The Cloud computing becomes an important topic
in the area of high performance distributed computing. On the
other hand, task scheduling is considered one the most significant
issues in the Cloud computing where the user has to pay for the
using resource based on the time. Therefore, distributing the
cloud resource among the users' applications should maximize
resource utilization and minimize task execution Time. The goal
of task scheduling is to assign tasks to appropriate resources that
optimize one or more performance parameters (i.e., completion
time, cost, resource utilization, etc.). In addition, the scheduling
belongs to a category of a problem known as an NP-complete
problem. Therefore, the heuristic algorithm could be applied to
solve this problem. In this paper, an enhanced dependent task
scheduling algorithm based on Genetic Algorithm (DTGA) has
been introduced for mapping and executing an application’s
tasks. The aim of this proposed algorithm is to minimize the
completion time. The performance of this proposed algorithm has
been evaluated using WorkflowSim toolkit and Standard Task
Graph Set (STG) benchmark.
Resource scheduling is a most important functioning area for the cloud manager and challenge as well. It plays very vital role to maintain the scalability in the cloud resources and ‘on demand’ availability of cloud. The challenges arise because the Cloud Service Provider (CSP) has to pretend to have infinite resource while he has limited amount of resource. Resource allocation in cloud computing means managing resources in such a way that every demand (task) must be fulfilled along with considering the parameter like throughput, cost, make span, availability, utilization of resource, time and reliability. The Modified Resource Allocation Mutation PSO (MRAMPSO) strategy based on the resource scheduling and allocation of cloud is proposed. In this paper MRAMPSO schedules the task with help of Extended Multi Queue (EMQ) by considering the resource availability and reschedule the task that fails to allocate. This approach is compared with slandered PSO and Longest Cloudlet to Fastest Processor (LCFP) algorithm to show that MRAMPSO can save execution time, makes span, transmission cost and round trip time.
Sharing of cluster resources among multiple Workflow Applicationsijcsit
Many computational solutions can be expressed as workflows. A Cluster of processors is a shared
resource among several users and hence the need for a scheduler which deals with multi-user jobs
presented as workflows. The scheduler must find the number of processors to be allotted for each workflow
and schedule tasks on allotted processors. In this work, a new method to find optimal and maximum
number of processors that can be allotted for a workflow is proposed. Regression analysis is used to find
the best possible way to share available processors, among suitable number of submitted workflows. An
instance of a scheduler is created for each workflow, which schedules tasks on the allotted processors.
Towards this end, a new framework to receive online submission of workflows, to allot processors to each
workflow and schedule tasks, is proposed and experimented using a discrete-event based simulator. This
space-sharing of processors among multiple workflows shows better performance than the other methods
found in literature. Because of space-sharing, an instance of a scheduler must be used for each workflow
within the allotted processors. Since the number of processors for each workflow is known only during
runtime, a static schedule can not be used. Hence a hybrid scheduler which tries to combine the advantages
of static and dynamic scheduler is proposed. Thus the proposed framework is a promising solution to
multiple workflows scheduling on cluster.
A novel scheduling algorithm for cloud computing environmentSouvik Pal
Cloud computing is the most recent computing paradigm, in the
Information Technology where the resources and information are provided
on-demand and accessed over the Internet. An essential factor in the cloud computing
system is Task Scheduling that relates to the efficiency of the entire cloud
computing environment. Mostly in a cloud environment, the issue of scheduling is
to apportion the tasks of the requesting users to the available resources. This paper
aims to offer a genetic based scheduling algorithm that reduces the waiting time of
the overall system. However the tasks enter the cloud environment and the users
have to wait until the resources are available that leads to more queue length and
increased waiting time. This paper introduces a Task Scheduling algorithm based
on genetic algorithm using a queuing model to minimize the waiting time and
queue length of the system.
The Cloud computing becomes an important topic
in the area of high performance distributed computing. On the
other hand, task scheduling is considered one the most significant
issues in the Cloud computing where the user has to pay for the
using resource based on the time. Therefore, distributing the
cloud resource among the users' applications should maximize
resource utilization and minimize task execution Time. The goal
of task scheduling is to assign tasks to appropriate resources that
optimize one or more performance parameters (i.e., completion
time, cost, resource utilization, etc.). In addition, the scheduling
belongs to a category of a problem known as an NP-complete
problem. Therefore, the heuristic algorithm could be applied to
solve this problem. In this paper, an enhanced dependent task
scheduling algorithm based on Genetic Algorithm (DTGA) has
been introduced for mapping and executing an application’s
tasks. The aim of this proposed algorithm is to minimize the
completion time. The performance of this proposed algorithm has
been evaluated using WorkflowSim toolkit and Standard Task
Graph Set (STG) benchmark.
Resource scheduling is a most important functioning area for the cloud manager and challenge as well. It plays very vital role to maintain the scalability in the cloud resources and ‘on demand’ availability of cloud. The challenges arise because the Cloud Service Provider (CSP) has to pretend to have infinite resource while he has limited amount of resource. Resource allocation in cloud computing means managing resources in such a way that every demand (task) must be fulfilled along with considering the parameter like throughput, cost, make span, availability, utilization of resource, time and reliability. The Modified Resource Allocation Mutation PSO (MRAMPSO) strategy based on the resource scheduling and allocation of cloud is proposed. In this paper MRAMPSO schedules the task with help of Extended Multi Queue (EMQ) by considering the resource availability and reschedule the task that fails to allocate. This approach is compared with slandered PSO and Longest Cloudlet to Fastest Processor (LCFP) algorithm to show that MRAMPSO can save execution time, makes span, transmission cost and round trip time.
Sharing of cluster resources among multiple Workflow Applicationsijcsit
Many computational solutions can be expressed as workflows. A Cluster of processors is a shared
resource among several users and hence the need for a scheduler which deals with multi-user jobs
presented as workflows. The scheduler must find the number of processors to be allotted for each workflow
and schedule tasks on allotted processors. In this work, a new method to find optimal and maximum
number of processors that can be allotted for a workflow is proposed. Regression analysis is used to find
the best possible way to share available processors, among suitable number of submitted workflows. An
instance of a scheduler is created for each workflow, which schedules tasks on the allotted processors.
Towards this end, a new framework to receive online submission of workflows, to allot processors to each
workflow and schedule tasks, is proposed and experimented using a discrete-event based simulator. This
space-sharing of processors among multiple workflows shows better performance than the other methods
found in literature. Because of space-sharing, an instance of a scheduler must be used for each workflow
within the allotted processors. Since the number of processors for each workflow is known only during
runtime, a static schedule can not be used. Hence a hybrid scheduler which tries to combine the advantages
of static and dynamic scheduler is proposed. Thus the proposed framework is a promising solution to
multiple workflows scheduling on cluster.
A novel scheduling algorithm for cloud computing environmentSouvik Pal
Cloud computing is the most recent computing paradigm, in the
Information Technology where the resources and information are provided
on-demand and accessed over the Internet. An essential factor in the cloud computing
system is Task Scheduling that relates to the efficiency of the entire cloud
computing environment. Mostly in a cloud environment, the issue of scheduling is
to apportion the tasks of the requesting users to the available resources. This paper
aims to offer a genetic based scheduling algorithm that reduces the waiting time of
the overall system. However the tasks enter the cloud environment and the users
have to wait until the resources are available that leads to more queue length and
increased waiting time. This paper introduces a Task Scheduling algorithm based
on genetic algorithm using a queuing model to minimize the waiting time and
queue length of the system.
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.
OPTIMIZED RESOURCE PROVISIONING METHOD FOR COMPUTATIONAL GRID ijgca
Grid computing is an accumulation of heterogeneous, dynamic resources from multiple administrative areas which are geographically distributed that can be utilized to reach a mutual end. Development of resource provisioning-based scheduling in large-scale distributed environments like grid computing brings in new requirement challenges that are not being believed in traditional distributed computing environments. Computational grid is applying the resources of many systems in a network to a single problem at the same time. Grid scheduling is the method by which work specified by some means is assigned to the resources that complete the work in the environment which cannot fulfill the user requirements considerably. The satisfaction of users while providing the resources might increase the beneficiary level of resource suppliers. Resource scheduling has to satisfy the multiple constraints specified by the user. The option of resource with the satisfaction of multiple constraints is the most tedious process. This trouble is solved by bringing out the particle swarm optimization based heuristic scheduling algorithm which attempts to select the most suitable resource from the set of available resources. The primary parameters that are taken in this work for selecting the most suitable resource are the makespan and cost. The experimental result shows that the proposed method yields optimal scheduling with the atonement of all user requirements.
Many computational solutions can be expressed as Di
rected Acyclic Graph (DAG), in which
nodes represent tasks to be executed and edges repr
esent precedence constraints among tasks.
A Cluster of processors is a shared resource among
several users and hence the need for a
scheduler which deals with multi-user jobs presente
d as DAGs. The scheduler must find the
number of processors to be allotted for each DAG an
d schedule tasks on allotted processors. In
this work, a new method to find optimal and maximum
number of processors that can be allotted
for a DAG is proposed. Regression analysis is used
to find the best possible way to share
available processors, among suitable number of subm
itted DAGs. An instance of a scheduler
for each DAG, schedules tasks on the allotted proce
ssors. Towards this end, a new framework
to receive online submission of DAGs, allot process
ors to each DAG and schedule tasks, is
proposed and experimented using a simulator. This s
pace-sharing of processors among multiple
DAGs shows better performance than the other method
s found in literature. Because of space-
sharing, an online scheduler can be used for each D
AG within the allotted processors. The use
of online scheduler overcomes the drawbacks of stat
ic scheduling which relies on inaccurate
estimated computation and communication costs. Thus
the proposed framework is a promising
solution to perform online scheduling of tasks usin
g static information of DAG, a kind of hybrid
scheduling
.
Task Scheduling using Hybrid Algorithm in Cloud Computing Environmentsiosrjce
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.
An enhanced adaptive scoring job scheduling algorithm with replication strate...eSAT Publishing House
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
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
Grid computing enlarge with computing platform which is collection of heterogeneous computing resources connected by a network across dynamic and geographically dispersed organization to form a distributed high performance computing infrastructure. Grid computing solves the complex computing problems amongst multiple machines. Grid computing solves the large scale computational demands in a high performance computing environment. The main emphasis in the grid computing is given to the resource management and the job scheduler .The goal of the job scheduler is to maximize the resource utilization and minimize the processing time of the jobs. Existing approaches of Grid scheduling doesn’t give much emphasis on the performance of a Grid scheduler in processing time parameter. Schedulers allocate resources to the jobs to be executed using the First come First serve algorithm. In this paper, we have provided an optimize algorithm to queue of the scheduler using various scheduling methods like Shortest Job First, First in First out, Round robin. The job scheduling system is responsible to select best suitable machines in a grid for user jobs. The management and scheduling system generates job schedules for each machine in the grid by taking static restrictions and dynamic parameters of jobs and machines into consideration. The main purpose of this paper is to develop an efficient job scheduling algorithm to maximize the resource utilization and minimize processing time of the jobs. Queues can be optimized by using various scheduling algorithms depending upon the performance criteria to be improved e.g. response time, throughput. The work has been done in MATLAB using the parallel computing toolbox.
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.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
Cloud computing is an incipient and quickly evolving model, with new expenses and capabilities being proclaimed frequently. The increases of user on cloud with the expansion of variety of services, with that the complete allocation of resource with the minimum latent time for Virtual machine is necessary. To allocate this virtual cloud computing resources to the cloud user is a key technical issue because user demand is dynamic in nature that required dynamic allocation of resource too. To improve the allocation there must be a correct balanced algorithmic scheduling for Resource Allocation Technique. The aim of this work is to allocate resource to scientific experiment request coming from multiple users, wherever customized Virtual machines (VM) are aloft in applicable host out there in cloud. Therefore, properly programmed scheduling cloud is extremely vital and it’s significant to develop efficient scheduling methods for appropriately allocation of VMs into physical resource. The planned formulas minimize the time interval quality so as of O (Log n) by adopting KD-Tree.
A HYPER-HEURISTIC METHOD FOR SCHEDULING THEJOBS IN CLOUD ENVIRONMENTieijjournal
Currently cloud computing has turned into a promising technology and has become a great key for satisfying a flexible service oriented , online provision and storage of computing resources and user’s information in lesser expense with dynamism framework on pay per use basis.In this technology Job Scheduling Problem is acritical issue. For well-organizedmanaging and handling resources, administrations, scheduling plays a vital role. This paper shares out the improved Hyper- Heuristic Scheduling Approach to schedule resources, by taking account of computation time and makespan with two detection operators. Operators are used to select the low-level heuristics automatically. Conditional
Revealing Algorithm (CRA)idea is applied for finding the job failures while allocating the resources. We believe that proposed hyper-heuristic achieve better results than other individual heuristics.
A hybrid approach for scheduling applications in cloud computing environment IJECEIAES
Cloud computing plays an important role in our daily life. It has direct and positive impact on share and update data, knowledge, storage and scientific resources between various regions. Cloud computing performance heavily based on job scheduling algorithms that are utilized for queue waiting in modern scientific applications. The researchers are considered cloud computing a popular platform for new enforcements. These scheduling algorithms help in design efficient queue lists in cloud as well as they play vital role in reducing waiting for processing time in cloud computing. A novel job scheduling is proposed in this paper to enhance performance of cloud computing and reduce delay time in queue waiting for jobs. The proposed algorithm tries to avoid some significant challenges that throttle from developing applications of cloud computing. However, a smart scheduling technique is proposed in our paper to improve performance processing in cloud applications. Our experimental result of the proposed job scheduling algorithm shows that the proposed schemes possess outstanding enhancing rates with a reduction in waiting time for jobs in queue list.
Optimization of energy consumption in cloud computing datacenters IJECEIAES
Cloud computing has emerged as a practical paradigm for providing IT resources, infrastructure and services. This has led to the establishment of datacenters that have substantial energy demands for their operation. This work investigates the optimization of energy consumption in cloud datacenter using energy efficient allocation of tasks to resources. The work seeks to develop formal optimization models that minimize the energy consumption of computational resources and evaluates the use of existing optimization solvers in testing these models. Integer linear programming (ILP) techniques are used to model the scheduling problem. The objective is to minimize the total power consumed by the active and idle cores of the servers’ CPUs while meeting a set of constraints. Next, we use these models to carry out a detailed performance comparison between a selected set of Generic ILP and 0-1 Boolean satisfiability based solvers in solving the ILP formulations. Simulation results indicate that in some cases the developed models have saved up to 38% in energy consumption when compared to common techniques such as round robin. Furthermore, results also showed that generic ILP solvers had superior performance when compared to SAT-based ILP solvers especially as the number of tasks and resources grow in size.
A optimized process for the synthesis of a key starting material for etodolac...IOSR Journals
Abstract An optimized process developed for the synthesis of 7-ethyltryptophol, a key starting material for etodolac, a non steroidal anti- inflammatory drug. Starting from commercially available 2-ethylphenylhydrazine. HCl and dihydro furan with con. H2SO4 as a catalyst in N, N- dimethyl acetamide ( DMAc). H2O (1:1) as a solvent in 75% yield . the method is easy, inexpensive , without purification getting pure solid. The process is very clean, high yielding & high quality and operationally simple.
Keywords: Etodolac, 7-ethyl tryptophol, 2-ethyl phenyl hydrazine hydrochloride, N,N-dimethyl acetamide.
Corporate Governance, Firm Size, and Earning Management: Evidence in Indonesi...IOSR Journals
Purpose –Thepurpose of this paper is to evaluate the impact of the corporate governance regulationsimplementation and firm size onthe earning management for food and beverages companies in Indonesian Stock Exchange. Design/methodology/approach –The multiple regression is utilized to test this relationship at 95% confidence.Corporate governance was proxied by board of director, audit quality, and board independence. Firm size was represented by natural logarithm of total assets. Earning management was measured by Jones model withdiscretionary accruals. Findings – Using data from the year 2005 annual reports of 51 food and beverages listed companies,including the composite index, the results showed that twoof the corporate governance variables, namely board of director and audit quality, as well as firm size are statistically significant in explaining earning management measured bydiscretionary accruals. Research limitations/implications – The regulations on corporate governance were implementedin 2005, but not all of food and beverages listed companies implemented the regulations in 2005. Practical implications – An implication of this finding is that regulatory efforts initiated after the1997 financial crisis to enhance corporate transparency and accountability did not appear to result on better corporate performance. Originality/value – This is one of the few studies which investigates the impact of regulatory actionson corporate governance on earning management immediately after its implementation.
Antibacterial Application of Novel Mixed-Ligand Dithiocarbamate Complexes of ...IOSR Journals
Nine stable mixed ligand dithiocarbamate complexes of Nickel (II) ion were prepared. The complexes were characterized with electronic spectroscopy, infrared spectroscopy, conductance measurement, melting point and percentage metal analysis. Resulting analytical data gave credence to the assignment of a tentative square planar geometry to all the complexes. The complexes were proposed to have a general formulae of [Ni(Sal)(Rdtc)], where Sal = salicylaldehyde; R = dibenzylamine(Bz2NH), methylphenylamine(MePhNH),pyrrolidineamine(pyrrolNH),piperidineamine(piperNH),morpholineamine(MorpNH), anilineamine(AnilNH), para-chloroanilineamine(p-ClAnilNH), toludineamine(TolNH) and anisidineamine(AnisNH); and dtc = dithiocarbamate anion. The metal complexes were screened against six different bacteria strain using Agar diffusion method. The antibacterial studies reveal that the metal complexes exhibit broad spectrum antibacterial activity against Escherichia coli, Staphylococcus aureus, Klebsiella oxytoca and Pseudomonas aureginosa with inhibitory range of 10.5.—20.0mm.
Impact of Frequency Offset on Interference between Zigbee and Wifi for Smart ...IOSR Journals
Abstract: The Zigbee is a low cost communication technology used for low data rate communication system such as industrial automation etc. Because of its low complexity it is widely adopted for many applications. But the utilization of the same spectrum band by the WLAN system causes interference between both the systems. The proposed approach presents an analysis of this interference effect on Zigbee system when operated with WLAN sources at different distances and different power. It also analyzes the effect when a frequency offset is established between both systems. The simulation results shows that a small offset can provide sufficient improvement in the performance. Keywords: Smart Grids, Zigbee Network, Mesh Network, Wireless LAN (WLAN), BER
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.
OPTIMIZED RESOURCE PROVISIONING METHOD FOR COMPUTATIONAL GRID ijgca
Grid computing is an accumulation of heterogeneous, dynamic resources from multiple administrative areas which are geographically distributed that can be utilized to reach a mutual end. Development of resource provisioning-based scheduling in large-scale distributed environments like grid computing brings in new requirement challenges that are not being believed in traditional distributed computing environments. Computational grid is applying the resources of many systems in a network to a single problem at the same time. Grid scheduling is the method by which work specified by some means is assigned to the resources that complete the work in the environment which cannot fulfill the user requirements considerably. The satisfaction of users while providing the resources might increase the beneficiary level of resource suppliers. Resource scheduling has to satisfy the multiple constraints specified by the user. The option of resource with the satisfaction of multiple constraints is the most tedious process. This trouble is solved by bringing out the particle swarm optimization based heuristic scheduling algorithm which attempts to select the most suitable resource from the set of available resources. The primary parameters that are taken in this work for selecting the most suitable resource are the makespan and cost. The experimental result shows that the proposed method yields optimal scheduling with the atonement of all user requirements.
Many computational solutions can be expressed as Di
rected Acyclic Graph (DAG), in which
nodes represent tasks to be executed and edges repr
esent precedence constraints among tasks.
A Cluster of processors is a shared resource among
several users and hence the need for a
scheduler which deals with multi-user jobs presente
d as DAGs. The scheduler must find the
number of processors to be allotted for each DAG an
d schedule tasks on allotted processors. In
this work, a new method to find optimal and maximum
number of processors that can be allotted
for a DAG is proposed. Regression analysis is used
to find the best possible way to share
available processors, among suitable number of subm
itted DAGs. An instance of a scheduler
for each DAG, schedules tasks on the allotted proce
ssors. Towards this end, a new framework
to receive online submission of DAGs, allot process
ors to each DAG and schedule tasks, is
proposed and experimented using a simulator. This s
pace-sharing of processors among multiple
DAGs shows better performance than the other method
s found in literature. Because of space-
sharing, an online scheduler can be used for each D
AG within the allotted processors. The use
of online scheduler overcomes the drawbacks of stat
ic scheduling which relies on inaccurate
estimated computation and communication costs. Thus
the proposed framework is a promising
solution to perform online scheduling of tasks usin
g static information of DAG, a kind of hybrid
scheduling
.
Task Scheduling using Hybrid Algorithm in Cloud Computing Environmentsiosrjce
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.
An enhanced adaptive scoring job scheduling algorithm with replication strate...eSAT Publishing House
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
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
Grid computing enlarge with computing platform which is collection of heterogeneous computing resources connected by a network across dynamic and geographically dispersed organization to form a distributed high performance computing infrastructure. Grid computing solves the complex computing problems amongst multiple machines. Grid computing solves the large scale computational demands in a high performance computing environment. The main emphasis in the grid computing is given to the resource management and the job scheduler .The goal of the job scheduler is to maximize the resource utilization and minimize the processing time of the jobs. Existing approaches of Grid scheduling doesn’t give much emphasis on the performance of a Grid scheduler in processing time parameter. Schedulers allocate resources to the jobs to be executed using the First come First serve algorithm. In this paper, we have provided an optimize algorithm to queue of the scheduler using various scheduling methods like Shortest Job First, First in First out, Round robin. The job scheduling system is responsible to select best suitable machines in a grid for user jobs. The management and scheduling system generates job schedules for each machine in the grid by taking static restrictions and dynamic parameters of jobs and machines into consideration. The main purpose of this paper is to develop an efficient job scheduling algorithm to maximize the resource utilization and minimize processing time of the jobs. Queues can be optimized by using various scheduling algorithms depending upon the performance criteria to be improved e.g. response time, throughput. The work has been done in MATLAB using the parallel computing toolbox.
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.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
Cloud computing is an incipient and quickly evolving model, with new expenses and capabilities being proclaimed frequently. The increases of user on cloud with the expansion of variety of services, with that the complete allocation of resource with the minimum latent time for Virtual machine is necessary. To allocate this virtual cloud computing resources to the cloud user is a key technical issue because user demand is dynamic in nature that required dynamic allocation of resource too. To improve the allocation there must be a correct balanced algorithmic scheduling for Resource Allocation Technique. The aim of this work is to allocate resource to scientific experiment request coming from multiple users, wherever customized Virtual machines (VM) are aloft in applicable host out there in cloud. Therefore, properly programmed scheduling cloud is extremely vital and it’s significant to develop efficient scheduling methods for appropriately allocation of VMs into physical resource. The planned formulas minimize the time interval quality so as of O (Log n) by adopting KD-Tree.
A HYPER-HEURISTIC METHOD FOR SCHEDULING THEJOBS IN CLOUD ENVIRONMENTieijjournal
Currently cloud computing has turned into a promising technology and has become a great key for satisfying a flexible service oriented , online provision and storage of computing resources and user’s information in lesser expense with dynamism framework on pay per use basis.In this technology Job Scheduling Problem is acritical issue. For well-organizedmanaging and handling resources, administrations, scheduling plays a vital role. This paper shares out the improved Hyper- Heuristic Scheduling Approach to schedule resources, by taking account of computation time and makespan with two detection operators. Operators are used to select the low-level heuristics automatically. Conditional
Revealing Algorithm (CRA)idea is applied for finding the job failures while allocating the resources. We believe that proposed hyper-heuristic achieve better results than other individual heuristics.
A hybrid approach for scheduling applications in cloud computing environment IJECEIAES
Cloud computing plays an important role in our daily life. It has direct and positive impact on share and update data, knowledge, storage and scientific resources between various regions. Cloud computing performance heavily based on job scheduling algorithms that are utilized for queue waiting in modern scientific applications. The researchers are considered cloud computing a popular platform for new enforcements. These scheduling algorithms help in design efficient queue lists in cloud as well as they play vital role in reducing waiting for processing time in cloud computing. A novel job scheduling is proposed in this paper to enhance performance of cloud computing and reduce delay time in queue waiting for jobs. The proposed algorithm tries to avoid some significant challenges that throttle from developing applications of cloud computing. However, a smart scheduling technique is proposed in our paper to improve performance processing in cloud applications. Our experimental result of the proposed job scheduling algorithm shows that the proposed schemes possess outstanding enhancing rates with a reduction in waiting time for jobs in queue list.
Optimization of energy consumption in cloud computing datacenters IJECEIAES
Cloud computing has emerged as a practical paradigm for providing IT resources, infrastructure and services. This has led to the establishment of datacenters that have substantial energy demands for their operation. This work investigates the optimization of energy consumption in cloud datacenter using energy efficient allocation of tasks to resources. The work seeks to develop formal optimization models that minimize the energy consumption of computational resources and evaluates the use of existing optimization solvers in testing these models. Integer linear programming (ILP) techniques are used to model the scheduling problem. The objective is to minimize the total power consumed by the active and idle cores of the servers’ CPUs while meeting a set of constraints. Next, we use these models to carry out a detailed performance comparison between a selected set of Generic ILP and 0-1 Boolean satisfiability based solvers in solving the ILP formulations. Simulation results indicate that in some cases the developed models have saved up to 38% in energy consumption when compared to common techniques such as round robin. Furthermore, results also showed that generic ILP solvers had superior performance when compared to SAT-based ILP solvers especially as the number of tasks and resources grow in size.
A optimized process for the synthesis of a key starting material for etodolac...IOSR Journals
Abstract An optimized process developed for the synthesis of 7-ethyltryptophol, a key starting material for etodolac, a non steroidal anti- inflammatory drug. Starting from commercially available 2-ethylphenylhydrazine. HCl and dihydro furan with con. H2SO4 as a catalyst in N, N- dimethyl acetamide ( DMAc). H2O (1:1) as a solvent in 75% yield . the method is easy, inexpensive , without purification getting pure solid. The process is very clean, high yielding & high quality and operationally simple.
Keywords: Etodolac, 7-ethyl tryptophol, 2-ethyl phenyl hydrazine hydrochloride, N,N-dimethyl acetamide.
Corporate Governance, Firm Size, and Earning Management: Evidence in Indonesi...IOSR Journals
Purpose –Thepurpose of this paper is to evaluate the impact of the corporate governance regulationsimplementation and firm size onthe earning management for food and beverages companies in Indonesian Stock Exchange. Design/methodology/approach –The multiple regression is utilized to test this relationship at 95% confidence.Corporate governance was proxied by board of director, audit quality, and board independence. Firm size was represented by natural logarithm of total assets. Earning management was measured by Jones model withdiscretionary accruals. Findings – Using data from the year 2005 annual reports of 51 food and beverages listed companies,including the composite index, the results showed that twoof the corporate governance variables, namely board of director and audit quality, as well as firm size are statistically significant in explaining earning management measured bydiscretionary accruals. Research limitations/implications – The regulations on corporate governance were implementedin 2005, but not all of food and beverages listed companies implemented the regulations in 2005. Practical implications – An implication of this finding is that regulatory efforts initiated after the1997 financial crisis to enhance corporate transparency and accountability did not appear to result on better corporate performance. Originality/value – This is one of the few studies which investigates the impact of regulatory actionson corporate governance on earning management immediately after its implementation.
Antibacterial Application of Novel Mixed-Ligand Dithiocarbamate Complexes of ...IOSR Journals
Nine stable mixed ligand dithiocarbamate complexes of Nickel (II) ion were prepared. The complexes were characterized with electronic spectroscopy, infrared spectroscopy, conductance measurement, melting point and percentage metal analysis. Resulting analytical data gave credence to the assignment of a tentative square planar geometry to all the complexes. The complexes were proposed to have a general formulae of [Ni(Sal)(Rdtc)], where Sal = salicylaldehyde; R = dibenzylamine(Bz2NH), methylphenylamine(MePhNH),pyrrolidineamine(pyrrolNH),piperidineamine(piperNH),morpholineamine(MorpNH), anilineamine(AnilNH), para-chloroanilineamine(p-ClAnilNH), toludineamine(TolNH) and anisidineamine(AnisNH); and dtc = dithiocarbamate anion. The metal complexes were screened against six different bacteria strain using Agar diffusion method. The antibacterial studies reveal that the metal complexes exhibit broad spectrum antibacterial activity against Escherichia coli, Staphylococcus aureus, Klebsiella oxytoca and Pseudomonas aureginosa with inhibitory range of 10.5.—20.0mm.
Impact of Frequency Offset on Interference between Zigbee and Wifi for Smart ...IOSR Journals
Abstract: The Zigbee is a low cost communication technology used for low data rate communication system such as industrial automation etc. Because of its low complexity it is widely adopted for many applications. But the utilization of the same spectrum band by the WLAN system causes interference between both the systems. The proposed approach presents an analysis of this interference effect on Zigbee system when operated with WLAN sources at different distances and different power. It also analyzes the effect when a frequency offset is established between both systems. The simulation results shows that a small offset can provide sufficient improvement in the performance. Keywords: Smart Grids, Zigbee Network, Mesh Network, Wireless LAN (WLAN), BER
Performance Evaluation of IEEE STD 802.16d TransceiverIOSR Journals
WiMAX ("Worldwide Interoperability for Microwave Access") technology is developed to meet the
growing demand of increased data rate and accessing the internet at high speeds. 802.16 family of standards is
officially called Wireless MAN in IEEE. Orthogonal frequency division multiplexing (OFDM) is multicarrier
modulation technique used in IEEE 802.16d (fixed WiMAX) communication standard. OFDM is used to
increase data rate of wireless medium with higher spectral efficiency. The proposed work is to evaluate
performance of IEEE Std 802.16d transceiver in MATLAB R2009b simulink environment. System performance
evaluated using BER vs SNR for different modulation technique such as 4 QAM, 16 QAM, 64 QAM under
different channel condition
A Comparative Study on Balance and Flexibility between Dancer and Non-Dancer ...IOSR Journals
Abstract: Dance is a form of art that normally involves rhythmic movement of the body and accompanied with
music. Movement of human body while performing dance can become a significant medium for communication,
feelings and emotions. It embraces movement, creation and performance. Dance helps to extend the limits of
human physical ability, expressiveness and spirit. When it comes to health dance can be a very effective way of
establishing a lasting healthy living. Anecdotally it can be said that dance potentially motivate and excite young
people. Dance is a non-competitive form of exercise which has positive effects on physical and mental health.
Young girls can be engaged in physical activity through dance. The author being a dancer in fervor and passion
as well as an aspirant of the profession Physical Education strived to conduct the study bearing the title “A
Comparative Study on Balance and Flexibility between Dancer and Non-Dancer Girls”. The researcher
selected 30 girls who are regularly involved in Dance and 30 girls who are non-dancer or rather sedentary on
the basis of purposive stratified random sampling from Bidhan Govt. Girl’s School, Dist. Nadia West Bengal as
the subjects of her study. She incorporated Sit and Reach test and Stork Stand Balance tests for assessment of
Flexibility and Balance respectively. With respect to data analysis initially descriptive statics like mean SD and
range and further paired sample T test was conducted to ascertain the degree of difference between the means
with the help of SPSS soft ware. Data analysis proved significant difference between the Dancer and NonDancer
girls both with respect to flexibility and Balance. In both the cases the Dancer girls proved to be better
though the differences were not statistically significant. Thus the author arrived at the conclusion that dance
involving passion, strength, stamina, enthusiasm, rhythm, amusement and many more could be a wonderful
fitness activity similar to other fitness activities like jogging, running, cycling, swimming etc.
Key words: Dance, Flexibility, Balance, Dancer, Non-Dancer.
Wear Analysis of Polytetrafluoroethylene (PTFE) and it’s Composites under Wet...IOSR Journals
In this paper, the effect of load, Velocity of sliding and sliding distance on friction and wear of
materials made of Polytetrafluoroethylele (PTFE) and PTFE composites under wet condition with filler
materials such as 25% bronze, 25% glass fiber and 25 % carbon have studied. The experimental work has
performed on pin-on-disc friction and wear test rig and analyzed with the help of Design Expert software. The
results of experiments are presented in tables and graphs which shows that the addition of bronze, glass and
carbon filler to the virgin PTFE decreases wear rate significantly and there is marginal increase in coefficient
of friction. The highest wear resistance was found for 25% carbon filled PTFE followed by 25% glass filled
PTFE, 25% bronze filled PTFE and virgin PTFE. Through this study, we can develop the best bearing material
for the various industrial applications which is available easily at the minimum cost.
Gc-Ms Analysis and Antimicrobial Activity of Essential Oil of Senecio Peduncu...IOSR Journals
The chemical composition of the essential oil obtained from the leaves of Senecio pedunculatus collected from the Kumaon region of Uttarakhand, was analyzed by GC-MS. The major constituent was found out to be caryophyllene oxide (23.28%). The antibacterial and antifungal activity of the oil was determined by disc diffusion method. Results showed that the oil exhibited mild antimicrobial activity.
Development Of Public Administration Program Development System in Rural Serv...IOSR Journals
This study aims to, knowing what aspects can be developed to increase the service capacity of village government, knowing the role of village and community in carrying out the functions and enhanced customer service and public administration, the factors that affect the improvement of rural public administration system to improve service capacity of village government, get a picture of the service capacity building and development of public administration system at the level of village government. The target to be achieved is to increase public administration system in the country so as to improve the capacity of government services to the rural community.From the study of theory, analysis and discussion on the findings of the field, it was found that the embodiment of the village administration, particularly on the object of research is still not optimal. Not optimal realization of the village administration, mainly reflected in: Still unclear performance standards that can be measured to determine the quality of the resulting output.
Field Programmable Gate Array for Data Processing in Medical SystemsIOSR Journals
Abstract: Two- dimensional &Three–dimensional (3-D) image segmentation is on of the most demanding tasks in image processing. It has been proven that only the 14-neighborship of a rhombic dodecahedron can satisfy the aforementioned requirements. The 3-D-GSC process is executed in the following three phases ,coding phases,linking phases,splitting phases. An FPGA-based digital signal processing board optimized for applications needing large memory with high bandwidth has been developed and successfully used for the parallelization of a modern image segmentation algorithm for medical and industrial real-time applications.The Use of this 128-bit coprocessor board is not limited to image segmentation.We propose the perfectly parallelizable 3-D Gray-Value Structure Code (3-D-GSC) for image segmentation on a new FPGA custom machine. This 128-Bit FPGA coprocessing board features an up-to-date Virtex-II Pro architecture, two large independent DDR-SDRAM channels, two fast independent ZBT-SRAM channels, and PCI-X bus and CameraLink interfaces. Key words: Field Programmable Gate Array, Segme-ntation, Vogel bruch, Gray-valve structure code ,Homogeneous, Linking phase, Coding phase, Splitting phase, SDRAM.
DYNAMIC TASK SCHEDULING BASED ON BURST TIME REQUIREMENT FOR CLOUD ENVIRONMENTIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
Dynamic Task Scheduling based on Burst Time Requirement for Cloud EnvironmentIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
Multi-objective tasks scheduling using bee colony algorithm in cloud computingIJECEIAES
Due to the development of communication device technology and the need to use up-to-date infrastructure ready to respond quickly and in a timely manner to computational needs, the competition for the use of processing resources is increasing nowadays. The scheduling tasks in the cloud computing environment have been remained a challenge to access a quick and efficient solution. In this paper, the aim is to present a new tactic for allocating the available processing resources based on the artificial bee colony (ABC) algorithm and cellular automata for solving the task scheduling problem in the cloud computing network. The results show the performance of the proposed method is better than its counterparts.
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.
RSDC (Reliable Scheduling Distributed in Cloud Computing)IJCSEA Journal
In this paper we will present a reliable scheduling algorithm in cloud computing environment. In this algorithm we create a new algorithm by means of a new technique and with classification and considering request and acknowledge time of jobs in a qualification function. By evaluating the previous algorithms, we understand that the scheduling jobs have been performed by parameters that are associated with a failure rate. Therefore in the roposed algorithm, in addition to previous parameters, some other important parameters are used so we can gain the jobs with different scheduling based on these parameters. This work is associated with a mechanism. The major job is divided to sub jobs. In order to balance the jobs we should calculate the request and acknowledge time separately. Then we create the scheduling of each job by calculating the request and acknowledge time in the form of a shared job. Finally efficiency of the system is increased. So the real time of this algorithm will be improved in comparison with the other algorithms. Finally by the mechanism presented, the total time of processing in cloud computing is improved in comparison with the other algorithms.
A cloud computing scheduling and its evolutionary approachesnooriasukmaningtyas
Despite the increasing use of cloud computing technology because it offers
unique features to serve its customers perfectly, exploiting the full potential
is very difficult due to the many problems and challenges. Therefore,
scheduling resources are one of these challenges. Researchers are still finding
it difficult to determine which of the scheduling algorithms are appropriate
and effective and that helps increases the performance of the system to
accomplish these tasks. This paper provides a broad and detailed examination
of resource scheduling algorithms in the environment of a cloud computing
environment and highlights the advantages and disadvantages of some
algorithms to help researchers in selecting the best algorithms to schedule a
particular workload to get a satisfy a quality of service, guarantee good
utilization of the cloud resources also minimizing the make-span.
Effective and Efficient Job Scheduling in Grid ComputingAditya Kokadwar
The integration of remote and diverse resources and the increasing computational needs of Grand Challenges problems combined with the faster growth of the internet and communication technologies leads to the development of global computational grids. Grid computing is a prevailing technology, which unites underutilized resources in order to support sharing of resources and services distributed across numerous administrative region. An efficient and effective scheduling system is essentially required in order to achieve the promising capacity of grids. The main goal of scheduling is to maximize the resource utilization and minimize processing time and cost of the jobs. In this research, the objective is to prioritize the jobs based on execution cost and then allocate the resources with minimum cost by merging it with conventional job grouping strategy to provide the solution for better and more efficient job scheduling which is beneficial to both user and resource broker. The proposed scheduling approach in grid computing employs a dynamic cost-based job scheduling algorithm for making an efficient mapping of a job to available resources in the grid. It also improves communication to computation ratio (CCR) and utilization of available resources by grouping the user jobs before resource allocation.
Cost-Efficient Task Scheduling with Ant Colony Algorithm for Executing Large ...Editor IJCATR
The aim of cloud computing is to share a large number of resources and pieces of equipment to compute and store knowledge and information for great scientific sources. Therefore, the scheduling algorithm is regarded as one of the most important challenges and problems in the cloud. To solve the task scheduling problem in this study, the ant colony optimization (ACO) algorithm was adapted from social theories with a fair and accurate resource allocation approach based on machine performance and capacity. This study was intended to decrease the runtime and executive costs. It was also meant to optimize the use of machines and reduce their idle time. Finally, the proposed method was compared with Berger and greedy algorithms. The simulation results indicate that the proposed algorithm reduced the makespan and executive cost when tasks were added. It also increased fairness and load balancing. Moreover, it made the optimal use of machines possible and increased user satisfaction. According to evaluations, the proposed algorithm improved the makespan by 80%.
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.
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
Grid computing enlarge with computing platform which is collection of heterogeneous computing resources connected by a network across dynamic and geographically dispersed organization to form a distributed high performance computing infrastructure. Grid computing solves the complex computing
problems amongst multiple machines. Grid computing solves the large scale computational demands in a high performance computing environment. The main emphasis in the grid computing is given to the resource management and the job scheduler .The goal of the job scheduler is to maximize the resource utilization and minimize the processing time of the jobs. Existing approaches of Grid scheduling doesn’t give much emphasis on the performance of a Grid scheduler in processing time parameter. Schedulers allocate resources to the jobs to be executed using the First come First serve algorithm. In this paper, we have provided an optimize algorithm to queue of the scheduler using various scheduling methods like Shortest Job First, First in First out, Round robin. The job scheduling system is responsible to select best suitable machines in a grid for user jobs. The management and scheduling system generates job schedules for each machine in the grid by taking static restrictions and dynamic parameters of jobs and machines
into consideration. The main purpose of this paper is to develop an efficient job scheduling algorithm to maximize the resource utilization and minimize processing time of the jobs. Queues can be optimized by using various scheduling algorithms depending upon the performance criteria to be improved e.g. response
time, throughput. The work has been done in MATLAB using the parallel computing toolbox.
A HYPER-HEURISTIC METHOD FOR SCHEDULING THEJOBS IN CLOUD ENVIRONMENTieijjournal1
Currently cloud computing has turned into a promising technology and has become a great key for
satisfying a flexible service oriented , online provision and storage of computing resources and user’s
information in lesser expense with dynamism framework on pay per use basis.In this technology Job
Scheduling Problem is acritical issue. For well-organizedmanaging and handling resources,
administrations, scheduling plays a vital role. This paper shares out the improved Hyper- Heuristic
Scheduling Approach to schedule resources, by taking account of computation time and makespan with two
detection operators. Operators are used to select the low-level heuristics automatically. Conditional
Revealing Algorithm (CRA)idea is applied for finding the job failures while allocating the resources. We
believe that proposed hyper-heuristic achieve better results than other individual heuristics
LOAD BALANCING ALGORITHM ON CLOUD COMPUTING FOR OPTIMIZE RESPONE TIMEijccsa
To improve the performance of cloud computing, there are many parameters and issues that we should consider, including resource allocation, resource responsiveness, connectivity to resources, unused resources exploration, corresponding resource mapping and planning for resource. The planning for the use of resources can be based on many kinds of parameters, and the service response time is one of them.
The users can easily figure out the response time of their requests, and it becomes one of the important QoSs. When we discover and explore more on this, response time can provide solutions for the distribution, the load balancing of resources with better efficiency. This is one of the most promising
research directions for improving the cloud technology. Therefore, this paper proposes a load balancing algorithm based on response time of requests on cloud with the name APRA (ARIMA Prediction of Response Time Algorithm), the main idea is to use ARIMA algorithms to predict the coming response time, thus giving a better way of effectively resolving resource allocation with threshold value. The experiment
result outcomes are potential and valuable for load balancing with predicted response time, it shows that prediction is a great direction for load balancing.
Cloud computing is an emerging technology. It process huge amount of data so scheduling mechanism
works as a vital role in the cloud computing. Thus my protocol is designed to minimize the switching time,
improve the resource utilization and also improve the server performance and throughput. This method or
protocol is based on scheduling the jobs in the cloud and to solve the drawbacks in the existing protocols.
Here we assign the priority to the job which gives better performance to the computer and try my best to
minimize the waiting time and switching time. Best effort has been made to manage the scheduling of jobs
for solving drawbacks of existing protocols and also improvise the efficiency and throughput of the server.
Providing a multi-objective scheduling tasks by Using PSO algorithm for cost ...Editor IJCATR
This article is intended to use the multi-PSO algorithm for scheduling tasks for cost management in cloud computing. This means that
any migration costs due to supply failure consider as a one objective and each task is a little particle and recognize by use of the
appropriate fitness schedule function (how the particles arrangement) that cost at least amount of total expense. In addition to, the weight
is granted to the each expenditure that reflects the importance of cost. The data which is used to simulate proposed method are series of
academic and research data that are prepared from the Internet and MATLAB software is used for simulation. We simulate two issues,
in the first issue, consider four task by four vehicles and divide tasks. In the second issue, make the issue more complicated and consider
six tasks by four vehicles. We write PSO's output for each two issues of various iterations. Finally, the particles dispersion and as well
as the output of the cost function were computed for each pa
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 18, Issue 3, Ver. V (May-Jun. 2016), PP 23-27
www.iosrjournals.org
DOI: 10.9790/0661-1803052327 www.iosrjournals.org 23 | Page
Workflow Scheduling for Public Cloud Using Genetic Algorithm
(WSGA)
Dr. D. I. George Amalarethinam1
, T. Lucia Agnes Beena2
1
(Dean of Science & Director MCA, Department of Computer Science, Jamal Mohamed College, Trichy, India)
2
(Assistant Professor, Department of Information Technology, St. Joseph’s College, Trichy, India)
Abstract : Workflow scheduling is a challenging issue in Cloud Computing. Though there are popular
schedulers available for workflow scheduling in Grid and other distributed environments, they are not
applicable to Cloud. Cloud differs from other distributed environments in resource pool and incurs less failure
rate.Workflow scheduling in Cloud has to concentrate on the QoS parameters such as deadline and budget.
Most heuristic algorithms are proposed in the literature. But the meta-heuristic algorithm like Genetic
Algorithm approach for the workflow scheduling in Cloud is expected to yield optimal results. This paper is an
attempt to minimize the execution cost of the workflow using the Genetic Algorithm. The new fitness function is
proposed to minimize the cost and the selection, crossover, mutation operators are applied with the arbitrary
task graphs given as input. It was observed that the proposed algorithm reduces the cost to the optimal value
when compared to the other list heuristic algorithms like HEFT, CFCSC and LBTP for communication intensive
graphs.
Keywords: Crossover, Fitness function, Genetic algorithm, Mutation, Selection, Workflow Scheduling
I. Introduction
Cloud computing has become a standardized way of providing IT services delivered through Internet
technologies in a pay-per-use and in a self-service way. Cost reduction and Organizational Agility have
attracted industries to adopt Cloud Computing. The different types of Clouds provide services for various
categories of users. The flexibility of Cloud Services support any individual or organization to go for Cloud
Computing. One of the important areas of Cloud application is Workflow scheduling.
Workflow scheduling tries to map the workflow tasks to the Virtual Machines (VMs) based on
different functional and non-functional requirements [1]. A workflow consists of a series of interdependent
tasks which are bounded together through data or functional dependencies and these dependencies should be
considered in the scheduling. However, workflow scheduling in the cloud computing is an NP-hard optimization
problem [1] and it is difficult to achieve an optimal schedule. As there are numerous VMs in a cloud, many user
tasks are to be scheduled by considering various scheduling objectives and factors. The common objective of
the workflow scheduling techniques is to minimize the makespan by properly allocating the tasks to the virtual
resources [2].
For scheduling problems, no known algorithm is able to generate an optimal solution within the
polynomial time. Thus there is a need to apply stochastic optimization methods to solve the scheduling problem
by using random variables. These methods include simulated annealing, swarm algorithms and evolutionary
algorithms. The main advantage of genetic algorithms over other traditional optimization methods is that genetic
algorithms are parallelizable. Genetic algorithms intrinsically work with many solutions in parallel which
enables them to explore the solution space in multiple directions at any time thereby converging faster [3]. This
paper attempts to apply Genetic algorithm for the workflow scheduling to optimize the cost and the
performance.
II. Motivation
Researchers prefer the evolutionary algorithms to optimize the performance of the workflows rather
than applying the heuristic scheduling algorithms and also Cloud Computing task scheduling differ from the
other distributed environments viz. Grid computing in resource sharing and cost of resource utilization [4].
Workflow based scheduling is able to efficiently determine an optimal solution for large and complex
applications by considering precedence constraints between potential tasks. One of the most challenging
problems with workflow scheduling in cloud computing is to optimize the cost of workflow execution. Sindhu
et. Al. [5] proposed a bi-objective Genetic Algorithm based scheduler for cloud that optimizes the makespan
(application-centric) and average processor utilization (resource-centric). QoS based task scheduling using
genetic algorithm for independent tasks was proposed by Jang et. al. [6]. Kaur et. al. [7] proposed a meta-
heuristic based scheduling, which minimizes execution time of the independent tasks at heavy loads. Sourav et.
al. [8] produces one scheduling algorithm based on Genetic Algorithm to optimize the waiting time of overall
2. Workflow Scheduling for Public Cloud Using Genetic Algorithm (WSGA)
DOI: 10.9790/0661-1803052327 www.iosrjournals.org 24 | Page
system. Ge and Yuan [9] presented a genetic algorithm MGA, optimized the total task completion time, average
task completion time with required costs.
Agarwal et. al. [10] presented a Generalized Priority algorithm for efficient execution of task and
compared the algorithm with FCFS and Round Robin Scheduling. This Algorithm was tested in CloudSim
toolkit and result showed better performance compared to other traditional scheduling algorithm. Various
scheduling algorithms [11][12][13][14][15][16] were proposed for distributed environment with execution time,
speed up and efficiency. Kaur et. al. [17] proposed a meta-heuristic based scheduling, which minimizes
execution time and execution cost for independent tasks. A cloud user reaches a Service Level Agreement
(SLA) with a cloud provider to process a task. A SLA document includes user requirements like time and
budgetary constraints of the task, which indicate acceptable deadline and payable budget of the cloud user [18].
From the literature, it is found that very few algorithms were proposed for workflow scheduling in Cloud. Some
Grid workflow management systems, like Pegasus [19] started supporting execution of workflows on Cloud
platforms. But it uses Heterogeneous Earliest Finish Time (HEFT) algorithm as the scheduling algorithm which
doesn’t include cost parameter. Juve et al. [20] found that Cloud is much easier to set up and use, more
predictable, capable of giving more uniform performance and incurring less failure than Grid. This background
motivates to propose a genetic algorithm based workflow scheduling for the Cloud, which optimizes the cost of
executing the workflow in the Cloud.
III. The Proposed Work (WSGA)
Scientific applications are usually represented by workflows. The workflows depict the number of
tasks and the data dependencies between the tasks of an application. It is advantageous to use Cloud to execute
the complex scientific applications due to its large-size resource pools. One of the most challenging problems in
Workflow Scheduling is to optimize the cost of workflow execution. The meta-heuristic scheduling schemes
yield the best result when compared to the heuristic algorithms. One of the best meta-heuristic algorithms is
Genetic algorithm. A Genetic Algorithm (GA) is a search algorithm which is based on the principle of
evolution and natural genetics. It combines the exploitation of past results with the exploration of new areas of
the search space [21]. By using the survival of the fittest techniques combined with a randomized information
exchange, the best solution is obtained. The experimental setup for this proposed algorithm WSGA was
tabulated in Table 1. Figure 1 gives the steps in GA.
Fig.1: Steps in Genetic Algorithm
1. Chromosome Representation (WSGA)
A GA for a scheduling problem must have the following five components:
Genetic representation for potential solutions to the problem
A way to create an initial population of the potential solutions
An evaluation function that plays the role of the environment, rating solutions in terms of their “fitness”
Genetic operators that alter the composition of children.
3. Workflow Scheduling for Public Cloud Using Genetic Algorithm (WSGA)
DOI: 10.9790/0661-1803052327 www.iosrjournals.org 25 | Page
Termination condition
In workflow scheduling, there are set of tasks to be scheduled. These tasks have to obey the following rules[21].
They are:
A task’s predecessors must have finished their execution before it can start executing
All tasks within the workflow must execute at least once.
Chromosomes are most commonly represented in binary alphabets {0,1}. The other representations
include ternary, integer and real values [21]. Based on these fundamentals, the chromosome for the workflow
scheduling is represented as an integer. The scheduling solution is a sequence of set of tasks to be scheduled.
Each task is represented by a pair of integers comprising the task and the resource in which the task is executed.
For example, (T7, 4) represents the task T7 to be executed on Resource 4. Thus a chromosome consists of n
tasks to be executed on m processors.
Table 1: Experimental setup
Population size 20
Selection method Roulette Wheel
Crossover method Single Point Crossover
Crossover rate 0.7
Mutation rate 0.1
No. of Iterations 200
2. Initial Population of WSGA
The initial population size is fixed as 20 and is built by generating chromosomes with the list –based
heuristic algorithms such as HEFT [22], CFCSC [23] and LBTP [24]. The remaining chromosomes are
generated randomly. The initial population is checked for its validity through the fitness function.
3. Fitness function of WSGA
The objective of the fitness function is to evaluate each chromosome in the population. In case of
minimization problem, the best fit chromosome will have the lowest numeric value for the objective function.
Based on the fitness value the chromosome may be selected for the next generation in the solution set. The
objective of WSGA is to minimize the cost of executing the workflow in the Cloud.In GA, the minimization
problem should be converted to maximization without sacrificing the optimal solution. Thus the fitness function
f(i) for the WSGA is given by
(1)
where MS is the makespan, AVGCOST is the average resource cost of the chromosome i. The fitness
value for all the chromosomes in the population is calculated using equation (1).
4. Selection operation of WSGA
The objective of the selection operation is to make duplicate copies of the good solution and eliminate
bad solutions in a population, while maintaining the population size. In order to identify the good solution, in
this paper, the Roulette wheel method is used. The roulette wheel selection operator maximizes the fitness
function. Also Stochastic remainder Roulette-Wheel Selection (SRWS) reduces the variance [25]. In SRWS
operator, each solution is first assigned a number of copies equal to the integral part of the expected number.
Thereafter, the usual roulette wheel selection operator is applied with the fractional part of the expected number
of all solutions to assign further copies.
5. Crossover Operation of WSGA
A crossover operation is applied next to create new solution from the chromosomes of the mating pool.
There are number of crossover operations available in the GA literature. In the proposed WSGA, single-point
crossover operation is applied. This is performed by randomly choosing a crossing site along the chromosome
and by exchanging all the pairs on the right side of the crossing site. In order to preserve some good
chromosomes in the population, a crossover probability pc has to be defined. In WSGA, the crossover
probability was 0.7% of the population size. The crossover probability was varied from 0.5 to 0.95, in steps of
0.2 and it was found that 0.7% providing the optimal result. In this paper, the population size is fixed as 20 and
14 chromosomes were selected in each iteration. In selecting the 14 chromosomes for crossover, each
chromosome’s cost is checked against the average cost. If the chromosome’s cost is less than the average cost,
that chromosome is selected for crossover operation. This is followed by the mutation operation.
4. Workflow Scheduling for Public Cloud Using Genetic Algorithm (WSGA)
DOI: 10.9790/0661-1803052327 www.iosrjournals.org 26 | Page
6. Mutation Operation of WSGA
The need for mutation operation is to keep diversity in the population. The mutation probability pm for
the proposed WSGA is 0.1%. Two chromosomes undergo mutation in each generation. The chromosomes for
mutation are selected randomly. In each chromosome, a random task is selected and its corresponding resource
is altered so that it may lead to lower cost.
7. The Termination of WSGA
Usually a GA is terminated after a certain number of generations or if a level of fitness has been
obtained or a point in the search space has been reached [21]. In WSGA, the generations are varied from 50 to
300 in steps of 50 and it was found the optimal solution is attained in the 200th
generation.
IV. Results and Discussion
The proposed WSGA is developed in Java in the Netbeans IDE 7.1. The input for the WSGA is the
arbitrary task graph generated by a program developed in Java [26]. This program generates the needed virtual
machine instance with various speeds randomly. Given the number of tasks to be generated and the number of
virtual machines, the program generates the arbitrary task graphs.
Table 2 : Makespan (Sec.)
No. of Tasks No. of Resources Algorithms
HEFT CFCSC LBTP WSGA
10 3 48.85 39.85 34 13
20 4 74.25 57.9 62 55
50 7 98.53 101.67 98 85
100 10 107.82 115.8 130 101
150 12 132 129 128 114
200 14 142 126.75 147 111
Table 3. Cost ($)
No. of Tasks No. of Resources Algorithms
HEFT CFCSC LBTP WSGA
10 3 7.7 2.2 2.07 1.23
20 4 8.25 5.09 4.74 3.23
50 7 14.07 6.46 4.62 3.81
100 10 47.72 19.9 22.1 11.58
150 12 74.8 31.29 29.45 14.95
200 14 96.99 46.59 46.64 14.5
From the initial population, after applying the reproduction operations like selection, crossover and
mutation for 200 generations, the results were observed for makespan of the arbitrary task graph and the
monetary cost for executing the task graph. The task graph size is varied from 10 to 200. Since Cloud follows
pay as you go formula for the service, the virtual machine instance is charged based on the Google AppEngine
[27]. The high speed CPU is costlier than the low speed CPU.
Fig 2:Graphical representation of Makespan Fig 3: Graphical representation of Cost
The result of WSGA is compared with the heuristic algorithms like HEFT, CFCSC and LBTP. It was
observed that cost was reduced to the optimal value. The results are tabulated in Table 2 and Table 3. The
graphical representations of the results are shown in Figure 2 and Figure 3.
5. Workflow Scheduling for Public Cloud Using Genetic Algorithm (WSGA)
DOI: 10.9790/0661-1803052327 www.iosrjournals.org 27 | Page
V. Conclusion
Using the Cloud environment, the scientists can take advantage of executing their workflows with
lower cost. Though some workflow scheduling algorithms were proposed for the scientific applications in the
distributed environment, they are not suitable for the Cloud environment. This paper, proposed a workflow
scheduling algorithm applying the Genetic algorithm to minimize the cost of executing the workflow in the
Cloud. The fitness function used in the proposed algorithm selects the appropriate chromosomes for the next
generation. The probability of the crossover and the probability of mutation were decided after conducting
various experiments with possible values. The termination condition is also finalized as 200 generations by
repeating the experiments with different values. This results in cost optimization. Thus the proposed genetic
algorithm outperforms the other list scheduling algorithms used in this paper. As a future work, this algorithm
will be tested in the Cloudsim tool to observe the performance. As this algorithm is tested with arbitrary task
graphs, real time workflows can be given as input to the algorithm and its results are expected to be consistent.
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