated in the vicinity area of the current
solution, Solutioncurrent
. If Enew
≤ Ecurrent
, the current solution is replaced with the new one. On the
other hand, if Enew
> Ecurrent
, the new solution may be accepted with the probability of Pw, which is
calculated as follows:
( )new current
w
E E
P exp
T
−
= −
(17)
where Ecurrent
and Enew
are the objective function value according to Eq. (1) for Solutioncurrent
and
Solutionnew
, respectively. The temperature T is typically considered to be decreased linearly from
the initial temperature Tinitial to the final temperature Tfinal, during execute algorithm. tSA and iterSA
are current iteration and the maximum number of iterations of SA, respectively. If T=0,
Solutionnew
never could be accepted, when Enew
> Ecurrent
. On the other hand, the larger T, the more
probability for accepting worse solutions. Accepting worse solutions allows SA to avoid trapping
in local minima points. In order to generate a new solution in the vicinity area of the current one,
the common approach as same as in ABC is used.
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( )SA
initial final initial
SA
t
T T T T
iter
= + × − (18)
3.4. Variable Neighborhood Search Mechanism
As mentioned above, we introduce a hybrid local and global scheme for the neighborhood search
in order to improve the convergence speed of both ABC and SA. Different neighborhood
operators are used for the neighborhood exploration. Each operator can avoid trapping a type of
local minima points. In this approach, Swap, Exchange, Relocation, and Or-opt operators [27-28]
are applied. The Relocation and Or-opt are performed in the first part (ordering part) of the
solution. The swap is applied in the second part (assignment part). Also, the Exchange can be
used in both parts, called Exchange-1 and Exchange-2, respectively. The five neighborhood
search operators can be shown in Fig. 5. Here, 18 neighborhood search structures are proposed
from the five mentioned operators, with different segment lengths. In order to explore the
neighborhood area of a solution (in both ABC and SA), each neighborhood structure may be
applied with the predefined probability.
Figure 5. The five operators in the proposed hybrid variable-neighborhood search mechanism.
4. PERFORMANCE EVALUATION
4.1. Simulation Settings
Experiments were implemented in MATLAB R2015a running on a PC with 2.53GHZ
processor
and 4GB
memory on windows 8. Setting the controllable parameters of the VNABCSA is very
important and affects on the efficiency of the algorithm. The 18 neighborhood search structures
and corresponding probabilities can be summarized in Table 1. Also, setting the controllable
parameters of the VNABCSA algorithm can be summarized in Table 2.
Table 1. Neighborhood search structures and probabilities.
Structure # Operator Segment Length Probability
1 Relocation 2 6%
2 Relocation 3 6%
3 Relocation 4 6%
4 Relocation 5 6%
5 Or-opt 1 , 2 3%
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Structure # Operator Segment Length Probability
6 Or-opt 2 , 2 3%
7 Or-opt 2 , 3 3%
8 Or-opt 2 , 4 3%
9 Or-opt 2 , 5 3%
10 Or-opt 3 , 3 3%
11 Or-opt 3 , 4 3%
12 Or-opt 3 , 5 3%
13 Or-opt 4 , 4 3%
14 Or-opt 4 , 5 3%
15 Or-opt 5 , 5 3%
16 Exchange-1 1 , 1 10%
17 Exchange-2 1 , 1 15%
18 Swap 1 18%
Table 2. Parameter setting for the proposed VNABCSA algorithm.
Parameter Value
Maximum number of iterations in ABC N×M
Population of artificial bees 50
Number of employed bees 25
Number of onlooker bees 25
α in Eq. (16) 3
Limit 0.7
Maximum number of iterations in SA 5×N×M
Tinitial in Eq. (18) 0.5
Tfinal in Eq. (18) 0
4.2. Benchmarks
In order to validate the proposed VNABCSA scheduling algorithm, several numerical
benchmarks [14] are performed. Here, a task graph with 10 tasks (Fig. 6) and a task graph with 50
tasks (Fig. 7) are considered for simulations. In the following, we called them Benchmark-1 and
Benchmark-2, respectively. The details of Benchmark-1 can be summarized in Table 3.
Table 3. Dataset details for Benchmark-1.
Ci
m
i suc (߬߬߬߬i) di Ci
1
Ci
2
Ci
3
Ci
4
1 8 19 5 3 11 8
2 6 9 6 5 4 13
3 4,5 18 11 8 6 7
4 6,7,8 37 10 13 5 6
5 6,10 38 10 13 8 11
6 9 37 2 11 11 3
7 --- 44 3 10 11 8
8 --- 30 4 12 10 5
9 10 37 6 9 7 10
10 --- 58 11 12 6 4
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Figure 6. Benchmark-1: the task graph with 10 tasks [14].
Figure 7. Benchmark-2: the task graph with 50 tasks [14].
4.3. Simulation Results
In order to evaluate the performance of the proposed VNABCSA scheduling algorithm, we
compare it against the four evolutionary-based algorithms named Monnier-GA by Monnier et al.
[9], Oh-GA by Oh et al. [10], Yoo-GASA by Yoo et al. [14], and Shokouhifar-ABC by
Shokouhifar et al. [16], in terms of the total tardiness, the computation time, the number of
utilized processors, and average utilization of the utilized processors. All algorithms were
simulated in the same situations in the same datasets.
In order to have an insight into the performance of the mentioned scheduling methodologies,
quantitative results are compared in terms of the total tardiness against the number of utilized
processors (Tables 4 and 6), and the best results achieved with no tardiness (Tables 5 and 7).
Comparison of the obtained results for Benchmark-1 can be seen in Tables 4 and 5. Also, the
results for Benchmark-2 are shown in Tables 6 and 7. Tables 4 and 6 depict comparison of the
total tardiness with the same number of utilized processors used in different algorithms. Also,
comparison of several terms (e.g., the computation time, the number of utilized processors, and
average utilization of the utilized processors) without tardiness can be shown in Tables 5 and 7.
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Table 4. Comparison of the total tardiness for Benchmark-1.
Algorithm
Total number of utilized processors
1 2 3
Monnier-GA [9] 35 19 0
Oh-GA [10] 36 13 0
Yoo-GASA [14] 25 0 0
Shokouhifar-ABC [16] 25 0 0
VNABCSA (Proposed) 25 0 0
Table 5. Comparison of the results with no tardiness for Benchmark-1.
Parameter
Monnier
-GA [9]
Oh-GA
[10]
Yoo-GASA
[14]
Shokouhifar
-ABC [16]
VNABCSA
(Proposed)
Number of utilized processors 3 3 2 2 2
Computation time 44 42 45 45 45
Utilization of processor P1 0.59 0.57 0.51 100 0.51
Utilization of processor P2 0.68 0.59 100 0.51 100
Utilization of processor P3 0.27 0.47 --- --- ---
Average utilization of processors 0.51 0.54 0.75 0.75 0.75
Table 6. Comparison of the total tardiness for Benchmark-2.
Algorithm
Total number of utilized processors
10 12 13 15 16 17
Monnier-GA [9] 78 --- --- 25 13 0
Oh-GA [10] 59 --- --- 19 0 0
Yoo-GASA [14] 22 --- --- 0 0 0
Shokouhifar-ABC [16] 17 9 0 0 0 0
VNABCSA (Proposed) 12 0 0 0 0 0
Table 7. Comparison of the results with no tardiness for Benchmark-2.
Parameter
Monnier
-GA [9]
Oh-GA
[10]
Yoo-GASA
[14]
Shokouhifar
-ABC [16]
VNABCSA
(Proposed)
Number of utilized processors 17 16 15 13 12
Computation time 43 46 47 49 49
Average utilization of processors 0.45 0.47 0.49 0.54 0.56
Results in Tables 4-7 clearly illustrate the positive impact of the proposed VNABCSA scheduling
algorithm. As seen, the total tardiness achieved via the VNABCSA algorithm is smaller than
those of the other scheduling algorithms. Moreover, the number of utilized processors achieved
by our algorithm is fewer than those of the others.
5. CONCLUSION
In this paper, we have proposed a new multi-objective algorithm based on artificial bee colony
and simulated annealing, named VNABCSA algorithm, for the scheduling of soft real-time tasks
in heterogeneous multiprocessor platforms. The objective was considered in such a way that
simultaneously minimize the total tardiness, the total number of utilized processors, the final
completion time, the total waiting time of tasks, and the total waiting time of processors. The
convergence speed of the artificial bee colony and simulated annealing has been improved by
introducing a new hybrid variable neighborhood search strategy. From the simulation results, the
12. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 5, Number 1, March 2016
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results of the proposed VNABCSA algorithm are better than that of the other algorithms. The
number of utilized processors achieved by the VNABCSA is fewer than those of the other
algorithms. Moreover, the variance of processor utilization rate is more desirable. However, the
total computation time achieved by the VNABCSA is a little bit longer than those of the other
algorithms. We plan to introduce other local search mechanisms, in order to improve the accuracy
and convergence speed of the proposed algorithm.
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AUTHORS
Hamideh Kazemi received her B.S. degree from Islamic Azad University of Sirjan,
Iran, in 2010. She received her M.S. degree from Islamic Azad University, Science
and Research Branch, Sirjan, Iran, in 2014. From 2013 till now she is with the
Computer Engineering Department at Nobonyad High Education Institude, Sirjan,
Iran. Her research interests include evolutionary algorithms, neural networks and
visualization of high dimensional datasets.
Zeynab Molay Zahedi received her B.S. degree from Islamic Azad University,
Shiraz, Iran, in 2010. She received her M.S. degree from Islamic Azad University,
Science and Research Branch, Shiraz, Iran, in 2014. Her research interests include
clustering and routing in wireless sensor networks, fuzzy sets and systems, real-time
soft task scheduling in multiprocessor systems, energy efficiency in grid and cloud
computing, and swarm intelligence algorithms.
Mohammad Shokouhifar received his B.S. from Islamic Azad University, Dezfoul,
Iran, in 2008. He received two M.S. degrees from Islamic Azad University of Central
Tehran Branch and Shahid Beheshti University, Tehran, Iran, in 2011 and 2013,
respectively. He is currently a Ph.D. candidate in Electronic Engineering at Shahid
Beheshti University, Tehran, Iran. His research interests include symbolic analysis
and design of analog OTAs, wireless sensor networks, fuzzy sets and systems and
swarm intelligence algorithms.