Computer industry has widely accepted that future performance increases must largely come from increasing the number of processing cores on a die. This has led to NoC processors. Task scheduling is one of the most challenging problems facing parallel programmers today which is known to be NP-complete. A good principle is space-sharing of cores and to schedule multiple DAGs simultaneously on NoC processor. Hence the need to find optimal number of cores for a DAG for a particular scheduling method and further which region of cores on NoC, to be allotted for a DAG . In this work, a method is proposed to find near-optimal minimal block of cores for a DAG on a NoC processor. Further, a time efficient framework and three on-line block allotment policies to the submitted DAGs are experimented. The objectives of the policies, is to improve the NoC throughput. The policies are experimented on a simulator and found to deliver better performance than the policies found in literature..
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
.
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 BAYE'S THEOREM BASED NODE SELECTION FOR LOAD BALANCING IN CLOUD ENVIRONMENThiij
Cloud computing is a popular computing model as it renders service to large number of users request on
the fly and has lead to the proliferation of large number of cloud users. This has lead to the overloaded
nodes in the cloud environment along with the problem of load imbalance among the cloud servers and
thereby impacts the performance. Hence, in this paper a heuristic Baye's theorem approach is considered
along with clustering to identify the optimal node for load balancing. Experiments using the proposed
approach are carried out on cloudsim simulator and are compared with the existing approach. Results
demonstrates that task deployment performed using this approach has improved performance in terms of
utilization and throughput when compared to the existing approaches
A Baye's Theorem Based Node Selection for Load Balancing in Cloud Environmentneirew J
Cloud computing is a popular computing model as it renders service to large number of users request on
the fly and has lead to the proliferation of large number of cloud users. This has lead to the overloaded
nodes in the cloud environment along with the problem of load imbalance among the cloud servers and
thereby impacts the performance. Hence, in this paper a heuristic Baye's theorem approach is considered
along with clustering to identify the optimal node for load balancing. Experiments using the proposed
approach are carried out on cloudsim simulator and are compared with the existing approach. Results
demonstrates that task deployment performed using this approach has improved performance in terms of
utilization and throughput when compared to the existing approaches.
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.
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
.
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 BAYE'S THEOREM BASED NODE SELECTION FOR LOAD BALANCING IN CLOUD ENVIRONMENThiij
Cloud computing is a popular computing model as it renders service to large number of users request on
the fly and has lead to the proliferation of large number of cloud users. This has lead to the overloaded
nodes in the cloud environment along with the problem of load imbalance among the cloud servers and
thereby impacts the performance. Hence, in this paper a heuristic Baye's theorem approach is considered
along with clustering to identify the optimal node for load balancing. Experiments using the proposed
approach are carried out on cloudsim simulator and are compared with the existing approach. Results
demonstrates that task deployment performed using this approach has improved performance in terms of
utilization and throughput when compared to the existing approaches
A Baye's Theorem Based Node Selection for Load Balancing in Cloud Environmentneirew J
Cloud computing is a popular computing model as it renders service to large number of users request on
the fly and has lead to the proliferation of large number of cloud users. This has lead to the overloaded
nodes in the cloud environment along with the problem of load imbalance among the cloud servers and
thereby impacts the performance. Hence, in this paper a heuristic Baye's theorem approach is considered
along with clustering to identify the optimal node for load balancing. Experiments using the proposed
approach are carried out on cloudsim simulator and are compared with the existing approach. Results
demonstrates that task deployment performed using this approach has improved performance in terms of
utilization and throughput when compared to the existing approaches.
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.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
Scalable and Adaptive Graph Querying with MapReduceKyong-Ha Lee
We address the problem of processing multiple graph queries over a massive set of data graphs in this letter. As the number of data graphs is growing rapidly, it is often hard to process graph queries with serial algorithms in a timely manner. We propose a distributed graph querying algorithm, which employs feature-based comparison and a filterand-verify scheme working on the MapReduce framework. Moreover, we devise an ecient scheme that adaptively tunes a proper feature size at runtime by sampling data graphs. With various experiments, we show that the proposed method outperforms conventional algorithms in terms of both scalability and efficiency.
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
A popular programming model for running data intensive applications on the cloud is map reduce. In
the Hadoop usually, jobs are scheduled in FIFO order by default. There are many map reduce
applications which require strict deadline. In Hadoop framework, scheduler wi t h deadline
con s t ra in t s has not been implemented. Existing schedulers d o not guarantee that the job will be
completed by a specific deadline. Some schedulers address the issue of deadlines but focus more on
improving s y s t em utilization. We have proposed an algorithm which facilitates the user to
specify a jobs deadline and evaluates whether the job can be finished before the deadline.
Scheduler with deadlines for Hadoop, which ensures that only jobs, whose deadlines can be met are
scheduled for execution. If the job submitted does not satisfy the specified deadline, physical or
virtual nodes can be added dynamically to complete the job within deadline[8].
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.
SASUM: A Sharing-based Approach to Fast Approximate Subgraph Matching for Lar...Kyong-Ha Lee
Subgraph matching is a fundamental operation for querying
graph-structured data. Due to potential errors and noises in real world graph data, exact subgraph matching is sometimes not appropriate in practice.
In this paper we consider an approximate subgraph matching model that allows missing edges. Based on this model, approximate subgraph matching finds all occurrences of a given query graph in a database graph,
allowing missing edges. A straightforward approach to this problem is to first generate query subgraphs of the query graph by deleting edges and then perform exact subgraph matching for each query subgraph. In this paper we propose a sharing based approach to approximate subgraph matching, called SASUM. Our method is based on the fact that query subgraphs are highly overlapped. Due to this overlapping nature of query subgraphs, the matches of a query subgraph can be computed from the matches of a smaller query subgraph, which results in reducing the number of query subgraphs that need costly exact subgraph matching. Our method uses a lattice framework to identify sharing opportunities between query subgraphs. To further reduce the number of graphs that need exact subgraph matching, SASUM generates small base graphs that are shared by query subgraphs and chooses the minimum number of base graphs whose matches are used to derive the matching results of all query subgraphs. A comprehensive set of experiments shows that our approach outperforms the state-of-the-art
approach by orders of magnitude in terms of query execution time.
A PROGRESSIVE MESH METHOD FOR PHYSICAL SIMULATIONS USING LATTICE BOLTZMANN ME...ijdpsjournal
In this paper, a new progressive mesh algorithm is introduced in order to perform fast physical simulations by the use of a lattice Boltzmann method (LBM) on a single-node multi-GPU architecture. This algorithm is able to mesh automatically the simulation domain according to the propagation of fluids. This method can also be useful in order to perform several types of physical simulations. In this paper, we associate this
algorithm with a multiphase and multicomponent lattice Boltzmann model (MPMC–LBM) because it is
able to perform various types of simulations on complex geometries. The use of this algorithm combined
with the massive parallelism of GPUs[5] allows to obtain very good performance in comparison with the
staticmesh method used in literature. Several simulations are shown in order to evaluate the algorithm.
UnaCloud is an opportunistic based cloud infrastructure
(IaaS) that allows to access on-demand computing
capabilities using commodity desktops. Although UnaCloud
tried to maximize the use of idle resources to deploy virtual
machines on them, it does not use energy-efficient resource
allocation algorithms. In this paper, we design and implement
different energy-aware techniques to operate in an energyefficient
way and at the same time guarantee the performance
to the users. Performance tests with different algorithms and
scenarios using real trace workloads from UnaCloud, show how
different policies can change the energy consumption patterns
and reduce the energy consumption in opportunistic cloud
infrastructures. The results show that some algorithms can
reduce the energy-consumption power up to 30% over the
percentage earned by opportunistic environment.
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.
Estimation of Optimized Energy and Latency Constraint for Task Allocation in ...ijcsit
In Network on Chip (NoC) rooted system, energy consumption is affected by task scheduling and allocation
schemes which affect the performance of the system. In this paper we test the pre-existing proposed
algorithms and introduced a new energy skilled algorithm for 3D NoC architecture. An efficient dynamic
and cluster approaches are proposed along with the optimization using bio-inspired algorithm. The
proposed algorithm has been implemented and evaluated on randomly generated benchmark and real life
application such as MMS, Telecom and VOPD. The algorithm has also been tested with the E3S benchmark
and has been compared with the existing mapping algorithm spiral and crinkle and has shown better
reduction in the communication energy consumption and shows improvement in the performance of the
system. On performing experimental analysis of proposed algorithm results shows that average reduction
in energy consumption is 49%, reduction in communication cost is 48% and average latency is 34%.
Cluster based approach is mapped onto NoC using Dynamic Diagonal Mapping (DDMap), Crinkle and
Spiral algorithms and found DDmap provides improved result. On analysis and comparison of mapping of
cluster using DDmap approach the average energy reduction is 14% and 9% with crinkle and spiral.
Congestion Control through Load Balancing Technique for Mobile Networks: A Cl...IDES Editor
The Optimal Routing Path (ORP) for mobile
cellular networks is proposed in this paper with the
introduction of cluster-based approach. Here an improved
dynamic selection procedure is used to elect cluster head.
The cluster head is only responsible for the computation of
least congested path. Hence the delay is reduced with the
significant reduction on the number of backtrackings.
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.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
Scalable and Adaptive Graph Querying with MapReduceKyong-Ha Lee
We address the problem of processing multiple graph queries over a massive set of data graphs in this letter. As the number of data graphs is growing rapidly, it is often hard to process graph queries with serial algorithms in a timely manner. We propose a distributed graph querying algorithm, which employs feature-based comparison and a filterand-verify scheme working on the MapReduce framework. Moreover, we devise an ecient scheme that adaptively tunes a proper feature size at runtime by sampling data graphs. With various experiments, we show that the proposed method outperforms conventional algorithms in terms of both scalability and efficiency.
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
A popular programming model for running data intensive applications on the cloud is map reduce. In
the Hadoop usually, jobs are scheduled in FIFO order by default. There are many map reduce
applications which require strict deadline. In Hadoop framework, scheduler wi t h deadline
con s t ra in t s has not been implemented. Existing schedulers d o not guarantee that the job will be
completed by a specific deadline. Some schedulers address the issue of deadlines but focus more on
improving s y s t em utilization. We have proposed an algorithm which facilitates the user to
specify a jobs deadline and evaluates whether the job can be finished before the deadline.
Scheduler with deadlines for Hadoop, which ensures that only jobs, whose deadlines can be met are
scheduled for execution. If the job submitted does not satisfy the specified deadline, physical or
virtual nodes can be added dynamically to complete the job within deadline[8].
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.
SASUM: A Sharing-based Approach to Fast Approximate Subgraph Matching for Lar...Kyong-Ha Lee
Subgraph matching is a fundamental operation for querying
graph-structured data. Due to potential errors and noises in real world graph data, exact subgraph matching is sometimes not appropriate in practice.
In this paper we consider an approximate subgraph matching model that allows missing edges. Based on this model, approximate subgraph matching finds all occurrences of a given query graph in a database graph,
allowing missing edges. A straightforward approach to this problem is to first generate query subgraphs of the query graph by deleting edges and then perform exact subgraph matching for each query subgraph. In this paper we propose a sharing based approach to approximate subgraph matching, called SASUM. Our method is based on the fact that query subgraphs are highly overlapped. Due to this overlapping nature of query subgraphs, the matches of a query subgraph can be computed from the matches of a smaller query subgraph, which results in reducing the number of query subgraphs that need costly exact subgraph matching. Our method uses a lattice framework to identify sharing opportunities between query subgraphs. To further reduce the number of graphs that need exact subgraph matching, SASUM generates small base graphs that are shared by query subgraphs and chooses the minimum number of base graphs whose matches are used to derive the matching results of all query subgraphs. A comprehensive set of experiments shows that our approach outperforms the state-of-the-art
approach by orders of magnitude in terms of query execution time.
A PROGRESSIVE MESH METHOD FOR PHYSICAL SIMULATIONS USING LATTICE BOLTZMANN ME...ijdpsjournal
In this paper, a new progressive mesh algorithm is introduced in order to perform fast physical simulations by the use of a lattice Boltzmann method (LBM) on a single-node multi-GPU architecture. This algorithm is able to mesh automatically the simulation domain according to the propagation of fluids. This method can also be useful in order to perform several types of physical simulations. In this paper, we associate this
algorithm with a multiphase and multicomponent lattice Boltzmann model (MPMC–LBM) because it is
able to perform various types of simulations on complex geometries. The use of this algorithm combined
with the massive parallelism of GPUs[5] allows to obtain very good performance in comparison with the
staticmesh method used in literature. Several simulations are shown in order to evaluate the algorithm.
UnaCloud is an opportunistic based cloud infrastructure
(IaaS) that allows to access on-demand computing
capabilities using commodity desktops. Although UnaCloud
tried to maximize the use of idle resources to deploy virtual
machines on them, it does not use energy-efficient resource
allocation algorithms. In this paper, we design and implement
different energy-aware techniques to operate in an energyefficient
way and at the same time guarantee the performance
to the users. Performance tests with different algorithms and
scenarios using real trace workloads from UnaCloud, show how
different policies can change the energy consumption patterns
and reduce the energy consumption in opportunistic cloud
infrastructures. The results show that some algorithms can
reduce the energy-consumption power up to 30% over the
percentage earned by opportunistic environment.
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.
Estimation of Optimized Energy and Latency Constraint for Task Allocation in ...ijcsit
In Network on Chip (NoC) rooted system, energy consumption is affected by task scheduling and allocation
schemes which affect the performance of the system. In this paper we test the pre-existing proposed
algorithms and introduced a new energy skilled algorithm for 3D NoC architecture. An efficient dynamic
and cluster approaches are proposed along with the optimization using bio-inspired algorithm. The
proposed algorithm has been implemented and evaluated on randomly generated benchmark and real life
application such as MMS, Telecom and VOPD. The algorithm has also been tested with the E3S benchmark
and has been compared with the existing mapping algorithm spiral and crinkle and has shown better
reduction in the communication energy consumption and shows improvement in the performance of the
system. On performing experimental analysis of proposed algorithm results shows that average reduction
in energy consumption is 49%, reduction in communication cost is 48% and average latency is 34%.
Cluster based approach is mapped onto NoC using Dynamic Diagonal Mapping (DDMap), Crinkle and
Spiral algorithms and found DDmap provides improved result. On analysis and comparison of mapping of
cluster using DDmap approach the average energy reduction is 14% and 9% with crinkle and spiral.
Congestion Control through Load Balancing Technique for Mobile Networks: A Cl...IDES Editor
The Optimal Routing Path (ORP) for mobile
cellular networks is proposed in this paper with the
introduction of cluster-based approach. Here an improved
dynamic selection procedure is used to elect cluster head.
The cluster head is only responsible for the computation of
least congested path. Hence the delay is reduced with the
significant reduction on the number of backtrackings.
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.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Front End Data Cleaning And Transformation In Standard Printed Form Using Neu...ijcsa
Front end of data collection and loading into database manually may cause potential errors in data sets and a very time consuming process. Scanning of a data document in the form of an image and recognition of corresponding information in that image can be considered as a possible solution of this challenge. This paper presents an automated solution for the problem of data cleansing and recognition of user written data to transform into standard printed format with the help of artificial neural networks. Three different neural models namely direct, correlation based and hierarchical have been developed to handle this issue. In a very hostile input environment, the solution is developed to justify the proposed logic.
COUPLER, POWER DIVIDER AND CIRCULATOR IN V-BAND SUBSTRATE INTEGRATED WAVEGUID...ijcsa
In recent years substrate integrated waveguide technology (SIW) has been applied successfully to the conception of planar compact components for the microwave and millimeter waves applications. In this study, a V-band substrate integrated waveguide coupler, power divider and circulator are conceived and optimized by Ansoft HFSS code. Thus, through this modeling, design considerations and results are discussed and presented. Attractive features including compact size and planar form make these devices structure easily integrated in planar circuits
Gold Design India is one of a leading Golf Course Architects and Designers in India provides construction, maintenance and management of Golf course in India
MULTIPLE DAG APPLICATIONS SCHEDULING ON A CLUSTER OF PROCESSORScscpconf
Many computational solutions can be expressed as Directed Acyclic Graph (DAG), in which
nodes represent tasks to be executed and edges represent 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 presented as DAGs. The scheduler must find the
number of processors to be allotted for each DAG 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 DAG is proposed. Regression analysis is used to find the best possible way to share
available processors, among suitable number of submitted DAGs. An instance of a scheduler
for each DAG, schedules tasks on the allotted processors. Towards this end, a new framework
to receive online submission of DAGs, allot processors to each DAG and schedule tasks, is
proposed and experimented using a simulator. This space-sharing of processors among multiple
DAGs shows better performance than the other methods found in literature. Because of spacesharing,
an online scheduler can be used for each DAG within the allotted processors. The use
of online scheduler overcomes the drawbacks of static scheduling which relies on inaccurate
estimated computation and communication costs. Thus the proposed framework is a promising
solution to perform online scheduling of tasks using static information of DAG, a kind of hybrid
scheduling.
A Framework and Methods for Dynamic Scheduling of a Directed Acyclic Graph on...IDES Editor
The data flow model is gaining popularity as a
programming paradigm for multi-core processors. Efficient
scheduling of an application modeled by Directed Acyclic
Graph (DAG) is a key issue when performance is very
important. DAG represents computational solutions, in which
the nodes represent tasks to be executed and edges represent
precedence constraints among the tasks. The task scheduling
problem in general is a NP-complete problem[2]. Several static
scheduling heuristics have been proposed. But the major
problem in static list scheduling is the inherent difficulty in
exact estimation of task cost and edge cost in a DAG and also
its inability to consider and manage with runtime behavior of
tasks. This underlines the need for dynamic scheduling of a
DAG. This paper presents how in general, dynamic scheduling
of a DAG can be done. Also proposes 4 simple methods to
perform dynamic scheduling of a DAG. These methods have
been simulated and experimented using a representative set
of DAG structured computations from both synthetic and real
problems. The proposed dynamic scheduler performance is
found to be in comparable with that of static scheduling
methods. The performance comparison of the proposed
dynamic scheduling methods is also carried out.
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The stringent power budget of fine grained power managed digital integrated circuits have driven chip designers to optimize power at the cost of area and delay, which were the traditional cost criteria for circuit optimization. The emerging scenario motivates us to revisit the classical operator scheduling problem under the availability of DVFS enabled functional units that can trade-off cycles with power. We study the design space defined due to this trade-off and present a branch-and-bound(B/B) algorithm to explore this state space and report the pareto-optimal front with respect to area and power. The scheduling also aims at maximum resource sharing and is able to attain sufficient area and power gains for complex benchmarks when timing constraints are relaxed by sufficient amount. Experimental results show that the algorithm that operates without any user constraint(area/power) is able to solve the problem for mostavailable benchmarks, and the use of power budget or area budget constraints leads to significant performance gain.
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using partial dynamic reconfiguration. One of the challenges for hardware multitasking in embedded
systems is the online management of the only reconfiguration port of present FPGA devices. This paper
presents different online reconfiguration scheduling strategies which assign the reconfiguration interface
resource using different criteria: workload distribution or task’ deadline. The online scheduling strategies
presented take efficient and fast decisions based on the information available at each moment. Experiments
have been made in order to analyze the performance and convenience of these reconfiguration strategies.
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.
PERFORMANCE FACTORS OF CLOUD COMPUTING DATA CENTERS USING [(M/G/1) : (∞/GDM O...ijgca
The ever-increasing status of the cloud computing h
ypothesis and the budding concept of federated clou
d
computing have enthused research efforts towards in
tellectual cloud service selection aimed at develop
ing
techniques for enabling the cloud users to gain max
imum benefit from cloud computing by selecting
services which provide optimal performance at lowes
t possible cost. Cloud computing is a novel paradig
m
for the provision of computing infrastructure, whic
h aims to shift the location of the computing
infrastructure to the network in order to reduce th
e maintenance costs of hardware and software resour
ces.
Cloud computing systems vitally provide access to l
arge pools of resources. Resources provided by clou
d
computing systems hide a great deal of services fro
m the user through virtualization. In this paper, t
he
cloud data center is modelled as
queuing system with a single task arrivals
and a task request buffer of infinite capacity.
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Intellectual Property (IP) which affect the performance of the system. In this paper we test the
antecedently extant proposed algorithms and introduced a new energy proficient algorithm
stand for 3D NoC architecture. In addition a hybrid method has also been implemented using
bioinspired optimization (particle swarm optimization) technique. The proposed algorithm has
been implemented and evaluated on randomly generated benchmark and real life application
such as MMS, Telecom and VOPD. The algorithm has also been tested with the E3S benchmark
and has been compared with the existing algorithm (spiral and crinkle) and has shown better
reduction in the communication energy consumption and shows improvement in the
performance of the system. Comparing our work with spiral and crinkle, experimental result
shows that the average reduction in communication energy consumption is 19% with spiral and
17% with crinkle mapping algorithms, while reduction in communication cost is 24% and 21%
whereas reduction in latency is of 24% and 22% with spiral and crinkle. Optimizing our work
and the existing methods using bio-inspired technique and having the comparison among them
an average energy reduction is found to be of 18% and 24%.
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Energy efficiency is one of the most critical issue in design of System on Chip. In Network On
Chip (NoC) based system, energy consumption is influenced dramatically by mapping of
Intellectual Property (IP) which affect the performance of the system. In this paper we test the
antecedently extant proposed algorithms and introduced a new energy proficient algorithm
stand for 3D NoC architecture. In addition a hybrid method has also been implemented using
bioinspired optimization (particle swarm optimization) technique. The proposed algorithm has
been implemented and evaluated on randomly generated benchmark and real life application
such as MMS, Telecom and VOPD. The algorithm has also been tested with the E3S benchmark
and has been compared with the existing algorithm (spiral and crinkle) and has shown better
reduction in the communication energy consumption and shows improvement in the
performance of the system. Comparing our work with spiral and crinkle, experimental result
shows that the average reduction in communication energy consumption is 19% with spiral and
17% with crinkle mapping algorithms, while reduction in communication cost is 24% and 21%
whereas reduction in latency is of 24% and 22% with spiral and crinkle. Optimizing our work
and the existing methods using bio-inspired technique and having the comparison among them
an average energy reduction is found to be of 18% and 24%.
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Macro-programming is the new generation advanced method of using Wireless Sensor Network (WSNs), where application developers can extract data from sensor nodes through a high level abstraction of the system. Instead of developing the entire application, task graph representation of the WSN model presents simplified approach of data collection. However, mapping of tasks onto sensor nodes highlights several problems in energy consumption and routing delay. In this paper, we present an efficient hybrid approach of task mapping for WSN – Hybrid Genetic Algorithm, considering multiple objectives of optimization – energy consumption, routing delay and soft real time requirement. We also present a method to configure the algorithm as per user's need by changing the heuristics used for optimization. The trade-off analysis between energy consumption and delivery delay was performed and simulation results are presented. The algorithm is applicable during macro-programming enabling developers to choose a better mapping according to their application requirements.
CONFIGURABLE TASK MAPPING FOR MULTIPLE OBJECTIVES IN MACRO-PROGRAMMING OF WIR...ijassn
Macro-programming is the new generation advanced method of using Wireless Sensor Network (WSNs),
where application developers can extract data from sensor nodes through a high level abstraction of the
system. Instead of developing the entire application, task graph representation of the WSN model presents
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International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
Crdom cell re ordering based domino on-the-fly mappingVLSICS Design
This Domino logic is often the choice for designing high speed CMOS circuits. Often VLSI designers
choose library based approaches to perform technology mapping of large scale circuits involving static
CMOS logic style. Cells designed using Domino logic style have the flexibility to accommodate wide range
of functions in them. Hence, there is a scope to adopt a library free synthesis approach for circuits
designed using Domino logic. In this work, we present an approach for mapping a domino logic circuit
using an On-the-fly technique. First, we present a node mapping algorithm which maps a given Domino
logic netlist using On-the-fly technique. Next, using an Equivalence Table, we re-order the cells along the
critical path for delay, area benefit. Finally, we find an optimum re-ordering set which can obtain
maximum delay savings for a minimum area penalty. We have tested the efficacy of our approach with a
set of standard benchmark circuits. Our proposed mapping approach (CRDOM) obtained 21%
improvement in area and 17% improvement in delay compared to existing work.
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.
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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.
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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.
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A Novel Framework and Policies for On-line Block of Cores Allotment for Multiple DAGs on NoC
1. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.6, December 2013
A Novel Framework and Policies for On-line Block
of Cores Allotment for Multiple DAGs on NoC
Uma Boregowda1 and Venugopal C R2
1
2
Malnad College of Engineering, Hassan, India
Sri Jayachamarajendra College of Engineering, Mysore, India
ABSTRACT
Computer industry has widely accepted that future performance increases must largely come from
increasing the number of processing cores on a die. This has led to NoC processors. Task scheduling is one
of the most challenging problems facing parallel programmers today which is known to be NP-complete. A
good principle is space-sharing of cores and to schedule multiple DAGs simultaneously on NoC processor.
Hence the need to find optimal number of cores for a DAG for a particular scheduling method and further
which region of cores on NoC, to be allotted for a DAG . In this work, a method is proposed to find nearoptimal minimal block of cores for a DAG on a NoC processor. Further, a time efficient framework and
three on-line block allotment policies to the submitted DAGs are experimented. The objectives of the
policies, is to improve the NoC throughput. The policies are experimented on a simulator and found to
deliver better performance than the policies found in literature..
KEYWORDS
DAG; task scheduling; NoC
1. INTRODUCTION
The advancement in VLSI has now led to the increased number of cores on a single chip. A
study at University of California, Berkeley[1] indicates that future chips will have thousands of
cores. Traditional bus based systems and cache hierarchies have given way to more flexible and
scalable Network-on-chip(NoC) architecture. Massive parallel computing performed on NoC is
the future computing, due to the large increased computing demands. With the increasing
number of cores, it is becoming further difficult and demanding to exploit the full power of all
cores on the chip.
The data flow model is gaining popularity as a programming paradigm for multi-core processors
and NoC. When the characteristics of an application is fully deterministic, including task's
execution time, size of data communicated between tasks, and task dependencies, the application
can be represented by a Directed Acyclic Graph (DAG). A DAG G=(V,E), where V = { vi |
i=1, ... V} is a set of vertices representing tasks and E={e i,j|i ,j) {1, ... V} * {1, ... V} is a set of
edges representing precedence constraints between tasks. DAG is assumed to have a single entry
and single exit task. The structure of a general DAG is shown in Fig. 1 with eleven number of
taks. The weight of a vertex v ∈ V corresponds to the execution time of the task, which can be
measured via benchmarking or can be calculated via a performance model. The weight of an
edge ei,j ∈ E, is the communication cost measured as the time taken to transfer data Di,j between
DOI:10.5121/ijcsa.2013.3606
55
2. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.6, December 2013
vertices vi and vj, which is a function of data volume Di,j, the network characteristics, the
processors allocated to it and data distribution cost.
Fig. 1 A General DAG
Communication-to-Computation (CCR) is the ratio of average communication cost to the average
computation cost of a DAG. This characterizes the nature of DAG. The objective of scheduling is
to map tasks onto cores and order their execution so that task dependencies are satisfied and
minimum overall completion time is achieved.
Scheduling can be static or dynamic. Static schedule can be generated only when the application
behaviour is fully deterministic and this has the advantage of being more efficient and a small
overhead during runtime. The full global knowledge of application in the form of DAG will help
to generate a better schedule. Makespan is the total time required to complete a DAG. The
challenge of scheduling tasks onto cores and also maintaining precedence constraint, with an
objective of minimizing overall execution time is proved to be a NP hard problem[2].
Not all applications can effectively utilize all cores on the chip, hence it would be advantageous
to multiprogram the NoC and space-partition the cores among applications, being
multiprogrammed. The design has now shifted from scheduling single application to multiple
applications. Hence the problem of scheduling DAG on NoC is twofold : first, to allot cores for a
DAG and second, to schedule tasks of DAG onto the allotted cores. This paper deals with the first
issue.
The contributions of this paper include (a) Finding near-optimal block of cores for a given DAG
for a particular scheduling method (b) To propose and implement a framework and three on-line
block of cores allotment policies for the DAGs submitted, with an objective of improving the
total time required to complete all DAGs and hence the throughput.
2. RELATED WORK
NoC processors are more interested in how many DAGs can be completed over a period of time
rather than how quickly an individual DAG gets completed, since the cores are not a very scarce
56
3. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.6, December 2013
resource on it. Thus the more emphasis is on throughput of DAGs. The scheduling problem has
been extensively studied for several decades. Sakellariou et. al.[3] have studied the problem of
scheduling multiple DAGs onto a set of heterogeneous resources. The main work is to optimize
overall makespan and also to achieve fairness for all DAGs. One approach is to combine several
DAGs into one and then use standard methods to schedule the task graph. Another way is to
perform task-by-task scheduling over all tasks in all task graphs. Ahmed et. al.[4] team have
proposed a game theory based scheduler on multicore processor with the objective of minimizing
energy consumption.
Cilk[5], Intel Thread Building Blocks(TBB) and OpenMP have proposed several task scheduling
techniques over multicore. These systems are optimized specifically for scheduling DAGs on
multicore but not for NoC processors. Viktor K Prasanna et. al.[6] have implemented a
hierarchical scheduler which could adapt to the input task dependency graph and demonstrated its
advantages for NoC architectures. They have divided the threads into groups, each having a
manager to perform scheduling at the group level and several workers to perform self-scheduling
for the tasks assigned by the manager. A super manager was used to dynamically adjust the group
size, so that the scheduler could adapt to the input task dependency graph.
Tang et. al.[7] proposed a two-step genetic algorithm and the related software for mapping
concurrent applications on a fixed NoC architecture. Murali et. al.[8] presented a methodology to
map multiple use-cases onto the NoC architecture, satisfying the constraints of each use-case. The
two-step mapping method [9] first finds a region on the NoC for a given application and then
maps all tasks of the application onto the region. Several strategies based on the Maximum Empty
Rectangle(MER) techniques, like BestSize, BestShape, BestSize BestShape and BestShape
BestSize are experimented. For each DAG to be scheduled, the number of cores required is
known before hand and DAG is scheduled only if specified cores are available. Hence, a
drawback of this work is that, it does not schedule a DAG if available cores is less than the
specified cores. But to increase the throughput of DAGs completion and also to maximize core
utilization, it is better to schedule a DAG even when the available cores are marginally less than
the specified cores.
This proposed work tries to schedule a DAG with less than specified cores whenever the specified
cores are not available and if it increases the throughput without much affecting that DAG's
makespan. It is generally beneficial in terms of throughput, to run more DAGs with less than
specified cores, than running few DAGs with full specified cores.
3. PROPOSED WORK
3.1. Finding Near-optimal block of cores
The reason behind the idea of allotting cores in the form of block for a DAG are (a) it would be
easier and time efficient to allot and free cores for a DAG in the form of block (b) the nodes
average distance (NAD) is minimum for a block and this minimizes the communication time
between the tasks, hence the completion time of the schedule generated. NAD is the average
distance between any two randomly selected nodes on NoC. The block can only be a square or
rectangle. To find the near-optimal block of cores, an initial schedule for the DAG is generated
using a well known list scheduling method and trying to use all cores on NoC. The scheduling
method also takes into account the number of hops required to move data between any two cores
on NoC, the amount of data to be moved and the bandwidth of the intermediate channels, hence
the generated schedule is more realistic. All the cores are not utilized to the maximum extent in
the generated schedule. Thus it would be beneficial to eliminate low utilized cores from the
schedule, without much affecting the completion time of the DAG. Since the cores allotted for a
57
4. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.6, December 2013
DAG must be in the form of a block, the low utilized rows or columns of cores are eliminated.
This method is given in the below algorithm.
Algorithm Near_Optimal_Block()
Input : DAG ‘G’
Output : Number of rows and columns of cores
1.
2.
3.
4.
Start
with
maximum
number
of
rows
and
column
of
cores
on the given NoC
Find the schedule for the given DAG ‘G’, using any well known list scheduling method
like- ETF, HLFET[2] etc.
Repeat
3.1 Reduce row or column of cores, whichever has least total number of tasks assigned
to it.
3.2 Generate the new schedule with revised row and
column number
3.3 Find the percentage increase in core utilization and
percentage
increase
in
makespan of the new schedule in comparison to previous schedule
Until percentage increase in makespan exceeds percenage increase in core utilization
end_algorithm
The iteration at which, percentage increase in makespan exceeds percentage increase in core
utilization, determines the number of rows and columns of cores for the DAG. It is the point
where any further reduction of row/column of cores will significantly increase the makespan.
Hence it fixes the near-optimal block of cores for a given DAG.
3.2. A Framework for On-line Block allotment
A Framework for on-line submission of DAGs, allotment of block of cores for each DAG,
scheduling of DAGs and finding its completion time is developed. An efficient method to track
the status of cores on NoC and hence to allot cores for a DAG is implemented. The NoC is
viewed as a single pool of cores, while previous methods[9] have maintained it as several blocks
of free or allotted cores. The disadvantages of maintaining NoC as a collection of free or allotted
blocks are (a) the job of splitting the blocks during DAG allotment when no free block with the
specified size is available and merging the blocks when a DAG is completed, is quite
computationally expensive to be used as an on-line method, (b) there will be few situations where
a DAG can not be allotted cores, as the required number of free cores are not available as a single
block but as a collection of few adjacent blocks. However the proposed method will allot block
when the required number of free cores are available either as a single block or as few adjacent
blocks.
Fig. 2 shows an instance where two blocks are allotted to DAG1 and DAG2, and another two
blocks FB1 and FB2 are free. Using methods[9], a demand for block of cores of size 4*2 or 5*2
can not be satisfied as the required number of cores are not available as a single block. But the
required free block is present as 2 adjacent blocks. The allotment of such adjacent blocks needs
frequent merging of blocks which is computationally expensive. The proposed method will never
fail to allot, whenever required cores are available either as a single free block or as few adjacent
free blocks. A mask with a dimension of the desired block size is used to locate the presence of
free block on NoC in a very simple manner.
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Fig. 2 An Instance of Block Allotment for DAGs
4. EXPERIMENT AND RESULTS
4.1. Experimental Methodology
A simple simulator using the proposed framework is developed to simulate the arrival,
scheduling, execution and completion of DAGs. Poisson distribution is used to simulate the
arrival time of DAGs. This software is run on multi2sim[12], a functional simulator to measure
total running time.
4.2. A Finding Near-optimal block of cores
The proposed method to find the near-optimal minimal block of cores is implemented using the
well known list scheduling method ETF[2]. This is experimented for several kinds of benchmark
DAGs taken from Task Graphs For Free[10], Standard Task Graph Set [11] and also DAGs of
real applications like Strassen matrix multiplication, FFT and complex matrix multiplication.
DAGs with CCR values of 0.1, 0.4, 1 and 5 are used in experiments. Fig. 3 shows the plot of
percentage increase in makespan and core utilization, as the row or column of cores, allotted to a
DAG, is reduced in subsequent iterations. For the DAG considered in Fig. 1, the block of cores
dimensions are 5*6, 4*5, 3*5 and 3*4 at iterations 1, 2, 3 and 4 respectively. Thus the optimal
block size is 3*5, as the cross-over of plots happens at iteration number 4, when the block
dimension is 3*4. This method can be used for any kind of DAG.
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Fig 3. Finding Minimal Block Size
4.3. A Comparison of Block Allotment Policies
To compare the performance of the proposed novel method of tracking the status of cores on
NoC, the policies BestSize[9] and BestShape[9] are implemented, by viewing the NoC as a
collection of free or alloted blocks. The proposed BestFit chooses the best fit block like the
BestSize but views NoC as a single pool of cores. Also, it can allot block of cores in few cases
when BestSize[9] fails to allot, because of the manner in which status of cores are maintained.
The simulator is run for 4 categories of DAG sets, each with 10, 20, 30 and 40 DAGs
respectively.
The performance metric used is complete makespan, which is the time to complete all DAGs. For
comparison, normalized complete makespan is used, which normalizes the obtained complete
makespan with respect to BestShape, as it is the best policy[9] found in literature. The average
normalized complete makespan of DAGs within each category is computed, for each block
allotment policy. Fig. 4 shows the comparison of average normalized complete makespan for
different policies. Proposed BestFit exhibits better performance against the other two policies, for
all categories. The improvement in performance is further enhanced with increase in number of
DAGs in a set. This happens as the number of times block allotment is successful in proposed
method, while it is a failure in previous method is more. But the significant advantage of
proposed method is its low execution time, which was evident from the recorded runtime on
simulator and thus proving that proposed method is the right candidate for on-line block allotment
for DAGs.
Fig. 4 Comparing New Method of Tracking Core Status with previous method
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Fig. 5 shows the performance comparison of all three proposed block allotment policies. Metric
used is normalized overall makespan with respect to BestFit policy. DAGs with both low and
high CCR, combined together with low and high arrival rates, are considered for each set of
DAGs. Each set has 50 DAGs. The set LoC_LoA includes DAG with low CCR and low arrival
rate. DAGs with high CCR and high arrival rate are included in the set HiC_HiA.
The DAGs with low CCR can better utilize more cores, as the communication cost is low. Thus
when allotted less number of cores for a DAG, resultant increase in makespan is slightly more.
For the set LoC_LoA, it is evident from Fig. 5 that performance of AvailableCore is better by
10% with reference to BestFit policy. When the arrival rate of DAGs is low, Adoptive policy
performance is close to AvailableCore. As the arrival rate is more in LoC_HiA, the adoptive
policy performs better by 9% in comparison to AvailableCore.
When CCR is high, DAGs can not effectively utilize more cores, hence a decrease in cores
allotted would not significantly affect the makespan. Thus for the set HiC_LoA, the performance
of AvailableCore is significantly better than BestFit by 18%. When CCR and arrival rate of
DAGs is high as in HiC_HiA, both Adoptive and AvailableCore show good performance
improvement of around 20%. For the set with all kinds of DAGs randomly chosen, Adoptive and
AvailableCore policies perform better by 17% in comparison to BestFit.
Fig. 5 Comparison of Block Allotment Policies
5. CONCLUSION
A framework for on-line DAGs submission, allotting block of cores, scheduling and completion
of DAGs is proposed and implemented on a simulator. Three block allotment policies – BestFit,
AvailableCores and Adoptive are compared with the policies found in literature. The simple
BestFit policy delivers improvement over the policies found in literature, because of the new
proposed method of tracking the status of cores on NoC. The novel methods – AvailableCore and
Adoptive delivers better performance than BestFit. Of the proposed policies, Adoptive one is
good, as it can adjust the allotment to the arrival rate of DAGs, thus reducing the overall
completion time. These experiments have proved that it is beneficial to schedule several DAGs
with less than specified cores, than running DAGs with full specified cores.
Future Enhancements – The experiment is now simulated using DAGs with dummy tasks. The
idea must be experimented on real NoC, with DAGs of real tasks.
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Author
C.R.Venugopal received his Ph. D from IIT, Bombay. Presently serving as a Professor
in Sri Jayachamarajendra College of Engineering, Mysore, India. His main research
interests are Cloud computing, High Performance computing, VLSI Technology and
File System development. Has authored more than 50 international conference and
journal papers.
Uma B completed M. Tech in Computer Science and Engineering from IIT, Delhi.
Currently working as a Associate Professor in Malnad College of engineering, Hassan,
India. Her research interests are Parallel Programming, High Performance Computing
and Task scheduling.
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