This document summarizes a research paper that proposes using Hidden Markov Model (HMM) forward and backward chaining algorithms for prefetching data in a distributed file system for cloud computing. It begins by introducing HMMs and their applications, as well as distributed file systems. It then describes using HMM forward and backward algorithms to analyze client I/O patterns, predict future requests, and prefetch data to storage servers. The authors implemented this approach and found it improved prefetching performance over other methods, with the HMM+DFS approach finding the shortest path with a value of 0.000804. In conclusion, HMM forward and backward chaining is an efficient way to perform predictive prefetching in distributed cloud file
CONTENT BASED DATA TRANSFER MECHANISM FOR EFFICIENT BULK DATA TRANSFER IN GRI...ijgca
A new class of Data Grid infrastructure is needed to support management, transport, distributed access, and analysis of terabyte and peta byte of data collections by thousands of users. Even though some of the existing data management systems (DMS) of Grid computing infrastructures provides methodologies to handle bulk data transfer. These technologies are not usable in addressing some kind of simultaneous data
access requirements. Often, in most of the scientific computing environments, a common data will be needed to access from different locations. Further, most of such computing entities will wait for a common scientific data (such as a data belonging to an astronomical phenomenon) which will be published only
when it is available. These kinds of data access needs were not yet addressed in the design of data component Grid Access to Secondary Storage (GASS) or GridFTP. In this paper, we address an application layer content based data transfer scheme for grid computing environments. By using the
proposed scheme in a grid computing environment, we can simultaneously move bulk data in an efficient way using simple subscribe and publish mechanism.
DISTRIBUTED AND BIG DATA STORAGE MANAGEMENT IN GRID COMPUTINGijgca
Big data storage management is one of the most challenging issues for Grid computing environments, since large amount of data intensive applications frequently involve a high degree of data access locality. Grid applications typically deal with large amounts of data. In traditional approaches high-performance computing consists dedicated servers that are used to data storage and data replication. In this paper we present a new mechanism for distributed and big data storage and resource discovery services. Here we proposed an architecture named Dynamic and Scalable Storage Management (DSSM) architecture in grid environments. This allows in grid computing not only sharing the computational cycles, but also share the storage space. The storage can be transparently accessed from any grid machine, allowing easy data sharing among grid users and applications. The concept of virtual ids that, allows the creation of virtual spaces has been introduced and used. The DSSM divides all Grid Oriented Storage devices (nodes) into multiple geographically distributed domains and to facilitate the locality and simplify the intra-domain storage management. Grid service based storage resources are adopted to stack simple modular service piece by piece as demand grows. To this end, we propose four axes that define: DSSM architecture and algorithms description, Storage resources and resource discovery into Grid service, Evaluate purpose prototype system, dynamically, scalability, and bandwidth, and Discuss results. Algorithms at bottom and upper level for standardization dynamic and scalable storage management, along with higher bandwidths have been designed.
Dynamic Resource Provisioning with Authentication in Distributed DatabaseEditor IJCATR
Data center have the largest consumption amounts of energy in sharing the power. The public cloud workloads of different
priorities and performance requirements of various applications [4]. Cloud data center have capable of sensing an opportunity to present
different programs. In my proposed construction and the name of the security level of imperturbable privacy leakage rarely distributed
cloud system to deal with the persistent characteristics there is a substantial increases and information that can be used to augment the
profit, retrenchment overhead or both. Data Mining Analysis of data from different perspectives and summarizing it into useful
information is a process. Three empirical algorithms have been proposed assignments estimate the ratios are dissected theoretically and
compared using real Internet latency data recital of testing methods
This document provides a summary of key concepts in computer networks:
1. It defines a computer network and describes the basic components - PCs, interconnections like network cards and cables, switches, and routers.
2. It discusses common network applications like email, web browsers, instant messaging, and collaboration tools.
3. It describes the seven-layer OSI model and compares it to the TCP/IP model, explaining the functions of the physical, data link, network, and transport layers.
4. It discusses networking software, network performance metrics like bandwidth and latency, and link layer services like acknowledged and unacknowledged connection-oriented services.
Distributed and Cloud Computing 1st Edition Hwang Solutions Manualkyxeminut
1. The document provides solutions to homework problems from a distributed and cloud computing textbook. It includes explanations and examples related to key concepts in high performance computing, distributed systems, cloud computing, and parallel architectures.
2. The problems cover topics such as high performance computing vs high throughput computing, peer to peer networks, computer clusters vs computational grids, and performance analysis of parallel systems using Amdahl's law and Gustafson's law.
3. Parallel architectures discussed include single-threaded superscalar, fine-grain multithreading, coarse-grain multithreading, and simultaneous multithreading. Their characteristics, advantages, and examples are summarized.
Java Abs Peer To Peer Design & Implementation Of A Tuple Spacencct
Final Year Projects, IEEE Projects, Final Year Projects in Chennai, Final Year IEEE Projects, final year projects, college projects, student projects, java projects, asp.net projects, software projects, software ieee projects, ieee 2009 projects, 2009 ieee projects, embedded projects, final year software projects, final year embedded projects, ieee embedded projects, matlab projects, microcontroller projects, vlsi projects, dsp projects, free projects, project review, project report, project presentation, free source code, free project report, Final Year Projects, IEEE Projects, Final Year Projects in Chennai, Final Year IEEE Projects, final year projects, college projects, student projects, java projects, asp.net projects, software projects, software ieee projects, ieee 2009 projects, 2009 ieee projects, embedded projects, final year software projects, final year embedded projects, ieee embedded projects, matlab projects, final year java projects, final year asp.net projects, final year vb.net projects, vb.net projects, c# projects, final year c# projects, electrical projects, power electronics projects, motors and drives projects, robotics projects, ieee electrical projects, ieee power electronics projects, ieee robotics projects, power system projects, power system ieee projects, engineering projects, ieee engineering projects, engineering students projects, be projects, mca projects, mtech projects, btech projects, me projects, mtech projects, college projects, polytechnic projects, real time projects, ieee projects, non ieee projects, project presentation, project ppt, project pdf, project source code, project review, final year project, final year projects
ODRS: Optimal Data Replication Scheme for Time Efficiency In MANETsIOSR Journals
This document proposes an Optimal Data Replication Scheme (ODRS) to improve data availability, reduce query delay, and increase hit ratio in mobile ad hoc networks (MANETs). In MANETs, frequent node and link failures can cause network partitions that decrease data access performance. ODRS aims to address this issue through proactive data replication across network partitions. It considers factors like node mobility, power consumption, and resource availability to determine what data to replicate, where to place replicas, and how to access and synchronize replicated data in order to optimize data availability and query efficiency in the dynamic MANET environment. The proposed scheme is evaluated through simulation to validate that it can achieve higher data availability, lower query delay, and increased
CONTENT BASED DATA TRANSFER MECHANISM FOR EFFICIENT BULK DATA TRANSFER IN GRI...ijgca
A new class of Data Grid infrastructure is needed to support management, transport, distributed access, and analysis of terabyte and peta byte of data collections by thousands of users. Even though some of the existing data management systems (DMS) of Grid computing infrastructures provides methodologies to handle bulk data transfer. These technologies are not usable in addressing some kind of simultaneous data
access requirements. Often, in most of the scientific computing environments, a common data will be needed to access from different locations. Further, most of such computing entities will wait for a common scientific data (such as a data belonging to an astronomical phenomenon) which will be published only
when it is available. These kinds of data access needs were not yet addressed in the design of data component Grid Access to Secondary Storage (GASS) or GridFTP. In this paper, we address an application layer content based data transfer scheme for grid computing environments. By using the
proposed scheme in a grid computing environment, we can simultaneously move bulk data in an efficient way using simple subscribe and publish mechanism.
DISTRIBUTED AND BIG DATA STORAGE MANAGEMENT IN GRID COMPUTINGijgca
Big data storage management is one of the most challenging issues for Grid computing environments, since large amount of data intensive applications frequently involve a high degree of data access locality. Grid applications typically deal with large amounts of data. In traditional approaches high-performance computing consists dedicated servers that are used to data storage and data replication. In this paper we present a new mechanism for distributed and big data storage and resource discovery services. Here we proposed an architecture named Dynamic and Scalable Storage Management (DSSM) architecture in grid environments. This allows in grid computing not only sharing the computational cycles, but also share the storage space. The storage can be transparently accessed from any grid machine, allowing easy data sharing among grid users and applications. The concept of virtual ids that, allows the creation of virtual spaces has been introduced and used. The DSSM divides all Grid Oriented Storage devices (nodes) into multiple geographically distributed domains and to facilitate the locality and simplify the intra-domain storage management. Grid service based storage resources are adopted to stack simple modular service piece by piece as demand grows. To this end, we propose four axes that define: DSSM architecture and algorithms description, Storage resources and resource discovery into Grid service, Evaluate purpose prototype system, dynamically, scalability, and bandwidth, and Discuss results. Algorithms at bottom and upper level for standardization dynamic and scalable storage management, along with higher bandwidths have been designed.
Dynamic Resource Provisioning with Authentication in Distributed DatabaseEditor IJCATR
Data center have the largest consumption amounts of energy in sharing the power. The public cloud workloads of different
priorities and performance requirements of various applications [4]. Cloud data center have capable of sensing an opportunity to present
different programs. In my proposed construction and the name of the security level of imperturbable privacy leakage rarely distributed
cloud system to deal with the persistent characteristics there is a substantial increases and information that can be used to augment the
profit, retrenchment overhead or both. Data Mining Analysis of data from different perspectives and summarizing it into useful
information is a process. Three empirical algorithms have been proposed assignments estimate the ratios are dissected theoretically and
compared using real Internet latency data recital of testing methods
This document provides a summary of key concepts in computer networks:
1. It defines a computer network and describes the basic components - PCs, interconnections like network cards and cables, switches, and routers.
2. It discusses common network applications like email, web browsers, instant messaging, and collaboration tools.
3. It describes the seven-layer OSI model and compares it to the TCP/IP model, explaining the functions of the physical, data link, network, and transport layers.
4. It discusses networking software, network performance metrics like bandwidth and latency, and link layer services like acknowledged and unacknowledged connection-oriented services.
Distributed and Cloud Computing 1st Edition Hwang Solutions Manualkyxeminut
1. The document provides solutions to homework problems from a distributed and cloud computing textbook. It includes explanations and examples related to key concepts in high performance computing, distributed systems, cloud computing, and parallel architectures.
2. The problems cover topics such as high performance computing vs high throughput computing, peer to peer networks, computer clusters vs computational grids, and performance analysis of parallel systems using Amdahl's law and Gustafson's law.
3. Parallel architectures discussed include single-threaded superscalar, fine-grain multithreading, coarse-grain multithreading, and simultaneous multithreading. Their characteristics, advantages, and examples are summarized.
Java Abs Peer To Peer Design & Implementation Of A Tuple Spacencct
Final Year Projects, IEEE Projects, Final Year Projects in Chennai, Final Year IEEE Projects, final year projects, college projects, student projects, java projects, asp.net projects, software projects, software ieee projects, ieee 2009 projects, 2009 ieee projects, embedded projects, final year software projects, final year embedded projects, ieee embedded projects, matlab projects, microcontroller projects, vlsi projects, dsp projects, free projects, project review, project report, project presentation, free source code, free project report, Final Year Projects, IEEE Projects, Final Year Projects in Chennai, Final Year IEEE Projects, final year projects, college projects, student projects, java projects, asp.net projects, software projects, software ieee projects, ieee 2009 projects, 2009 ieee projects, embedded projects, final year software projects, final year embedded projects, ieee embedded projects, matlab projects, final year java projects, final year asp.net projects, final year vb.net projects, vb.net projects, c# projects, final year c# projects, electrical projects, power electronics projects, motors and drives projects, robotics projects, ieee electrical projects, ieee power electronics projects, ieee robotics projects, power system projects, power system ieee projects, engineering projects, ieee engineering projects, engineering students projects, be projects, mca projects, mtech projects, btech projects, me projects, mtech projects, college projects, polytechnic projects, real time projects, ieee projects, non ieee projects, project presentation, project ppt, project pdf, project source code, project review, final year project, final year projects
ODRS: Optimal Data Replication Scheme for Time Efficiency In MANETsIOSR Journals
This document proposes an Optimal Data Replication Scheme (ODRS) to improve data availability, reduce query delay, and increase hit ratio in mobile ad hoc networks (MANETs). In MANETs, frequent node and link failures can cause network partitions that decrease data access performance. ODRS aims to address this issue through proactive data replication across network partitions. It considers factors like node mobility, power consumption, and resource availability to determine what data to replicate, where to place replicas, and how to access and synchronize replicated data in order to optimize data availability and query efficiency in the dynamic MANET environment. The proposed scheme is evaluated through simulation to validate that it can achieve higher data availability, lower query delay, and increased
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.
Abstract— Cloud storage is usually distributed infrastructure, where data is not stored in a single device but is spread to several storage nodes which are located in different areas. To ensure data availability some amount of redundancy has to be maintained. But introduction of data redundancy leads to additional costs such as extra storage space and communication bandwidth which required for restoring data blocks. In the existing system, the storage infrastructure is considered as homogeneous where all nodes in the system have same online availability which leads to efficiency losses. The proposed system considers that distributed storage system is heterogeneous where each node exhibit different online availability. Monte Carlo Sampling is used to measure the online availability of storage nodes. The parallel version of Particle Swarm Optimization is used to assign redundant data blocks according to their online availability. The optimal data assignment policy reduces the redundancy and their associated cost.
Information Extraction from Wireless Sensor Networks: System and ApproachesM H
Recent advances in wireless communication have made it possible to develop low-cost, and low power Wireless Sensor Networks (WSN). The WSN can be used for several application areas (e.g., habitat monitoring, forest fire detection, and health care). WSN Information Extraction (IE) techniques can be classified into four categories depending on the factors that drive data acquisition: event-driven, time-driven, query-based, and hybrid. This paper presents a survey of the state-of-the-art IE techniques in WSNs. The benefits and shortcomings of different IE approaches are presented as motivation for future work into automatic hybridization and adaptation of IE mechanisms.
The document discusses using network coding with multi-generation mixing to improve data recovery in cloud storage systems. It provides a literature review of several papers that use techniques like Maximum Distance Separation codes, random linear network coding, and instantly decodable network coding. The proposed work develops an architecture that uses multi-generation mixing and the DODEX+ encoding scheme to encode and retrieve data across multiple mobile clients and cloud storage. This aims to provide more efficient and reliable data delivery over wireless mesh networks. Tools like Amazon S3 and the NS2 network simulator are used to implement and test the proposed system.
Lately, the Wireless Sensors Networks (WSN) have moved to the concept of the hybrids networks in order to get universal platforms in various types of monitoring and information collecting applications. The work presented in this paper aims in designing a hybrid remote monitoring architecture, largely secured by a high availability and resilience WSN. The modeling approach intends to describe the main operation of polling and dispatching between the communications channels with the purpose of ensuring the information availability and reducing the resilience time. To achieve our goal, we have realized an experimental platform of measuring, processing and routing data through hybrid communications technologies. We have illustrated, via curves, the routing of the data measured by a WSN (ZigBee Technology) to a final user through several communication technologies (HTTPS, SMS, ...).
Lecture 01 - Chapter 1 (Part 01): This Lecture show the Overview of Course, What is an Operating System, Operating System Functions, Definition of a Distributed System, Properties of Distributed Systems, Software Concepts, Transparency in a Distributed System, Challenges, Approaches, Scalability Problems, Scalability Examples, Web Search, Financial Transactions, Multiplayer Games. Some basic concept of Operating System (OS).
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.
This document summarizes a study on a new dynamic load balancing approach in cloud environments. It begins by outlining some of the major challenges of load balancing in cloud systems, including uneven distribution of workloads across CPUs. It then proposes a new approach with three main components: 1) A queueing and job assignment process that prioritizes assigning jobs to faster CPUs, 2) A timeout chart to determine when jobs should be migrated or terminated to avoid delays, and 3) Use of a "super node" to act as a proxy and backup in case other nodes fail. The approach is intended to more efficiently distribute jobs and help cloud systems maintain optimal performance. Finally, the document discusses how this approach could be integrated into existing cloud architectures
This document summarizes and categorizes different weight-based clustering algorithms that have been designed for mobile ad hoc networks (MANETs). It discusses:
1) The basic concept of clustering in MANETs and the roles of standard nodes, cluster heads, and cluster gateways.
2) Two categories of clustering algorithms - simple (e.g. lowest ID, connectivity-based) and enhanced (e.g. k-cluster, hierarchical) approaches.
3) Weighted clustering, where each node calculates a weight based on attributes like speed, degree, power, and energy to elect cluster heads.
A New Architecture for Group Replication in Data GridEditor IJCATR
Nowadays, grid systems are vital technology for programs running with high performance and problems solving with largescale
in scientific, engineering and business. In grid systems, heterogeneous computational resources and data should be shared
between independent organizations that are scatter geographically. A data grid is a kind of grid types that make relations computational
and storage resources. Data replication is an efficient way in data grid to obtain high performance and high availability by saving
numerous replicas in different locations e.g. grid sites. In this research, we propose a new architecture for dynamic Group data
replication. In our architecture, we added two components to OptorSim architecture: Group Replication Management component
(GRM) and Management of Popular Files Group component (MPFG). OptorSim developed by European Data Grid projects for
evaluate replication algorithm. By using this architecture, popular files group will be replicated in grid sites at the end of each
predefined time interval.
Efficient load rebalancing for distributed file system in CloudsIJERA Editor
Cloud computing is an upcoming era in software industry. It’s a very vast and developing technology.
Distributed file systems play an important role in cloud computing applications based on map reduce
techniques. While making use of distributed file systems for cloud computing, nodes serves computing and
storage functions at the same time. Given file is divided into small parts to use map reduce algorithms in
parallel. But the problem lies here since in cloud computing nodes may be added, deleted or modified any time
and also operations on files may be done dynamically. This causes the unequal load distribution of load among
the nodes which leads to load imbalance problem in distributed file system. Newly developed distributed file
system mostly depends upon central node for load distribution but this method is not helpful in large-scale and
where chances of failure are more. Use of central node for load distribution creates a problem of single point
dependency and chances of performance of bottleneck are more. As well as issues like movement cost and
network traffic caused due to migration of nodes and file chunks need to be resolved. So we are proposing
algorithm which will overcome all these problems and helps to achieve uniform load distribution efficiently. To
verify the feasibility and efficiency of our algorithm we will be using simulation setup and compare our
algorithm with existing techniques for the factors like load imbalance factor, movement cost and network traffic.
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.
An efficient hybrid peer to-peersystemfordistributeddatasharingambitlick
The document proposes a hybrid peer-to-peer system that combines the advantages of structured and unstructured networks. It consists of two parts: 1) a structured core network that forms the backbone and provides efficient data lookup; 2) multiple unstructured networks attached to each core node, allowing flexible peer joining/leaving. This two-tier design decouples efficiency and flexibility. Simulation results show the hybrid system balances these properties better than single-approach networks.
Charm a cost efficient multi cloud data hosting scheme with high availabilityKamal Spring
More and more enterprises and organizations are hosting their data into the cloud, in order to reduce the IT maintenance cost and enhance the data reliability. However, facing the numerous cloud vendors as well as their heterogenous pricing policies, customers may well be perplexed with which cloud(s) are suitable for storing their data and what hosting strategy is cheaper. The general status quo is that customers usually put their data into a single cloud (which is subject to the vendor lock-in risk) and then simply trust to luck. Based on comprehensive analysis of various state-of-the-art cloud vendors, this paper proposes a novel data hosting scheme (named CHARM) which integrates two key functions desired. The first is selecting several suitable clouds and an appropriate redundancy strategy to store data with minimized monetary cost and guaranteed availability. The second is triggering a transition process to re-distribute data according to the variations of data access pattern and pricing of clouds. We evaluate the performance of CHARM using both trace-driven simulations and prototype experiments. The results show that compared with the major existing schemes, CHARM not only saves around 20% of monetary cost but also exhibits sound adaptability to data and price adjustments.
This document summarizes a research paper that proposes a new permission-based clustering mutual exclusion algorithm for mobile ad-hoc networks. The algorithm uses a cluster-based hierarchical approach where only cluster leaders are responsible for granting or denying permission to enter the critical section, thereby reducing message complexity. Nodes are partitioned into clusters, with the heaviest weighted node selected as the cluster leader. When a node wants to enter the critical section, it sends a request to its cluster leader. If the cluster leader has obtained over 50% of the total votes, it can grant permission. Otherwise, it requests votes from other cluster leaders until it reaches the majority. This clustering approach helps solve the mutual exclusion problem in mobile ad-hoc networks in an efficient
Cryptographic Cloud Storage with Hadoop ImplementationIOSR Journals
This document proposes a scheme for cryptographic cloud storage using Hadoop implementation. It introduces parallel homomorphic encryption schemes that allow computation over encrypted data through an evaluation algorithm that can run efficiently in parallel. This allows a client to outsource function evaluation on private inputs to a Hadoop cluster while maintaining data confidentiality. The scheme uses erasure coding to distribute encrypted data across servers and generate verification tokens to check integrity and locate errors. It analyzes how Hadoop security can be enhanced using Kerberos authentication and capabilities to control data access. The proposed approach aims to efficiently ensure cloud data storage security, correctness, and availability.
Network clustering is an important technique used in many large-scale distributed systems. Given good design and implementation, network clustering can significantly enhance the system\'s scalability and efficiency. However, it is very challenging to design a good clustering protocol for networks that scale fast and change continuously. In this paper, we propose a distributed network clustering protocol SDC targeting large-scale decentralized systems. In SDC, clusters are dynamically formed and adjusted based on SCM, a practical clustering accuracy measure. Based on SCM, each node can join or leave a cluster such that the clustering accuracy of the whole network can be improved. A big advantage of SDC is it can recover accurate clusters from node dynamics with very small message overhead. Through extensive simulations, we conclude that SDC is able to discover good quality clusters very efficiently.
Cloud computing is a technological paradigm that enables the consumer to enjoy the benefits of computing
services and applications without necessarily worrying about the investment and maintenance costs. This paper focuses on
the applicability of a new fully homomorphic encryption scheme (FHE) in solving data security in cloud computing. Different types
of existing homomorphic encryption schemes, including both partial and fully homomorphic encryption schemes are reviewed. The
study was aimed at constructing a fully homomorphic encryption scheme that lessens the computational strain on the computing
assets as compared to Gentry’s contribution on partial homomorphic encryption schemes where he constructed homomorphic
encryption based on ideal lattices using both additive and multiplicative Homomorphisms. In this study both addition and
composition operations implementing a fully homomorphic encryption scheme that secures data within cloud computing is used. The
work is founded on mathematical theory that is translated into an algorithm implementable in JAVA. The work was tested by a single
computing hardware to ascertain its suitability. The newly developed FHE scheme posted better results that confirmed its suitability
for data security in cloud computing.
‘Grids’areanapproachforbuildingdynamicallyconstructedproblem-solvingenvironmentsusing
geographically and organizationally dispersed,
high-performance computing and
data handling resources.
Gridsalsoprovideimportantinfrastructuresupportingmulti-institutionalcollaboration.
This document discusses using Hidden Markov Model (HMM) forward chaining techniques for prefetching in distributed file systems (DFS) for cloud computing. It begins by introducing DFS for cloud storage and issues like load balancing. It then discusses using HMM to analyze client I/O and predict future requests to prefetch relevant data. The HMM forward algorithm would be used to prefetch data from storage servers to clients proactively. This could improve performance by reducing client wait times for requested data in DFS for cloud applications.
This dissertation proposal discusses using a Hidden Markov Model (HMM) forward chaining technique for prefetching data in a distributed file system (DFS) for cloud computing. The technique would analyze I/O from client machines using HMM and send prefetched data from storage servers to clients before it is requested. This would improve performance by reducing wait times for requests. The proposal outlines using HMM algorithms like forward, backward, and Viterbi to model I/O sequences and train a model. It proposes a system where client I/O is sent to storage servers, which would use HMM to predict and prefetch future requests and send data proactively to clients.
This document provides a review of simulation techniques for parallel and distributed computing. It discusses several key topics:
1) It defines parallel computing, distributed computing, and parallel and distributed computing systems. Various classification schemes for parallel and distributed systems are also described.
2) It examines several modeling techniques for parallel and distributed systems including system modeling, network modeling, performance modeling, and mathematical modeling. It provides details on parallel discrete event simulation.
3) It reviews several simulation software tools used for modeling parallel and distributed systems including SimOS, SimJava, and MicroGrid.
4) It concludes with a focused discussion on cloud computing as the latest development in parallel and distributed computing.
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.
Abstract— Cloud storage is usually distributed infrastructure, where data is not stored in a single device but is spread to several storage nodes which are located in different areas. To ensure data availability some amount of redundancy has to be maintained. But introduction of data redundancy leads to additional costs such as extra storage space and communication bandwidth which required for restoring data blocks. In the existing system, the storage infrastructure is considered as homogeneous where all nodes in the system have same online availability which leads to efficiency losses. The proposed system considers that distributed storage system is heterogeneous where each node exhibit different online availability. Monte Carlo Sampling is used to measure the online availability of storage nodes. The parallel version of Particle Swarm Optimization is used to assign redundant data blocks according to their online availability. The optimal data assignment policy reduces the redundancy and their associated cost.
Information Extraction from Wireless Sensor Networks: System and ApproachesM H
Recent advances in wireless communication have made it possible to develop low-cost, and low power Wireless Sensor Networks (WSN). The WSN can be used for several application areas (e.g., habitat monitoring, forest fire detection, and health care). WSN Information Extraction (IE) techniques can be classified into four categories depending on the factors that drive data acquisition: event-driven, time-driven, query-based, and hybrid. This paper presents a survey of the state-of-the-art IE techniques in WSNs. The benefits and shortcomings of different IE approaches are presented as motivation for future work into automatic hybridization and adaptation of IE mechanisms.
The document discusses using network coding with multi-generation mixing to improve data recovery in cloud storage systems. It provides a literature review of several papers that use techniques like Maximum Distance Separation codes, random linear network coding, and instantly decodable network coding. The proposed work develops an architecture that uses multi-generation mixing and the DODEX+ encoding scheme to encode and retrieve data across multiple mobile clients and cloud storage. This aims to provide more efficient and reliable data delivery over wireless mesh networks. Tools like Amazon S3 and the NS2 network simulator are used to implement and test the proposed system.
Lately, the Wireless Sensors Networks (WSN) have moved to the concept of the hybrids networks in order to get universal platforms in various types of monitoring and information collecting applications. The work presented in this paper aims in designing a hybrid remote monitoring architecture, largely secured by a high availability and resilience WSN. The modeling approach intends to describe the main operation of polling and dispatching between the communications channels with the purpose of ensuring the information availability and reducing the resilience time. To achieve our goal, we have realized an experimental platform of measuring, processing and routing data through hybrid communications technologies. We have illustrated, via curves, the routing of the data measured by a WSN (ZigBee Technology) to a final user through several communication technologies (HTTPS, SMS, ...).
Lecture 01 - Chapter 1 (Part 01): This Lecture show the Overview of Course, What is an Operating System, Operating System Functions, Definition of a Distributed System, Properties of Distributed Systems, Software Concepts, Transparency in a Distributed System, Challenges, Approaches, Scalability Problems, Scalability Examples, Web Search, Financial Transactions, Multiplayer Games. Some basic concept of Operating System (OS).
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.
This document summarizes a study on a new dynamic load balancing approach in cloud environments. It begins by outlining some of the major challenges of load balancing in cloud systems, including uneven distribution of workloads across CPUs. It then proposes a new approach with three main components: 1) A queueing and job assignment process that prioritizes assigning jobs to faster CPUs, 2) A timeout chart to determine when jobs should be migrated or terminated to avoid delays, and 3) Use of a "super node" to act as a proxy and backup in case other nodes fail. The approach is intended to more efficiently distribute jobs and help cloud systems maintain optimal performance. Finally, the document discusses how this approach could be integrated into existing cloud architectures
This document summarizes and categorizes different weight-based clustering algorithms that have been designed for mobile ad hoc networks (MANETs). It discusses:
1) The basic concept of clustering in MANETs and the roles of standard nodes, cluster heads, and cluster gateways.
2) Two categories of clustering algorithms - simple (e.g. lowest ID, connectivity-based) and enhanced (e.g. k-cluster, hierarchical) approaches.
3) Weighted clustering, where each node calculates a weight based on attributes like speed, degree, power, and energy to elect cluster heads.
A New Architecture for Group Replication in Data GridEditor IJCATR
Nowadays, grid systems are vital technology for programs running with high performance and problems solving with largescale
in scientific, engineering and business. In grid systems, heterogeneous computational resources and data should be shared
between independent organizations that are scatter geographically. A data grid is a kind of grid types that make relations computational
and storage resources. Data replication is an efficient way in data grid to obtain high performance and high availability by saving
numerous replicas in different locations e.g. grid sites. In this research, we propose a new architecture for dynamic Group data
replication. In our architecture, we added two components to OptorSim architecture: Group Replication Management component
(GRM) and Management of Popular Files Group component (MPFG). OptorSim developed by European Data Grid projects for
evaluate replication algorithm. By using this architecture, popular files group will be replicated in grid sites at the end of each
predefined time interval.
Efficient load rebalancing for distributed file system in CloudsIJERA Editor
Cloud computing is an upcoming era in software industry. It’s a very vast and developing technology.
Distributed file systems play an important role in cloud computing applications based on map reduce
techniques. While making use of distributed file systems for cloud computing, nodes serves computing and
storage functions at the same time. Given file is divided into small parts to use map reduce algorithms in
parallel. But the problem lies here since in cloud computing nodes may be added, deleted or modified any time
and also operations on files may be done dynamically. This causes the unequal load distribution of load among
the nodes which leads to load imbalance problem in distributed file system. Newly developed distributed file
system mostly depends upon central node for load distribution but this method is not helpful in large-scale and
where chances of failure are more. Use of central node for load distribution creates a problem of single point
dependency and chances of performance of bottleneck are more. As well as issues like movement cost and
network traffic caused due to migration of nodes and file chunks need to be resolved. So we are proposing
algorithm which will overcome all these problems and helps to achieve uniform load distribution efficiently. To
verify the feasibility and efficiency of our algorithm we will be using simulation setup and compare our
algorithm with existing techniques for the factors like load imbalance factor, movement cost and network traffic.
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.
An efficient hybrid peer to-peersystemfordistributeddatasharingambitlick
The document proposes a hybrid peer-to-peer system that combines the advantages of structured and unstructured networks. It consists of two parts: 1) a structured core network that forms the backbone and provides efficient data lookup; 2) multiple unstructured networks attached to each core node, allowing flexible peer joining/leaving. This two-tier design decouples efficiency and flexibility. Simulation results show the hybrid system balances these properties better than single-approach networks.
Charm a cost efficient multi cloud data hosting scheme with high availabilityKamal Spring
More and more enterprises and organizations are hosting their data into the cloud, in order to reduce the IT maintenance cost and enhance the data reliability. However, facing the numerous cloud vendors as well as their heterogenous pricing policies, customers may well be perplexed with which cloud(s) are suitable for storing their data and what hosting strategy is cheaper. The general status quo is that customers usually put their data into a single cloud (which is subject to the vendor lock-in risk) and then simply trust to luck. Based on comprehensive analysis of various state-of-the-art cloud vendors, this paper proposes a novel data hosting scheme (named CHARM) which integrates two key functions desired. The first is selecting several suitable clouds and an appropriate redundancy strategy to store data with minimized monetary cost and guaranteed availability. The second is triggering a transition process to re-distribute data according to the variations of data access pattern and pricing of clouds. We evaluate the performance of CHARM using both trace-driven simulations and prototype experiments. The results show that compared with the major existing schemes, CHARM not only saves around 20% of monetary cost but also exhibits sound adaptability to data and price adjustments.
This document summarizes a research paper that proposes a new permission-based clustering mutual exclusion algorithm for mobile ad-hoc networks. The algorithm uses a cluster-based hierarchical approach where only cluster leaders are responsible for granting or denying permission to enter the critical section, thereby reducing message complexity. Nodes are partitioned into clusters, with the heaviest weighted node selected as the cluster leader. When a node wants to enter the critical section, it sends a request to its cluster leader. If the cluster leader has obtained over 50% of the total votes, it can grant permission. Otherwise, it requests votes from other cluster leaders until it reaches the majority. This clustering approach helps solve the mutual exclusion problem in mobile ad-hoc networks in an efficient
Cryptographic Cloud Storage with Hadoop ImplementationIOSR Journals
This document proposes a scheme for cryptographic cloud storage using Hadoop implementation. It introduces parallel homomorphic encryption schemes that allow computation over encrypted data through an evaluation algorithm that can run efficiently in parallel. This allows a client to outsource function evaluation on private inputs to a Hadoop cluster while maintaining data confidentiality. The scheme uses erasure coding to distribute encrypted data across servers and generate verification tokens to check integrity and locate errors. It analyzes how Hadoop security can be enhanced using Kerberos authentication and capabilities to control data access. The proposed approach aims to efficiently ensure cloud data storage security, correctness, and availability.
Network clustering is an important technique used in many large-scale distributed systems. Given good design and implementation, network clustering can significantly enhance the system\'s scalability and efficiency. However, it is very challenging to design a good clustering protocol for networks that scale fast and change continuously. In this paper, we propose a distributed network clustering protocol SDC targeting large-scale decentralized systems. In SDC, clusters are dynamically formed and adjusted based on SCM, a practical clustering accuracy measure. Based on SCM, each node can join or leave a cluster such that the clustering accuracy of the whole network can be improved. A big advantage of SDC is it can recover accurate clusters from node dynamics with very small message overhead. Through extensive simulations, we conclude that SDC is able to discover good quality clusters very efficiently.
Cloud computing is a technological paradigm that enables the consumer to enjoy the benefits of computing
services and applications without necessarily worrying about the investment and maintenance costs. This paper focuses on
the applicability of a new fully homomorphic encryption scheme (FHE) in solving data security in cloud computing. Different types
of existing homomorphic encryption schemes, including both partial and fully homomorphic encryption schemes are reviewed. The
study was aimed at constructing a fully homomorphic encryption scheme that lessens the computational strain on the computing
assets as compared to Gentry’s contribution on partial homomorphic encryption schemes where he constructed homomorphic
encryption based on ideal lattices using both additive and multiplicative Homomorphisms. In this study both addition and
composition operations implementing a fully homomorphic encryption scheme that secures data within cloud computing is used. The
work is founded on mathematical theory that is translated into an algorithm implementable in JAVA. The work was tested by a single
computing hardware to ascertain its suitability. The newly developed FHE scheme posted better results that confirmed its suitability
for data security in cloud computing.
‘Grids’areanapproachforbuildingdynamicallyconstructedproblem-solvingenvironmentsusing
geographically and organizationally dispersed,
high-performance computing and
data handling resources.
Gridsalsoprovideimportantinfrastructuresupportingmulti-institutionalcollaboration.
This document discusses using Hidden Markov Model (HMM) forward chaining techniques for prefetching in distributed file systems (DFS) for cloud computing. It begins by introducing DFS for cloud storage and issues like load balancing. It then discusses using HMM to analyze client I/O and predict future requests to prefetch relevant data. The HMM forward algorithm would be used to prefetch data from storage servers to clients proactively. This could improve performance by reducing client wait times for requested data in DFS for cloud applications.
This dissertation proposal discusses using a Hidden Markov Model (HMM) forward chaining technique for prefetching data in a distributed file system (DFS) for cloud computing. The technique would analyze I/O from client machines using HMM and send prefetched data from storage servers to clients before it is requested. This would improve performance by reducing wait times for requests. The proposal outlines using HMM algorithms like forward, backward, and Viterbi to model I/O sequences and train a model. It proposes a system where client I/O is sent to storage servers, which would use HMM to predict and prefetch future requests and send data proactively to clients.
This document provides a review of simulation techniques for parallel and distributed computing. It discusses several key topics:
1) It defines parallel computing, distributed computing, and parallel and distributed computing systems. Various classification schemes for parallel and distributed systems are also described.
2) It examines several modeling techniques for parallel and distributed systems including system modeling, network modeling, performance modeling, and mathematical modeling. It provides details on parallel discrete event simulation.
3) It reviews several simulation software tools used for modeling parallel and distributed systems including SimOS, SimJava, and MicroGrid.
4) It concludes with a focused discussion on cloud computing as the latest development in parallel and distributed computing.
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.
This document provides an overview of cloud computing and distributed systems. It discusses large scale distributed systems, cloud computing paradigms and models, MapReduce and Hadoop. MapReduce is introduced as a programming model for distributed computing problems that handles parallelization, load balancing and fault tolerance. Hadoop is presented as an open source implementation of MapReduce and its core components are HDFS for storage and the MapReduce framework. Example use cases and running a word count job on Hadoop are also outlined.
This document discusses security issues in grid computing and proposes an enhanced amalgam encryption approach. It begins with an overview of distributed, cloud, and grid computing. Grid computing involves coordinating shared resources across distributed, heterogeneous environments. Major security issues in grid computing include integration with existing security systems, interoperability across domains, and establishing trust relationships. The document then discusses cryptography approaches used to provide security, including symmetric and asymmetric encryption. It proposes a hybrid encryption solution combining AES and RC4 algorithms to address overhead limitations of previous approaches for large distributed networks like smart grids.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Effective & Flexible Cryptography Based Scheme for Ensuring User`s Data Secur...ijsrd.com
Cloud computing has been envisioned as the next-generation architecture of IT enterprise. In contrast to traditional solutions, where the IT services are under proper physical, logical and personnel controls, cloud computing moves the application software and databases to the large data centers, where the management of the data and services may not be fully trustworthy. This unique attribute, however, poses many new security challenges which have not been well understood. In this article, we focus on cloud data storage security, which has always been an important aspect of quality of service. To ensure the correctness of users' data in the cloud, we propose an effective and flexible cryptography based scheme. Extensive security and performance analysis shows that the proposed scheme is highly efficient and resilient against malicious data modification attack.
This document proposes an integrated cloud-based framework for collecting and processing sensory data from mobile phones to support diverse people-centric applications. The framework includes modules for user adaptation, storage, application interfaces, and mobile cloud engines. A prototype is implemented to demonstrate how the framework can reduce mobile device energy consumption while meeting application requirements such as for emergency response systems.
This document summarizes a research paper that proposes a scheme for ensuring security and reliability of data stored in the cloud. The scheme utilizes erasure coding to redundantly store encrypted data fragments across multiple cloud servers. It generates homomorphic tokens that allow auditing of the data storage and identification of any misbehaving servers. The scheme supports secure dynamic operations like modification, deletion and append of cloud data files. Analysis shows the scheme is efficient and resilient against various security threats like server compromises or failures. It ensures storage correctness and fast localization of data errors in the cloud.
Suitability_of_Addition-Composition_Full_Homomorphic_Encryption_Scheme.pdfDr. Richard Otieno
This document discusses the suitability of a new fully homomorphic encryption scheme called addition-composition for securing data in cloud computing. It first reviews existing homomorphic encryption schemes, both partial and fully homomorphic. It then describes developing an FHE scheme using both addition and composition operations to address limitations of previous schemes in terms of computational strain. The scheme is implemented in Java and tested on a single computing hardware to confirm its suitability for data security in cloud computing by posting better results than previous approaches.
Evaluation Of The Data Security Methods In Cloud Computing Environmentsijfcstjournal
This document discusses methods for ensuring data security in cloud computing environments. It begins by introducing cloud computing models including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). The main goals of data security - confidentiality, integrity, and availability - are then described. Several methods for data security are proposed, including data fragmentation where sensitive data is divided and distributed across different domains. Encryption techniques are also discussed as ways to protect confidential data during storage and transmission. Overall, the document aims to evaluate approaches for addressing key issues around securing user data in cloud systems.
Agent based frameworks for distributed association rule mining an analysis ijfcstjournal
Distributed Association Rule Mining (DARM) is the task for generating the globally strong association
rules from the global frequent itemsets in a distributed environment. The intelligent agent based model, to
address scalable mining over large scale distributed data, is a popular approach to constructing
Distributed Data Mining (DDM) systems and is characterized by a variety of agents coordinating and
communicating with each other to perform the various tasks of the data mining process. This study
performs the comparative analysis of the existing agent based frameworks for mining the association rules
from the distributed data sources.
MAP/REDUCE DESIGN AND IMPLEMENTATION OF APRIORIALGORITHM FOR HANDLING VOLUMIN...acijjournal
Apriori is one of the key algorithms to generate frequent itemsets. Analysing frequent itemset is a crucial
step in analysing structured data and in finding association relationship between items. This stands as an
elementary foundation to supervised learning, which encompasses classifier and feature extraction
methods. Applying this algorithm is crucial to understand the behaviour of structured data. Most of the
structured data in scientific domain are voluminous. Processing such kind of data requires state of the art
computing machines. Setting up such an infrastructure is expensive. Hence a distributed environment
such as a clustered setup is employed for tackling such scenarios. Apache Hadoop distribution is one of
the cluster frameworks in distributed environment that helps by distributing voluminous data across a
number of nodes in the framework. This paper focuses on map/reduce design and implementation of
Apriori algorithm for structured data analysis.
Cloud Computing: A Perspective on Next Basic Utility in IT World IRJET Journal
This document discusses cloud computing and its architecture. It begins with an introduction to cloud computing, defining it as a model that provides infrastructure, platforms, and software as services. The key characteristics and service models of cloud computing are described.
The document then discusses the architecture of cloud computing, including the layers of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). It also describes the deployment models of private cloud, public cloud, community cloud, and hybrid cloud.
The document outlines several challenges of cloud computing, such as resource allocation and scheduling, cost optimization, processing time and speed, memory management, load balancing, security issues, fault
PROVABLE MULTICOPY DYNAMIC DATA POSSESSION IN CLOUD COMPUTING SYSTEMSNexgen Technology
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DIVISION AND REPLICATION OF DATA IN GRID FOR OPTIMAL PERFORMANCE AND SECURITYijgca
Using Grid Storage, users can remotely store their data and enjoy the on-demand high quality applications and services from a shared networks of configurable computing resources, without the burden of local data storage and maintenance. In this project based on the dynamic secrets proposed design an encryption scheme for SG wireless communication, named as dynamic secret-based encryption (DSE). Dynamic encryption key (DEK) is updated by XOR the previous DEK with current DS. In this project based on the dynamic secrets proposed design an encryption scheme for SG wireless communication, named as dynamic secret-based encryption (DSE). The basic idea of dynamic secrets is to generate a series of secrets from unavoidable transmission errors and other random factors in wireless communications In DSE, the previous packets are coded as binary values 0 and 1 according to whether they are retransmitted due to channel error. This 0/1 sequence is called as retransmission sequence (RS) which is applied to generate dynamic secret (DS). Dynamic encryption key (DEK) is updated by XOR the previous DEK with current DS.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
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Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
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2. II. CLOUD COMPUTING
Cloud Computing refers to manipulating, configuring, and
accessing the applications online. It offers online data
storage, infrastructure and application. Simply put, cloud
computing is the delivery of computing services servers,
storage, databases, networking, software, analytics and
more over the Internet (“the cloud”). Companies offering
these computing services are called cloud providers and
typically charge for cloud computing services based on
usage, similar to how you are billed for water or electricity
at home. Cloud computing is a type of computing that relies
on sharing computing resources rather than having local
servers or personal devices to handle applications. Cloud
computing is comparable to grid, a type of computing where
unused processing cycles of all computers in a network are
harnesses to solve problems too intensive for any stand-
alone machine. Also denoted to as a network cloud. In
telecommunications, a cloud refers to a public or semi-
public space on transmission lines that exists between the
end points of a transmission. Data that is transmitted across
a WAN enters the network from one end point using a
standard protocol suite such as Frame Relay and then enters
the network cloud where it shares space with other data
transmissions. The data emerges from the cloud where it
may be encapsulated, translated and transported in myriad
ways in the same format as when it entered the cloud. A
network cloud exists because when data is transmitted
across a packet switched network in a packet, no two
packets will necessarily follow the same physical path. The
unpredictable area that the data enters before it is received is
the cloud. A place where clients can access apps and
services, and where client’s data can be stored securely.
III. DISTRIBUTED FILE SYSTEM FOR CLOUD
Cloud uses distributed file system for storage purpose. If
one of the storage resource fails, then it can be extracted
from another one which makes cloud computing more
reliable Cloud computing refers to applications and services
that run on a distributed network using virtualized resources
and accessed by common Internet protocols and networking
standards.
Distributed file system allows to multiple clients to access
the data and support operations (create, delete, modify, read,
write) on that data. Every data file may be segregated into
several parts named chunks. Every chunk might be stored
on dissimilar remote machines, facilitating the parallel
performance of applications. Typically, documents are kept
in files in a hierarchical tree, wherever the nodes denote
directories. There are numerous ways to share records in a
distributed architecture: each solution must be appropriate
for a certain type of application, conditional on how
composite the application is. Meanwhile, the safety of the
System essential is ensured.
Confidentiality, availability and integrity are the foremost
keys designed for a secure system. Distributed file system
allows several big, medium, and small originalities to store
and access their isolated data as they ensure local data,
enabling the use of variable resources.
In a cloud computing environment, failure is the norm and
chunk servers may be upgraded, replaced, and added to the
system. Documents can be enthusiastically created, deleted,
and appended. Those indications to load imbalance in a
distributed file system, meaning that the file chunks are not
distributed equitably between the servers.
IV. HIDDEN MARKOV MODEL (HMM)
The Hidden Markov Model is a restricted set of states, each
one of which is associated with a (commonly
multidimensional) possibility distribution. A hidden Markov
model can be considered a broad view of a mixture
model someplace the hidden variables or dormant variables,
which control the mixture constituent to be designated for
each observation, are related over a Markov process
somewhat than self-determining of each other. Recently,
hidden Markov models have been comprehensive to
pairwise Markov models and threesome Markov models
which permit thought of more difficult data structures and
the forming of non-stationary data. A Hidden Markov
Model is unique in which perceive a series of emissions, but
do not recognize the series of states the model pass away
over to generate the emissions. Evaluates of Hidden Markov
Models seek to recuperate of states from the detected data.
Common HMM Types: major one is Ergodic (completely
connected).Every single state of model can be touched in a
single step from all other state of the model. Next one is
Bakiss (left-right).Such as time increases, states keep from
left to right ensure with an HMM, the forward algorithm,
backward algorithm, the forward-backward algorithm and
the Viterbi algorithms are used. Around are situated three
core problems in hmm. Three problems must be explained
for HMMs to be valuable in real-world applications,
Evaluation, Decoding and Learning. HMM Incentive is
Real-world has constructions and processes which have or
yield recognizable outputs.
V. IMPLEMENTATION
Compare the HMM Forward, Backward and Viterbi
Algorithm is the best way for find the shortest path without
knowing the inside data process. In this Algorithms are used
to training for the data that are accessed and processed with
frequently. So we get trained data that is used for finding
the shortest path with values.
A.Forward Algorithm
The Forward Algorithm is a recursive algorithm for
calculating αt(i) for the observation sequence of increasing
length t .
24AJCST Vol.6 No.2 July-December 2017
V.Thilaganga, M.Karthika and M.Maha Lakshmi
3. 1. Initialization
α1(i) = pi bi(o(1)) , i =1, ... , N
2.Recursion
α t+1 (i) = [ ∑j-1
N
αt (j) αji ] bi ( O (t+1) )
here i =1, ... , N , t =1, ... , T – 1
3.Termination
P (O (1) O (2) … O (T)) = ∑j-1
N
αT (j)
Use the forward algorithm to calculate the probability of a T
long observation sequence. An each of the y is one of the
observable set. Intermediate probabilities ( 's) are
calculated recursively by first calculating for all states at
t=1.
B. Backward Algorithm
The Backward Algorithm calculates recursively backward
variables going backward along the observation sequence.
1.Initialization
βT (i) = 1 , i =1, ... , N
According to the above definition, βT(i) does not exist. This
is a formal extension of the below recursion to t = T.
2.Recursion
βT (i) = ∑j-1
N
αtbj ( O (t+1) ) βt+1(j)
here i =1, ... , N , t = T - 1, T - 2 , . . . , 1
3.Termination
P (O (1) O (2) … O (T)) = ∑j-1
N
pj bj ( O (1) β1(j)
Obviously both Forward and Backward algorithms must
give the same results for total probabilities P(O)
= P(o(1), o(2), ... , o(T) ).
In this forward and backward are calculating and finding the
best path value is equal 0.000804. Which means all the
HMM algorithms brings the same value using matrix
calculations. In HMM chaining algorithms are finding best
solution path with values. It is Combined and worked with
Distributed File System for Cloud and bring a best solution
for the clients and servers.
The HMM combined with Distributed File Systems
Prefetching Technique algorithms are used to fetch the
client’s data proactively and forward into the storage server.
In previously using this prefetching techniques are chaotic
time series and regression prediction algorithms. In this
paper represents replace the prefetching algorithms such as
Forward and Backward algorithms in Hidden Markov
Model. It is mostly find the clients data as previously and
forward into relevant storage server. The main advantage
for using HMM forward and backward chain the data
prefetching process and data should be trained and will
forward to the nearest storage server as soon as possible. So
the prefetching technique processed will take quick time
with trained data. Here include the Values for founded path
values HMM and DFS and also include the previous
Prediction algorithms are a founded value that is below
Figure Table I.
TABLE 1 EVALUATION OF PREVIOUS METHODS
Learning algorithm Results
Wang and Mendel [11] 0.091
Kim and Kim [12] 0.026
Liner Predictive Model [11] 0.55
ANFIS [13] 0.007
Hidden Markov Model + Neural Nets 0.0017
Hidden Markov Model + DFS 0.5153
Hidden Markov Model + DFS 0.0315
Hidden Markov Model + DFS 0.003843
Hidden Markov Model + DFS 0.000804
VI. CONCLUSION
The Hidden Markov Model Froward and Backward
Algorithm compute probability much more efficiently than
the naïve approach, which very rapidly ends up in
combinational explosion [11]. It can offer the likelihood of
a given emission or observation at each position in the
sequence of observations. In this proposed mechanism is,
implemented and evaluated in data prefetching Distributed
File System for Cloud, which the client engines can collect
relevant data proactively through by the loading server in a
cloud environs.
25 AJCST Vol.6 No.2 July-December 2017
A Prefetching Technique Using HMM Forward and Backward Chaining for the DFS in Cloud
4. The loading servers are capable to analyze and predict the
client I/O process and then they proactively push data into
the relevant client machines for adequate client’s future
applications requests. The purpose of forward I/O data and
about the client machine information’s are piggybacked and
then transferred to corresponding storage server from the
client nodes [12].
The current implementation of proposed data prefetching
process in Distributed File System for cloud using Hidden
Markov Model Froward and backward prediction chains for
proactively fetch the clients I/O events. Using HMM
forward and backward chain the data is trained and growths
the learning process [13]. There are different workloads
bang up-to-date in the system by client, categorizing block
access patterns from the block I/O events are sketched and
bring about by several workloads with using HMM Viterbi
algorithm.
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26AJCST Vol.6 No.2 July-December 2017
V.Thilaganga, M.Karthika and M.Maha Lakshmi