Banker’s algorithm referred to as resource allocation and deadlock avoidance algorithm that checks for the safety by simulating the allocation of predetermined maximum possible of resources and makes the system into s-state by checking the possible deadlock conditions for all other pending processes. It needs to know how much of each resource a process could possibly request. Number of processes are static in algorithm, but in most of system processes varies dynamically and no additional process will be started while it is in execution. The number of resources are not allow to go down while it is in execution. In this research an approach for Dynamic Banker's algorithm is proposed which allows the number of resources to be changed at runtime that prevents the system to fall in unsafe state. It also give details about all the resources and processes that which one require resources and in what quantity. This also allocates the resource automatically to the stopped process for the execution and will always give the appropriate safe sequence for the given processes.
Get a piece of basic knowledge about deadlock and what it is. What are the measures., conditions, and more. Also, know about Banker's algorithm and how does the calculation work.
Recourse Management Using a Fair Share SchedulerCSCJournals
Resource management is a vital task of all operating systems. It is the responsibility of operating system to ensure that all programs requesting resources are getting resources in a timely manner. Various recourse allocation strategies are there which provide guidance for operating systems to make resource allocation decisions. This article describes about the resource management using a fare share scheduler. The fair share scheduler ensures that resources are allocated to programs in an efficient manner and this ensures fairness in resource allocation.
Certain Investigation on Dynamic Clustering in Dynamic Dataminingijdmtaiir
Clustering is the process of grouping a set of objects
into classes of similar objects. Dynamic clustering comes in a
new research area that is concerned about dataset with dynamic
aspects. It requires updates of the clusters whenever new data
records are added to the dataset and may result in a change of
clustering over time. When there is a continuous update and
huge amount of dynamic data, rescan the database is not
possible in static data mining. But this is possible in Dynamic
data mining process. This dynamic data mining occurs when
the derived information is present for the purpose of analysis
and the environment is dynamic, i.e. many updates occur.
Since this has now been established by most researchers and
they will move into solving some of the problems and the
research is to concentrate on solving the problem of using data
mining dynamic databases. This paper gives some
investigation of existing work done in some papers related with
dynamic clustering and incremental data clustering
Data mining is a very popular research topic over the years. Sequential pattern mining or sequential rule mining is very useful application of data mining for the prediction purpose. In this paper, we have presented a review over sequential rule cum sequential pattern mining. The advantages & drawbacks of each popular sequential mining method is discussed in brief.
Get a piece of basic knowledge about deadlock and what it is. What are the measures., conditions, and more. Also, know about Banker's algorithm and how does the calculation work.
Recourse Management Using a Fair Share SchedulerCSCJournals
Resource management is a vital task of all operating systems. It is the responsibility of operating system to ensure that all programs requesting resources are getting resources in a timely manner. Various recourse allocation strategies are there which provide guidance for operating systems to make resource allocation decisions. This article describes about the resource management using a fare share scheduler. The fair share scheduler ensures that resources are allocated to programs in an efficient manner and this ensures fairness in resource allocation.
Certain Investigation on Dynamic Clustering in Dynamic Dataminingijdmtaiir
Clustering is the process of grouping a set of objects
into classes of similar objects. Dynamic clustering comes in a
new research area that is concerned about dataset with dynamic
aspects. It requires updates of the clusters whenever new data
records are added to the dataset and may result in a change of
clustering over time. When there is a continuous update and
huge amount of dynamic data, rescan the database is not
possible in static data mining. But this is possible in Dynamic
data mining process. This dynamic data mining occurs when
the derived information is present for the purpose of analysis
and the environment is dynamic, i.e. many updates occur.
Since this has now been established by most researchers and
they will move into solving some of the problems and the
research is to concentrate on solving the problem of using data
mining dynamic databases. This paper gives some
investigation of existing work done in some papers related with
dynamic clustering and incremental data clustering
Data mining is a very popular research topic over the years. Sequential pattern mining or sequential rule mining is very useful application of data mining for the prediction purpose. In this paper, we have presented a review over sequential rule cum sequential pattern mining. The advantages & drawbacks of each popular sequential mining method is discussed in brief.
Complex Event Processing (CEP) plays a major role in real-time analytics such as identifying possible frauds in credit card transactions and geospatial analysis. In CEP, events that are received from different data sources are stored in memory and processed on the fly. Scaling is one of the most important features of a CEP engine. Contemporary CEP engines provide several options to scale event processing vertically and horizontally. For example, these include scaling with Storm cluster, distributed object cache, and publisher-subscriber model, all of which come under random or attribute based partitioning. These approaches help to handle a large number of queries, queries that need a large memory, events which come in high rate, and complex queries that might not fit within a single machine. However, it is difficult to scale pattern and sequence detection in CEP for high event rate because the pattern and sequence detection depend on a set of events happened overtime. Existing scaling approaches based on random or attribute based partitioning affects the continuous event flow and event ordering, which are most important attributes for pattern and sequence matching.
We propose a novel approach to scale pattern and sequence detection queries for high incoming event rate. In the proposed approach incoming events are kept in a queue, grouped into partitions based on time interval defined in the query with some overlapping events, then the events are pushed to several CEP engines and processed simultaneously. Finally, the processed events are filtered and reordered before publishing out from the CEP. Performance analysis showed that the proposed technique increase the throughput by 800%, while increasing the per event latency from 2-3 milliseconds to 8-10 milliseconds due to queuing nature of the solution.
In this paper, we present a literature survey of existing frequent item set mining algorithms. The concept of frequent item set mining is also discussed in brief. The working procedure of some modern frequent item set mining techniques is given. Also the merits and demerits of each method are described. It is found that the frequent item set mining is still a burning research topic.
Document Classification Using Expectation Maximization with Semi Supervised L...ijsc
As the amount of online document increases, the demand for document classification to aid the analysis and management of document is increasing. Text is cheap, but information, in the form of knowing what classes a document belongs to, is expensive. The main purpose of this paper is to explain the expectation maximization technique of data mining to classify the document and to learn how to improve the accuracy while using semi-supervised approach. Expectation maximization algorithm is applied with both supervised and semi-supervised approach. It is found that semi-supervised approach is more accurate and effective. The main advantage of semi supervised approach is “DYNAMICALLY GENERATION OF NEW CLASS”. The algorithm first trains a classifier using the labeled document and probabilistically classifies the
unlabeled documents. The car dataset for the evaluation purpose is collected from UCI repository dataset in which some changes have been done from our side.
An Improved Differential Evolution Algorithm for Data Stream ClusteringIJECEIAES
A Few algorithms were actualized by the analysts for performing clustering of data streams. Most of these algorithms require that the number of clusters (K) has to be fixed by the customer based on input data and it can be kept settled all through the clustering process. Stream clustering has faced few difficulties in picking up K. In this paper, we propose an efficient approach for data stream clustering by embracing an Improved Differential Evolution (IDE) algorithm. The IDE algorithm is one of the quick, powerful and productive global optimization approach for programmed clustering. In our proposed approach, we additionally apply an entropy based method for distinguishing the concept drift in the data stream and in this way updating the clustering procedure online. We demonstrated that our proposed method is contrasted with Genetic Algorithm and identified as proficient optimization algorithm. The performance of our proposed technique is assessed and cr eates the accuracy of 92.29%, the precision is 86.96%, recall is 90.30% and F-measure estimate is 88.60%.
Comparative analysis of association rule generation algorithms in data streamsIJCI JOURNAL
Data mining technology is engaged in establishing helpful and unfamiliar data from the huge databases. Generally, data mining methods are useful for static databases for knowledge extraction wherever currently available data mining techniques are not appropriate and it also has a number of limitations for managing dynamic databases. A data stream manages dynamic data sets and it has become one of the essential research domains in data mining. The fundamental definition of the data stream is an arrival of continuous and unlimited data which may not be stored fully because it needs more storage capacity. In
order to perform data analysis with this, many new data mining techniques are to be required. Data analysis is carried out by using clustering, classification, frequent item set mining and association rule generation. Association rule mining is one of the significant research problems in the data stream which helps to find out the relationship between the data items in the transactional databases. This research work concentrated on how the traditional algorithms are used for generating association rules in data streams. The algorithms used in this work are Assoc Outliers, Frequent Item sets and Supervised Association Rule. A number of rules generated by an algorithm and execution time are considered as the performance factors. Experimental results give that Frequent Item set algorithm efficiency is better than Assoc Outliers and Supervised Association Rule Algorithms. This implementation work is executed in the Tanagra data mining tool.
Many resources discuss machine learning and data analytics from a technology deployment perspective. From the business standpoint, however, the real value of analytics is in the methodology for solving some systemic holistic problems, rather than a specific technology or platform.
In this presentation, the focus is shifted from the technology deployment to the analytics methodology for solving some holistic business problems. Two examples will be covered in detail:
(i) Analysis of the performance and the optimal staffing of a team of doctors, nurses, and technicians for a large local hospital unit using discrete event simulation with a live demonstration. This simulation methodology is not included in most Machine Learning algorithms libraries.
(ii) Identifying a few factors (or variables) that contribute most to the financial outcome of a local hospital using principal component decomposition (PCD) of the large observational dataset of population demographic and disease prevalence.
DESIGN, IMPLEMENTATION AND PERFORMANCE ANALYSIS OF CONCURRENCY CONTROL ALGORI...ijdms
Concurrency control mechanism is one of the critical factors which affect the overall performance of
database system. The conflicting situations are normal phenomena in multiuser distributed transaction
environment. Concurrency situation is not desirable for time dependent and time critical large database
applications; it must be efficiently controlled and managed to ensure consistency of database system. The
time aspect of data can be easily represented by temporal database, it allows you to associate time element
with data through temporal validity support. Concurrency control mechanism elevates to a new height
when it is implemented on temporal database especially for multiuser distributed transaction environment
and it requires a special treatment. For consistency requirement to be true different user sessions of a
distributed transaction running simultaneously must produce meaningful and consistent results otherwise it
leaves database into inconsistent state. Traditionally timestamp and locking based approaches are used to
encounter the concurrency problem. This research study proposes an efficient concurrency algorithm
which is a mixture of both timestamp and locking approach suitable for temporal database environment
where more than one users of distributed transaction tries to access the common resource. The scope of
research involves design, implementation, experimental study and performance analysis of proposed
algorithm in comparison with pessimistic and optimistic concurrency control mechanisms. The proposed
algorithm is implemented through trigger using Oracle 12c which supports temporal database consistency
through temporal validity support and efficient locking mechanism. Concurrency and locking situation is
graphically represented in the experiment with the help of Oracle 12c enterprise manager.
A Dynamic and Improved Implementation of Banker’s Algorithmrahulmonikasharma
Banker’s algorithm can be described as deadlock avoidance and resource allocation algorithm which ensure the execution safety by simulating the allocation of already determined maximum possible of resources and makes the system into s-state by checking the possible deadlock conditions for all other pending processes. It needs to know how much of each resource a process could possibly request. Number of processes is static in algorithm, but in most of system processes varies dynamically and no additional process will be started while it is in execution. The number of resources is not allowed to go down while it is in execution. In this research an approach for Dynamic Banker's algorithm is proposed which allows the number of resources to be changed at runtime that prevents the system to fall in unsafe state. It also gives details about all the resources and processes that which one requires resources and in what quantity. This modified banker’s algorithm performs the process arrangement on the basis of their needs that leads to solve the problem in less time.
A LIGHT-WEIGHT DISTRIBUTED SYSTEM FOR THE PROCESSING OF REPLICATED COUNTER-LI...ijdpsjournal
In order to increase availability in a distributed system some or all of the data items are replicated and
stored at separate sites. This is an issue of key concern especially since there is such a proliferation of
wireless technologies and mobile users. However, the concurrent processing of transactions at separate
sites can generate inconsistencies in the stored information. We have built a distributed service that
manages updates to widely deployed counter-like replicas. There are many heavy-weight distributed
systems targeting large information critical applications. Our system is intentionally, relatively lightweight
and useful for the somewhat reduced information critical applications. The service is built on our
distributed concurrency control scheme which combines optimism and pessimism in the processing of
transactions. The service allows a transaction to be processed immediately (optimistically) at any
individual replica as long as the transaction satisfies a cost bound. All transactions are also processed in a
concurrent pessimistic manner to ensure mutual consistency
Resource Availability Prediction in the Grid: Taxonomy and Review of State of...IJEACS
Use of Grid Computing for carrying out cooperative work from distributed resources has been into practice for the past several years. Efficient execution of various tasks on the grid comes with various challenges. One of them is to ensure that a particular resource remains available during the execution of task. The dynamic nature of resources makes it even more challenging to predict resource availability for complete task duration. This paper is an attempt to address this issue by providing a comprehensive review of the existing methods along with a stated taxonomy of the approaches used.
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
Deadlock- System model, resource types, deadlock problem, deadlock characteri...Wakil Kumar
Help to other students on research paper on Deadlock- System model, resource types, deadlock problem, deadlock characterization, methods for deadlock handling,
Complex Event Processing (CEP) plays a major role in real-time analytics such as identifying possible frauds in credit card transactions and geospatial analysis. In CEP, events that are received from different data sources are stored in memory and processed on the fly. Scaling is one of the most important features of a CEP engine. Contemporary CEP engines provide several options to scale event processing vertically and horizontally. For example, these include scaling with Storm cluster, distributed object cache, and publisher-subscriber model, all of which come under random or attribute based partitioning. These approaches help to handle a large number of queries, queries that need a large memory, events which come in high rate, and complex queries that might not fit within a single machine. However, it is difficult to scale pattern and sequence detection in CEP for high event rate because the pattern and sequence detection depend on a set of events happened overtime. Existing scaling approaches based on random or attribute based partitioning affects the continuous event flow and event ordering, which are most important attributes for pattern and sequence matching.
We propose a novel approach to scale pattern and sequence detection queries for high incoming event rate. In the proposed approach incoming events are kept in a queue, grouped into partitions based on time interval defined in the query with some overlapping events, then the events are pushed to several CEP engines and processed simultaneously. Finally, the processed events are filtered and reordered before publishing out from the CEP. Performance analysis showed that the proposed technique increase the throughput by 800%, while increasing the per event latency from 2-3 milliseconds to 8-10 milliseconds due to queuing nature of the solution.
In this paper, we present a literature survey of existing frequent item set mining algorithms. The concept of frequent item set mining is also discussed in brief. The working procedure of some modern frequent item set mining techniques is given. Also the merits and demerits of each method are described. It is found that the frequent item set mining is still a burning research topic.
Document Classification Using Expectation Maximization with Semi Supervised L...ijsc
As the amount of online document increases, the demand for document classification to aid the analysis and management of document is increasing. Text is cheap, but information, in the form of knowing what classes a document belongs to, is expensive. The main purpose of this paper is to explain the expectation maximization technique of data mining to classify the document and to learn how to improve the accuracy while using semi-supervised approach. Expectation maximization algorithm is applied with both supervised and semi-supervised approach. It is found that semi-supervised approach is more accurate and effective. The main advantage of semi supervised approach is “DYNAMICALLY GENERATION OF NEW CLASS”. The algorithm first trains a classifier using the labeled document and probabilistically classifies the
unlabeled documents. The car dataset for the evaluation purpose is collected from UCI repository dataset in which some changes have been done from our side.
An Improved Differential Evolution Algorithm for Data Stream ClusteringIJECEIAES
A Few algorithms were actualized by the analysts for performing clustering of data streams. Most of these algorithms require that the number of clusters (K) has to be fixed by the customer based on input data and it can be kept settled all through the clustering process. Stream clustering has faced few difficulties in picking up K. In this paper, we propose an efficient approach for data stream clustering by embracing an Improved Differential Evolution (IDE) algorithm. The IDE algorithm is one of the quick, powerful and productive global optimization approach for programmed clustering. In our proposed approach, we additionally apply an entropy based method for distinguishing the concept drift in the data stream and in this way updating the clustering procedure online. We demonstrated that our proposed method is contrasted with Genetic Algorithm and identified as proficient optimization algorithm. The performance of our proposed technique is assessed and cr eates the accuracy of 92.29%, the precision is 86.96%, recall is 90.30% and F-measure estimate is 88.60%.
Comparative analysis of association rule generation algorithms in data streamsIJCI JOURNAL
Data mining technology is engaged in establishing helpful and unfamiliar data from the huge databases. Generally, data mining methods are useful for static databases for knowledge extraction wherever currently available data mining techniques are not appropriate and it also has a number of limitations for managing dynamic databases. A data stream manages dynamic data sets and it has become one of the essential research domains in data mining. The fundamental definition of the data stream is an arrival of continuous and unlimited data which may not be stored fully because it needs more storage capacity. In
order to perform data analysis with this, many new data mining techniques are to be required. Data analysis is carried out by using clustering, classification, frequent item set mining and association rule generation. Association rule mining is one of the significant research problems in the data stream which helps to find out the relationship between the data items in the transactional databases. This research work concentrated on how the traditional algorithms are used for generating association rules in data streams. The algorithms used in this work are Assoc Outliers, Frequent Item sets and Supervised Association Rule. A number of rules generated by an algorithm and execution time are considered as the performance factors. Experimental results give that Frequent Item set algorithm efficiency is better than Assoc Outliers and Supervised Association Rule Algorithms. This implementation work is executed in the Tanagra data mining tool.
Many resources discuss machine learning and data analytics from a technology deployment perspective. From the business standpoint, however, the real value of analytics is in the methodology for solving some systemic holistic problems, rather than a specific technology or platform.
In this presentation, the focus is shifted from the technology deployment to the analytics methodology for solving some holistic business problems. Two examples will be covered in detail:
(i) Analysis of the performance and the optimal staffing of a team of doctors, nurses, and technicians for a large local hospital unit using discrete event simulation with a live demonstration. This simulation methodology is not included in most Machine Learning algorithms libraries.
(ii) Identifying a few factors (or variables) that contribute most to the financial outcome of a local hospital using principal component decomposition (PCD) of the large observational dataset of population demographic and disease prevalence.
DESIGN, IMPLEMENTATION AND PERFORMANCE ANALYSIS OF CONCURRENCY CONTROL ALGORI...ijdms
Concurrency control mechanism is one of the critical factors which affect the overall performance of
database system. The conflicting situations are normal phenomena in multiuser distributed transaction
environment. Concurrency situation is not desirable for time dependent and time critical large database
applications; it must be efficiently controlled and managed to ensure consistency of database system. The
time aspect of data can be easily represented by temporal database, it allows you to associate time element
with data through temporal validity support. Concurrency control mechanism elevates to a new height
when it is implemented on temporal database especially for multiuser distributed transaction environment
and it requires a special treatment. For consistency requirement to be true different user sessions of a
distributed transaction running simultaneously must produce meaningful and consistent results otherwise it
leaves database into inconsistent state. Traditionally timestamp and locking based approaches are used to
encounter the concurrency problem. This research study proposes an efficient concurrency algorithm
which is a mixture of both timestamp and locking approach suitable for temporal database environment
where more than one users of distributed transaction tries to access the common resource. The scope of
research involves design, implementation, experimental study and performance analysis of proposed
algorithm in comparison with pessimistic and optimistic concurrency control mechanisms. The proposed
algorithm is implemented through trigger using Oracle 12c which supports temporal database consistency
through temporal validity support and efficient locking mechanism. Concurrency and locking situation is
graphically represented in the experiment with the help of Oracle 12c enterprise manager.
A Dynamic and Improved Implementation of Banker’s Algorithmrahulmonikasharma
Banker’s algorithm can be described as deadlock avoidance and resource allocation algorithm which ensure the execution safety by simulating the allocation of already determined maximum possible of resources and makes the system into s-state by checking the possible deadlock conditions for all other pending processes. It needs to know how much of each resource a process could possibly request. Number of processes is static in algorithm, but in most of system processes varies dynamically and no additional process will be started while it is in execution. The number of resources is not allowed to go down while it is in execution. In this research an approach for Dynamic Banker's algorithm is proposed which allows the number of resources to be changed at runtime that prevents the system to fall in unsafe state. It also gives details about all the resources and processes that which one requires resources and in what quantity. This modified banker’s algorithm performs the process arrangement on the basis of their needs that leads to solve the problem in less time.
A LIGHT-WEIGHT DISTRIBUTED SYSTEM FOR THE PROCESSING OF REPLICATED COUNTER-LI...ijdpsjournal
In order to increase availability in a distributed system some or all of the data items are replicated and
stored at separate sites. This is an issue of key concern especially since there is such a proliferation of
wireless technologies and mobile users. However, the concurrent processing of transactions at separate
sites can generate inconsistencies in the stored information. We have built a distributed service that
manages updates to widely deployed counter-like replicas. There are many heavy-weight distributed
systems targeting large information critical applications. Our system is intentionally, relatively lightweight
and useful for the somewhat reduced information critical applications. The service is built on our
distributed concurrency control scheme which combines optimism and pessimism in the processing of
transactions. The service allows a transaction to be processed immediately (optimistically) at any
individual replica as long as the transaction satisfies a cost bound. All transactions are also processed in a
concurrent pessimistic manner to ensure mutual consistency
Resource Availability Prediction in the Grid: Taxonomy and Review of State of...IJEACS
Use of Grid Computing for carrying out cooperative work from distributed resources has been into practice for the past several years. Efficient execution of various tasks on the grid comes with various challenges. One of them is to ensure that a particular resource remains available during the execution of task. The dynamic nature of resources makes it even more challenging to predict resource availability for complete task duration. This paper is an attempt to address this issue by providing a comprehensive review of the existing methods along with a stated taxonomy of the approaches used.
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
Deadlock- System model, resource types, deadlock problem, deadlock characteri...Wakil Kumar
Help to other students on research paper on Deadlock- System model, resource types, deadlock problem, deadlock characterization, methods for deadlock handling,
Comparison of Dynamic Scheduling Techniques in Flexible Manufacturing SystemIJERA Editor
Scheduling is an important tool in the manufacturing area since productivity is inherently linked to how well the resources are used to increase efficiency and reduce waste. The present article analyzes and provides comparison of modern techniques used for solving dynamic scheduling problem in flexible manufacturing system. These techniques are often impractical in dynamic real world environments where there are complex constraints and a variety of unexpected disruptions. This paper defines the modern techniques of dynamic scheduling and provides a literature survey of scheduling which are presented in recent few years. The principles of several dynamic scheduling techniques, namely dispatching rules, heuristics, genetic algorithms and artificial intelligence techniques are describe in details and comparison of their potential.
In the recent years the scope of data mining has evolved into an active area of research because of the previously unknown and interesting knowledge from very large database collection. The data mining is applied on a variety of applications in multiple domains like in business, IT and many more sectors. In Data Mining the major problem which receives great attention by the community is the classification of the data. The classification of data should be such that it could be they can be easily verified and should be easily interpreted by the humans. In this paper we would be studying various data mining techniques so that we can find few combinations for enhancing the hybrid technique which would be having multiple techniques involved so enhance the usability of the application. We would be studying CHARM Algorithm, CM-SPAM Algorithm, Apriori Algorithm, MOPNAR Algorithm and the Top K Rules.
A deadlock occurs when there is a setoff process waiting for a resource held by the other processes in the same set. This paper describes the deadlock detection and prevention using wait for graph and some deadlock resolution algorithms which resolves the deadlock by selecting victims using different criteria. In a multi process system, deadlock is a situation, which arises in a shared resource environment where a process indefinitely waits for a resource, which is held by some other process, which in turn waiting for a resource held by some other process. To prevent any deadlock situation in the system, the DBMS aggressively inspects all the operations which transactions are about to execute. DBMS inspects operations and analyzes if they can create a deadlock situation. If it finds that a deadlock situation might occur, then that transaction is never allowed to be executed. Durgesh Raghuvanshi "Deadlock in DBMS" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd24064.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/24064/deadlock-in-dbms/durgesh-raghuvanshi
A deadlock occurs when there is a setoff process waiting for a resource held by the other processes in the same set. This paper describes the deadlock detection and prevention using wait for graph and some deadlock resolution algorithms which resolves the deadlock by selecting victims using different criteria. In a multi process system, deadlock is a situation, which arises in a shared resource environment where a process indefinitely waits for a resource, which is held by some other process, which in turn waiting for a resource held by some other process. To prevent any deadlock situation in the system, the DBMS aggressively inspects all the operations which transactions are about to execute. DBMS inspects operations and analyzes if they can create a deadlock situation. If it finds that a deadlock situation might occur, then that transaction is never allowed to be executed. Durgesh Raghuvanshi "Deadlock in DBMS" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd24064.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/24064/deadlock-in-dbms/durgesh-raghuvanshi
Similar to Implementation of Banker’s Algorithm Using Dynamic Modified Approach (20)
Data Mining is a significant field in today’s data-driven world. Understanding and implementing its concepts can lead to discovery of useful insights. This paper discusses the main concepts of data mining, focusing on two main concepts namely Association Rule Mining and Time Series Analysis
A Review on Real Time Integrated CCTV System Using Face Detection for Vehicle...rahulmonikasharma
We are describes the technique for real time human face detection and counting the number of passengers in vehicle and also gender of the passengers.The Image processing technology is very popular,now at present all are going to use it for various purpose. It can be applied to various applications for detecting and processing the digital images. Face detection is a part of image processing. It is used for finding the face of human in a given area. Face detection is used in many applications such as face recognition, people tracking, or photography. In this paper,The webcam is installed in public vehicle and connected with Raspberry Pi model. We use face detection technique for detecting and counting the number of passengers in public vehicle via webcam with the help of image processing and Raspberry Pi.
Considering Two Sides of One Review Using Stanford NLP Frameworkrahulmonikasharma
Sentiment analysis is a type of natural language processing for tracking the mood of the public about a particular product or a topic and is useful in several ways. Polarity shift is the most classical task which aims at classifying the reviews either positive or negative. But in many cases, in addition to the positive and negative reviews, there still many neutral reviews exist. However, the performance sometimes limited due to the fundamental deficiencies in handling the polarity shift problem. We propose an Improvised Dual Sentiment Analysis (IDSA) model to address this problem for sentiment classification. We first propose a novel data expansion technique by creating sentiment-reversed review for each training and test review. We develop a corpus based method to construct a pseudo-antonym dictionary. It removes DSA’s dependency on an external antonym dictionary for review reversion. We conduct a range of experiments and the results demonstrates the effectiveness of DSA in addressing the polarity shift in sentiment classification. .
A New Detection and Decoding Technique for (2×N_r ) MIMO Communication Systemsrahulmonikasharma
The requirements of fifth generation new radio (5G- NR) access networks are very high capacity and ultra-reliability. In this paper, we proposed a V-BLAST2 × N_r MIMO system that is analyzed, improved, and expected to achieve both very high throughput and ultra- reliability simultaneously.A new detection technique called parallel detection algorithm is proposed. The performance of the proposed algorithm compared with existing linear detection algorithms. It was seen that the proposed technique increases the speed of signal transmission and prevents error propagation which may be present in serial decoding techniques. The new algorithm reduces the bit error probability and increases the capacity simultaneouslywithout using a standard STC technique. However, it was seen that the BER of systems using the proposed algorithm is slightly higher than a similar system using only STC technique. Simulation results show the advantages of using the proposed technique.
Broadcasting Scenario under Different Protocols in MANET: A Surveyrahulmonikasharma
A wireless network enables people to communicate and access applications and information without wires. This provides freedom of movement and the ability to extend applications to different parts of a building, city, or nearly anywhere in the world. Wireless networks allow people to interact with e-mail or browse the Internet from a location that they prefer. Adhoc Networks are self-organizing wireless networks, absent any fixed infrastructure. broadcasting of data through proper channel is essential. Various protocols are designed to avoid the loss of data. In this paper an overview of different broadcast protocols are discussed.
Sybil Attack Analysis and Detection Techniques in MANETrahulmonikasharma
Security is important for many sensor network applications. A particularly harmful attack against sensor and ad hoc networks is known as the Sybil attack [6], where a node Illegitimately claims multiple identities.Mobility cause a main problem when we talk about security in Mobile Ad-hoc networks. It doesn’t depend on fixed architecture, the nodes are continuously moving in a random fashion. In this article we will focus on identifying the Sybil attack in MANET. It uses air medium for communication so it is more prone to the attack. Sybil attack is one in which single node present multiple fake identities to other nodes, which cause destruction.
A Landmark Based Shortest Path Detection by Using A* and Haversine Formularahulmonikasharma
In 1900, less than 20 percent of the world populace lived in cities, in 2007, fair more than 50 percent of the world populace lived in cities. In 2050, it has been anticipated that more than 70 percent of the worldwide population (about 6.4 billion individuals) will be city tenants. There's more weight being set on cities through this increment in population [1]. With approach of keen cities, data and communication technology is progressively transforming the way city regions and city inhabitants organize and work in reaction to urban development. In this paper, we create a nonspecific plot for navigating a route throughout city A asked route is given by utilizing combination of A* Algorithm and Haversine equation. Haversine Equation gives least distance between any two focuses on spherical body by utilizing latitude and longitude. This least distance is at that point given to A* calculation to calculate minimum distance. The method for identifying the shortest path is specify in this paper.
Processing Over Encrypted Query Data In Internet of Things (IoTs) : CryptDBs,...rahulmonikasharma
Internet of Things (IoT) is the developing technologies that would be the biggest agents to modify the current world. Machine-to-machine communications perform with virtual, mobile and instantaneous connections. In IoT system, it consists of data-gathering sensors various other household devices. Intended for protecting IoT system, the end-to-end secure communication is a necessary measure to protect against unauthorized entities (e.g., modification attacks and eavesdropping,) and the data unprotected on the Cloud. The most important concern hereby is how to preserve the insightful information and to provide the privacy of user data. In IoT, the encrypted data computing is based on techniques appear to be promising approaches. In this paper, we discuss about the recent secure database systems, which are capable to execute SQL queries over encrypted data.
Quality Determination and Grading of Tomatoes using Raspberry Pirahulmonikasharma
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Network management and Routing is supportively done by performing with the nodes, due to infrastructure-less nature of the network in Ad hoc networks or MANET. The nodes are maintained itself from the functioning of the network, for that reason the MANET security challenges several defects. Routing process and Scheduling is a significant idea to enhance the security in MANET. Other than, scheduling has been recognized to be a key issue for implementing throughput/capacity optimization in Ad hoc networks. Designed underneath conventional (LT) light tailed assumptions, traffic fundamentally faces Heavy-tailed (HT) assumption of the validity of scheduling algorithms. Scheduling policies are utilized for communication networks such as Max-Weight, backpressure and ACO, which are provably throughput optimality and the Pareto frontier of the feasible throughput region under maximal throughput vector. In wireless ad-hoc network, the issue of routing and optimal scheduling performs with time varying channel reliability and multiple traffic streams. Depending upon the security issues within MANETs in this paper presents a comparative analysis of existing scheduling policies based on their performance to progress the delay performance in most scenarios. The security issues of MANETs considered from this paper presents a relative analysis of existing scheduling policies depend on their performance to progress the delay performance in most developments.
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This paper discusses a new algorithm of a univariate method, which is vitally important to develop a short-term load forecasting module for planning and operation of distribution system. It has many applications including purchasing of energy, generation and infrastructure development etc. We have discussed different time series forecasting approaches in this paper. But ARIMA has proved itself as the most appropriate method in forecasting of the load profile for West Bengal using the historical data of the year of 2017. Auto Regressive Integrated Moving Average model gives more accuracy level of load forecast than any other techniques. Mean Absolute Percentage Error (MAPE) has been calculated for the mentioned forecasted model.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Implementation of Banker’s Algorithm Using Dynamic Modified Approach
1. International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
Volume: 5 Issue: 11 157 – 163
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Implementation of Banker’s Algorithm Using Dynamic Modified Approach
Mrinal Gaur
M.Tech Scholar,
CGI Bharatpur
E-Mail: iammrinnal@gmail.com
Dushyant Singh
Assistant Professor,
CGI Bharatpur
Abstract: Banker’s algorithm referred to as resource allocation and deadlock avoidance algorithm that checks for the safety by simulating the
allocation of predetermined maximum possible of resources and makes the system into s-state by checking the possible deadlock conditions for
all other pending processes.
It needs to know how much of each resource a process could possibly request. Number of processes are static in algorithm, but in most of system
processes varies dynamically and no additional process will be started while it is in execution. The number of resources are not allow to go down
while it is in execution.
In this research an approach for Dynamic Banker's algorithm is proposed which allows the number of resources to be changed at runtime that
prevents the system to fall in unsafe state. It also give details about all the resources and processes that which one require resources and in what
quantity. This also allocates the resource automatically to the stopped process for the execution and will always give the appropriate safe
sequence for the given processes.
__________________________________________________*****_________________________________________________
1. Introduction
1.1 Deadlock
A set of processes is deadlocked if each process in the set is
waiting for an event that only another process in the set can
cause. Usually the event is release of a currently held
resource. None of the processes can run and release
resources.
In an operating system, a deadlock occurs when a process or
thread enters a waiting state because a requested system
resource is held by another waiting process, which in turn is
waiting for another resource held by another waiting
process. If a process is unable to change its state indefinitely
because the resources requested by it are being used by
another waiting process, then the system is said to be in a
deadlock [1].
1.1.1 Conditions for Deadlock
Deadlock can arise if four conditions hold simultaneously
(i) Mutual exclusion condition: Only one process at a
time can use a resource (non-shareable resource). Each
resource is assigned to a process or is available.
(ii) Hold and wait condition: A process holding at least
one resource can request for additional resources.
(iii) No preemption condition: A resource can be released
only voluntarily by the process holding it. That is
previously granted resources cannot be forcibly taken
away.
(iv) Circular wait condition: There exists a set
{P0,P1,…,P0} of waiting processes such that P0 is
waiting for a resource that is held by P1, P1 is waiting
for a resource that is held by P2,…,Pn–1 is waiting for
a resource that is held by Pn, and P0 is waiting for a
resource that is held by P0.
1.2 Methods for Handling Deadlocks
Generally speaking there are three ways of handling
deadlocks:
1. Deadlock prevention or avoidance - Do not allow the
system to get into a deadlocked state.
2. Deadlock detection and recovery - Abort a process
or preempt some resources when deadlocks are
detected.
Ignore the problem all together - If deadlocks only occur
once a year or so, it may be better to simply let them happen
and reboot as necessary than to incur the constant overhead
and system performance penalties associated with deadlock
prevention or detection. This is the approach that both
Windows and UNIX take.
1.3 Banker's Algorithm
For resource categories that contain more than one instance
the resource-allocation graph method does not work, and
more complex (and less efficient) methods must be chosen.
The Banker's Algorithm gets its name because it is a method
that bankers could use to assure that when they lend out
resources they will still be able to satisfy all their clients. A
banker won't loan out a little money to start building a house
unless they are assured that they will later be able to loan
out the rest of the money to finish the house.
When a process starts up, it must state in advance the
maximum allocation of resources it may request, up to the
amount available on the system.
When a request is made, the scheduler determines whether
granting the request would leave the system in a safe state.
2. International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
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If not, then the process must wait until the request can be
granted safely.
The banker's algorithm relies on several key data structures:
(where n is the number of processes and m is the number of
resource categories.)
Available[m] indicates how many resources are
currently available of each type.
Max[n] [m] indicates the maximum demand of each
process of each resource.
Allocation[n] [m] indicates the number of each
resource category allocated to each process.
Need[n] [m] indicates the remaining resources
needed of each type for each process. Note that
Need[ i ][ j ] = Max[ i ][ j ] - Allocation[ i ][ j ] for all
i, j.)
For simplification of discussions, we make the following
notations / observations:
One row of the Need vector, Need[ i ], can be treated
as a vector corresponding to the needs of process i,
and similarly for Allocation and Max.
A vector X is considered to be <= a vector Y if X[ i ] <= Y[ i ]
for all i.
1.4 An Illustrative Example
Consider the following situation:
Figure 1.1:Process, Allocated Resources, Max Resources
and Needed Resources
And now consider what happens if process P1
requests 1 instance of A and 2 instances of C. (
Request[ 1 ] = ( 1, 0, 2 )
Figure 1.2: Process, Allocated Resources, Needed
Resources and Available Resource
What about requests of (3, 3, 0) by P4? Or (0, 2, 0) by P0?
Can these be safely granted? Why or why not?
2. Literature Review
In 2014, Pankaj Kawadkar, Shiv Prasad, Amiya Dhar
Dwivedi in their research "Deadlock Avoidance based on
Banker’s Algorithm for Waiting State Processes" proposed
an algorithm for deadlock avoidance used for Waiting State
processes.
They proposed that if process is going to waiting state then
the consideration of number of allocated resources and/or
number of instances as well as need of resources in order to
select a waiting process for the execution will make
Banker’s Algorithm more efficient. But they didn't give any
solution when the system is in unsafe state [15].
In 2013, Smriti Agrawal, Madhavi Devi Botlagunta and
Chennupalli Srinivasulu in their research titled “A Total
Need based Resource Reservation Technique for Effective
Resource Management” and proposed an approach for Total
Need Based Resource Reservation (TNRR) that suggests
reserving some resources so as to ensure that at least one
process will complete after it. The simulation results
indicate that the frequency of deadlocks has reduced by
approximately 75% for higher load (above 80%) as
compared to the Deadlock Recovery technique, while for
lower load it tends to be zero. The turnaround time of the
TNRR is approximately 9% better than the existing
Banker’s algorithm. But in case of insufficient resources
when there is no safe sequence is possible they didn’t
provide details for resources and processes that causes the
deadlock if executed or no safe sequence [4].
In 1999, Sheau-Dong Lang in his research titled “An
Extended Banker’s Algorithm for Deadlock Avoidance”
proposed an approach for safety in banker’s algorithm
assuming that the control flow of the resource-related calls
of each process forms a rooted tree, they
proposed a quadratic-time algorithm which decomposes
these trees into regions and computes the associated
maximum resource claims, prior to process execution. This
information is then used at runtime to test the system safety
using the original banker’s algorithm. But this approach was
unable to recognize patterns of resource-related calls in real-
time system and the practicality was also not proven [16].
3. Motivation
Many research has been done for the improvement of
Banker’s Algorithm. Most of the researchers has worked on
the limitations of waiting time and resource allocation to
improve the performance or minimizing the deadlocks. But
if at the end when system is not in safe state and traditional
Banker’s algorithm cannot be applied then what? We do not
have any information about the process or resources due to
which the system was in unsafe state. This specific problem
leads us towards this approach. Proposed approach gives the
details about all the resources and processes who require
resources in what quantity. This also allocates the resource
automatically to the stopped process for the execution and
3. International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
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will always give the appropriate safe sequence for the given
processes.
4. Proposed Approach
Banker's algorithm was originally designed to check
whether the allocation of resources leave the system in safe
state or not and if it is in safe state then it gives the safe
sequence of processes and allocate the resources.
In banker's algorithm when, a new process enters the
system, it must declare the maximum number of instances of
each resource type that it may need. This number may not
exceed the total number of resources in the system.
When a user requests a set of resources, the system must
determine whether the allocation of these resources will
leave the system in a safe state. If it will, the resources are
allocated; otherwise, the process must wait until some other
process releases enough resources.
Steps for Banker’s Algorithm
1. Let Request[ n ][ m ] indicate the number of
resources of each type currently requested by
processes. If Request[ i ] > Need[ i ] for any process
i, raise an error condition.
2. If Request[ i ] > Available for any process i, then that
process must wait for resources to become available.
Otherwise the process can continue to step 3.
3. Check to see if the request can be granted safely, by
pretending it has been granted and then seeing if the
resulting state is safe. If so, grant the request, and if
not, then the process must wait until its request can
be granted safely. The procedure for granting a
request ( or pretending to for testing purposes ) is:
a) Available = Available – Request
b) Allocation = Allocation + Request
c) Need = Need - Request
Several data structures must be maintained to implement the
banker's algorithm. These data structures encode the state of
the resource-allocation system.
In this research an approach for Dynamic Banker's
algorithm is proposed which allows the number of resources
to be changed at runtime that prevents the system to fall in
unsafe state.
Proposed Algorithm
Proposed algorithm that provides a safe sequence. It
depends upon need (i)=1,2…n,
Allocation (i) =1, 2…n and Available resources.
It works as follows:
After the Resource-Request-Algorithm if process is going
to waiting state then these steps must be followed:
Step.1- Need (i) is compare to Need(i+1 to last).
Step.2- Take the process with minimum Need and
maximum Allocation.
Step.3- If Available<Need (i) then Execute process. Set
state is executed.
Step.4-Available=Available + Allocation (i)
Step.5- Repeat step 1 to 4 until all the process is going to
executed state.
Whenever a process requests for instance of resource it
checks the availability and demand of instance of resource.
If availability is more than demand then instance of resource
is allocated to process according to the priority of which
process free the most number of resources.
It checks for the needed availability to execute the process.
Return the information about the instance of resource
needed to system along with associated process.
5. Performing Banker’s Algorithm with Existing
Approach
Here in the first experiment of this research, implementing
the original Banker’s Algorithm having 3 types of different
resources and 5 processes. Here some resources are already
allocated to the processes and there is also given the
maximum demand and remaining needs of these processes.
After implementing the Banker’s Algorithm results are
evaluated to determine the safe sequence.
Here in this experiment there are five processes (p0 to p4)
are used and three types of resources are used with different
number of instances as A-10, B-5 and C-7.
TABLE 5.1
AVAILABLE RESOURCES
Available
A B C
10 5 7
Table 5.1 shows the number of resources available for the
process allocations. These are having several instances of
same type.
TABLE 5.2
RESOURCES ALLOCATIONS
Allocations
A B C
p0 0 1 0
p1 2 0 0
p2 3 0 2
p3 2 1 1
p4 0 0 2
Table 5.2 displays the resources allocated to the number of
processes with different types of instances. These resources
are already allocated to the processes and processes are
executing on these resources.
4. International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
Volume: 5 Issue: 11 157 – 163
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TABLE 5.3
MAX DEMAND
Available
A B C
p0 7 5 3
p0 3 2 2
p2 9 0 2
p3 2 2 2
p4 11 3 3
Table 5.3 shows the max demand of the processes for the
resources. It also shows that which resource is having in
what number of instances of which type of resource. Max
demand also containing the allocated resources so here it is
must to calculate the remaining needs of the process for the
execution of the Banker’s Algorithm.
TABLE 5.4
REMAINING NEED
Remaining Need
A B C
p0 7 4 3
p1 1 2 2
p2 6 0 0
p3 0 1 1
p4 11 3 1
Table 5.4 shows the remaining needs of the processes for the
resources. It also shows that which resource is having in
what number of instances of which type of resource.
Remaining need is calculating through the max demand and
allocation of the resources. The formula which is used to
calculate the need is given below.
Need = maximum resources - currently allocated resources
Processes (possibly needed resources):
After calculating the need for the processes then need to
check the execution of the processes that these are in safe
state or not.
Now to check whether above state is safe sequence in which
above requests can be fulfilled. After evaluating the
Banker’s Algorithm shows that it is not in the safe state
because any of the process needs for more resources which
are not available to fulfill the requirements. Here Banker’s
Algorithm does not show that particular process which
needs more resources of what type. It just shows that it is
not in the safe sequence, so it is very difficult to say that
which resource is needed to which process so the problem
can be solved.
A state is considered unsafe if it is not possible for all
processes to completing executing. Since the system does
not know when a process will finish termination, or how
many resources it requested by then, the system assumes
that all processes will not able to acquire their stated
maximum resources and terminated. This is a reasonable
assumption in most of the cases the system is not
particularly concerned with how long each process runs.
Also, if a process terminates without having its maximum
resources it only makes it simpler on the system. A safe
state is considered as to be the decision maker if it is going
to process ready queue. Any state where such set exists is a
safe state.
5.1 Performing Modified Banker’s Algorithm with New
Approach
Here in the second experiment of this research,
implementing the modified Banker’s Algorithm having 3
types of different resources and 5 processes. Here some
resources are already allocated to the processes and there is
also given the maximum demand and remaining needs of
these processes. After implementing the modified Banker’s
Algorithm results are evaluated to determine the safe
sequence.
Here in this experiment there are five processes (p0 to p4)
are used and three types of resources are used with different
number of instances as A-10, B-5 and C-7.
TABLE 5.5
AVAILABLE RESOURCES
Available
A B C
10 5 7
Table 5.5 shows the number of resources
available for the process allocations. These are
having several instances of same type.
Available
A B C
10 5 7
TABLE 5.6
RESOURCES ALLOCATIONS
Table 5.6 displays the resources allocated to the number of
processes with different types of instances. These resources
are already allocated to the processes and processes are
executing on these resources.
Allocation
A B C
p0 0 1 0
p1 2 0 0
p2 3 0 2
p3 2 1 1
p4 0 0 2
5. International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
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TABLE 5.7
MAX DEMAND
Max Demand
A B C
p0 7 5 3
p1 3 2 2
p2 9 0 2
p3 2 2 2
p4 11 3 3
Table 5.7 shows the max demand of the processes for the
resources. It also shows that which resource is having in
what number of instances of which type of resource. Max
demand also containing the allocated resources so here it is
must to calculate the remaining needs of the process for the
execution of the Banker’s Algorithm.
TABLE 5.8
REMAINING NEED
Remaining Need
A B C
p0 7 4 3
p1 1 2 2
p2 6 0 0
p3 0 1 1
p4 11 3 1
Table 5.8 shows the remaining needs of the processes for the
resources. It also shows that which resource is having in
what number of instances of which type of resource.
Remaining need is calculating through the max demand and
allocation of the resources. The formula which is used to
calculate the need is given below.
Need = maximum resources - currently allocated resources
Processes (possibly needed resources):
After calculating the need for the processes then need to
check the execution of the processes that these are in safe
state or not.
Now to check whether above state is safe sequence in which
above requests can be fulfilled. After evaluating the
Modified Banker’s Algorithm shows that which process
needs what instance of resource in what quantity? After
knowing that which instance of resources need to be added
more, then it is very easy to add the more resources for the
process. Here modified Banker’s Algorithm shows that
particular process which needs more required resources of
what type. It also shows that it is not in the safe sequence, so
it is very easy to find out & add which resource is required
to the process so the problem can be solved by this
approach.
TABLE 5.9
REQUIRE RESOURCES
Require Resources
A B C
p0 4 1 1
Table 5.9 displays the require resources of the process. It
shows after calculating that which resources is not available
for the process. Then it is easy to add more resources further
and then calculate the new safe sequence. It ensure that it
will always give the safe sequence which never lead to
deadlock.
After adding more resources in the system then calculate the
safe sequence as follow P0, p1, p2, p3, p4.
Finally evaluating the results of both experiments it is very
clear to say that original Banker’s algorithm gives only
assumption that the execution of the processes are in safe
state or not in safe state.
Banker’s Algorithm does not show that particular process
which needs more resources of what kind of type. It just
shows that it is not in the safe sequence, so it is very
difficult to say that which resource is needed to which
process so the problem can be solved.
Modified Banker’s Algorithm shows that which process
needs what instance of resource in what quantity? After
knowing that which instance of resources need to be added
more, then it is very easy to add the more resources for the
process. Here modified Banker’s Algorithm shows that
particular process which needs multiple required resources
of what type. It also shows that it is not in the safe sequence,
so it is very easy to add which resource is needed to the
process so the problem can be solved by this approach.
6. Results
Performance is calculated through executing several
examples which prove that new approach of Banker’s
Algorithm is capable to solve more number of problems
than existing approach of Banker’s Algorithm.
Here is result comparison of both algorithms.
TABLE 6.1
PERFORMANCE ANALYSIS
No. of
Experiments
Banker's
Algorithm
New
Approach
20 7 16
Table 6.1 displayed the total 20 experiments were performed
with different problems (set of process and resources) out of
which Banker's algorithm was able to solve 6 problems, on
the other hand improved banker's algorithm was able to
solve 16 out of the 20 problems.
6. International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
Volume: 5 Issue: 11 157 – 163
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Performance Chart
20
15
10
5
0
Banker's Algorithm New Approach
Figure 6.1 Performance Chart
Figure 6.1 defines that the total number of experiments were
performed with different problems. Out of which traditional
banker's approach was able to inform about no deadlock in
35% problems. The modified banker's algorithm was able to
avoid deadlock in 80% of total experiments. Thus the
performance of improved algorithm was 45% better than the
banker's approach.
7. Conclusion & Future Work
7.1 Conclusion
This research shows the Banker’s Algorithm working,
problem in original algorithm to identify the reason for
failing the process execution. Here Dynamic Banker’s
algorithm solve the existing problem of the original
Banker’s algorithm.
Results prove that modified Banker’s Algorithm shows that
particular process which needs more resources of what type.
It also shows that it is in the safe sequence or not, so it is
very easy to add which resource is needed to the process so
the problem can be solved by this approach.
7.2 Future Scope
The present and future of this area is bright, and full of
opportunities and great challenges as it processes high
demands.
In future it can be used for the auto added process and
killing the undesired process.
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