Today the most commonly used system architectures in data processing can be divided into three categories, general purpose processors, application specific architectures and reconfigurable architectures. Application specific architectures are efficient and give good performance, but are inflexible. Recently reconfigurable systems have drawn increasing attention due to their combination of flexibility and efficiency. Re-configurable architectures limit their flexibility to a particular algorithm. This paper introduces approaches to mapping point arithmetic. After presenting an optimal formulation using applications onto CGRAs supporting both integer and floating point unit.High-level design entry tools are essential for reconfigurable systems, especially coarse-grained reconfigurable architectures.Coarse-grained reconfigurable architectures have drawn increasing attention due to their performance and flexibility. However, their applications have been restricted to domains based on integer arithmetic since typical CGRAs support only integer arithmetic or logical operations. In this project we introduce an approach to map applications onto CGRAs supporting floating point addition. The increase in requirements for more flexibility and higher performance in embedded systems design, reconfigurable computing is becoming more and more popular.
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...IRJET Journal
This document proposes a scheduling algorithm for allocating resources in cloud computing based on the Project Evaluation and Review Technique (PERT). It aims to address issues like starvation of lower priority tasks. The algorithm models task allocation as a directed acyclic graph and uses PERT to schedule critical and non-critical tasks, prioritizing higher priority tasks. The algorithm is evaluated against other scheduling methods and shows improvements in reducing completion time and optimizing resource allocation for all tasks.
Improving the Performance of Mapping based on Availability- Alert Algorithm U...AM Publications
Performance of Mapping can be improved and it is needed arise in several fields of science and engineering.
They'll be parallelized in master-worker fashion and relevant programming ways have been projected to cut back
applications. In existing system, the performance of application is considered only for homogenous systems due to simplicity.
In this we use Availability-Alert algorithm using Poisson arrival to extend our approach for Heterogeneous systems in Multi
core Architecture systems. Our proposed algorithm also considers the requirement needed for the application for their
execution in Heterogeneous systems in Multi core Architecture systems while maintaining good performance. Performance
prediction errors are minimized by using this approach at the end of the execution. We present simulation results to quantify
the benefits of our approach.
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.
In recent years the data mining applications become musty and outmoded over time. Energy wastage is the major
problem more in big data analytics and applications. More workload and more computational time will increase high energy
cost and decrease efficiency. The Incremental computational time processing is a promising approach to refreshing mining
results. It utilizes previously saved states to avoid the expense of re-computation from scratch. In this paper, we propose
Energy efficiency Map Reduce Scheduling Algorithm, a novel incremental processing extension to reduce the Map, the most
widely used framework for mining big data. Map reduce is a programming model for processing and generating large amount of data in parallel processing time. In this paper, Energy Efficiency reduce Map (EEMP) is algorithm provide more energy
and less maps in big data. Priority based scheduling is a task will allocate the schedules based on necessary and utilization of
the Jobs. For reducing the maps, it will reduce the system computational time so easily energy has improved in terms of big data applications.. Final results show the experimental comparison of the different algorithms involved in the paper.
An enhanced adaptive scoring job scheduling algorithm with replication strate...eSAT Publishing House
This document describes an enhanced adaptive scoring job scheduling algorithm with replication strategy for grid environments. The algorithm aims to improve upon an existing adaptive scoring job scheduling algorithm by identifying whether jobs are data-intensive or computation-intensive. It then divides large jobs into subtasks, replicates the subtasks, and allocates the replicas to clusters based on a computed cluster score in order to improve resource utilization and job completion times. The algorithm is evaluated through simulation using the GridSim toolkit.
Hybrid Task Scheduling Approach using Gravitational and ACO Search AlgorithmIRJET Journal
The document proposes a hybrid task scheduling approach for cloud computing called ACGSA that combines ant colony optimization and gravitational search algorithms. It describes using the Cloudsim simulator to test the performance of ACGSA and comparing it to ant colony optimization. The results show that ACGSA achieves better performance than the basic ant colony approach on relevant parameters like task scheduling time and resource utilization.
HyPR: Hybrid Page Ranking on Evolving Graphs (NOTES)Subhajit Sahu
Highlighted notes on A Parallel Algorithm Template for Updating Single-Source Shortest Paths in Large-Scale Dynamic Networks.
While doing research work under Prof. Dip Banerjee, Prof, Kishore Kothapalli.
In Hybrid Pagerank the vertices are divided in 3 groups, V_old, V_border, V_new. Scaling for old, border vertices is N/N_new, and 1/N_new for V_new (i do this too ). Then PR is run only on V_border, V_new.
"V_border which is the set of nodes which have edges in Bi connecting V_old and V_new and is reachable using a breadth first traversal."
Does that mean V_border = V_batch(i) ∩ V_old? BFS from where?
"We can assume that the new batch of updates is topologically sorted since the PR scores of the new nodes in Bi is guaranteed to be lower than those in Co."
Is sum(PR) in V_old > sum(PR) in V_new always?
"For performing the comparisons with GPMA and GPMA+, we configure the experiment to run HyPR on the same platform as used in [1] which is a Intel Xeon CPU connected to a Titan X Pascal GPU, and also the same datasets."
Old GPUs are going to be slower ...
Like we were discussing last time, it is not possible to scale old ranks, and skip the unchanged components (or here V_old). Please check this simple counter example that shows skipping leads to incorrect ranks.
https://github.com/puzzlef/pagerank-levelwise-skip-unchanged-components
Another omission in the paper is that Hybrid PR (just like STICD) wont work for graphs which have dead ends. This is a pre-condition for the algorithm.
Parallel KNN for Big Data using Adaptive IndexingIRJET Journal
This document presents an evaluation of different algorithms for performing parallel k-nearest neighbor (kNN) queries on big data using the MapReduce framework. It first discusses how kNN algorithms do not scale well for large datasets. It then reviews existing MapReduce-based kNN algorithms like H-BNLJ, H-zkNNJ, and RankReduce that improve performance by partitioning data and distributing computation. The document also proposes using an adaptive indexing technique with the RankReduce algorithm. An implementation of this approach on a airline on-time statistics dataset shows it achieves better precision and speed than other algorithms.
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...IRJET Journal
This document proposes a scheduling algorithm for allocating resources in cloud computing based on the Project Evaluation and Review Technique (PERT). It aims to address issues like starvation of lower priority tasks. The algorithm models task allocation as a directed acyclic graph and uses PERT to schedule critical and non-critical tasks, prioritizing higher priority tasks. The algorithm is evaluated against other scheduling methods and shows improvements in reducing completion time and optimizing resource allocation for all tasks.
Improving the Performance of Mapping based on Availability- Alert Algorithm U...AM Publications
Performance of Mapping can be improved and it is needed arise in several fields of science and engineering.
They'll be parallelized in master-worker fashion and relevant programming ways have been projected to cut back
applications. In existing system, the performance of application is considered only for homogenous systems due to simplicity.
In this we use Availability-Alert algorithm using Poisson arrival to extend our approach for Heterogeneous systems in Multi
core Architecture systems. Our proposed algorithm also considers the requirement needed for the application for their
execution in Heterogeneous systems in Multi core Architecture systems while maintaining good performance. Performance
prediction errors are minimized by using this approach at the end of the execution. We present simulation results to quantify
the benefits of our approach.
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.
In recent years the data mining applications become musty and outmoded over time. Energy wastage is the major
problem more in big data analytics and applications. More workload and more computational time will increase high energy
cost and decrease efficiency. The Incremental computational time processing is a promising approach to refreshing mining
results. It utilizes previously saved states to avoid the expense of re-computation from scratch. In this paper, we propose
Energy efficiency Map Reduce Scheduling Algorithm, a novel incremental processing extension to reduce the Map, the most
widely used framework for mining big data. Map reduce is a programming model for processing and generating large amount of data in parallel processing time. In this paper, Energy Efficiency reduce Map (EEMP) is algorithm provide more energy
and less maps in big data. Priority based scheduling is a task will allocate the schedules based on necessary and utilization of
the Jobs. For reducing the maps, it will reduce the system computational time so easily energy has improved in terms of big data applications.. Final results show the experimental comparison of the different algorithms involved in the paper.
An enhanced adaptive scoring job scheduling algorithm with replication strate...eSAT Publishing House
This document describes an enhanced adaptive scoring job scheduling algorithm with replication strategy for grid environments. The algorithm aims to improve upon an existing adaptive scoring job scheduling algorithm by identifying whether jobs are data-intensive or computation-intensive. It then divides large jobs into subtasks, replicates the subtasks, and allocates the replicas to clusters based on a computed cluster score in order to improve resource utilization and job completion times. The algorithm is evaluated through simulation using the GridSim toolkit.
Hybrid Task Scheduling Approach using Gravitational and ACO Search AlgorithmIRJET Journal
The document proposes a hybrid task scheduling approach for cloud computing called ACGSA that combines ant colony optimization and gravitational search algorithms. It describes using the Cloudsim simulator to test the performance of ACGSA and comparing it to ant colony optimization. The results show that ACGSA achieves better performance than the basic ant colony approach on relevant parameters like task scheduling time and resource utilization.
HyPR: Hybrid Page Ranking on Evolving Graphs (NOTES)Subhajit Sahu
Highlighted notes on A Parallel Algorithm Template for Updating Single-Source Shortest Paths in Large-Scale Dynamic Networks.
While doing research work under Prof. Dip Banerjee, Prof, Kishore Kothapalli.
In Hybrid Pagerank the vertices are divided in 3 groups, V_old, V_border, V_new. Scaling for old, border vertices is N/N_new, and 1/N_new for V_new (i do this too ). Then PR is run only on V_border, V_new.
"V_border which is the set of nodes which have edges in Bi connecting V_old and V_new and is reachable using a breadth first traversal."
Does that mean V_border = V_batch(i) ∩ V_old? BFS from where?
"We can assume that the new batch of updates is topologically sorted since the PR scores of the new nodes in Bi is guaranteed to be lower than those in Co."
Is sum(PR) in V_old > sum(PR) in V_new always?
"For performing the comparisons with GPMA and GPMA+, we configure the experiment to run HyPR on the same platform as used in [1] which is a Intel Xeon CPU connected to a Titan X Pascal GPU, and also the same datasets."
Old GPUs are going to be slower ...
Like we were discussing last time, it is not possible to scale old ranks, and skip the unchanged components (or here V_old). Please check this simple counter example that shows skipping leads to incorrect ranks.
https://github.com/puzzlef/pagerank-levelwise-skip-unchanged-components
Another omission in the paper is that Hybrid PR (just like STICD) wont work for graphs which have dead ends. This is a pre-condition for the algorithm.
Parallel KNN for Big Data using Adaptive IndexingIRJET Journal
This document presents an evaluation of different algorithms for performing parallel k-nearest neighbor (kNN) queries on big data using the MapReduce framework. It first discusses how kNN algorithms do not scale well for large datasets. It then reviews existing MapReduce-based kNN algorithms like H-BNLJ, H-zkNNJ, and RankReduce that improve performance by partitioning data and distributing computation. The document also proposes using an adaptive indexing technique with the RankReduce algorithm. An implementation of this approach on a airline on-time statistics dataset shows it achieves better precision and speed than other algorithms.
A MULTI-OBJECTIVE PERSPECTIVE FOR OPERATOR SCHEDULING USING FINEGRAINED DVS A...VLSICS Design
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.
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.
IRJET- A Statistical Approach Towards Energy Saving in Cloud ComputingIRJET Journal
This document proposes a statistical approach to save energy in cloud computing through predictive monitoring and optimization techniques. It discusses using Gaussian process regression to predict infrastructure workload and then applying convex optimization to determine the optimal subset of physical machines needed. Virtual machines would be migrated to this subset and idle physical machines could then be powered off to reduce energy consumption while maintaining system performance. An evaluation using 29 days of Google trace data showed the potential for significant power savings without affecting quality of service.
A survey on the performance of job scheduling in workflow applicationiaemedu
This document summarizes a survey on job scheduling performance in workflow applications on grid platforms. It discusses an adaptive dual objective scheduling (ADOS) algorithm that takes both completion time and resource usage into account for measuring schedule performance. The study shows ADOS delivers good performance in completion time, resource usage, and robustness to changes in resource performance. It also describes the system architecture used, which includes a planner and executor component. The planner focuses on scheduling to minimize completion time while considering resource usage, and can reschedule if needed. The executor enacts the schedule on the grid resources.
A HIGH SPEED LOW POWER CAM AND TCAM WITH A PARITY BIT AND POWER GATED ML SENSINGpharmaindexing
The document describes a proposed improvement to content addressable memory (CAM) to reduce power consumption and increase search speed. It introduces using a parity bit that requires less than 1% additional area and power overhead but can reduce sensing delay by 39% by making the 1-mismatch case stronger. It also proposes a power gated sense amplifier that can auto-turn off power to unused comparison elements and reduce average power consumption by 64%. The design can operate down to 0.5V supply voltage.
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...ijccsa
Fast development of knowledge and communication has established a new computational style which is
known as cloud computing. One of the main issues considered by the cloud infrastructure providers, is to
minimize the costs and maximize the profitability. Energy management in the cloud data centers is very
important to achieve such goal. Energy consumption can be reduced either by releasing idle nodes or by
reducing the virtual machines migrations. To do the latter, one of the challenges is to select the placement
approach of the migrated virtual machines on the appropriate node. In this paper, an approach to reduce
the energy consumption in cloud data centers is proposed. This approach adapts harmony search
algorithm to migrate the virtual machines. It performs the placement by sorting the nodes and virtual
machines based on their priority in descending order. The priority is calculated based on the workload.
The proposed approach is simulated. The evaluation results show the reduction in the virtual machine
migrations, the increase of efficiency and the reduction of energy consumption.
KEYWORDS
Energy Consumption, Virtual Machine Placement, Harmony Search Algorithm, Server Consolidati
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.
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.
TASK-DECOMPOSITION BASED ANOMALY DETECTION OF MASSIVE AND HIGH-VOLATILITY SES...ijdpsjournal
This document summarizes a research paper that presents a task-decomposition based anomaly detection system for analyzing massive and highly volatile session data from the Science Information Network (SINET), Japan's academic backbone network. The system uses a master-worker design with dynamic task scheduling to process over 1 billion sessions per day. It discriminates incoming and outgoing traffic using GPU parallelization and generates histograms of traffic volumes over time. Long short-term memory (LSTM) neural networks detect anomalies like spikes in incoming traffic volumes. The experiment analyzed SINET data from February 27 to March 8, 2021, detecting some anomalies while processing 500-650 gigabytes of daily session data.
This document summarizes a research paper that aims to predict delays in bus travel times in Dublin, Ireland using machine learning models. The researchers collected over 22 million records of real-time bus location and schedule data. They cleaned and preprocessed the data, engineered features, and applied support vector regression, XGBoost regression, and random forest regression models. Feature engineering improved the prediction accuracy of the models, with XGBoost achieving the best results at 69.25% accuracy. The researchers concluded that feature engineering and XGBoost are effective for predicting bus delays using transit data.
TOWARDS REDUCTION OF DATA FLOW IN A DISTRIBUTED NETWORK USING PRINCIPAL COMPO...cscpconf
For performing distributed data mining two approaches are possible: First, data from several sources are copied to a data warehouse and mining algorithms are applied in it. Secondly,
mining can performed at the local sites and the results can be aggregated. When the number of
features is high, a lot of bandwidth is consumed in transferring datasets to a centralized location. For this dimensionality reduction can be done at the local sites. In dimensionality reduction a certain encoding is applied on data so as to obtain its compressed form. The
reduced features thus obtained at the local sites are aggregated and data mining algorithms are applied on them. There are several methods of performing dimensionality reduction. Two most important ones are Discrete Wavelet Transforms (DWT) and Principal Component Analysis (PCA). Here a detailed study is done on how PCA could be useful in reducing data flow across a distributed network.
This paper presents efficient parallel algorithms for hypergraph processing implemented in a new framework called Hygra. Hygra extends the Ligra graph processing framework to support hypergraphs. It represents hypergraphs using a bipartite graph and implements optimizations from Ligra. The paper introduces parallel hypergraph algorithms for betweenness centrality, maximal independent set, k-core decomposition, hypertrees, hyperpaths, connected components, PageRank, and single-source shortest paths. Experiments show the algorithms in Hygra achieve good parallel speedup and outperform existing hypergraph frameworks.
Parallel Processing Technique for Time Efficient Matrix MultiplicationIJERA Editor
The document proposes a parallel-parallel input single output (PPI-SO) design for matrix multiplication that reduces hardware resources compared to existing designs. It uses fewer multipliers and registers than existing designs, trading off increased completion time. Simulation results show the PPI-SO design uses 30% less energy and involves 70% less area-delay product than other designs.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
www.irjes.com
A Review on Scheduling in Cloud Computingijujournal
This document reviews scheduling techniques in cloud computing. It discusses key concepts like virtualization and different scheduling algorithms. The review surveys various scheduling algorithms for tasks, workflows, real-time applications and energy efficiency. It analyzes algorithms based on parameters like makespan, cost, energy consumption and concludes many algorithms can improve resource utilization and performance while reducing energy costs.
Power consumption prediction in cloud data center using machine learningIJECEIAES
The flourishing development of the cloud computing paradigm provides several ser- vices in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.
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 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.
This document proposes a feedforward without cutset (FCF) pipelining technique for machine learning accelerators.
Traditional pipelined multiply-accumulate (MAC) units require many flip-flops along the feedforward cutset to ensure functional correctness. This increases area and power overhead as the number of pipeline stages increases.
The proposed FCF technique removes some flip-flops by relaxing the feedforward cutset rule. This is possible for machine learning applications where only the final output value is used, not intermediate values.
Simulation results showed the proposed MAC unit achieved a 20% reduction in energy and 20% reduction in area compared to a traditional pipelined MAC design.
Last four/five decades have seen revolutionary development in the field of electronics, computer and automation. Naturally avionics and C.N.S facilities also have adopted these technologies to the best of their advantage. The present paper shows how these technologies have modernized the aircraft cockpit and how C.N.S facilities have been modernised to give smooth and safe flying. The description is based on author’s observation of the development in civil aviation for the last more than four decades and future trends in this field
El documento presenta información sobre el Índice de Precios al Consumidor (IPC). Explica que el IPC mide los cambios en los precios de una canasta de bienes y servicios adquiridos por los hogares, y que sirve para calcular la inflación. También describe los cinco pasos para calcular el IPC, que incluyen fijar la cesta de bienes, averiguar los precios, calcular el costo de la cesta en diferentes años, elegir un año base para las comparaciones, y utilizar el IPC para calcular la tasa
A MULTI-OBJECTIVE PERSPECTIVE FOR OPERATOR SCHEDULING USING FINEGRAINED DVS A...VLSICS Design
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.
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.
IRJET- A Statistical Approach Towards Energy Saving in Cloud ComputingIRJET Journal
This document proposes a statistical approach to save energy in cloud computing through predictive monitoring and optimization techniques. It discusses using Gaussian process regression to predict infrastructure workload and then applying convex optimization to determine the optimal subset of physical machines needed. Virtual machines would be migrated to this subset and idle physical machines could then be powered off to reduce energy consumption while maintaining system performance. An evaluation using 29 days of Google trace data showed the potential for significant power savings without affecting quality of service.
A survey on the performance of job scheduling in workflow applicationiaemedu
This document summarizes a survey on job scheduling performance in workflow applications on grid platforms. It discusses an adaptive dual objective scheduling (ADOS) algorithm that takes both completion time and resource usage into account for measuring schedule performance. The study shows ADOS delivers good performance in completion time, resource usage, and robustness to changes in resource performance. It also describes the system architecture used, which includes a planner and executor component. The planner focuses on scheduling to minimize completion time while considering resource usage, and can reschedule if needed. The executor enacts the schedule on the grid resources.
A HIGH SPEED LOW POWER CAM AND TCAM WITH A PARITY BIT AND POWER GATED ML SENSINGpharmaindexing
The document describes a proposed improvement to content addressable memory (CAM) to reduce power consumption and increase search speed. It introduces using a parity bit that requires less than 1% additional area and power overhead but can reduce sensing delay by 39% by making the 1-mismatch case stronger. It also proposes a power gated sense amplifier that can auto-turn off power to unused comparison elements and reduce average power consumption by 64%. The design can operate down to 0.5V supply voltage.
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...ijccsa
Fast development of knowledge and communication has established a new computational style which is
known as cloud computing. One of the main issues considered by the cloud infrastructure providers, is to
minimize the costs and maximize the profitability. Energy management in the cloud data centers is very
important to achieve such goal. Energy consumption can be reduced either by releasing idle nodes or by
reducing the virtual machines migrations. To do the latter, one of the challenges is to select the placement
approach of the migrated virtual machines on the appropriate node. In this paper, an approach to reduce
the energy consumption in cloud data centers is proposed. This approach adapts harmony search
algorithm to migrate the virtual machines. It performs the placement by sorting the nodes and virtual
machines based on their priority in descending order. The priority is calculated based on the workload.
The proposed approach is simulated. The evaluation results show the reduction in the virtual machine
migrations, the increase of efficiency and the reduction of energy consumption.
KEYWORDS
Energy Consumption, Virtual Machine Placement, Harmony Search Algorithm, Server Consolidati
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.
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.
TASK-DECOMPOSITION BASED ANOMALY DETECTION OF MASSIVE AND HIGH-VOLATILITY SES...ijdpsjournal
This document summarizes a research paper that presents a task-decomposition based anomaly detection system for analyzing massive and highly volatile session data from the Science Information Network (SINET), Japan's academic backbone network. The system uses a master-worker design with dynamic task scheduling to process over 1 billion sessions per day. It discriminates incoming and outgoing traffic using GPU parallelization and generates histograms of traffic volumes over time. Long short-term memory (LSTM) neural networks detect anomalies like spikes in incoming traffic volumes. The experiment analyzed SINET data from February 27 to March 8, 2021, detecting some anomalies while processing 500-650 gigabytes of daily session data.
This document summarizes a research paper that aims to predict delays in bus travel times in Dublin, Ireland using machine learning models. The researchers collected over 22 million records of real-time bus location and schedule data. They cleaned and preprocessed the data, engineered features, and applied support vector regression, XGBoost regression, and random forest regression models. Feature engineering improved the prediction accuracy of the models, with XGBoost achieving the best results at 69.25% accuracy. The researchers concluded that feature engineering and XGBoost are effective for predicting bus delays using transit data.
TOWARDS REDUCTION OF DATA FLOW IN A DISTRIBUTED NETWORK USING PRINCIPAL COMPO...cscpconf
For performing distributed data mining two approaches are possible: First, data from several sources are copied to a data warehouse and mining algorithms are applied in it. Secondly,
mining can performed at the local sites and the results can be aggregated. When the number of
features is high, a lot of bandwidth is consumed in transferring datasets to a centralized location. For this dimensionality reduction can be done at the local sites. In dimensionality reduction a certain encoding is applied on data so as to obtain its compressed form. The
reduced features thus obtained at the local sites are aggregated and data mining algorithms are applied on them. There are several methods of performing dimensionality reduction. Two most important ones are Discrete Wavelet Transforms (DWT) and Principal Component Analysis (PCA). Here a detailed study is done on how PCA could be useful in reducing data flow across a distributed network.
This paper presents efficient parallel algorithms for hypergraph processing implemented in a new framework called Hygra. Hygra extends the Ligra graph processing framework to support hypergraphs. It represents hypergraphs using a bipartite graph and implements optimizations from Ligra. The paper introduces parallel hypergraph algorithms for betweenness centrality, maximal independent set, k-core decomposition, hypertrees, hyperpaths, connected components, PageRank, and single-source shortest paths. Experiments show the algorithms in Hygra achieve good parallel speedup and outperform existing hypergraph frameworks.
Parallel Processing Technique for Time Efficient Matrix MultiplicationIJERA Editor
The document proposes a parallel-parallel input single output (PPI-SO) design for matrix multiplication that reduces hardware resources compared to existing designs. It uses fewer multipliers and registers than existing designs, trading off increased completion time. Simulation results show the PPI-SO design uses 30% less energy and involves 70% less area-delay product than other designs.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
www.irjes.com
A Review on Scheduling in Cloud Computingijujournal
This document reviews scheduling techniques in cloud computing. It discusses key concepts like virtualization and different scheduling algorithms. The review surveys various scheduling algorithms for tasks, workflows, real-time applications and energy efficiency. It analyzes algorithms based on parameters like makespan, cost, energy consumption and concludes many algorithms can improve resource utilization and performance while reducing energy costs.
Power consumption prediction in cloud data center using machine learningIJECEIAES
The flourishing development of the cloud computing paradigm provides several ser- vices in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.
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 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.
This document proposes a feedforward without cutset (FCF) pipelining technique for machine learning accelerators.
Traditional pipelined multiply-accumulate (MAC) units require many flip-flops along the feedforward cutset to ensure functional correctness. This increases area and power overhead as the number of pipeline stages increases.
The proposed FCF technique removes some flip-flops by relaxing the feedforward cutset rule. This is possible for machine learning applications where only the final output value is used, not intermediate values.
Simulation results showed the proposed MAC unit achieved a 20% reduction in energy and 20% reduction in area compared to a traditional pipelined MAC design.
Last four/five decades have seen revolutionary development in the field of electronics, computer and automation. Naturally avionics and C.N.S facilities also have adopted these technologies to the best of their advantage. The present paper shows how these technologies have modernized the aircraft cockpit and how C.N.S facilities have been modernised to give smooth and safe flying. The description is based on author’s observation of the development in civil aviation for the last more than four decades and future trends in this field
El documento presenta información sobre el Índice de Precios al Consumidor (IPC). Explica que el IPC mide los cambios en los precios de una canasta de bienes y servicios adquiridos por los hogares, y que sirve para calcular la inflación. También describe los cinco pasos para calcular el IPC, que incluyen fijar la cesta de bienes, averiguar los precios, calcular el costo de la cesta en diferentes años, elegir un año base para las comparaciones, y utilizar el IPC para calcular la tasa
Too often we want the benefits of calling without the commitment that comes with it. Follow along with our podcast via our Elevate Church AU App, iTunes (https://itunes.apple.com/us/podcast/elevate-church/id634597356?mt=2), or your favourite podcatcher.
The Analysis of Marketing Personnel Performance Appraisal Problems of Small a...AM Publications,India
The marketing personnel play a very important position in market economy activity which enterprises engaged in. Aiming at the status of difficult to "stay" for the small and medium-sized enterprises, the paper uses performance appraisal theory systematically, analyses performance appraisal problems of marketing personnel for small and medium-sized enterprises, and provide relevant performance appraisal advices and effective measures for the marketing personnel of small and medium-sized enterprises, so as to cultivate talents, retain staff, play the role of talent and ensure the stability of the enterprise marketing team.
LinkedIn Series D Opportunity - Investment RecommendationEunice Chou
LinkedIn is the largest professional social network with over 20 million members. It generates revenue through online subscriptions, online job postings, advertising, and corporate sales. LinkedIn has experienced rapid growth in recent years and is profitable. The company is well positioned for further expansion and an IPO within 1-2 years.
Investor readiness: 99 questions from investors by Startups.beStartUps.be
Sharpen up your pitching skills: how to talk about your product, metrics, business model, team and more! Want to get investor ready? Visit www.startups.be/fundraising
Investor readiness: Startup valuation by Startups.beStartUps.be
Check out the most reliable methodologies and become aware of the risk factors. More information on getting investor-ready: www.startups.be/fundraising
Study of Effect of Rotor Speed, Combing-Roll Speed and Type of Recycled Waste...IOSR Journals
This document studies the effect of rotor speed, combing roll speed, and type of recycled waste on rotor yarn quality using response surface methodology. A central composite design was used to evaluate how these three variables impact four yarn quality responses: total imperfections, yarn strength, elongation percentage, and end breakage rate. Results show that 85,000 rpm rotor speed and 8,500 rpm combing roll speed produced the best yarn quality in terms of strength and elongation. End breakage increased significantly at higher speeds but could be reduced by adding 15% pneumafil recycled fiber. Overall, yarn quality was improved by 5-25% when using a blend of 15% pneumafil fiber compared to 100
DONA ANDERSON KUNKEL has over 20 years of experience in human resources, administrative, logistics, and import/export compliance roles. She currently serves as Human Resources Director for Culberson Construction, managing payroll, benefits, and employee relations for over 500 employees. Prior to this, she held roles managing customs clearance, import documentation, licensing, and logistics for various companies. Kunkel has a proven track record of streamlining processes, improving customer service and reducing costs. She is proficient in customs compliance, import/export documentation, financial reconciliation, and human resources.
With online shopping spreading quickly, China ' s e-commerce market is rapidly expanding; the e-commerce distribution bottlenecks gradual emerge. Logistics distribution has restricted the development of Taobao.It is proved that during the process of distributing activity, information processing and dispatching speed can be speeded up with computer, telecommunication and network technologies, so improving efficiency and competence greatly. Alibaba set up rookie network aimed at long-term growth. The necessary of setting up “Rookie network” as well as business models of“Rookie network” were detailed in this article. Last,the effects of Rookie Network on market was analyzed.
Programming Modes and Performance of Raspberry-Pi ClustersAM Publications
In present times, updated information and knowledge has become readily accessible to researchers, enthusiasts, developers, and academics through the Internet on many different subjects for wider areas of application. The underlying framework facilitating such possibilities is networking of servers, nodes, and personal computers. However, such setups, comprising of mainframes, servers and networking devices are inaccessible to many, costly, and are not portable. In addition, students and lab-level enthusiasts do not have the requisite access to modify the functionality to suit specific purposes. The Raspberry-Pi (R-Pi) is a small device capable of many functionalities akin to super-computing while being portable, economical and flexible. It runs on open source Linux, making it a preferred choice for lab-level research and studies. Users have started using the embedded networking capability to design portable clusters that replace the costlier machines. This paper introduces new users to the most commonly used frameworks and some recent developments that best exploit the capabilities of R-Pi when used in clusters. This paper also introduces some of the tools and measures that rate efficiencies of clusters to help users assess the quality of cluster design. The paper aims to make users aware of the various parameters in a cluster environment.
Implementation of p pic algorithm in map reduce to handle big dataeSAT Publishing House
This document presents an implementation of the p-PIC clustering algorithm using the MapReduce framework to handle big data. P-PIC is a parallel version of the Power Iteration Clustering (PIC) algorithm that is able to cluster large datasets in a distributed environment. The document first provides background on PIC and challenges with scaling to big data. It then describes how p-PIC addresses these challenges using MPI for parallelization. The design of implementing p-PIC within MapReduce is presented, including the map and reduce functions. Experimental results on synthetic datasets up to 100,000 records show that p-PIC using MapReduce has increased performance and scalability compared to the original p-PIC implementation using MPI.
Energy-Efficient Task Scheduling in Cloud EnvironmentIRJET Journal
1. The document discusses developing an energy-efficient task scheduling approach for cloud data centers using deep reinforcement learning.
2. It aims to minimize computational costs and cooling costs by optimizing task assignment to servers based on factors like temperature, CPU, and memory.
3. The proposed approach uses a greedy algorithm to schedule tasks to servers maintaining the lowest temperature, thus reducing energy consumption and improving data center performance.
Improved Utilization of Infrastructure of Clouds by using Upgraded Functional...AM Publications
This paper discusses a propose cloud infrastructure that combines On-Demand allocation of resources with
improved utilization, opportunistic provisioning of cycles from idle cloud nodes to other processes. Because for cloud
computing to avail all the demanded services to the cloud consumers is very difficult. It is a major issue to meet cloud
consumer’s requirements. Hence On-Demand cloud infrastructure using Hadoop configuration with improved CPU
utilization and storage utilization is proposed using splitting algorithm by using Map-Reduce. Hence all cloud nodes which
remains idle are all in use and also improvement in security challenges and achieves load balancing and fast processing of
large data in less amount of time. Here we compare the FTP and HDFS for file uploading and file downloading; and
enhance the CPU utilization and storage utilization. Cloud computing moves the application software and databases to the
large data centres, where the management of the data and services may not be fully trustworthy. Therefore this security
problem is solve by encrypting the data using encryption/decryption algorithm and Map-Reducing algorithm which solve
the problem of utilization of all idle cloud nodes for larger data.
An Energy Efficient Data Transmission and Aggregation of WSN using Data Proce...IRJET Journal
The document proposes a system for efficient data transmission and aggregation in wireless sensor networks (WSNs) using MapReduce processing. Sensors are grouped into three clusters, with a cluster head elected in each based on distance, memory, and battery to reduce energy consumption. Sensor data is encrypted and sent to cluster heads, which aggregate the data and append a signature before sending to the base station. The signature is verified and data is stored in Hadoop and processed using MapReduce. The system aims to provide data integrity and privacy during concealed data aggregation to reduce overhead in heterogeneous WSNs.
Distributed Feature Selection for Efficient Economic Big Data AnalysisIRJET Journal
The document proposes a new framework for efficiently analyzing large and high-dimensional economic big data. The framework combines methods for economic feature selection and econometric model construction to identify patterns in economic development from vast amounts of economic indicator data. It relies on three key aspects: 1) novel data pre-processing techniques to prepare high-quality economic data, 2) an innovative distributed feature identification solution to locate important economic indicators from multidimensional datasets, and 3) new econometric models to capture patterns of economic development. The framework is demonstrated on economic data collected over 30 years from over 300 towns in Dalian, China.
PERFORMANCE FACTORS OF CLOUD COMPUTING DATA CENTERS USING [(M/G/1) : (∞/GDMOD...ijgca
This document discusses modeling cloud computing data centers as queuing systems to analyze performance factors. It begins with background on cloud computing and queuing theory. It then models a cloud data center as an [(M/G/1) : (∞/GDMODEL)] queuing system with single task arrivals and infinite task buffer capacity. Key performance factors analyzed include mean number of tasks in the system. Analytical results are obtained by solving the model to estimate response time distribution and other metrics. The modeling approach allows determining the relationship between performance and number of servers/buffer size.
PERFORMANCE FACTORS OF CLOUD COMPUTING DATA CENTERS USING [(M/G/1) : (∞/GDMOD...ijgca
This document discusses modeling cloud computing data centers as queuing systems to analyze performance factors. It presents an analytical model of a cloud data center as a [(M/G/1) : (∞/GDMODEL)] queuing system with single task arrivals and infinite task buffer capacity. The model is solved to obtain important performance metrics like mean number of tasks in the system. Prior work on modeling cloud systems and queuing theory concepts are also reviewed. Key assumptions of the proposed model include tasks following a Poisson arrival process and service times having a general probability distribution.
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.
Optimized Resource Provisioning Method for Computational Gridijgca
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
IRJET-Framework for Dynamic Resource Allocation and Efficient Scheduling Stra...IRJET Journal
This document discusses a framework for dynamic resource allocation and efficient scheduling strategies in cloud computing platforms for high-performance computing (HPC). It proposes using a parallel genetic algorithm to find optimal allocation of virtual machines to physical resources in order to maximize resource utilization. The algorithm represents the resource allocation problem as an unbalanced job scheduling problem. It uses genetic operators like mutation and crossover to efficiently allocate requests for resources to idle nodes. Compared to a traditional genetic algorithm, the parallel genetic algorithm improves the speed of finding the best allocation and increases resource utilization. Future work could explore implementing dynamic load balancing and using big data concepts on the cloud.
High Dimensionality Structures Selection for Efficient Economic Big data usin...IRJET Journal
This document proposes a new framework for efficient analysis of high-dimensional economic big data using feature selection and k-means clustering algorithms. It introduces challenges in analyzing large volumes of economic data with high dimensionality. The framework combines methods for economic feature selection and model construction to identify patterns for economic development. It uses novel data preprocessing, distributed feature identification to select important indicators, and new econometric models to capture hidden patterns for economic analysis. The results on economic data sets demonstrate superior performance of the proposed methods.
Service Request Scheduling in Cloud Computing using Meta-Heuristic Technique:...IRJET Journal
This document discusses using the Teaching Learning Based Optimization (TLBO) meta-heuristic technique for service request scheduling between users and cloud service providers. TLBO is a nature-inspired algorithm that mimics the teacher-student learning process. It is compared to other meta-heuristic algorithms like Genetic Algorithm. The key steps of TLBO involve initializing a population, evaluating fitness, selecting the best solution as teacher, and updating the population through teacher and learner phases until termination criteria is met. The document proposes using number of users and virtual machines as parameters for TLBO scheduling in cloud computing. MATLAB simulation results show the initial and final iterations converging to an optimal scheduling solution.
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.
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.
Evolutionary Multi-Goal Workflow Progress in ShadeIRJET Journal
This document summarizes an evolutionary multi-goal workflow scheduling algorithm for cloud computing environments. It begins by highlighting challenges with applying existing scheduling algorithms to clouds, which differ from traditional heterogeneous environments. It formulates the cloud workflow scheduling problem to optimize makespan and cost simultaneously as a multi-objective optimization problem. The paper then proposes an evolutionary algorithm-based approach using novel encoding, population initialization, fitness evaluation, and genetic operators tailored for this problem. Experimental results show the algorithm can achieve better solutions than existing QoS optimization scheduling algorithms in most cases.
Qo s aware scientific application scheduling algorithm in cloud environmentAlexander Decker
This document summarizes a research paper that proposes a scheduling algorithm for scientific applications in cloud environments. The algorithm aims to schedule tasks in workflows based on user preferences for quality of service (QoS), like time and cost. It ranks tasks and uses an UPFF function to select resources that meet the user's desired QoS. The algorithm is compared to other similar algorithms through scenarios, and results show it has better efficiency. The full paper provides more details on scientific workflows, cloud computing, related work on workflow scheduling algorithms, and defines the problem of scheduling tasks to resources while considering costs and times.
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Discovering the Best Indian Architects A Spotlight on Design Forum Internatio...Designforuminternational
India’s architectural landscape is a vibrant tapestry that weaves together the country's rich cultural heritage and its modern aspirations. From majestic historical structures to cutting-edge contemporary designs, the work of Indian architects is celebrated worldwide. Among the many firms shaping this dynamic field, Design Forum International stands out as a leader in innovative and sustainable architecture. This blog explores some of the best Indian architects, highlighting their contributions and showcasing the most famous architects in India.
Architectural and constructions management experience since 2003 including 18 years located in UAE.
Coordinate and oversee all technical activities relating to architectural and construction projects,
including directing the design team, reviewing drafts and computer models, and approving design
changes.
Organize and typically develop, and review building plans, ensuring that a project meets all safety and
environmental standards.
Prepare feasibility studies, construction contracts, and tender documents with specifications and
tender analyses.
Consulting with clients, work on formulating equipment and labor cost estimates, ensuring a project
meets environmental, safety, structural, zoning, and aesthetic standards.
Monitoring the progress of a project to assess whether or not it is in compliance with building plans
and project deadlines.
Attention to detail, exceptional time management, and strong problem-solving and communication
skills are required for this role.
International Upcycling Research Network advisory board meeting 4Kyungeun Sung
Slides used for the International Upcycling Research Network advisory board 4 (last one). The project is based at De Montfort University in Leicester, UK, and funded by the Arts and Humanities Research Council.
Best Digital Marketing Strategy Build Your Online Presence 2024.pptxpavankumarpayexelsol
This presentation provides a comprehensive guide to the best digital marketing strategies for 2024, focusing on enhancing your online presence. Key topics include understanding and targeting your audience, building a user-friendly and mobile-responsive website, leveraging the power of social media platforms, optimizing content for search engines, and using email marketing to foster direct engagement. By adopting these strategies, you can increase brand visibility, drive traffic, generate leads, and ultimately boost sales, ensuring your business thrives in the competitive digital landscape.