In our days, there are many real-time applications that use data which are geographically
dispersed. The distributed real-time database management systems (DRTDBMS) have been used
to manage large amount of distributed data while meeting the stringent temporal requirements
in real-time applications. Providing Quality of Service (QoS) guarantees in DRTDBMSs is a
challenging task. To address this problem, different research works are often based on
distributed feedback control real-time scheduling architecture (DFCSA). Data replication is an
efficient method to help DRTDBMS meeting the stringent temporal requirements of real-time
applications. In literature, many works have designed algorithms that provide QoS guarantees
in distributed real-time databases using only full temporal data replication policy.
In this paper, we have applied two data replication policies in a distributed feedback control
scheduling architecture to manage a QoS performance for DRTDBMS. The first proposed data
replication policy is called semi-total replication, and the second is called partial replication
policy.
1) The document discusses quality of service (QoS)-aware data replication for data-intensive applications in cloud computing systems. It aims to minimize data replication cost and number of QoS violated replicas.
2) It presents a mathematical model and algorithm to optimally place QoS-satisfied and QoS-violated data replicas. The algorithm uses minimum-cost maximum flow to obtain the optimal placement.
3) The algorithm takes as input a set of requested nodes and outputs the optimal placement for QoS-satisfied and QoS-violated replicas by modeling the problem as a network flow graph and applying existing polynomial-time algorithms.
Data Distribution Handling on Cloud for Deployment of Big Dataijccsa
Cloud computing is a new emerging model in the field of computer science. For varying workload Cloud computing presents a large scale on demand infrastructure. The primary usage of clouds in practice is to process massive amounts of data. Processing large datasets has become crucial in research and business environments. The big challenges associated with processing large datasets is the vast infrastructure required. Cloud computing provides vast infrastructure to store and process Big data. Vms can be provisioned on demand in cloud to process the data by forming cluster of Vms . Map Reduce paradigm can be used to process data wherein the mapper assign part of task to particular Vms in cluster and reducer combines individual output from each Vms to produce final result. we have proposed an algorithm to reduce the overall data distribution and processing time. We tested our solution in Cloud Analyst Simulation environment wherein, we found that our proposed algorithm significantly reduces the overall data processing time in cloud.
This document discusses synchronization and replication in occasionally connected mobile database systems. It begins by describing the architecture of such systems, where mobile clients maintain local copies of shared data and synchronize with a central server when reconnected. It then discusses using a data-centric approach where data is grouped and clients subscribe to relevant groups. The document proposes a grouping estimation algorithm to determine optimal data groupings. Finally, it describes how primary and secondary copies are used for replication among clients and servers, and the need for synchronization when clients operate offline.
This document discusses scheduling algorithms for batches of MapReduce jobs in heterogeneous cloud environments with budget and deadline constraints. It proposes two optimization problems: 1) Given a fixed budget B, how to efficiently schedule tasks to minimize workflow completion time without exceeding the budget. 2) Given a fixed deadline D, how to efficiently schedule tasks to minimize monetary cost without missing the deadline. It presents an optimal dynamic programming algorithm for the first problem that runs in O(κB2) time, and two faster greedy algorithms. It also briefly discusses reducing the second problem to a knapsack problem. The goal is to help cloud service providers deploy MapReduce cost-effectively given user constraints.
Differentiating Algorithms of Cloud Task Scheduling Based on various Parametersiosrjce
Cloud computing is a new design structure for large, distributed data centers. Cloud computing
system promises to offer end user “pay as go” model. To meet the expected quality requirements of users, cloud
computing need to offer differentiated services to users. QoS differentiation is very important to satisfy
different users with different QoS requirements. In this paper, various QoS based scheduling algorithms,
scheduling parameters and the future scope of discussed algorithms have been studied. This paper summarizes
various cloud scheduling algorithms, findings of algorithms, scheduling factors, type of scheduling and
parameters considered
THRESHOLD BASED VM PLACEMENT TECHNIQUE FOR LOAD BALANCED RESOURCE PROVISIONIN...IJCNCJournal
The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.
IRJET- Improving Data Availability by using VPC Strategy in Cloud Environ...IRJET Journal
This document discusses improving data availability in cloud environments using virtual private cloud (VPC) strategies and data replication strategies (DRS). It proposes using VPC to define private networks in public clouds and deploying cloud resources into those private networks for improved security and control. It also proposes using DRS to store multiple copies of data across different nodes to increase data availability, reduce bandwidth usage, and provide fault tolerance. The proposed approach identifies popular data files for replication, selects the best storage sites based on factors like request frequency, failure probability, and storage usage, and decides when to replace replicas to optimize resource usage. A simulation showed this hybrid VPC and DRS approach improved performance metrics like response time, network usage, and load balancing compared to
In this paper we explore the issue of store determination in a portable shared specially appointed system. In our vision reserve determination ought to fulfill the accompanying prerequisites: (i) it ought to bring about low message overhead and (ii) the data ought to be recovered with least postponement. In this paper, we demonstrate that these objectives can be accomplished by part the one bounce neighbors into two sets in view of the transmission run. The proposed approach lessens the quantity of messages overflowed into the system to discover the asked for information. This plan is completely circulated and comes requiring little to no effort as far as store overhead. The test comes about gives a promising outcome in view of the measurements of studies.
1) The document discusses quality of service (QoS)-aware data replication for data-intensive applications in cloud computing systems. It aims to minimize data replication cost and number of QoS violated replicas.
2) It presents a mathematical model and algorithm to optimally place QoS-satisfied and QoS-violated data replicas. The algorithm uses minimum-cost maximum flow to obtain the optimal placement.
3) The algorithm takes as input a set of requested nodes and outputs the optimal placement for QoS-satisfied and QoS-violated replicas by modeling the problem as a network flow graph and applying existing polynomial-time algorithms.
Data Distribution Handling on Cloud for Deployment of Big Dataijccsa
Cloud computing is a new emerging model in the field of computer science. For varying workload Cloud computing presents a large scale on demand infrastructure. The primary usage of clouds in practice is to process massive amounts of data. Processing large datasets has become crucial in research and business environments. The big challenges associated with processing large datasets is the vast infrastructure required. Cloud computing provides vast infrastructure to store and process Big data. Vms can be provisioned on demand in cloud to process the data by forming cluster of Vms . Map Reduce paradigm can be used to process data wherein the mapper assign part of task to particular Vms in cluster and reducer combines individual output from each Vms to produce final result. we have proposed an algorithm to reduce the overall data distribution and processing time. We tested our solution in Cloud Analyst Simulation environment wherein, we found that our proposed algorithm significantly reduces the overall data processing time in cloud.
This document discusses synchronization and replication in occasionally connected mobile database systems. It begins by describing the architecture of such systems, where mobile clients maintain local copies of shared data and synchronize with a central server when reconnected. It then discusses using a data-centric approach where data is grouped and clients subscribe to relevant groups. The document proposes a grouping estimation algorithm to determine optimal data groupings. Finally, it describes how primary and secondary copies are used for replication among clients and servers, and the need for synchronization when clients operate offline.
This document discusses scheduling algorithms for batches of MapReduce jobs in heterogeneous cloud environments with budget and deadline constraints. It proposes two optimization problems: 1) Given a fixed budget B, how to efficiently schedule tasks to minimize workflow completion time without exceeding the budget. 2) Given a fixed deadline D, how to efficiently schedule tasks to minimize monetary cost without missing the deadline. It presents an optimal dynamic programming algorithm for the first problem that runs in O(κB2) time, and two faster greedy algorithms. It also briefly discusses reducing the second problem to a knapsack problem. The goal is to help cloud service providers deploy MapReduce cost-effectively given user constraints.
Differentiating Algorithms of Cloud Task Scheduling Based on various Parametersiosrjce
Cloud computing is a new design structure for large, distributed data centers. Cloud computing
system promises to offer end user “pay as go” model. To meet the expected quality requirements of users, cloud
computing need to offer differentiated services to users. QoS differentiation is very important to satisfy
different users with different QoS requirements. In this paper, various QoS based scheduling algorithms,
scheduling parameters and the future scope of discussed algorithms have been studied. This paper summarizes
various cloud scheduling algorithms, findings of algorithms, scheduling factors, type of scheduling and
parameters considered
THRESHOLD BASED VM PLACEMENT TECHNIQUE FOR LOAD BALANCED RESOURCE PROVISIONIN...IJCNCJournal
The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.
IRJET- Improving Data Availability by using VPC Strategy in Cloud Environ...IRJET Journal
This document discusses improving data availability in cloud environments using virtual private cloud (VPC) strategies and data replication strategies (DRS). It proposes using VPC to define private networks in public clouds and deploying cloud resources into those private networks for improved security and control. It also proposes using DRS to store multiple copies of data across different nodes to increase data availability, reduce bandwidth usage, and provide fault tolerance. The proposed approach identifies popular data files for replication, selects the best storage sites based on factors like request frequency, failure probability, and storage usage, and decides when to replace replicas to optimize resource usage. A simulation showed this hybrid VPC and DRS approach improved performance metrics like response time, network usage, and load balancing compared to
In this paper we explore the issue of store determination in a portable shared specially appointed system. In our vision reserve determination ought to fulfill the accompanying prerequisites: (i) it ought to bring about low message overhead and (ii) the data ought to be recovered with least postponement. In this paper, we demonstrate that these objectives can be accomplished by part the one bounce neighbors into two sets in view of the transmission run. The proposed approach lessens the quantity of messages overflowed into the system to discover the asked for information. This plan is completely circulated and comes requiring little to no effort as far as store overhead. The test comes about gives a promising outcome in view of the measurements of studies.
Data Warehouses store integrated and consistent data in a subject-oriented data repository dedicated
especially to support business intelligence processes. However, keeping these repositories updated usually
involves complex and time-consuming processes, commonly denominated as Extract-Transform-Load tasks.
These data intensive tasks normally execute in a limited time window and their computational requirements
tend to grow in time as more data is dealt with. Therefore, we believe that a grid environment could suit
rather well as support for the backbone of the technical infrastructure with the clear financial advantage of
using already acquired desktop computers normally present in the organization. This article proposes a
different approach to deal with the distribution of ETL processes in a grid environment, taking into account
not only the processing performance of its nodes but also the existing bandwidth to estimate the grid
availability in a near future and therefore optimize workflow distribution.
Role of Operational System Design in Data Warehouse Implementation: Identifyi...iosrjce
Data warehouse designing process takes input from operational system of the organization. Quality
of data warehousing solution depends on design of operational system. Often, operational system
implementations of organizations have some limitations. Thus, we cannot proceed for data warehouse
designing so easily. In this paper, we have tried to investigate operational system of the organization for
identifying such limitations and determine role of operational system design in the process of data warehouse
design and implementation. We have worked out to find possible methods to handle such limitations and have
proposed techniques to get a quality data warehousing solution under such limitations. To make the work based
on live example, National Rural Health Mission (NRHM) Project has been taken. It is a national project of
health sector, managed by Indian Government across the country. The complex structure and high volume of
data makes it an ideal case for data warehouse implementation.
RESOURCE ALLOCATION METHOD FOR CLOUD COMPUTING ENVIRONMENTS WITH DIFFERENT SE...IJCNCJournal
In a cloud computing environment with multiple data centers over a wide area, it is highly likely that each data center would provide the different service quality to users at different locations. It is also required to consider the nodes at the edge of the network (local cloud) which support applications such as IoTs that require low latency and location awareness. The authors proposed the joint multiple resource allocation method in a cloud computing environment that consists of multiple data centers and each data center provides the different network delay. However, the existing method does not take account of cases where requests that require a short network delay occur more than expected. Moreover, the existing method does not take account of service processing time in data centers and therefore cannot provide the optimal resource allocation when it is necessary to take the total processing time (both network delay and service processing time in a data center) into consideration in resource allocation.
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.
Overlapped clustering approach for maximizing the service reliability ofIAEME Publication
This document discusses an overlapped clustering approach for maximizing the reliability of heterogeneous distributed computing systems. It proposes assigning tasks to nodes based on their requirements in order to reduce network bandwidth usage and enable local communication. It calculates the reliability of each node and assigns more resource-intensive tasks to more reliable nodes. When nodes fail, it uses load balancing techniques like redistributing tasks from overloaded or failed nodes to idle nodes in the same cluster. The goal is to improve system reliability through approaches like minimizing network communication, assigning tasks based on node reliability, and handling failures through load balancing at the cluster level.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
This document summarizes a research paper that proposes a new algorithm called KD-Tree approach for efficient virtual machine (VM) allocation in cloud computing environments. The algorithm aims to minimize the response time for allocating VMs to user requests. It does this by adopting a KD-Tree data structure to index physical host machines, allowing the scheduler to quickly find the host that can accommodate a new VM request with the minimum latency in O(Log n) time. The proposed approach is evaluated through simulations using the CloudSim toolkit and is shown to outperform an existing linear scheduling strategy (LSTR) algorithm in terms of reducing VM allocation times.
PROVABLE MULTICOPY DYNAMIC DATA POSSESSION IN CLOUD COMPUTING SYSTEMSNexgen Technology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
This document evaluates the performance of a hybrid differential evolution-genetic algorithm (DE-GA) approach for load balancing in cloud computing. It first provides background on cloud computing and load balancing. It then describes the DE-GA approach, which uses differential evolution initially and switches to genetic algorithm if needed. The results show that the hybrid DE-GA approach improves performance over differential evolution and genetic algorithm alone, reducing makespan, average response time, and improving resource utilization. The study demonstrates the benefits of the hybrid evolutionary algorithm for an important problem in cloud computing.
Many real-time systems are naturally distributed and these distributed systems require not only highavailability
but also timely execution of transactions. Consequently, eventual consistency, a weaker type of
strong consistency is an attractive choice for a consistency level. Unfortunately, standard eventual
consistency, does not contain any real-time considerations. In this paper we have extended eventual
consistency with real-time constraints and this we call real-time eventual consistency. Followed by this new
definition we have proposed a method that follows this new definition. We present a new algorithm using
revision diagrams and fork-join data in a real-time distributed environment and we show that the proposed
method solves the problem.
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.
Challenges in Dynamic Resource Allocation and Task Scheduling in Heterogeneou...rahulmonikasharma
This document discusses the challenges of dynamic resource allocation and task scheduling in heterogeneous cloud environments. It outlines that resource allocation involves deciding how to allocate resources to tasks to maximize utilization, while task scheduling assigns tasks to processors to minimize execution time. The major challenges are optimizing allocated resources to minimize costs while meeting customer demands and application requirements. Allocating resources dynamically in heterogeneous cloud environments is difficult due to issues like resource contention, scarcity, and fragmentation. The document also discusses approaches to resource modeling, allocation, offering, discovery and monitoring that algorithms must address to effectively allocate resources on demand.
Provable Multicopy Dynamic Data Possession in Cloud Computing Systems1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
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Phone : +91 97518 00789 / +91 72999 51536
This document summarizes a research paper that proposes a new join operator called C JOIN for highly concurrent data warehouses. C JOIN improves upon the traditional query-at-a-time model by employing a single physical plan that can share I/O, computation, and tuple storage across concurrent join queries. The design allows the query engine to scale gracefully to large datasets and numbers of concurrent queries, provide predictable execution times, and reduce contention compared to commercial and open-source database systems. An empirical evaluation found that C JOIN outperforms these other systems by an order of magnitude for tens to hundreds of concurrent queries on the Star Schema Benchmark.
An asynchronous replication model to improve data available into a heterogene...Alexander Decker
This document summarizes a research paper that proposes an asynchronous replication model to improve data availability in heterogeneous systems. The proposed model uses a loosely coupled architecture between main and replication servers to reduce dependencies. It also supports heterogeneous systems, allowing different parts of an application to run on different systems for better performance. This makes it a cost-effective solution for data replication across different system types.
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.
Ankur Shukla is seeking a position that utilizes his 2 years of experience in the telecom industry and qualifications in electrical engineering. He currently works as a Quality Engineer for Bharti Infratel Ltd, where he is responsible for quality assurance, inspections, and providing feedback to improve site progress. His experience includes training in deployment, supply chain management, planning, quality, and operations and maintenance functions. He aims to contribute leadership skills and a drive for productivity and quality improvement.
El documento presenta información sobre sistemas educativos en la nube. Explica conceptos como cloud storage, ventajas de la computación en la nube como ser económico e independiente, y beneficios para la educación como el trabajo colaborativo a distancia y el uso de recursos de manera razonada. También identifica herramientas tecnológicas como Skype, Facebook, blogs y Twitter y cómo pueden usarse en el ámbito educativo.
1) The document is a newsletter from the Huldah Ministry, a Christian organization. It discusses several topics related to scripture and its importance.
2) It summarizes Romans 13, which talks about obeying governing authorities as established by God. It also discusses living righteously and with love.
3) The newsletter argues that scripture alone provides true future hope. It says the continued existence of Jews despite persecution is proof of God's existence, as scripture promised their survival.
Data Warehouses store integrated and consistent data in a subject-oriented data repository dedicated
especially to support business intelligence processes. However, keeping these repositories updated usually
involves complex and time-consuming processes, commonly denominated as Extract-Transform-Load tasks.
These data intensive tasks normally execute in a limited time window and their computational requirements
tend to grow in time as more data is dealt with. Therefore, we believe that a grid environment could suit
rather well as support for the backbone of the technical infrastructure with the clear financial advantage of
using already acquired desktop computers normally present in the organization. This article proposes a
different approach to deal with the distribution of ETL processes in a grid environment, taking into account
not only the processing performance of its nodes but also the existing bandwidth to estimate the grid
availability in a near future and therefore optimize workflow distribution.
Role of Operational System Design in Data Warehouse Implementation: Identifyi...iosrjce
Data warehouse designing process takes input from operational system of the organization. Quality
of data warehousing solution depends on design of operational system. Often, operational system
implementations of organizations have some limitations. Thus, we cannot proceed for data warehouse
designing so easily. In this paper, we have tried to investigate operational system of the organization for
identifying such limitations and determine role of operational system design in the process of data warehouse
design and implementation. We have worked out to find possible methods to handle such limitations and have
proposed techniques to get a quality data warehousing solution under such limitations. To make the work based
on live example, National Rural Health Mission (NRHM) Project has been taken. It is a national project of
health sector, managed by Indian Government across the country. The complex structure and high volume of
data makes it an ideal case for data warehouse implementation.
RESOURCE ALLOCATION METHOD FOR CLOUD COMPUTING ENVIRONMENTS WITH DIFFERENT SE...IJCNCJournal
In a cloud computing environment with multiple data centers over a wide area, it is highly likely that each data center would provide the different service quality to users at different locations. It is also required to consider the nodes at the edge of the network (local cloud) which support applications such as IoTs that require low latency and location awareness. The authors proposed the joint multiple resource allocation method in a cloud computing environment that consists of multiple data centers and each data center provides the different network delay. However, the existing method does not take account of cases where requests that require a short network delay occur more than expected. Moreover, the existing method does not take account of service processing time in data centers and therefore cannot provide the optimal resource allocation when it is necessary to take the total processing time (both network delay and service processing time in a data center) into consideration in resource allocation.
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.
Overlapped clustering approach for maximizing the service reliability ofIAEME Publication
This document discusses an overlapped clustering approach for maximizing the reliability of heterogeneous distributed computing systems. It proposes assigning tasks to nodes based on their requirements in order to reduce network bandwidth usage and enable local communication. It calculates the reliability of each node and assigns more resource-intensive tasks to more reliable nodes. When nodes fail, it uses load balancing techniques like redistributing tasks from overloaded or failed nodes to idle nodes in the same cluster. The goal is to improve system reliability through approaches like minimizing network communication, assigning tasks based on node reliability, and handling failures through load balancing at the cluster level.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
This document summarizes a research paper that proposes a new algorithm called KD-Tree approach for efficient virtual machine (VM) allocation in cloud computing environments. The algorithm aims to minimize the response time for allocating VMs to user requests. It does this by adopting a KD-Tree data structure to index physical host machines, allowing the scheduler to quickly find the host that can accommodate a new VM request with the minimum latency in O(Log n) time. The proposed approach is evaluated through simulations using the CloudSim toolkit and is shown to outperform an existing linear scheduling strategy (LSTR) algorithm in terms of reducing VM allocation times.
PROVABLE MULTICOPY DYNAMIC DATA POSSESSION IN CLOUD COMPUTING SYSTEMSNexgen Technology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
This document evaluates the performance of a hybrid differential evolution-genetic algorithm (DE-GA) approach for load balancing in cloud computing. It first provides background on cloud computing and load balancing. It then describes the DE-GA approach, which uses differential evolution initially and switches to genetic algorithm if needed. The results show that the hybrid DE-GA approach improves performance over differential evolution and genetic algorithm alone, reducing makespan, average response time, and improving resource utilization. The study demonstrates the benefits of the hybrid evolutionary algorithm for an important problem in cloud computing.
Many real-time systems are naturally distributed and these distributed systems require not only highavailability
but also timely execution of transactions. Consequently, eventual consistency, a weaker type of
strong consistency is an attractive choice for a consistency level. Unfortunately, standard eventual
consistency, does not contain any real-time considerations. In this paper we have extended eventual
consistency with real-time constraints and this we call real-time eventual consistency. Followed by this new
definition we have proposed a method that follows this new definition. We present a new algorithm using
revision diagrams and fork-join data in a real-time distributed environment and we show that the proposed
method solves the problem.
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.
Challenges in Dynamic Resource Allocation and Task Scheduling in Heterogeneou...rahulmonikasharma
This document discusses the challenges of dynamic resource allocation and task scheduling in heterogeneous cloud environments. It outlines that resource allocation involves deciding how to allocate resources to tasks to maximize utilization, while task scheduling assigns tasks to processors to minimize execution time. The major challenges are optimizing allocated resources to minimize costs while meeting customer demands and application requirements. Allocating resources dynamically in heterogeneous cloud environments is difficult due to issues like resource contention, scarcity, and fragmentation. The document also discusses approaches to resource modeling, allocation, offering, discovery and monitoring that algorithms must address to effectively allocate resources on demand.
Provable Multicopy Dynamic Data Possession in Cloud Computing Systems1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
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This document summarizes a research paper that proposes a new join operator called C JOIN for highly concurrent data warehouses. C JOIN improves upon the traditional query-at-a-time model by employing a single physical plan that can share I/O, computation, and tuple storage across concurrent join queries. The design allows the query engine to scale gracefully to large datasets and numbers of concurrent queries, provide predictable execution times, and reduce contention compared to commercial and open-source database systems. An empirical evaluation found that C JOIN outperforms these other systems by an order of magnitude for tens to hundreds of concurrent queries on the Star Schema Benchmark.
An asynchronous replication model to improve data available into a heterogene...Alexander Decker
This document summarizes a research paper that proposes an asynchronous replication model to improve data availability in heterogeneous systems. The proposed model uses a loosely coupled architecture between main and replication servers to reduce dependencies. It also supports heterogeneous systems, allowing different parts of an application to run on different systems for better performance. This makes it a cost-effective solution for data replication across different system types.
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.
Ankur Shukla is seeking a position that utilizes his 2 years of experience in the telecom industry and qualifications in electrical engineering. He currently works as a Quality Engineer for Bharti Infratel Ltd, where he is responsible for quality assurance, inspections, and providing feedback to improve site progress. His experience includes training in deployment, supply chain management, planning, quality, and operations and maintenance functions. He aims to contribute leadership skills and a drive for productivity and quality improvement.
El documento presenta información sobre sistemas educativos en la nube. Explica conceptos como cloud storage, ventajas de la computación en la nube como ser económico e independiente, y beneficios para la educación como el trabajo colaborativo a distancia y el uso de recursos de manera razonada. También identifica herramientas tecnológicas como Skype, Facebook, blogs y Twitter y cómo pueden usarse en el ámbito educativo.
1) The document is a newsletter from the Huldah Ministry, a Christian organization. It discusses several topics related to scripture and its importance.
2) It summarizes Romans 13, which talks about obeying governing authorities as established by God. It also discusses living righteously and with love.
3) The newsletter argues that scripture alone provides true future hope. It says the continued existence of Jews despite persecution is proof of God's existence, as scripture promised their survival.
Carolynn Wicks' supervisor recommends her for a leadership position. Carolynn has exceeded expectations in her role as Administrative Manager, demonstrating initiative, competent judgment, and effective communication. She manages her office efficiently, multitasking and filling in wherever needed. Carolynn is also highly trustworthy, maintaining confidentiality and honoring all stakeholders. The principal is certain Carolynn will excel in a new leadership role.
Kristina Toto is a graphic and web designer seeking a new design position. She has over 10 years of experience as both a freelance and in-house designer. She has created designs for menus, brochures, flyers, posters, magazines, web pages, logos, branding, invitations and photography. Kristina is proficient in Adobe Creative Suite and has a Bachelor's and Associate's degree in graphic design from Herzing University. She is detail-oriented, able to meet deadlines, and has experience managing projects from initial client meetings through final delivery.
A sat encoding for solving games with energy objectivescsandit
This document presents a reduction from the problem of solving energy games to the satisfiability problem for formulas of propositional logic. Energy games model resource-constrained reactive systems and are equivalent to mean-payoff games. The document proposes encoding winning strategies for energy games using difference logic and propositional logic. It reports tight size bounds for these encodings and argues they could lead to more efficient solving algorithms by leveraging modern SAT solvers.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms for those who already suffer from conditions like depression and anxiety.
QUALITY OF SERVICE MANAGEMENT IN DISTRIBUTED FEEDBACK CONTROL SCHEDULING ARCH...cscpconf
The document discusses two approaches to managing quality of service (QoS) in distributed real-time database management systems (DRTDBMS) using different data replication policies. The first approach uses a semi-total data replication policy that replicates both real-time and non-real-time data between nodes based on access thresholds. The second approach uses a partial data replication policy that replicates the most accessed data objects between the most accessed nodes based on transaction access histories. Both approaches are applied within a distributed feedback control scheduling architecture and their results are compared to an existing approach using full data replication.
This document discusses QoS aware replica control strategies for distributed real-time database management systems. It proposes a heuristic approach called Greedy-Cover Firefly algorithm that dynamically places replicas based on QoS requirements and replaces replicas using an adaptive algorithm. The algorithm calculates replication costs and selects optimal nodes for replica placement based on access history. It aims to improve system performance by reducing resources consumed over time while meeting QoS requirements. Simulation results show the proposed algorithms greatly improve the system performance.
A DDS-Based Scalable and Reconfigurable Framework for Cyber-Physical Systemsijseajournal
Cyber-Physical Systems (CPSs) involve the interconnection of heterogeneous computing devices which are
closely integrated with the physical processes under control. Often, these systems are resource-constrained
and require specific features such as the ability to adapt in a timeliness and efficient fashion to dynamic
environments. Also, they must support fault tolerance and avoid single points of failure. This paper
describes a scalable framework for CPSs based on the OMG DDS standard. The proposed solution allows
reconfiguring this kind of systems at run-time and managing efficiently their resources.
Data Distribution Handling on Cloud for Deployment of Big Dataneirew J
This document summarizes a research paper that proposes an algorithm to reduce data distribution and processing time in cloud computing for big data deployment. The paper discusses different data distribution techniques for virtual machines (VMs) in cloud computing, such as centralized, semi-centralized, hierarchical, and peer-to-peer approaches. It also reviews related work on MapReduce frameworks and load balancing algorithms. The authors implemented their proposed peer-to-peer distribution technique and Round Robin and Throttled load balancing algorithms in CloudSim. Experimental results showed the Throttled algorithm achieved significantly lower average response times than Round Robin.
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.
International Journal of Engineering Research and DevelopmentIJERD Editor
This document summarizes a research paper on developing an efficient dynamic resource scheduling model called CRAM for cloud computing. The proposed model uses Stochastic Reward Nets to model cloud resources and client requests in an analytical way. It captures key concepts like virtualization, federation between clouds, and defines performance metrics from the perspective of both cloud providers and users. The model is scalable and can represent systems with thousands of resources to analyze the impact of different resource management strategies.
A Reconfigurable Component-Based Problem Solving EnvironmentSheila Sinclair
This technical report describes a reconfigurable component-based problem solving environment called DISCWorld. The key features discussed are:
1) DISCWorld uses a data flow model represented as directed acyclic graphs (DAGs) of operators to integrate distributed computing components across networks.
2) It supports both long running simulations and parameter search applications by allowing complex processing requests to be composed graphically or through scripting and executed on heterogeneous platforms.
3) Operators can be simple "pure Java" implementations or wrappers to fast platform-specific implementations, and some operators may represent sub-graphs that can be reconfigured to run across multiple servers for faster execution.
This document proposes a novel distributed architecture for a NoSQL datastore that supports strong consistency while maintaining high scalability. It is based on the Scalable Distributed Two-Layer Data Store (SD2DS) model, which has proven efficient. The architecture considers concurrent and unfinished operations to ensure consistency. Algorithms for scheduling operations are presented and proven theoretically correct. An implementation is evaluated experimentally against MongoDB and MemCached, showing high performance compared to existing NoSQL systems. The architecture aims to augment SD2DS with consistency mechanisms without impacting scalability.
A load balancing strategy for reducing data loss risk on cloud using remodif...IJECEIAES
This document summarizes a research paper that proposes a load balancing strategy called the re-modified throttled algorithm (RMTA) to reduce the risk of data loss on cloud computing. The RMTA aims to address limitations in previous algorithms by considering both the availability and capacity of virtual machines (VMs) during load distribution and migration processes. It maintains two index tables to track available and unavailable VMs. When a new request arrives, the RMTA load balancer selects a VM that has sufficient available storage and bandwidth to handle the request size without risk of data overflow. This is intended to minimize data loss or hampering during migration. The performance of the RMTA is evaluated through simulation and analysis on the CloudAnalyst tool.
An efficient resource sharing technique for multi-tenant databases IJECEIAES
Multi-tenancy is a key component of Software as a Service (SaaS) paradigm. Multi-tenant software has gained a lot of attention in academics, research and business arena. They provide scalability and economic benefits for both cloud service providers and tenants by sharing same resources and infrastructure in isolation of shared databases, network and computing resources with Service level agreement (SLA) compliances. In a multitenant scenario, active tenants compete for resources in order to access the database. If one tenant blocks up the resources, the performance of all the other tenants may be restricted and a fair sharing of the resources may be compromised. The performance of tenants must not be affected by resource-intensive activities and volatile workloads of other tenants. Moreover, the prime goal of providers is to accomplish low cost of operation, satisfying specific schemas/SLAs of each tenant. Consequently, there is a need to design and develop effective and dynamic resource sharing algorithms which can handle above mentioned issues. This work presents a model referred as MultiTenant Dynamic Resource Scheduling Model (MTDRSM) embracing a query classification and worker sorting technique enabling efficient and dynamic resource sharing among tenants. The experiments show significant performance improvement over existing model.
IRJET- A Survey on Remote Data Possession Verification Protocol in Cloud StorageIRJET Journal
This document summarizes a survey on remote data possession verification protocols for cloud storage. It begins with an abstract describing the problem of verifying integrity of outsourced data files on remote cloud servers. It then provides background on remote data possession verification (RDPV) protocols and discusses related work on ensuring data integrity and supporting dynamic operations. The document describes the system framework, RDPV protocol, use of homomorphic hash functions, and an optimized implementation using an operation record table to efficiently support dynamic operations like modifications. It concludes that the presented efficient and secure RDPV protocol is suitable for cloud storage applications.
Automatic Management of Wireless Sensor Networks through Cloud Computingyousef emami
This document presents a framework for integrating wireless sensor networks (WSNs) with cloud computing. The framework uses policy-based network management to automate WSN management tasks. It proposes adding a policy analyzer to a publish/subscribe broker to match sensor data with stored policies and mandate appropriate actions. The framework includes software as a service, virtualization management, a publish/subscribe broker, management services like SLA and change management, fault tolerance, and security measures. The goal is to facilitate and automate WSN management through the use of cloud computing and policy-based network rules.
BI-TEMPORAL IMPLEMENTATION IN RELATIONAL DATABASE MANAGEMENT SYSTEMS: MS SQ...lyn kurian
Traditional database management systems (DBMS) are the computation
storage and reservoir of large amounts of information. The data accumulated by these
database systems is the information valid at present time, valid now. It is the data that
is true at the present moment. Past data is the information that was kept in the
database at an earlier time, data that is hold to be existed in the past, were valid at
some point before now. Future data is the information supposed to be valid at a future
time instance, data that will be true in the near future, valid at some point after now.
The commercial DBMS of today used by organizations and individuals, such as MS
SQL Server, Oracle, DB2, Sybase, Postgres etc., do not provide models to support and
process (retrieving, modifying, inserting and removing) past and future data.
The implementation of bi-temporal modelling in Microsoft SQL Server is important
to know how relational database management system handles data the bi-temporal
property. In bi-temporal database, data saved is never deleted and additional values
are always appended. Therefore, the paper explores one of the way we can build bitemporal handling of data. The paper aims to build the core concepts of bi-temporal
data storage and querying techniques used in bi-temporal relational DBMS i.e., from
data structures to normalized storage, and to extraction or slicing of data.
The unlimited growth of data results relational data to become complicated in terms
of management and storage of data. Thus, the developers working in various
commercial and industrial applications should know how bi-temporal concepts apply to relational databases, especially due to their increased flexibility in the bi-temporal
storage as well as in analyzing data. Thereby, the paper demonstrates how bi-temporal
data structures and their operations are applied in Relational Database Management
System
This document summarizes a paper that presents a novel method for passive resource discovery in cluster grid environments. The method monitors network packet frequency from nodes' network interface cards to identify nodes with available CPU cycles (<70% utilization) by detecting latency signatures from frequent context switching. Experiments on a 50-node testbed showed the method can consistently and accurately discover available resources by analyzing existing network traffic, including traffic passed through a switch. The paper also proposes algorithms for distributed two-level resource discovery, replication and utilization to optimize resource allocation and access costs in distributed computing environments.
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Nexgen Technology Address:
Nexgen Technology
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Near SBI ATM,
Puducherry.
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www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Provable multicopy dynamic data possessionnexgentech15
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
PROVABLE MULTICOPY DYNAMIC DATA POSSESSION IN CLOUD COMPUTING SYSTEMSNexgen Technology
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Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
A latency-aware max-min algorithm for resource allocation in cloud IJECEIAES
Cloud computing is an emerging distributed computing paradigm. However, it requires certain initiatives that need to be tailored for the cloud environment such as the provision of an on-the-fly mechanism for providing resource availability based on the rapidly changing demands of the customers. Although, resource allocation is an important problem and has been widely studied, there are certain criteria that need to be considered. These criteria include meeting user’s quality of service (QoS) requirements. High QoS can be guaranteed only if resources are allocated in an optimal manner. This paper proposes a latency-aware max-min algorithm (LAM) for allocation of resources in cloud infrastructures. The proposed algorithm was designed to address challenges associated with resource allocation such as variations in user demands and on-demand access to unlimited resources. It is capable of allocating resources in a cloud-based environment with the target of enhancing infrastructure-level performance and maximization of profits with the optimum allocation of resources. A priority value is also associated with each user, which is calculated by analytic hierarchy process (AHP). The results validate the superiority for LAM due to better performance in comparison to other state-of-the-art algorithms with flexibility in resource allocation for fluctuating resource demand patterns.
IMPACT OF RESOURCE MANAGEMENT AND SCALABILITY ON PERFORMANCE OF CLOUD APPLICA...IJCSEA Journal
Cloud computing facilitates service providers to rent their computing capabilities for deploying
applications depending on user requirements. Applications of cloud have diverse composition,
configuration and deployment requirements. Quantifying the performance of applications in Cloud
computing environments is a challenging task. In this paper, we try to identify various parameters
associated with performance of cloud applications and analyse the impact of resource management and
scalability among them.
Similar to A quality of service management in distributed feedback control scheduling architecture using different replication policies (20)
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
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deadlines. To address this problem, more studies focus on feedback control techniques have been
proposed [1,5] to provide better QoS guarantees in replicated environment.
Data replication consists on replicating a data on participating nodes. Given that this technique
increases the data availability, it can help DRTDBMS to meet the stringent temporal
requirements. In literature, two data replication policies are presented: the full data replication
policy and the partial data replication policy [8].
Wei et al [8] have proposed a QoS management algorithm in distributed real-time databases with
full temporal data replication. They proposed a replication model in which only real-time data are
replicated and the updating replicas data is propagate at the same time to all replicas in each other
nodes. In replicated environment, user operations on data replicas are often read operations [8]. In
this case, to guarantee the replicas data freshness, many efficient replica management algorithms
have been added in literature. All proposed algorithms aim to meet deadlines of transactions and
data freshness guarantees.
This proposed solution in [8] is shown to be appropriate for a fixed number of participating
nodes, e.g., 8 nodes. However, in the presence of a high number of nodes (more than 8 nodes),
using a full data replication policy is inefficient in these systems, and it may have many
limitations. Those issues are related to communication costs between nodes, highly message loss
and periodically collected data performance.
To address this problem, we proposed in this work a new data replication policy called a semitotal data replication policy and, we have applied it in feedback control scheduling architecture in
replicated environment. Furthermore, we have applied the partial replication policy in this
architecture, and we have presented a comparison between obtained results with these data
replication policies and the existing results with full data replication policy proposed in previous
work. Compared to previous work [8], we increment the number of nodes. Also, in our replication
model, we proposed to replicate both types of data; the classical data and the temporal data. In
this model, temporal replicas data are updating transparently, and the classical replicas data are
updating according to the RT-RCP policy presented in [3].
The main objective of our work is to limit the miss ratio of the arrived transactions to the system.
At the same time, our work aims to tolerate a certain imprecise value of replicas data and to
control timely transactions, which must guarantee access only to fresh replicas data, even in the
presence of unpredictable workloads.
In this article, we begin by presenting a distributed real-time database model. In the section 3, we
present the related works on which, we base our approach. In section 4, we present the proposed
QoS management approach based on DFCS architecture to provide QoS guarantees in
DRTDBMS. Section 5 shows the details of the simulation and the evaluation results. We
conclude this article by discussing this work and by focusing on its major perspectives.
2. RELATED WORK
In this section, we present the QoS management architecture proposed in [3], on which we based
our work. We describe, also, an overview of the data replication policies presented in literature,
which is used for the QoS enhancement.
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2.1. Architecture for QoS management in DRTDBMS
The QoS can be considered as a metric which measures the overall system performance. In fact,
QoS is a collective measure of the service level provided to the customer. It can be evaluated by
different performance criteria that include basic availability, error rate, response time, the rate of
successful transactions before their deadline, etc.
The DRTDBMS as RTDBMS have to maintain both the logical consistency of the database and
its temporal consistency. Since it seems to be difficult to reach these two goals simultaneously
because of the lack of predictability of such systems, some researchers have designed new
techniques to manage real-time transactions. These techniques use feedback control scheduling
theory in order to provide an acceptable DRTDBMS behaviour. They also attempt to provide a
fixed QoS by considering the data and the transactions behaviour.
In RTDBMS, many works are presented to provide a QoS guarantees. Those works consist of the
applicability of most of the management techniques of the real-time transactions and/or real-time
data [1,2,9,11].
A significant contribution on QoS guarantees for real-time data services in mid-size scale of
DRTDBMS is presented by Wei et al. [8] (cf. Figure 1). The authors have designed an
architecture, which we base our work, that provides QoS guarantees in DRTDBMS with full
replication of only temporal data with small number of nodes (8 nodes). This architecture, called
Distributed FCSA (DFCSA), consists of heuristic Feedback-based local controllers and global
load balancers (GLB) working at each site.
The general outline of the DFCSA is shown in Figure 1. In what follows, we give a brief
description of its basic components.
The admission controller is used to regulate the system workload in order to prevent its
overloading. Its functioning is based on the estimated CPU utilization and the target utilization set
point. For each admitted transaction, its estimated execution time is added to the estimated CPU
utilization. Therefore, transactions will be discarded from the system if the estimated CPU
utilization is higher than the target utilization set by the local controller.
The transaction manager handles the execution of transactions. It is composed of a concurrency
controller (CC), a freshness manager (FM), a data manager (DM) and a replica manager (RM).
Figure 1 FCS architecture for QoS guarantee in DRTDBMS [8].
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2.2. Distributed real-time database model
In this section, we present the distributed real-time database model on which we base our work.
This model is issued from many works about QoS management in DRTDBMS [6]. The main
difference is the applicability of different data replication policies. In our works, DRTDBMS
model is defined by the interconnection between many centralized RTDBMS. We consider a
main memory database model on each site, in which the CPU is the main system resource taken
into account.
2.2.1. Data model
In this model, data objects are classified into either real-time or non real-time data. In our model,
we consider both types of data objects. Non real-time data are classical data found in
conventional databases, whereas real-time data have a validity interval beyond which they
become stale. These data may change continuously to reflect the real world state (e.g., the current
temperature value). Each real-time data has a timestamps which define its last observation in the
real world state [9], a validity interval and a value.
2.2.2. Data replication model
In DRTDBMS, data replication is very attractive in order to increase the chance of distributed
real-time transaction to meet its deadline, system throughput and provide fault-tolerance.
However, it is a challenge to keep data copies consistent. For that purpose, two types of data
replication policies have been developed in literature.
The first data replication policy is the full data replication; the entire database at each node is
replicated to all other sites which admit transactions that can use their replicas data. Therefore, all
the data of this database will be available in different sites which facilitates access by the various
local and remote transactions.
The second policy data replication is the partial data replication policy, which is based on access
history of all transactions in each node. In fact, for each node, if the number of access on one data
object by current transactions reach a threshold then current node request to replicate locally this
data object. Therefore, this policy consist of replicate the most accessed data object of the most
accessed nodes to satisfy various user requests.
In this paper, we present the third policy. We called it the "Semi-total data replication". Wei et al.
[8] have proposed a replication model that only temporal data had replicated and the updating
replicas data is propagate at the same time to all replicas in each other nodes. In the replicated
environment, the frequently user operations on data replicas are read operations. In this case, to
guarantee the replicas data freshness, many efficient replica management algorithms was added in
literature to manage the data replicas at every node that supported data replication. All proposed
algorithms aims to meet transactions deadlines and data freshness guarantees.
Here, we discuss the difference between the two data replication policies. The full replication is
characterized by a maximum number of replicated data. Then, full database replication means that
all sites store copies of all data items. An analytical study in [12] has shown the scalability limits
of full replication. Therefore, the time needed for updating replicated data is quite important. In
some protocols update transactions are executed to preserve all the databases consistency. By
taking into consideration the time of transmission of messages between sites, the chance to
respect distributed real-time transactions decreases. However, partial data replication policy only
assigns copies of a frequently accessed data item. When there is a high update workload, full
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replication has too much overhead to keep all copies consistent and the individual replicas have
little resources left to execute read operations. In contrast, with partial replication, updating
protocols for replica data only has to execute the updates for mostly accessed replica data items,
and thus, it has more potential to execute read operations.
In the next section, we present our QoS management algorithms based on a different data
replication policy.
2.2.3. Transaction model
In this model, we use the firm transaction model, in which tardy transactions are aborted because
they can't meet their deadlines. In fact, transactions are classified into two classes: update
transactions and user transactions. Update transactions are used to update the values of real-time
data in order to reflect the real world state. These transactions are executed periodically and have
only to write real-time data. User transactions, representing user requests, arrive aperiodically.
They may read real-time data, and read or write non real-time data.
Furthermore, each update transaction is composed only by one write operation on real-time data.
User transactions are composed of a set of sub-transactions which are executed at local node or at
remote nodes that participate in the execution of the global system.
We consider that a user transaction may arrive at any node of the global system and define its
data needs. If all data needed by the transaction exist at the current site, then the transaction is
executed locally. Otherwise, the transaction, called real-time distributed user transaction, is split
into sub-transactions according to the location of their data. Those sub-transactions are transferred
and executed at corresponding nodes.
There are two sub-types of real-time distributed user transaction: remote and local [3]. Remote
transactions are executed at more than one node, whereas the local transactions are executed only
at one node. There is one process called coordinator which is executed at the site where the
transaction is submitted (master node). There is also a set of other processes called cohorts that
execute on behalf of the transaction at other sites accessed by the transaction(cohort node). The
transaction is an atomic unit of work, which is either entirely complete or not at all. Hence, a
distributed commit protocol is needed to guarantee uniform commitment of distributed
transaction execution. The commit operation implies that the transaction is successful, and hence
all of its updates should be incorporated into the database permanently. An abort operation
indicates that the transaction has failed, and hence requires the database management system to
cancel or abolish all of its effects in the database system. In short, a transaction is an all or
nothing unit of execution.
2.3. Performance metrics
The main performance metric, we consider in our model, is the Success Ratio (SR). It is a QoS
parameter which measures the percentage of transactions that meet their deadlines. It is defined as
follows:
SR = 100 ×
# T im e ly
# T im e ly + # T a r d y
(% )
Where #timely and #tardy represent, respectively, the number of transactions that have met and
missed their deadlines.
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3. QOS MANAGEMENT APPROACHES USING DIFFERENT DATA
REPLICATION POLICIES
In this paper, the number of nodes is larger than the number used in experiments in [8] for full
data replication policy. We have also proposed to apply other data replication policies that
dedicated to mid-size scale system.
Our work consists of a new approach to enhance the QoS in DRTDBMS. We apply two data
replication policies in conventional DFCS architecture; the semi-total replication and partial
replication, on the DFCS architecture using a greater number of nodes (16 nodes) of overall
system than classical DFCS architecture. Then, we have compared obtained results with those
both data replication policies and the result obtained with full data replication policy used in [8].
Moreover, in our replication model, we proposed to replicate both types of data; the classical data
and the temporal data. In this model, temporal replicas data are updating transparently, and the
classical replicas data are updating as RT-RCP protocol presented by Haj Said et al. [3].
3.1 Approach using semi-total replication policy
In the first part, we propose a new algorithm replication called semi-total replication of real-time
and non real-time data (cf. Algorithm 1). In this proposed data replication policy, the system
execution start running without any database replication. Distributed real-time transactions
require data located in local or in remote nodes. Each node define an access counter for
transactions which require data located on remote sites which is define by an access counter of
mostly accessed sites. In case where the number of accessed remote data at each node ni reaches a
maximum threshold, then, the current node request the full replication of the database from node
ni. In other case, if the number of accessed remote data at each node ni reaches a minimum
threshold, then the current node decide to remove the full replicated database from node ni. The
maximum and the minimum threshold parameters are defined as input parameter to the system.
Algorithm 1: The semi-total data replication policy
nbAccNode: number of accessed remote node by all transactions from current node
nbAccDataNi: number of accessed remote data by all transactions from current node Ni
MaxAccNodeThres: maximum number of accessed remote node by all transactions.
MinAccNodeThres: minimum number of accessed remote node by all transactions.
Ni : Node i.
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3.2 Approach using partial replication policy
In the second part of our work, we propose to apply a partial data replication policy of real-time
and non real-time data (cf. Algorithm 2) in DFCS architecture. This technique may provide good
management of database storage by reducing the updating replica data of the overfull system.
The principle of this algorithm is to start running system execution without any real-time and non
real-time data replication. User’s transactions require data located in local or in remote nodes. We
use an accumulator of accessed remote data on other nodes by all transactions of the current node.
In this policy, the access counter is calculated for each data, this means that it uses two
accumulators; the first consists of the number of the frequently accessed remote nodes, and the
second consists of the number of the accessed remote data on frequently accessed nodes. in the
first case, if the number of accessed remote data at each node ni reaches a maximum threshold,
then the current node requests the data replication from node ni. In another case, if the number of
accessed remote data at each node ni reaches a minimum threshold, then the current node decide
to remove the current replicated data from node ni. The maximum and the minimum threshold
parameters are defined as input parameter to the system.
The both of those algorithms do not overload the system any more by updating replica data
workload compared with full data replication policy. Each one of them has advantages to
enhancing the QoS performance in DRTDBMS. This assertion for both algorithms is validated
through a set of simulations.
Algorithm 2: The partial data replication policy
nbAccNode: number of accessed remote node by all transactions from current node
nbAccDataNi: number of accessed remote data by all transactions from current node Ni
nbAccDataOcc: number of occurrences of accessed remote data from current node Ni
MaxAccNodeThres: maximum number of accessed remote node by all transactions.
MinAccNodeThres: minimum number of accessed remote node by all transactions.
MaxAccDataThres: maximum number of accessed remote data from current node by all
transactions.
MinAccDataThres: minimum number of accessed remote data from current node by all
transactions.
Ni : Node i.
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4. SIMULATIONS AND RESULTS
To validate our QoS management approach, we have developed a simulator. In this section, we
describe the overall architecture of the simulator, and we present and comments the obtained
results.
4.1. Simulation model
Our simulator is composed of:
•
Database: it consists of a data generator that generates data randomly avoiding duplicate
information. The consistency in the database is provided by update transactions. In our
simulator, the database contains real-time data and classic data.
•
Generator of transactions: it is composed of two parts.
o
User transactions generator: it generates user transactions using a random
distribution, taking into account their unpredictable arrival.
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o
83
Update transactions generator: it generates update transactions according to an
arrival process that respects the periodicity of transactions.
•
Precision controller: it rejects update transactions when the data to be updated are
sufficiently representative of the real world, based on the value of MDE.
•
Scheduler: it is used to schedule transactions according to their priorities.
•
Freshness manager: it checks the freshness of data that will be accessed by transactions.
If the accessed data object is fresh, then the transaction can be executed, and it will be
sent to the transactions handler. Otherwise, it will be sent to the block queue. Then, it will
be reinserted in the ready queue when the update of the accessed data object is
successfully executed.
•
Concurrency controller: it is responsible for the resolution of data access conflicts.
Based on priorities (deadlines) of transactions, it determines which one can continue its
execution and which one should be blocked, aborted or restarted.
•
Distributed commit protocol: it manages the global distributed real-time transaction to
ensure that all participating nodes agree with the final transaction result (validation or
abortion) [9,10]. In our simulator, we use the commit protocol called “Permits Reading
Of Modified Prepared-data for Timeliness” (PROMPT) defined in [4] which allows
transactions accessing data not committed (optimistic protocol).
•
Admission controller: the main role of this component is to filter the arrival of user
transactions, depending on the system workload and respecting deadlines of transactions.
Then, accepted transactions will be sent to the ready queue.
•
Handler: it is used to execute transactions. If a conflict between transactions appears, it
calls the concurrency controller.
•
Global load balancers (GLB): it ensures that the load on each node does not affect the
functioning of other nodes. So the GLB is used to balance the system load by transferring
transactions from overloaded nodes to less loaded nodes in order to maintain QoS.
The input interface of our simulator allows choosing parameter values by which each simulation
runs. It includes general parameters of the system, parameters for generating database, generating
update transactions and generating user transactions. It also enables to choose the scheduling
algorithm, the concurrency control protocol and the real-time distributed validation protocol.
Once the simulation is well finished, results are saved in an output file. We can also show results
in the form of curves, pie charts or histograms.
4.2. Simulation settings
The performance evaluation of the DRTDBMS is achieved by a set of simulation experiments, in
which we varied some of the parameter values. Table 1 summarizes the general system
parameters settings used in our simulations. The DRTDBMS is composed of 16 nodes. In each
node we have 200 real-time data objects and 10000 classic data objects. Validity values of realtime data are distributed between 500 milliseconds and 1000 milliseconds. For real-time data, the
value of MDE varies by 1 between 1 and 10. We choose the universal method to update real-time
data. Within each queue in our system, transactions are scheduled according to the EDF
algorithm. We use the 2PL-HP protocol for concurrency control. For the validation of
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transactions, the distributed algorithm PROMPT is chosen. Update transactions are generated
according to the number of real-time data in the database.
Table 1. System parameter settings.
Parameter
Simulation time
Number of nodes
Number of real-time data
Number of classic data
Validity of real-time data
MDE value
Method to update real-time data
Scheduling algorithm
Concurrency control algorithm
Distributed validation algorithm
Value
3000 ms
16
200/node
10000/node
[500,1000]
[1,10]
Universal
EDF
2PL-HP
PROMPT
Parameter settings for user transactions are defined in Table 2. User transactions are a set of read
and write operations (from 1 to 4). Read operations (from 0 to 2) can access real-time data objects
and classic data objects, however, write operations (from 1 to 2) access only classic data objects.
The time of one read operations is used to be 1 millisecond, and for one write operation is set to 2
milliseconds. We set the slack factor of transactions to 10. The remote data ratio, which
represents the ratio of the number of remote data operations to that of all data operations, is set to
20%. We note that user transactions are generated at arrival times calculated according to the
"Poisson" process, which uses the value of the lambda parameter to vary the number of
transactions.
Table 2. User transactions parameter settings.
Parameter
Number of write operations
Number of read operations
Write operation time (ms)
Read operation time (ms)
Slack factor
Remote data ratio
Value
[1,2]
[0,2]
2
1
10
20 %
Parameter settings for the real-time and non real-time data replication parameter settings are
given in Table 3. To simulate using the partial data replication policy, we have to fix a minimum
and a maximum threshold for nodes supporting replication which are set respectively to 0.2 and
0.5. We have to define, also, the value of the minimum and the maximum threshold of the number
of accessed remote data at each node, which are set to 0.1 and 0.2. In case of a simulation with
semi-total replication, only maximum values of the threshold of accessed remote nodes to
replicate their database have to be fixed.
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Table 3. Data replication parameter settings.
Parameter
Maximum threshold of nodes
supporting replication
Maximum threshold of data to be
replicated
Minimum threshold of nodes
supporting replication
Minimum threshold of data to be
replicated
Value
0.5
0.2
0.2
0.1
4.3. Simulation principle
To evaluate the performance of the proposed QoS management approach to enhance the
performance of the overall system, we conducted a series of simulations by varying values of
some parameters. Each transaction, whatever its type (user or update), undergoes a series of tests
since its creation to its execution. In experiments, the workload distribution is initially balanced
between all participating nodes.
4.3.1. Simulation using semi-total data replication policy
The first set of experiments evaluates the QoS management using semi-total data replication
policy. For each node, an accumulator counter is used to calculate the mostly accessed remote
nodes which the current node requests to replicate fully their databases.
4.3.2. Simulation using partial data replication policy
In this set of simulations, we evaluate the QoS management using partial data replication policy.
For each node, as with semi-total replication, an accumulator counter is used to calculate the
mostly accessed remote nodes. For each frequently accessed node, an accumulator counter is used
to calculate the mostly accessed data which are requested to be replicated in the current node.
4.4. Results and discussions
As shown in Figure 2, the transactions success ratio is not affected by the increase of incoming
user transactions. In fact, the system workload remains balanced and the system behaviour is
maintained in stable state. Also, it is shown that the number of successful transactions with partial
and semi-total data replication policies is greater than with full data replication policy. Indeed, the
QoS guarantees is defined by increasing the number of transactions that meet their deadlines
using fresh data.
We can say that the use of semi-total and partial data replication policy is suitable when
increasing the number of participating nodes in DRTDBMS. By this way, the applicability of
these data replication policies provides an optimistic management of the database storage while
using fresh data by reducing the time of updating replicas.
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100
90
Success ratio
80
70
60
50
full replication
40
semi-total
replication
30
20
10
0
0
200
400
600
800
1000
1200
1400
1600
Number of transactions
Figure 2. Simulation results for user transactions
The proposed QoS management, using semi-total and partial data replication policies, provides a
better QoS guarantees than full data replication policy, ensuring stability and robustness of
DRTDBMS.
5. CONCLUSION AND FUTURE WORK
In this article, we presented our QoS management approach to provide QoS guarantees in
DRTDBMS. This approach is an extension work of the DFCS architecture proposed by Wei et al.
[8]. It consists on applying two data replication policies, semi-total replication and partial
replication of both classical data and real-time data on the conventional DFCS architecture, in
order to make DRTDBMS more robust and stable. Indeed, the proposed approach is defined by a
set of modules for data and transaction management in distributed real-time environment. The
proposed approach helps to establish a compromise between real-time and data storage
requirements by applying different data replication policies.
In future work, we will propose an approach for QoS enhancement in DRTDBMS using multiversions data with both semi-total and partial data replication policies.
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