Scheduling -Issues in Load Distributing, Components for Load Distributing Algorithms,
Different Types Distributed of Load Distributing Algorithms, Fault-tolerant services Highly
available services, Introduction to Distributed Database and Multimedia system
Distributed Objects and Remote Invocation: Communication between distributed objects
Remote procedure call, Events and notifications, operating system layer Protection, Processes
and threads, Operating system architecture. Introduction to Distributed shared memory,
Design and implementation issue of DSM.Case Study: CORBA and JAVA RMI.
Clock synchronization: Clocks, events and process states, Synchronizing physical clocks,
Logical time and logical clocks, Lamport’s Logical Clock, Global states, Distributed mutual
exclusion algorithms: centralized, decentralized, distributed and token ring algorithms,
election algorithms, Multicast communication.
Introduction: Definition, Design Issues, Goals, Types of distributed systems, Centralized
Computing, Advantages of Distributed systems over centralized system .Limitation of
Distributed systems Architectural models of distributed system, Client-server
communication, Introduction to DCE
This document discusses different approaches to resource management in distributed systems, including task assignment, load balancing, and load sharing. The task assignment approach views each process as a collection of tasks and schedules the tasks across nodes to improve performance. The load balancing approach distributes processes across nodes to equalize workloads. The load sharing approach aims to ensure no nodes are idle while processes wait. Effective resource management requires algorithms that make quick decisions with minimal overhead while optimizing resource usage and response times.
IRJET- HHH- A Hyped-up Handling of Hadoop based SAMR-MST for DDOS Attacks...IRJET Journal
This document proposes a novel scheme called SAMR-MST to detect DDoS attacks using Hadoop's MapReduce framework more efficiently. It introduces the SAMR (Self-Adaptive MapReduce) scheduling algorithm, which uses historical task performance data to identify slow tasks and launch backup tasks. It then enhances SAMR with Minimum Spanning Tree clustering to tune SAMR's parameters, improving its ability to find slow tasks. The proposed approach is evaluated against existing MapReduce schedulers like FIFO and LATE, showing it can reduce execution time by up to 25% in heterogeneous cloud environments subject to DDoS attacks.
Intrusion detection in heterogeneous network by multipath routing based toler...eSAT Publishing House
This document summarizes a research paper that proposes a new scheme called weighted-based voting to overcome the problem of "badmouthing" attacks in wireless sensor networks with multipath routing. Badmouthing occurs when malicious nodes fail to drop packets even after knowing the packet was already delivered. The weighted-based voting protocol assigns weights based on success rates to identify trusted nodes. It uses weighted voting to make multipath routing decisions and remove malicious nodes detected by a distributed intrusion detection system based on votes from random voter nodes. The goal is to maximize network lifetime while satisfying quality of service requirements in heterogeneous wireless sensor networks.
Intrusion detection in heterogeneous network by multipath routing based toler...eSAT Journals
Abstract The key theory of our redundancy management is to achieve the tradeoff between energy consumption vs. the gain in timeliness, security, and reliability to increase the system useful lifetime. A Innovative probability model to analyze the best redundancy level in terms of source redundancy, path redundancy and as well as the best intrusion detection settings in terms of the number of voters and the intrusion invocation break under which the lifetime of a HWSN [Heterogeneous Wireless Sensor Network] is maximized. In redundancy management “badmouthing” is the major problem in managing the redundancy. This badmouthing is malicious node which will never drop the packet even after knowing that the packet has been sent already. In this paper we propose a new scheme to overcome the problem of badmouthing by weighted based voting, this protocol will weight (Success Rate) all the nodes in the network to find the non-malicious node in the network which having more packet drop. In “weighted voting” main function is to find trust/reputation of neighbor nodes, as well as to tackle the “what paths to use” problem in multipath routing decision making for intrusion tolerance in WSNs. Keywords: Bad mouthing, Wireless Sensor Network, Weighted Based Voting, HWSN.
Distributed Objects and Remote Invocation: Communication between distributed objects
Remote procedure call, Events and notifications, operating system layer Protection, Processes
and threads, Operating system architecture. Introduction to Distributed shared memory,
Design and implementation issue of DSM.Case Study: CORBA and JAVA RMI.
Clock synchronization: Clocks, events and process states, Synchronizing physical clocks,
Logical time and logical clocks, Lamport’s Logical Clock, Global states, Distributed mutual
exclusion algorithms: centralized, decentralized, distributed and token ring algorithms,
election algorithms, Multicast communication.
Introduction: Definition, Design Issues, Goals, Types of distributed systems, Centralized
Computing, Advantages of Distributed systems over centralized system .Limitation of
Distributed systems Architectural models of distributed system, Client-server
communication, Introduction to DCE
This document discusses different approaches to resource management in distributed systems, including task assignment, load balancing, and load sharing. The task assignment approach views each process as a collection of tasks and schedules the tasks across nodes to improve performance. The load balancing approach distributes processes across nodes to equalize workloads. The load sharing approach aims to ensure no nodes are idle while processes wait. Effective resource management requires algorithms that make quick decisions with minimal overhead while optimizing resource usage and response times.
IRJET- HHH- A Hyped-up Handling of Hadoop based SAMR-MST for DDOS Attacks...IRJET Journal
This document proposes a novel scheme called SAMR-MST to detect DDoS attacks using Hadoop's MapReduce framework more efficiently. It introduces the SAMR (Self-Adaptive MapReduce) scheduling algorithm, which uses historical task performance data to identify slow tasks and launch backup tasks. It then enhances SAMR with Minimum Spanning Tree clustering to tune SAMR's parameters, improving its ability to find slow tasks. The proposed approach is evaluated against existing MapReduce schedulers like FIFO and LATE, showing it can reduce execution time by up to 25% in heterogeneous cloud environments subject to DDoS attacks.
Intrusion detection in heterogeneous network by multipath routing based toler...eSAT Publishing House
This document summarizes a research paper that proposes a new scheme called weighted-based voting to overcome the problem of "badmouthing" attacks in wireless sensor networks with multipath routing. Badmouthing occurs when malicious nodes fail to drop packets even after knowing the packet was already delivered. The weighted-based voting protocol assigns weights based on success rates to identify trusted nodes. It uses weighted voting to make multipath routing decisions and remove malicious nodes detected by a distributed intrusion detection system based on votes from random voter nodes. The goal is to maximize network lifetime while satisfying quality of service requirements in heterogeneous wireless sensor networks.
Intrusion detection in heterogeneous network by multipath routing based toler...eSAT Journals
Abstract The key theory of our redundancy management is to achieve the tradeoff between energy consumption vs. the gain in timeliness, security, and reliability to increase the system useful lifetime. A Innovative probability model to analyze the best redundancy level in terms of source redundancy, path redundancy and as well as the best intrusion detection settings in terms of the number of voters and the intrusion invocation break under which the lifetime of a HWSN [Heterogeneous Wireless Sensor Network] is maximized. In redundancy management “badmouthing” is the major problem in managing the redundancy. This badmouthing is malicious node which will never drop the packet even after knowing that the packet has been sent already. In this paper we propose a new scheme to overcome the problem of badmouthing by weighted based voting, this protocol will weight (Success Rate) all the nodes in the network to find the non-malicious node in the network which having more packet drop. In “weighted voting” main function is to find trust/reputation of neighbor nodes, as well as to tackle the “what paths to use” problem in multipath routing decision making for intrusion tolerance in WSNs. Keywords: Bad mouthing, Wireless Sensor Network, Weighted Based Voting, HWSN.
A study of load distribution algorithms in distributed schedulingeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Peer-to-peer Systems – Introduction – Napster and its legacy – Peer-to-peer – Middleware – Routing overlays. Overlay case studies: Pastry, Tapestry- Distributed File Systems –Introduction – File service architecture – Andrew File system. File System: Features-File model -File accessing models – File sharing semantics Naming: Identifiers, Addresses, Name Resolution – Name Space Implementation – Name Caches – LDAP.
This document discusses resource management techniques in distributed systems. It describes three main approaches: task assignment, load balancing, and load sharing. Task assignment involves scheduling related tasks to optimize performance metrics like turnaround time. Load balancing aims to evenly distribute workloads across nodes to utilize resources efficiently. Load sharing is a simpler approach that prevents idle nodes when others are heavily loaded. The document also outlines desirable properties for scheduling algorithms and categorizes different types of load balancing techniques.
This is a presentation for Chapter 7 Distributed system management
Book: DISTRIBUTED COMPUTING , Sunita Mahajan & Seema Shah
Prepared by Students of Computer Science, Ain Shams University - Cairo - Egypt
A survey of various scheduling algorithm in cloud computing environmenteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Securing Privacy of User’s Data on Cloud Using Back Propagation Neural Networkstheijes
To improve the accuracy of learning result, in practice multiple parties may collaborate through conducting joint Backpropagation neural network learning on the union of their respective data sets. During this process no party wants to disclose her/his data to others. Existing schemes supporting this kind of collaborative learning are either limited in the way of data partition or just consider two parties. There lacks a solution that allows two or more parties, each with an arbitrarily portioned data set, to collaboratively conduct the learning. this paper solves this open problem by utilizing the power of cloud computing. In our proposed scheme, each party encrypts his/her private data locally and uploads the ciphertexts into the cloud. The cloud then executes most of the operations pertaining to the learning algorithms over ciphertexts without knowing the original private data. To support flexible operations over ciphertexts, we adopt and tailor the BGN ‘doubly homomorphic’ encryption algorithm for the multi-party setting..
This document discusses load balancing in distributed systems. It provides definitions of static and dynamic load balancing, compares their approaches, and describes several dynamic load balancing algorithms. Static load balancing assigns tasks at compile time without migration, while dynamic approaches migrate tasks at runtime based on current system state. Dynamic approaches have overhead from migration but better utilize resources. Specific dynamic algorithms discussed include nearest neighbor, random, adaptive contracting with neighbor, and centralized information approaches.
The cloud user can remotely access software, services, application whenever they require over the
internet. The user can put their data remotely to the cloud storage. So, It is necessary that the cloud must have to
ensure data integrity and privacy of data of user.
The security is the major issue about cloud computing. The user may feel insecure for storing the data in
cloud storage. To overcome this issue, here we are giving public auditing mechanism for cloud storage. For this,
we studied Oruta system that providing public auditing mechanism. Revocation is all about the problems with
security occur in system. And we are revoked these many problems from the system. We are also revoking
existing members and adding new members in a group. In this way, we overcome the problem of static group. In
this system, TPA is Third Party Auditor which maintains all the log credentials of user and it verifies the proof of
data integrity and identity privacy of user. So, TPA plays a very important role in our system. Here we defining
statement of our model as,“Privacy Preserving using PAM in Cloud Computing ”.
.Keywords: Cloud Service Provider, Provable Data Possesion, Third Part Auditor, Public Auditing, Identity
Privacy, Shared Data, Cloud Computing.
The document discusses processes and process scheduling in an operating system. It covers key concepts like process state, process control blocks, CPU scheduling, and process synchronization techniques like cooperating processes and interprocess communication. Process scheduling involves allocating processes between ready, waiting, running and terminated states using schedulers like long-term and short-term schedulers. Context switching and process creation/termination are also summarized.
This document discusses using a genetic algorithm for routing in delay tolerant networks. It proposes using anycast routing between groups of nodes and applying crossover between groups using genetic algorithms. The algorithm initializes a network of nodes divided into groups. It then applies crossover between groups by swapping node IDs. A random fitness function is used to decrement the node population by deleting source and destination nodes after message transfer. Simulation results show the genetic algorithm approach effectively routes messages between groups in the delay tolerant network.
This document discusses a hierarchical scheduling method for efficiently scheduling varying length tasks in grid computing. It proposes using a two-level hierarchical approach. The first level uses a permutation-based method like Chemical Reaction Optimization (CRO) to schedule jobs to resources. The second level uses Shortest Job First to select and prioritize shorter jobs on each resource. This prevents shorter jobs from waiting for longer jobs to finish. Results show the hierarchical method reduces flowtime compared to CRO alone and improves performance for varying length job scheduling.
Quality of Service based Task Scheduling Algorithms in Cloud Computing IJECEIAES
In cloud computing resources are considered as services hence utilization of the resources in an efficient way is done by using task scheduling and load balancing. Quality of service is an important factor to measure the trustiness of the cloud. Using quality of service in task scheduling will address the problems of security in cloud computing. This paper studied quality of service based task scheduling algorithms and the parameters used for scheduling. By comparing the results the efficiency of the algorithm is measured and limitations are given. We can improve the efficiency of the quality of service based task scheduling algorithms by considering these factors arriving time of the task, time taken by the task to execute on the resource and the cost in use for the communication.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A major role is played in the layout and evaluation of any empirical wireless structure to manifest is the goal of this paper that counterfeit mode architectures affect counterfeit conduct, regarding structure accomplishment metrics, essentially and therefore, the excellent architecture should be explored in order to accomplish the most accurate and reliable results. It is found that the most analytical factors it is found that that actuate counterfeit mode accomplishment are counterfeit time, structure event organizing and
grade of adequate. It is, also, found that counterfeit time in relation to event existence in the real structure
along with the usage of modern architectural concepts such as multi-interweave technology complement
analytical issues too in the advancement of an adequate counterfeit organization for wireless communications. In order to evaluate the above findings an extensive empirical review has been
demeanored analysising several distinct events counterfeitorganizations towards presenting the relation
between channel designing collections, counterfeit time and structure accomplishment.
Load Balancing in Parallel and Distributed DatabaseMd. Shamsur Rahim
This document discusses load balancing techniques in distributed database systems. It describes different types of parallelism including inter-query, intra-query, intra-operation, and inter-operation parallelism. It also discusses problems that can occur with parallel execution such as initialization, interference, and skew. The document then focuses on techniques for load balancing within operators and between operators, including adaptive and specialized techniques. It describes how activations, activation queues, and threads can be used to improve load balancing in shared-memory systems.
Redundant Actor Based Multi-Hole Healing System for Mobile Sensor NetworksEditor IJCATR
In recent years, the Mobile Wireless Sensor Network
is the emerging solution for monitoring of a specified region of
interest. Several anomalies can occur in WSNs that impair their
desired functionalities resulting in the formation of different
kinds of holes, namely: coverage holes, routing holes. Our
ultimate aim is to cover total area without coverage hole in
wireless sensor networks. We propose a comprehensive solution,
called holes detection and healing. We divided our proposed
work into two phases. The first phase consists of three sub- tasks;
Hole-identification, Hole-discovery and border detection. The
second phase treats the Hole-healing with novel concept, hole
healing area. It consists of two sub-tasks; Hole healing area
determination and node relocation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Communication synchronization in cluster based wireless sensor network a re...eSAT Journals
Abstract A wireless sensor network is acquiring more popularity in different sectors. A scalable, low latency and energy efficient are desire challenges that should meet by wireless sensor network. Clustering permits sensors to systematically communicate among clusters. Cluster based sensor network satisfies these challenges as it provides flexible, energy saving and QoS. The communication efficiency and network performance degrades if the interaction between inter-cluster and intra-cluster communication are not managed properly. The proposed work uses two approaches to solve this problem. At aiming low packet delay and high throughput first approach uses cycle- based synchronous scheduling. By completely removing necessity of communication synchronization second approach send packets with no synchronization delay. The combined scheme can take benefit of both approaches. Keywords: Wireless sensor network, clustering, communication synchronization, QoS.
Perceiving and recovering degraded data on secure cloudIAEME Publication
This document discusses securing data stored on cloud systems. It proposes a method using tokens to represent file blocks distributed across multiple servers. A third party auditor verifies the integrity of tokens and can detect corrupted data by checking signatures. The system uses erasure coding and fault tolerance techniques like retransmission to recover lost data blocks and make the file system tolerant to node failures without data loss. Performance is evaluated, showing that optimal token size balances processing time against overhead of managing many small tokens.
A Survey of Job Scheduling Algorithms Whit Hierarchical Structure to Load Ba...Editor IJCATR
Due to the advances in human civilization, problems in science and engineering are becoming more complicated than ever
before. To solve these complicated problems, grid computing becomes a popular tool. a grid environment collects, integrates, and uses
heterogeneous or homogeneous resources scattered around the globe by a high-speed network. Scheduling problems are at the heart of
any Grid-like computational system. a good scheduling algorithm can assign jobs to resources efficiently and can balance the system
load. in this paper we survey three algorithms for grid scheduling and compare benefit and disadvantages of their based on makespan.
A survey of various scheduling algorithm in cloud computing environmenteSAT Journals
Abstract Cloud computing is known as a provider of dynamic services using very large scalable and virtualized resources over the Internet. Due to novelty of cloud computing field, there is no many standard task scheduling algorithm used in cloud environment. Especially that in cloud, there is a high communication cost that prevents well known task schedulers to be applied in large scale distributed environment. Today, researchers attempt to build job scheduling algorithms that are compatible and applicable in Cloud Computing environment Job scheduling is most important task in cloud computing environment because user have to pay for resources used based upon time. Hence efficient utilization of resources must be important and for that scheduling plays a vital role to get maximum benefit from the resources. In this paper we are studying various scheduling algorithm and issues related to them in cloud computing. Index Terms: cloud computing, scheduling, algorithm
A study of load distribution algorithms in distributed schedulingeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Peer-to-peer Systems – Introduction – Napster and its legacy – Peer-to-peer – Middleware – Routing overlays. Overlay case studies: Pastry, Tapestry- Distributed File Systems –Introduction – File service architecture – Andrew File system. File System: Features-File model -File accessing models – File sharing semantics Naming: Identifiers, Addresses, Name Resolution – Name Space Implementation – Name Caches – LDAP.
This document discusses resource management techniques in distributed systems. It describes three main approaches: task assignment, load balancing, and load sharing. Task assignment involves scheduling related tasks to optimize performance metrics like turnaround time. Load balancing aims to evenly distribute workloads across nodes to utilize resources efficiently. Load sharing is a simpler approach that prevents idle nodes when others are heavily loaded. The document also outlines desirable properties for scheduling algorithms and categorizes different types of load balancing techniques.
This is a presentation for Chapter 7 Distributed system management
Book: DISTRIBUTED COMPUTING , Sunita Mahajan & Seema Shah
Prepared by Students of Computer Science, Ain Shams University - Cairo - Egypt
A survey of various scheduling algorithm in cloud computing environmenteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Securing Privacy of User’s Data on Cloud Using Back Propagation Neural Networkstheijes
To improve the accuracy of learning result, in practice multiple parties may collaborate through conducting joint Backpropagation neural network learning on the union of their respective data sets. During this process no party wants to disclose her/his data to others. Existing schemes supporting this kind of collaborative learning are either limited in the way of data partition or just consider two parties. There lacks a solution that allows two or more parties, each with an arbitrarily portioned data set, to collaboratively conduct the learning. this paper solves this open problem by utilizing the power of cloud computing. In our proposed scheme, each party encrypts his/her private data locally and uploads the ciphertexts into the cloud. The cloud then executes most of the operations pertaining to the learning algorithms over ciphertexts without knowing the original private data. To support flexible operations over ciphertexts, we adopt and tailor the BGN ‘doubly homomorphic’ encryption algorithm for the multi-party setting..
This document discusses load balancing in distributed systems. It provides definitions of static and dynamic load balancing, compares their approaches, and describes several dynamic load balancing algorithms. Static load balancing assigns tasks at compile time without migration, while dynamic approaches migrate tasks at runtime based on current system state. Dynamic approaches have overhead from migration but better utilize resources. Specific dynamic algorithms discussed include nearest neighbor, random, adaptive contracting with neighbor, and centralized information approaches.
The cloud user can remotely access software, services, application whenever they require over the
internet. The user can put their data remotely to the cloud storage. So, It is necessary that the cloud must have to
ensure data integrity and privacy of data of user.
The security is the major issue about cloud computing. The user may feel insecure for storing the data in
cloud storage. To overcome this issue, here we are giving public auditing mechanism for cloud storage. For this,
we studied Oruta system that providing public auditing mechanism. Revocation is all about the problems with
security occur in system. And we are revoked these many problems from the system. We are also revoking
existing members and adding new members in a group. In this way, we overcome the problem of static group. In
this system, TPA is Third Party Auditor which maintains all the log credentials of user and it verifies the proof of
data integrity and identity privacy of user. So, TPA plays a very important role in our system. Here we defining
statement of our model as,“Privacy Preserving using PAM in Cloud Computing ”.
.Keywords: Cloud Service Provider, Provable Data Possesion, Third Part Auditor, Public Auditing, Identity
Privacy, Shared Data, Cloud Computing.
The document discusses processes and process scheduling in an operating system. It covers key concepts like process state, process control blocks, CPU scheduling, and process synchronization techniques like cooperating processes and interprocess communication. Process scheduling involves allocating processes between ready, waiting, running and terminated states using schedulers like long-term and short-term schedulers. Context switching and process creation/termination are also summarized.
This document discusses using a genetic algorithm for routing in delay tolerant networks. It proposes using anycast routing between groups of nodes and applying crossover between groups using genetic algorithms. The algorithm initializes a network of nodes divided into groups. It then applies crossover between groups by swapping node IDs. A random fitness function is used to decrement the node population by deleting source and destination nodes after message transfer. Simulation results show the genetic algorithm approach effectively routes messages between groups in the delay tolerant network.
This document discusses a hierarchical scheduling method for efficiently scheduling varying length tasks in grid computing. It proposes using a two-level hierarchical approach. The first level uses a permutation-based method like Chemical Reaction Optimization (CRO) to schedule jobs to resources. The second level uses Shortest Job First to select and prioritize shorter jobs on each resource. This prevents shorter jobs from waiting for longer jobs to finish. Results show the hierarchical method reduces flowtime compared to CRO alone and improves performance for varying length job scheduling.
Quality of Service based Task Scheduling Algorithms in Cloud Computing IJECEIAES
In cloud computing resources are considered as services hence utilization of the resources in an efficient way is done by using task scheduling and load balancing. Quality of service is an important factor to measure the trustiness of the cloud. Using quality of service in task scheduling will address the problems of security in cloud computing. This paper studied quality of service based task scheduling algorithms and the parameters used for scheduling. By comparing the results the efficiency of the algorithm is measured and limitations are given. We can improve the efficiency of the quality of service based task scheduling algorithms by considering these factors arriving time of the task, time taken by the task to execute on the resource and the cost in use for the communication.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A major role is played in the layout and evaluation of any empirical wireless structure to manifest is the goal of this paper that counterfeit mode architectures affect counterfeit conduct, regarding structure accomplishment metrics, essentially and therefore, the excellent architecture should be explored in order to accomplish the most accurate and reliable results. It is found that the most analytical factors it is found that that actuate counterfeit mode accomplishment are counterfeit time, structure event organizing and
grade of adequate. It is, also, found that counterfeit time in relation to event existence in the real structure
along with the usage of modern architectural concepts such as multi-interweave technology complement
analytical issues too in the advancement of an adequate counterfeit organization for wireless communications. In order to evaluate the above findings an extensive empirical review has been
demeanored analysising several distinct events counterfeitorganizations towards presenting the relation
between channel designing collections, counterfeit time and structure accomplishment.
Load Balancing in Parallel and Distributed DatabaseMd. Shamsur Rahim
This document discusses load balancing techniques in distributed database systems. It describes different types of parallelism including inter-query, intra-query, intra-operation, and inter-operation parallelism. It also discusses problems that can occur with parallel execution such as initialization, interference, and skew. The document then focuses on techniques for load balancing within operators and between operators, including adaptive and specialized techniques. It describes how activations, activation queues, and threads can be used to improve load balancing in shared-memory systems.
Redundant Actor Based Multi-Hole Healing System for Mobile Sensor NetworksEditor IJCATR
In recent years, the Mobile Wireless Sensor Network
is the emerging solution for monitoring of a specified region of
interest. Several anomalies can occur in WSNs that impair their
desired functionalities resulting in the formation of different
kinds of holes, namely: coverage holes, routing holes. Our
ultimate aim is to cover total area without coverage hole in
wireless sensor networks. We propose a comprehensive solution,
called holes detection and healing. We divided our proposed
work into two phases. The first phase consists of three sub- tasks;
Hole-identification, Hole-discovery and border detection. The
second phase treats the Hole-healing with novel concept, hole
healing area. It consists of two sub-tasks; Hole healing area
determination and node relocation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Communication synchronization in cluster based wireless sensor network a re...eSAT Journals
Abstract A wireless sensor network is acquiring more popularity in different sectors. A scalable, low latency and energy efficient are desire challenges that should meet by wireless sensor network. Clustering permits sensors to systematically communicate among clusters. Cluster based sensor network satisfies these challenges as it provides flexible, energy saving and QoS. The communication efficiency and network performance degrades if the interaction between inter-cluster and intra-cluster communication are not managed properly. The proposed work uses two approaches to solve this problem. At aiming low packet delay and high throughput first approach uses cycle- based synchronous scheduling. By completely removing necessity of communication synchronization second approach send packets with no synchronization delay. The combined scheme can take benefit of both approaches. Keywords: Wireless sensor network, clustering, communication synchronization, QoS.
Perceiving and recovering degraded data on secure cloudIAEME Publication
This document discusses securing data stored on cloud systems. It proposes a method using tokens to represent file blocks distributed across multiple servers. A third party auditor verifies the integrity of tokens and can detect corrupted data by checking signatures. The system uses erasure coding and fault tolerance techniques like retransmission to recover lost data blocks and make the file system tolerant to node failures without data loss. Performance is evaluated, showing that optimal token size balances processing time against overhead of managing many small tokens.
A Survey of Job Scheduling Algorithms Whit Hierarchical Structure to Load Ba...Editor IJCATR
Due to the advances in human civilization, problems in science and engineering are becoming more complicated than ever
before. To solve these complicated problems, grid computing becomes a popular tool. a grid environment collects, integrates, and uses
heterogeneous or homogeneous resources scattered around the globe by a high-speed network. Scheduling problems are at the heart of
any Grid-like computational system. a good scheduling algorithm can assign jobs to resources efficiently and can balance the system
load. in this paper we survey three algorithms for grid scheduling and compare benefit and disadvantages of their based on makespan.
A survey of various scheduling algorithm in cloud computing environmenteSAT Journals
Abstract Cloud computing is known as a provider of dynamic services using very large scalable and virtualized resources over the Internet. Due to novelty of cloud computing field, there is no many standard task scheduling algorithm used in cloud environment. Especially that in cloud, there is a high communication cost that prevents well known task schedulers to be applied in large scale distributed environment. Today, researchers attempt to build job scheduling algorithms that are compatible and applicable in Cloud Computing environment Job scheduling is most important task in cloud computing environment because user have to pay for resources used based upon time. Hence efficient utilization of resources must be important and for that scheduling plays a vital role to get maximum benefit from the resources. In this paper we are studying various scheduling algorithm and issues related to them in cloud computing. Index Terms: cloud computing, scheduling, algorithm
Modified Active Monitoring Load Balancing with Cloud Computingijsrd.com
Cloud computing is internet-based computing in which large groups of remote servers are networked to allow the centralized data storage, and online access to computer services or resources. Load Balancing is essential for efficient operations in distributed environments. As Cloud Computing is growing rapidly and clients are demanding more services and better results, load balancing for the Cloud has become a very interesting and important research area. In the absence of proper load balancing strategy/technique the growth of CC will never go as per predictions. The main focus of this paper is to verify the approach that has been proposed in the model paper [3]. An efficient load balancing algorithm has the ability to reduce the data center processing time, overall response time and to cope with the dynamic changes of cloud computing environments. The traditional load balancing Active Monitoring algorithm has been modified to achieve better data center processing time and overall response time. The algorithm presented in this paper efficiently distributes the requests to all the VMs for their execution, considering the CPU utilization of all VMs.
Scalable Distributed Job Processing with Dynamic Load Balancingijdpsjournal
We present here a cost effective framework for a robust scalable and distributed job processing system that
adapts to the dynamic computing needs easily with efficient load balancing for heterogeneous systems. The
design is such that each of the components are self contained and do not depend on each other. Yet, they
are still interconnected through an enterprise message bus so as to ensure safe, secure and reliable
communication based on transactional features to avoid duplication as well as data loss. The load
balancing, fault-tolerance and failover recovery are built into the system through a mechanism of health
check facility and a queue based load balancing. The system has a centralized repository with central
monitors to keep track of the progress of various job executions as well as status of processors in real-time.
The basic requirement of assigning a priority and processing as per priority is built into the framework.
The most important aspect of the framework is that it avoids the need for job migration by computing the
target processors based on the current load and the various cost factors. The framework will have the
capability to scale horizontally as well as vertically to achieve the required performance, thus effectively
minimizing the total cost of ownership
This document proposes a fair scheduling algorithm with dynamic load balancing for grid computing. It begins by introducing grid computing and the need for efficient load balancing algorithms to distribute tasks. It then describes dynamic load balancing approaches, including information, triggering, resource type, location, and selection policies. The proposed algorithm uses a fair scheduling approach that assigns tasks to processors based on their estimated fair completion times to ensure tasks receive equal shares of computing resources. It also includes a dynamic load balancing component that migrates tasks between processors to maintain balanced loads across all resources. Simulation results demonstrated the algorithm achieved balanced loads across processors and reduced overall task completion times.
This document discusses load balancing in computational grid systems. It defines load balancing as distributing workloads across computing nodes to improve system performance and node utilization. Static and dynamic load balancing algorithms are described, with dynamic being more complex but able to adapt in real-time. The document presents a model of a grid system with job queues and computing nodes, and discusses factors like job arrival rates, service times, and load definitions that impact load balancing strategies.
Job Resource Ratio Based Priority Driven Scheduling in Cloud Computingijsrd.com
Cloud Computing is an emerging technology in the area of parallel and distributed computing. Clouds consist of a collection of virtualized resources, which include both computational and storage facilities that can be provisioned on demand, depending on the users' needs. Job scheduling is one of the major activities performed in all the computing environments. Cloud computing is one the upcoming latest technology which is developing drastically. To efficiently increase the working of cloud computing environments, job scheduling is one the tasks performed in order to gain maximum profit. In this paper we proposed a new scheduling algorithm based on priority and that priority is based on ratio of job and resource. To calculate priority of job we use analytical hierarchy process. In this paper we also compare result with other algorithm like First come first serve and round robin algorithms.
This document describes a report submitted for a seminar assignment on dynamic load balancing in grid computing using a multi-agent system and tree structure. It was submitted by Vishnu Kumar Prajapati in 2013 for his M.Tech. in Advanced Networking at ABV Indian Institute of Information Technology and Management in Gwalior, India. The report discusses the historical background and motivation for grid computing and load balancing. It then reviews literature on dynamic load balancing policies, multi-agent systems, grid computing service architectures and objectives. The methodology section proposes reducing communication overhead compared to previous pool-based approaches to improve grid system performance.
Grid scheduling is a process of mapping Grid jobs to resources over multiple administrative domains.
A Grid job can be split into many small tasks.
The scheduler has the responsibility of selecting resources and scheduling jobs in such a way that the user and application requirements are met,in terms of overall execution time (throughput) and cost of the resources utilized.
IRJET - Efficient Load Balancing in a Distributed EnvironmentIRJET Journal
This document discusses load balancing algorithms for distributed computing environments. It begins by defining load balancing and describing its importance in distributed systems for optimizing resource utilization and system performance. Several static and dynamic load balancing algorithms are then summarized, including round robin, random, min-min, and max-min algorithms. The document also outlines key issues in load balancing, advantages, metrics for evaluating algorithms, and provides more detailed descriptions of 13 load balancing algorithms.
Cs 704 d aos-resource&processmanagementDebasis Das
Resource and process management approaches include task assignment, load balancing, and load sharing. Task assignment involves assigning tasks to suitable nodes. Load balancing distributes processes to balance load across nodes. Load sharing equitably distributes processes so no node remains idle. Good scheduling considers factors like dynamic decision-making, balanced performance/overhead, fairness, and scalability. Process migration moves processes between nodes for load balancing. Issues include freezing processes during transfer, address space transfer mechanisms, and maintaining communication between related processes. Threads allow finer-grained parallelism and resource sharing within a process. They present challenges for synchronization, scheduling, and signal handling.
Hybrid Scheduling Algorithm for Efficient Load Balancing In Cloud ComputingEswar Publications
This document presents a hybrid scheduling algorithm for efficient load balancing in cloud computing. The algorithm uses both round robin and priority-based scheduling approaches. It first assigns priorities to incoming job requests and then executes them in a round robin fashion. The algorithm aims to minimize overall response time and data center processing time. It is evaluated through simulation and found to perform better than round robin, priority-based, and equally spread current execution algorithms alone in terms of optimized response time and data center service time.
The document discusses process migration in Linux. It begins with an abstract and introduction on process migration and its benefits. It then provides details on the characteristics and motivations for process migration, including load balancing, resource sharing, fault tolerance, and mobility. The document discusses homogeneous migration in Linux in detail, including user-level and kernel-level approaches. It also describes the key components involved in process migration in Linux like the central server, load balancer, checkpointer, and file transferrer. Finally, it discusses ELF files and their structure including the ELF header and various fields.
The Concept of Load Balancing Server in Secured and Intelligent NetworkIJAEMSJORNAL
Hundreds and thousands of data packets are routed every second by computer networks which are complex systems. The data should be routed efficiently to handle large amounts of data in network. A core networking solution which is responsible for distribution of incoming traffic among servers hosting the same content is load balancing. For example, if there are ten servers within a network and two of them are doing 95% of the work, the network is not running very efficiently. If each server was handling about 10% of the traffic, the network would run much faster.Networks get more efficient with the help of Load balancing. The traffic is evenly distributed amongst the network making sure no single device is overwhelmed.When a request is balanced across multiple servers, it prevents any server from becoming a single point of failure. It improves overall availability and responsiveness. To evenly split the traffic load among several different servers web servers; often use load balancing.Load balancing requires hardware or software that divides incoming traffic amongst the available serverseither it is done on a local network or a large web server. High amount of traffic is received by a network that have one server dedicated to balance the load among other servers and devices in the network. This server is often known as load balancer. Load balancing is used by clusters or multiple computers that work together, to spread out processing jobs among the available systems.
This document discusses and compares various load balancing techniques in cloud computing. It begins by introducing load balancing as an important issue in cloud computing for efficiently scheduling user requests and resources. Several load balancing algorithms are then described, including honeybee foraging algorithm, biased random sampling, active clustering, OLB+LBMM, and Min-Min. Metrics for evaluating and comparing load balancing techniques are defined, such as throughput, overhead, fault tolerance, migration time, response time, resource utilization, scalability, and performance. The algorithms are then analyzed based on these metrics.
This document proposes an efficient implementation of the slot shifting algorithm that reduces computational overhead. Slot shifting is an algorithm that schedules both periodic and aperiodic tasks by allocating spare processing capacity to aperiodic tasks. The proposed approach removes the discrete time "slots" used in slot shifting and replaces it with a method using job release times and deadlines. It also provides a way to delay capacity updates to further reduce runtime complexity while preserving the slot shifting concept. Experimental results show this new approach can reduce scheduling overhead by 45-60% on average compared to the original slot shifting algorithm. It also presents a new aperiodic task admission algorithm with lower time complexity than the existing slot shifting approach.
A SURVEY ON STATIC AND DYNAMIC LOAD BALANCING ALGORITHMS FOR DISTRIBUTED MULT...IRJET Journal
This document summarizes a survey of static and dynamic load balancing algorithms for distributed multicore systems. It discusses how efficient load balancing is essential for distributing work across cores in large supercomputers. Both static and dynamic algorithms are reviewed. Static algorithms allocate work deterministically or probabilistically without considering runtime conditions, while dynamic algorithms can adapt based on network conditions and core capabilities. The paper evaluates various performance metrics for different load balancing algorithms and concludes that modern distributed multicore systems require more reliable dynamic algorithms to optimize performance.
This document contains information about a course on Data Mining and Warehousing taught by Mr. Sagar Pandya at Medi-Caps University. The course code is IT3ED02 and it is a 3 credit course covering 5 units: introduction to data mining, association and classification, clustering, and business analysis. It provides reference books and textbooks for the course. It also contains lecture materials from Mr. Pandya covering topics like querying and reporting tools, applications of data mining, OLAP, and OLAP cubes.
Here are the answers to the questions:
1. Pipeline cycle time = Maximum delay of any stage + Latch delay
= 90 ns + 10 ns = 100 ns
2. Non-pipeline execution time for one task = Total delay of all stages
= 60 + 50 + 90 + 80 = 280 ns
3. Speed up ratio = Non-pipeline time/Pipeline time
= 280/100 = 2.8
4. Pipeline time for 1000 tasks = Pipeline cycle time x Number of tasks
= 100 ns x 1000 = 100,000 ns = 100 μs
5. Sequential time for 1000 tasks = Non-pipeline time per task x Number of tasks
= 280 ns x 1000 = 280,
I/O subsystems: Input/output devices such as Disk, CD,ROM, Printer etc.; Interfacing with IO devices, keyboard and display interfaces; Basic concepts Bus Control, Read Write operations, Programmed IO, Concept of handshaking, Polled and Interrupt driven I/O, DMA data transfer
Information representation, Floating point representation (IEEE 754), computer arithmetic and their implementation; Fixed-point Arithmetic: Addition, Subtraction, Multiplication and Division, Memory Technology, static and dynamic memory, Random Access and Serial Access Memories, Cache memory and Memory Hierarchy, Address Mapping, Cache updation schemes, Virtual memory and memory management unit.
Direct and Indirect Address, addressing modes; Arithmetic Logic Units control and data path, data path components, design of ALU and data path, Stack Organization, discussions about RISC versus CISC architectures, controller design; Hardwired and Micro programmed Control
Basic architecture and organization of computers, Von Neumann Model, Registers and storage, Register Transfer Language, Bus and Memory Transfer, Common Bus System, Machine instructions, functional units and execution of a program; instruction cycles, Instruction set architectures, instruction formats
Clustering: Introduction, Types of clustering;
Partition-based clustering: K-Means, K-Medoids;
Density based clustering: DBSCAN, Clustering evaluation.
Mining Data Stream, Mining Time-Series Data, Mining Sequence Patterns in Transactional Database,
Social Network analysis and Multirelational Data Mining.
This document contains information about a Data Mining and Warehousing course taught by Mr. Sagar Pandya at Medi-Caps University. The course code is IT3ED02 and it is a 3 credit course taught over 3 hours per week. The document provides details about the course units which include introductions to data mining, association and classification, clustering, and business analysis. It also lists reference textbooks and includes sections taught by Mr. Pandya on topics like the basics of data mining, techniques, applications and challenges.
Data Warehousing (Need,Application,Architecture,Benefits), Data Mart, Schema,...Medicaps University
Data warehousing Components –Building a Data warehouse,
Need for data warehousing,
Basic elements of data warehousing,
Data Mart,
Data Extraction, Clean-up, and Transformation Tools –Metadata,
Star, Snow flake and Galaxy Schemas for Multidimensional databases,
Fact and dimension data,
Partitioning Strategy-Horizontal and Vertical Partitioning.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
2. Distributed System
Mr. Sagar Pandya
Information Technology Department
sagar.pandya@medicaps.ac.in
Course
Code
Course Name Hours Per
Week
Total Hrs. Total
Credits
L T P
IT3EL04 Distributed System 3 0 0 3 3
3. Reference Books
Text Book:
1. G. Coulouris, J. Dollimore and T. Kindberg, Distributed Systems: Concepts
and design, Pearson.
2. P K Sinha, Distributed Operating Systems: Concepts and design, PHI
Learning.
3. Sukumar Ghosh, Distributed Systems - An Algorithmic approach, Chapman
and Hall/CRC
Reference Books:
1. Tanenbaum and Steen, Distributed systems: Principles and Paradigms,
Pearson.
2. Sunita Mahajan & Shah, Distributed Computing, Oxford Press.
3. Distributed Algorithms by Nancy Lynch, Morgan Kaufmann.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
4. Unit-5
Scheduling
Issues in Load Distributing,
Components for Load Distributing Algorithms,
Different Types Distributed of Load Distributing Algorithms,
Fault-tolerant services Highly available services,
Introduction to Distributed Database and
Multimedia system
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
5. INTRODUCTION
The goal of distributed scheduling is to distribute a system’s load
across available resources in a way that optimizes overall system
performance while maximizing resource utilization.
The primary concept is to shift workloads from strongly laden
machines to idle or lightly loaded machines.
To fully utilize the computing capacity of the Distributed Systems,
good resource allocation schemes are required.
A distributed scheduler is a resource management component of a
DOS that focuses on dispersing the system’s load among the
computers in a reasonable and transparent manner.
The goal is to maximize the system’s overall performance.
A locally distributed system is made up of a group of independent
computers connected by a local area network.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
6. INTRODUCTION
A distributed scheduler is a resource management component of a
distributed operating system that focuses on judiciously and
transparently redistributing the load of the system among the
individual units to enhance overall performance.
Users submit tasks at their host computers for processing.
The need for load distribution arises in such environments because,
due to the random arrival of tasks and their random CPU service time
requirements, there is a good possibility that several computers are
idle or lightly loaded and some others are heavily loaded,
which would degrade the performance.
In real life applications there is always a possibility that one server or
system is idle while a task is being waited upon at another server.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
7. INTRODUCTION
Users submit tasks for processing at their host computers.
Because of the unpredictable arrival of tasks and their random CPU
service time, load distribution is essential in such an environment.
The length of resource queues, particularly the length of CPU
queues, are useful indicators of demand since they correlate closely
with task response time.
It is also fairly simple to determine the length of a queue.
However, there is a risk in oversimplifying scheduling decisions.
A number of remote servers, for example, could notice at the same
time that a particular site had a short CPU queue length and start a lot
of process transfers.
As a result, that location may become overburdened with processes,
and its initial reaction may be to try to relocate them.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
8. INTRODUCTION
We don’t want to waste resources (CPU time and bandwidth) by
making poor decisions that result in higher migration activity
because migration is an expensive procedure. Therefore, we need
proper load distributing algorithms.
Load on a system/node can correspond to the queue length of tasks/
processes that need to be processed.
Queue length of waiting tasks: proportional to task response time,
hence a good indicator of system load.
Distributing load: transfer tasks/processes among nodes.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
9. What is Load?
CPU queue length can act as a good indicator of load and it is also
easy to determine.
If the task transfer involves large delay then using CPU queue length
will make the load to accept more task while the already accepted
task are in transit.
When all the accepted task arrives at the node then the load becomes
overloaded and requires further task transfer.
This problem can be solved by artificially incrementing CPU queue
length whenever a task is accepted.
If the task transfer does not occur in a specified amount of time
period then time out occurs and if time out occurs then CPU queue
length is automatically decremented.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
10. INTRODUCTION
Static, dynamic, and adaptive load distribution algorithms are
available.
Static indicates that decisions on process assignment to processors
are hardwired into the algorithm, based on a priori knowledge, such
as that gleaned via an analysis of the application’s graph model.
Dynamic algorithms use system state information to make
scheduling decisions, allowing them to take use of under utilized
system resources at runtime while incurring the cost of gathering
system data.
To adjust to system loading conditions, an adaptive algorithm alters
the parameters of its algorithm.
When system demand or communication is high, it may lower the
amount of information required for scheduling decisions.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
11. Components of Load Distributing Algorithm
Components of Load Distributing Algorithm :A load distributing
algorithm has, typically, four components:- transfer, selection,
location and information policies.
Transfer Policy –
Determine whether or not a node is in a suitable state for a task
transfer.
Process Selection Policy –
Determines the task to be transferred.
Site Location Policy –
Determines the node to which a task should be transferred to when it
is selected for transfer.
Information Policy –
It is in-charge of initiating the gathering of system state data.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
12. Components of Load Distributing Algorithm
1. Transfer Policy
When a process is a about to be created, it could run on the local
machine or be started elsewhere.
Bearing in mind that migration is expensive, a good initial choice of
location for a process could eliminate the need for future system
activity.
Many policies operate by using a threshold.
If the machine's load is below the threshold then it acts as a potential
receiver for remote tasks.
If the load is above the threshold, then it acts as a sender for new
tasks.
Local algorithms using thresholds are simple but may be far from
optimal.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
13. Components of Load Distributing Algorithm
Transfer policy indicates when a node (system) is in a suitable state
to participate in a task transfer.
The most popular proposed concept for transfer policy is based on a
optimum threshold.
Thresholds are nothing but units of load.
When a load or task originates in a particular node and the number of
load goes beyond the threshold T, the node becomes a sender (i.e.
the node is overloaded and has additional task(s) that should be
transferred to another node).
Similarly, when the loads at a particular node falls bellow T it
becomes a receiver.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
14. Components of Load Distributing Algorithm
2. Process Selection Policy
A selection policy chooses a task for transfer.
This decision will be based on the requirement that the overhead
involved in the transfer will be compensated by an improvement in
response time for the task and/or the system.
Some means of knowing that the task is long-lived will be necessary to
avoid needless migration. This could be based perhaps on past history.
A number of other factors could influence the decision.
The size of the task's memory space is the main cost of migration.
Small tasks are more suited.
Also, for efficiency purposes, the number of location dependent calls
made by the chosen task should be minimal because these must be
mapped home transparently.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
15. Components of Load Distributing Algorithm
A selection policy determines which task in the node (selected by the
transfer policy), should be transferred.
If the selection policy fails to find a suitable task in the node, then the
transfer procedure is stopped until the transfer policy indicates that the
site is again a sender.
Here there are two approaches viz.: preemptive and non-pre-emptive.
Non-pre-emptive the approach is simple, we select the newly
originated task that has caused the node to be a sender, for migration.
But often this is not the best approach as the overhead incurred in the
transfer of task should be compensated for by the reduction in the
response time realised by the task.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
16. Components of Load Distributing Algorithm
Also there are some other factors, firstly the overhead incurred by the
transfer should be minimal (a task of small size carries less overhead)
and secondly, the number of location dependent system calls made
by the selected task should be minimal.
This phenomenon of location dependency is called location affinity
and must be executed at the node where the task originated because
they use resources such as windows, or mouse that only exist at the
node.
Other criteria to consider in a task selection approach are: first, the
overhead imposed by the transfer should be as low as possible, and
second, the number of location-dependent calls made by the selected
task should be as low as possible.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
17. Components of Load Distributing Algorithm
3. Site Location Policy
Once the transfer policy has decided to send a particular task, the
location policy must decide where the task is to be sent.
This will be based on information gathered by the information policy.
Polling is a widely used sender-initiated technique.
A site polls other sites serially or in parallel to determine if they are
suitable sites for a transfer and/or if they are willing to accept a
transfer.
Nodes could be selected at random for polling, or chosen more
selectively based on information gathered during previous polls. The
number of sites polled may vary.
A receiver-initiated scheme depends on idle machines to announce
their availability for work.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
18. Components of Load Distributing Algorithm
The goal of the idle site is to find some work to do. An interesting
idea is for it to offer to do work at a price, leaving the sender to make
a cost/performance decision in relation to the task to be migrated.
Polling is a widely used approach for locating a suitable node. In
polling, a node polls another node to see if it is a suitable load-
sharing node.
Nodes can be polled sequentially or concurrently.
A site polls other sites in a sequential or parallel manner to see
whether they are acceptable for a transfer and/or if they are prepared
to accept one.
For polling, nodes could be chosen at random or more selectively
depending on information obtained during prior polls.
It’s possible that the number of sites polled will change.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
19. Components of Load Distributing Algorithm
4. Information Policy – The information policy is in charge of
determining when information regarding the states of the other nodes
in the system should be collected. Most information policies fall into
one of three categories.
Demand driven – Using sender initiated or receiver initiated polling
techniques, a node obtains the state of other nodes only when it
desires to get involved in either sending or receiving tasks.
Because their actions are dependent on the status of the system,
demand-driven policies are inherently adaptive and dynamic.
The policy here can be sender initiative : sender looks for receivers
to transfer the load, receiver initiated – receivers solicit load from the
senders and symmetrically initiated – a combination of both sender
& receiver initiated.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
20. Components of Load Distributing Algorithm
Periodic – At regular intervals, nodes exchange data. To inform
localization algorithms, each site will have a significant history of
global resource utilization over time. At large system loads, the
benefits of load distribution are negligible, and the periodic exchange
of information may thus be an unnecessary overhead.
State change driven –When a node’s state changes by a specific
amount, it sends out state information. This data could be forwarded
to a centralized load scheduling point or shared with peers.
It does not collect information about other nodes like demand-driven
policy. This policy does not alter its operations in response to
changes in system state.
For example, if the system is already overloaded, exchanging system
state information on a regular basis will exacerbate the problem.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
21. Design Issues for Processor Allocation Algorithms
The major design decisions can be summed up as follows::
Deterministic versus Heuristic
Deterministic algorithms are appropriate when everything about
process behavior is known in advance.
If the computing, memory and communication requirements of all
processes can be established, then a graph may be constructed
depicting the system state.
The problem is to partition the graph into a number of subgraphs
according to a stated policy so that each sub graph of processes is
mapped onto one machine.
This is achieved subject to the resource constraints imposed by each
machine.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
22. Design Issues for Processor Allocation Algorithms
Heuristics refer to principles used in making decisions when all
possibilities cannot be fully explored.
For example, consider a site location algorithm where a machine
sends out a probe to a randomly chosen machine, asking if its
workload is below some threshold.
If not, it probes another and if no suitable host is found within N
probes, the algorithm terminates and the process runs on the
originating machine.
Optimal versus Suboptimal Algorithms: Optimal solutions require
extensive system information and devote significant time to analysis.
A study of the complexity of this analysis versus the success of
solutions might reveal that a simple suboptimal algorithm can yield
acceptable results and will be easier to implement.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
23. Design Issues for Processor Allocation Algorithms
Deterministic algorithms impose excessive costs on all modules
within the policy hierarchy and are not scaleable, but can achieve
optimal results.
Heuristic techniques are invariably less expensive and often
demonstrate acceptable but suboptimal results.
Local versus Global Algorithms
When a process is being considered for migration and a new
destination is being selected, there is a choice between allowing this
decision to be made in isolation by the current host or, to require
some consideration of the status of the intended destination.
It may be better to collect information about load elsewhere before
deciding whether the current host is under greater pressure or not.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
24. Design Issues for Processor Allocation Algorithms
One globally continuous technique, is to associate an 'income' with
each process so that the larger this value is relative to other processes
at this site, the greater the percentage of processor cycles received.
This income is adjusted based on the operational characteristics
revealed by the process to the operating system.
For heavily loaded sites the percentage of processor cycles received
makes poor economic sense and processes migrate to where they can
obtain more cycles per current income.
Sender Initiated versus Receiver Initiated Algorithms
Maintaining a complete database of idle or lightly loaded machines
for migration can be a challenging problem in a distributed system.
Various alternatives to this have been proposed.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
25. Design Issues for Processor Allocation Algorithms
With sender initiated schemes the overloaded machine takes the
initiative by random polling or broadcasting and waiting for replies.
With receiver initiated schemes an idle machine offers itself for work
to a group of machines and accepts tasks from them.
In some situations a machine can become momentarily idle even
though on average it is reasonably busy.
As discussed earlier, care must be taken to coordinate migration
activity so that senders do not capitalize on this transient period and
send a flood of processes to this node.
One approach is to assign each site a local load value and an export
load value. The local load value reflects the true state of the machine
while the export load value decreases more slowly to dampen short-
lived fluctuations.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
26. Types of Load Distribution Algorithms:
Load Distribution Algorithms are classified as static, dynamic or
adaptive. In static algorithms all load distribution decisions are
hardwired in an algorithm on the basis of prior knowledge of the
system.
Dynamic algorithms make their load distribution decision on the
basis of current system state, so they have more scope of
improvement as compared to static algorithm.
However the overhead incurred in correcting the system state
information may overweigh the load benefit of load distribution.
Adaptive Algorithms are a special class of dynamic algorithms and
they can adapt their activities by changing the parameter of the
algorithm to suite the system state.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
27. Types of Load Distribution Algorithms:
Example, at high system load, dynamic algorithms continue the
correction of system state information thereby further increasing the
system load. On the other hand, adaptive algorithms will discontinue
this procedure.
Load Balancing v/s Load Sharing :
Load Distribution Algorithms are further classified as load balancing
and load sharing.
The main aim of both the algorithms is to reduce the unshared state
(a system in which some sites are idle while the other sites are
heavily loaded) by transferring the task.
Load Balancing algorithms goes one step ahead by equally
distributing the load on all computers.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
28. Types of Load Distribution Algorithms:
Such algorithms have more task transfer so the overhead incurred in
task transfer may overweigh the potential performance improvement.
Preemptive v/s Non- Preemptive Transfer:
Preemptive tasks are those tasks which are partially executed.
Their transfer is an expansive up gradation as the task state
consisting of virtual memory image , process control block, list of
higher buffers and timers is also to be transferred.
Non-Preemptive tasks are those tasks which have not begin their
execution such task transfer are also called task placement. In both
type of task transfer, the environment (such as the privileges
inherited by task ) in which the terms are to be executed is also
transferred.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
29. Static and Dynamic Load Balancing Algorithms
Static and Dynamic Load Balancing Algorithms:
Static Load Balancing In static algorithm the processes are assigned
to the processors at the compile time according to the performance of
the nodes. Once the processes are assigned, no change or
reassignment is possible at the run time.
Number of jobs in each node is fixed in static load balancing
algorithm.
Static algorithms do not collect any information about the nodes.
The assignment of jobs is done to the processing nodes on the basis
of the following factors: incoming time, extent of resource needed,
mean execution time and inter-process communications.
Since these factors should be measured before the assignment, this is
why static load balance is also called probabilistic algorithm.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
30. Static and Dynamic Load Balancing Algorithms
As there is no migration of job at the runtime no overhead occurs or
a little over head may occur.
Since load is balanced prior to the execution, several fundamental
flaws with static load balancing even if a mathematical solution
exist: Very difficult to estimate accurately the execution times of
various parts of a program without actually executing the parts.
Communication delays that vary under different circumstances Some
problems have an indeterminate number of steps to reach their
solution.
In static load balancing it is observed that as the number of tasks is
more than the processors, better will be the load balancing.
Fig shows a schematic diagram of static load balancing where local
tasks arrive at the assignment queue.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
31. Static and Dynamic Load Balancing Algorithms
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
32. Static and Dynamic Load Balancing Algorithms
A job either be transferred to a remote node or can be assigned to
threshold queue from the assignment queue.
A job from remote node similarly be assigned to threshold queue.
Once a job is assigned to a threshold queue, it can not be migrated to
any node.
A job arriving at any node either processed by that node or
transferred to another node for remote processing through the
communication network.
The static load balancing algorithms can be divided into two sub
classes: optimal static load balancing and sub optimal static load
balancing.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
33. Static and Dynamic Load Balancing Algorithms
Dynamic Load Balancing During the static load balancing too much
information about the system and jobs must be known before the
execution. These information may not be available in advance.
A full study on the system state and the jobs quite tedious approach
in advance.
So, dynamic load balancing algorithm came into existence. The
assignment of jobs is done at the runtime.
In DLB jobs are reassigned at the runtime depending upon the
situation that is the load will be transferred from heavily loaded
nodes to the lightly loaded nodes.
In this case communication over heads occur and becomes more
when number of processors increase. In dynamic load balancing no
decision is taken until the process gets execution.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
34. Static and Dynamic Load Balancing Algorithms
This strategy collects the information about the system state and
about the job information.
As more information is collected by an algorithm in a short time,
potentially the algorithm can make better decision.
Dynamic load balancing is mostly considered in heterogeneous
system because it consists of nodes with different speeds, different
communication link speeds, different memory sizes, and variable
external loads due to the multiple.
The numbers of load balancing strategies have been developed and
classified so far for getting the high performance of a system.
Fig shows a simple dynamic load balancing for transferring jobs
from heavily loaded to the lightly loaded nodes.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
35. Static and Dynamic Load Balancing Algorithms
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
36. COMPARISON BETWEEN SLB and DLB
ALGORITHM
COMPARISON BETWEEN SLB and DLB ALGORITHM:
Some qualitative parameters for comparative study have been listed
below.
1. Nature: Whether the applied algorithm is static or dynamic is
determined by this factor.
2. Overhead: Involved In static load balancing algorithm
redistribution of tasks are not possible and there is no overhead
involved at runtime.
But a little overhead may occur due to the inter process
communications. In case of dynamic load balancing algorithm
redistribution of tasks are done at the run time so considerable over
heads may involve. Hence it clear that SLB involves a less amount of
overheads as compared to DLB.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
37. COMPARISON BETWEEN SLB and DLB
ALGORITHM
3. Utilization of Resource:
Though the response time is minimum in case of SLB, it has poor
resource utilization capability because it is impractical to get all the
submitted jobs to the corresponding processors will completed at the
same time that means there is a great chance that some would be idle
after completing their assigned jobs and some will remain busy due
to the absence of reassignment policy.
In case of dynamic algorithm since there is reassignment policy exist
at run time, it is possible to complete all the jobs approximately at
the same time. So, better resource utilization occurs in DLB.
4. Thrashing or Process Dumping: A processor is called in
thrashing if it is spending more time in migration of jobs than
executing any useful work.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
38. COMPARISON BETWEEN SLB and DLB
ALGORITHM
As the degree of migration is less, processor thrashing will be less.
So SLB is out of thrashing but DLB incurs considerable thrashing
due to the process migration during run time.
5. State Woggling
It corresponds to the frequent change of the status by the processors
between low and high. It is a performance degrading factor.
6. Predictability:
Predictability corresponds to the fact that whether it is possible to
predict about the behavior of an algorithm.
The behavior of the SLB algorithm is predictable as everything is
known before compilation.
DLB algorithm’s behavior is unpredictable, as everything is done at
run time.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
39. COMPARISON BETWEEN SLB and DLB
ALGORITHM
7. Adaptability:
Adaptability determines whether an algorithm will adjust by itself
with the change of the system state.
SLB has no ability to adapt with changing environment. But DLB
has that ability.
8. Reliability: Reliability of a system is concerned with if a node
fails still the system will work without any error.
SLB is not so reliable as there is no ability to adapt with the changing
of a system’s state. But DLB has adaptation power, so DLB is more
reliable.
9. Response Time: Response time measures how much time is taken
by a system applying a particular load balancing algorithm to
respond for a job.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
40. COMPARISON BETWEEN SLB and DLB
ALGORITHM
SLB algorithm has shorter response time because processors fully
involved in processing due to the absence of job transferring.
But DLB algorithm has larger response time because processors can
not fully involved in processing due to the presence of job
transferring policy.
10. Stability: SLB is more stable as every thing is known before
compilation and work load transfer is done. But DLB is not so stable
as SLB because it involves both the compile time assignment of jobs
and distribution of work load as needed.
11. Complexity: Involved SLB algorithms are easy to construct
while DLB algorithms are not so easy to develop because nothing is
known in advance. Although the dynamic load balancing is complex
phenomenon, the benefits from it is much more than its complexity .
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
41. Benefits of Load balancing
Benefits of Load balancing
a) Load balancing improves the performance of each node and hence
the overall system performance.
b) Load balancing reduces the job idle time
c) Small jobs do not suffer from long starvation
d) Maximum utilization of resources
e) Response time becomes shorter
f) Higher throughput
g) Higher reliability
h) Low cost but high gain
i) Extensibility and incremental growth
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
42. Introduction to Distributed Database System
A distributed database is basically a database that is not limited to
one system, it is spread over different sites, i.e, on multiple
computers or over a network of computers.
A distributed database system is located on various sites that don’t
share physical components.
This may be required when a particular database needs to be
accessed by various users globally.
It needs to be managed such that for the users it looks like one single
database.
Types:
1. Homogeneous Database:
In a homogeneous database, all different sites store database
identically.
Mr. Sagar Pandya
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43. Introduction to Distributed Database System
The operating system, database management system and the data
structures used – all are same at all sites. Hence, they’re easy to
manage.
2. Heterogeneous Database:
In a heterogeneous distributed database, different sites can use
different schema and software that can lead to problems in query
processing and transactions.
Also, a particular site might be completely unaware of the other sites.
Different computers may use a different operating system, different
database application.
They may even use different data models for the database.
Hence, translations are required for different sites to communicate.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
44. Introduction to Distributed Database System
A distributed database is a collection of multiple interconnected
databases, which are spread physically across various locations that
communicate via a computer network.
Features:
Databases in the collection are logically interrelated with each other.
Often they represent a single logical database.
Data is physically stored across multiple sites. Data in each site can
be managed by a DBMS independent of the other sites.
The processors in the sites are connected via a network. They do not
have any multiprocessor configuration.
A distributed database is not a loosely connected file system.
A distributed database incorporates transaction processing, but it is
not synonymous with a transaction processing system.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
45. Introduction to Distributed Database System
Distributed Data Storage :
There are 2 ways in which data can be stored on different sites. These
are:
1. Replication –
In this approach, the entire relation is stored redundantly at 2 or more
sites. If the entire database is available at all sites, it is a fully
redundant database. Hence, in replication, systems maintain copies of
data.
This is advantageous as it increases the availability of data at
different sites.
Also, now query requests can be processed in parallel.
However, it has certain disadvantages as well. Data needs to be
constantly updated.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
46. Introduction to Distributed Database System
Any change made at one site needs to be recorded at every site that
relation is stored or else it may lead to inconsistency.
This is a lot of overhead. Also, concurrency control becomes way
more complex as concurrent access now needs to be checked over a
number of sites.
2. Fragmentation –
In this approach, the relations are fragmented (i.e., they’re divided
into smaller parts) and each of the fragments is stored in different sites
where they’re required.
It must be made sure that the fragments are such that they can be used
to reconstruct the original relation (i.e, there isn’t any loss of data).
Fragmentation is advantageous as it doesn’t create copies of data,
consistency is not a problem.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
47. Introduction to Distributed Database System
Fragmentation of relations can be done in two ways:
Horizontal fragmentation – Splitting by rows –
The relation is fragmented into groups of tuples so that each tuple is
assigned to at least one fragment.
Vertical fragmentation – Splitting by columns –
The schema of the relation is divided into smaller schemas. Each
fragment must contain a common candidate key so as to ensure
lossless join.
Advantages of Distributed Databases:
Modular Development − If the system needs to be expanded to new
locations or new units, in centralized database systems, the action
requires substantial efforts and disruption in the existing functioning.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
48. Introduction to Distributed Database System
However, in distributed databases, the work simply requires adding new
computers and local data to the new site and finally connecting them to the
distributed system, with no interruption in current functions.
More Reliable − In case of database failures, the total system of centralized
databases comes to a halt. However, in distributed systems, when a
component fails, the functioning of the system continues may be at a reduced
performance. Hence DDBMS is more reliable.
Better Response − If data is distributed in an efficient manner, then user
requests can be met from local data itself, thus providing faster response. On
the other hand, in centralized systems, all queries have to pass through the
central computer for processing, which increases the response time.
Lower Communication Cost − In distributed database systems, if data is
located locally where it is mostly used, then the communication costs for data
manipulation can be minimized. This is not feasible in centralized systems.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
49. Introduction to Multimedia Database
Multimedia database is the collection of interrelated multimedia data
that includes text, graphics (sketches, drawings), images, animations,
video, audio etc and have vast amounts of multisource multimedia data.
The framework that manages different types of multimedia data which
can be stored, delivered and utilized in different ways is known as
multimedia database management system.
There are three classes of the multimedia database which includes static
media, dynamic media and dimensional media.
Content of Multimedia Database management system :
1. Media data – The actual data representing an object.
2. Media format data – Information such as sampling rate, resolution,
encoding scheme etc. about the format of the media data after it goes
through the acquisition, processing and encoding phase.
Mr. Sagar Pandya
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50. Introduction to Multimedia Database
3. Media keyword data – Keywords description relating to the
generation of data. It is also known as content descriptive data.
Example: date, time and place of recording.
4. Media feature data – Content dependent data such as the
distribution of colors, kinds of texture and different shapes present in
data.
Types of multimedia applications based on data management
characteristic are :
1. Repository applications – A Large amount of multimedia data as
well as meta-data(Media format date, Media keyword data, Media
feature data) that is stored for retrieval purpose, e.g., Repository of
satellite images, engineering drawings, radiology scanned pictures.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
51. Introduction to Multimedia Database
2. Presentation applications – They involve delivery of multimedia
data subject to temporal constraint. Optimal viewing or listening
requires DBMS to deliver data at certain rate offering the quality of
service above a certain threshold. Here data is processed as it is
delivered. Example: Annotating of video and audio data, real-time
editing analysis.
3. Collaborative work using multimedia information – It involves
executing a complex task by merging drawings, changing
notifications. Example: Intelligent healthcare network.
There are still many challenges to multimedia databases:
1. Modelling – Working in this area can improve database versus
information retrieval techniques thus, documents constitute a
specialized area and deserve special consideration.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
52. Introduction to Multimedia Database
2. Design – The conceptual, logical and physical design of
multimedia databases has not yet been addressed fully as
performance and tuning issues at each level are far more complex as
they consist of a variety of formats like JPEG, GIF, PNG, MPEG
which is not easy to convert from one form to another.
3. Storage – Storage of multimedia database on any standard disk
presents the problem of representation, compression, mapping to
device hierarchies, archiving and buffering during input-output
operation. In DBMS, a ”BLOB”(Binary Large Object) facility allows
untyped bitmaps to be stored and retrieved.
4. Performance – For an application involving video playback or
audio-video synchronization, physical limitations dominate.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
53. Introduction to Multimedia Database
The use of parallel processing may alleviate some problems but such
techniques are not yet fully developed. Apart from this multimedia
database consume a lot of processing time as well as bandwidth.
5. Queries and retrieval – For multimedia data like images, video,
audio accessing data through query opens up many issues like
efficient query formulation, query execution and optimization which
need to be worked upon.
Areas where multimedia database is applied are :
Documents and record management : Industries and businesses
that keep detailed records and variety of documents. Example:
Insurance claim record.
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
54. Introduction to Multimedia Database
Knowledge dissemination : Multimedia database is a very effective
tool for knowledge dissemination in terms of providing several
resources. Example: Electronic books.
Education and training : Computer-aided learning materials can be
designed using multimedia sources which are nowadays very popular
sources of learning. Example: Digital libraries.
Marketing, advertising, retailing, entertainment and travel. Example:
a virtual tour of cities.
Real-time control and monitoring : Coupled with active database
technology, multimedia presentation of information can be very
effective means for monitoring and controlling complex tasks
Example: Manufacturing operation contro
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
58. Thank You
Great God, Medi-Caps, All the attendees
Mr. Sagar Pandya
sagar.pandya@medicaps.ac.in
www.sagarpandya.tk
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