Cache Manager to Reduce the Workload of MapReduce Framework

Cache Manager to Reduce the Workload of MapReduce Framework Provision of Cache Manager to Reduce the Workload of MapReduce Framework for Bigdata application Ms.S.Rengalakshmi,  Mr.S.Alaudeen Basha Abstract: The term big-data refers to the large-scale distributed data processing applications that operate on large amounts of data. MapReduce and Apache’s Hadoop of Google, are the essential software systems for big-data applications. A large amount of intermediate data are generated by MapReduce framework. After the completion of the task this abundant information is thrown away .So MapReduce is unable to utilize them. In this approach, we propose provision of cache manager to reduce the workload of MapReduce framework along with the idea of data filter method for big-data applications. In provision of cache manager, tasks submit their intermediate results to the cache manager. A task checks the cache manager before executing the actual computing work. A cache description scheme and a cache request and reply protocol are designed. It is expected that provision of cache manager to reduce the workload of MapReduce will improve the completion time of MapReduce jobs. Key words: big-data; MapReduce; Hadoop; Caching. I. Introduction With the evolution of information technology, enormous expanses of data have become increasingly obtainable at outstanding volumes. Amount of data being gathered today is so much that, 90% of the data in the world nowadays has been created in the last two years [1]. The Internet impart a resource for compiling extensive amounts of data, Such data have many sources including large business enterprises, social networking, social media, telecommunications, scientific activities, data from traditional sources like forms, surveys and government organizations, and research institutions [2]. The term Big Data refers to 3 v’s as volume, variety, velocity and veracity. This provides the functionalities of Apprehend, analysis, storage, sharing, transfer and visualization [3].For analyzing unstructured and structured data, Hadoop Distributed File System (HDFS) and Mapreduce paradigm provides a Parallelization and distributed processing. Huge amount data is complex and difficult to process using on-hand database management tools, desktop statistics, database management systems or traditional data processing applications and visualization packages. The traditional method in data processing had only smaller amount of data and has very slow processing [4]. A big data might be petabytes (1,024 terabytes) or exabytes (1,024 petabytes) of data composed of billions to trillions of records of millions of people—all from different sources (e.g. Web, sales, customer center for communication, social media. The data is loosely structured and most of the data are not in a complete manner and not easily accessible[5]. The challenges include capturing of data, analysis for the requirement, searching the data, sharing, storage of data and privacy violations. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of data which are related to one another, as matched to distinguish smaller sets with the same total density of data, expressing correlations to be found to identify business routines[10].Scientists regularly find constraints because of large data sets in areas, including meteorology, genomics. The limitations also affect Internet search, financial transactions and information related business trends. Data sets develop in size in fraction because they are increasingly accumulated by ubiquitous information-sensing devices relating mobility. The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization. MapReduce is useful in a wide range of applications,such as distributed pattern-based searching technique, sorting in a distributed system, web link-graph reversal, Singular Value Decomposition, web access log stats, index construction in an inverted manner, document clustering , machine learning, and machine translation in statistics. Moreover, the MapReduce model has been adapted to several computing environments. Googles index of the World Wide Web is regenerated using MapReduce. Early stages of ad hoc programs that updates the index and various analyses can be executedis replaced by MapReduce. Google has moved on to technologies such as Percolator, Flume and MillWheel that provides the operation of streaming and updates instead of batch processing, to allow integrating live search results without rebuilding the complete index. Stable input data and output results of MapReduce are stored in a distributed file system. The ephemeral data is stored on local disk and retrieved by the reducers remotely. In 2001,Big data defined by industry analyst Doug Laney (currently with Gartner) as the three Vs : namevolume, velocity and variety [11]. Big data can be characterized by well-known 3Vs: the extreme density of data, the various types of data and the swiftness at which the data must be processed. II. Literature survey Minimization of execution time in data processing of MapReduce jobs has been described by Abhishek Verma, Ludmila Cherkasova, Roy H. Campbell [6]. This is to buldge their MapReduce clusters utilization to reduce their cost and to optimize the Mapreduce jobs execution on the Cluster. Subset of production workloads developed by unstructured information that consists of MapReduce jobs without dependency and the order in which these jobs are performed can have good impact on their inclusive completion time and the cluster resource utilization is recognized. Application of the classic Johnson algorithm that was meant for developing an optimal two-stage job schedule for identifying the shortest path in directed weighted graph has been allowed. Performance of the constructed schedule via unquantifiable set of simulations over a various workloads and cluster-size dependent. L. Popa, M. Budiu, Y. Yu, and M. Isard [7]: Based on append-only, partitioned datasets, many large-scale (cloud) computations will operate. In these circumstances, two incremental computation frameworks to reuse prior work in these can be shown: (1) reusing similar computations already performed on data partitions, and (2) computing just on the newly appended data and merging the new and previous results. Advantage: Similar Computation is used and partial results can be cached and reused. Machine learning algorithm on Hadoop at the core of data analysis, is described by Asha T, Shravanthi U.M, Nagashree N, Monika M [1] . Machine Learning Algorithms are recursive and sequential and the accuracy of Machine Learning Algorithms depend on size of the data where, considerable the data more accurate is the result. Reliable framework for Machine Learning is to work for bigdata has made these algorithms to disable their ability to reach the fullest possible. Machine Learning Algorithms need data to be stored in single place because of its recursive nature. MapRedure is the general and technique for parallel programming of a large class of machine learning algorithms for multicore processors. To achieve speedup in the multi-core system this is used. P. Scheuermann, G. Weikum, and P. Zabback [9] I_O parallelism can be exploited in two ways by Parallel disk systems namely inter-request and intra-request parallelism. There are some main issues in performance tuning of such systems.They are: striping and load balancing. Load balancing is performed by allocation and dynamic redistributions of the data when access patterns change. Our system uses simple but heuristics that incur only little overhead. D. Peng and F. Dabek [12] an index of the web is considered as documents can be crawled. It needs a continuous transformation of a large repository of existing documents when new documents arrive.Due to these tasks, databases do not meet the the requirements of storage or throughput of these tasks: Huge amount of data(in petabytes) can be stored by Google’s indexing system and processes billions of millions updates per day on wide number of machines. Small updates cannot be processed individually by MapReduce and other batch-processing systems because of their dependency on generating large batches for efficiency. By replacing a batch-based indexing system with an indexing system based on incremental processing using Percolator, we process the similar number of data documents averagely per day, happens during the reduction of the average age of documents in Google search which is resulted by 50%. Utilization of the big data application in Hadoop clouds is described by Weiyi Shang, Zhen Ming Jiang, Hadi Hemmati, Bram Adams, Ahmed E. Hassan, Patrick Martin[13]. To analyze huge parallel processing frameworks, Big Data Analytics Applications is used. These applications build up them using a little model of data in a pseudo-cloud environment. Afterwards, they arrange the applications in a largescale cloud situation with notably more processing organize and larger input data. Runtime analysis and debugging of such applications in the deployment stage cannot be easily addressed by usual monitoring and debugging approaches. This approach drastically reduces the verification effort when verifying the deployment of BDA Apps in the cloud. Matei Zaharia, Mosharaf Chowdhury, Michael J. Franklin, Scott Shenker, Ion Stoica [14] MapReduce and its variants have been highly successful in implementing large-scale data-intensive applications on clusters of commodity base. These systems are built around an model which is acyclic in data flow which is very less suitable for other applications. This paper focuses on one such class of applications: those that reuse a working set of data across multiple operations which is parallel. This encompasses many machine learning algorithms which are iterative. A framework cnamed Spark which ropes these applications and retains the scalability and tolerantes fault of MapReduce has been proposed. To achieve these goals, Spark introduces an abstraction called resilient distributed datasets (RDDs). An RDD is a read-only collection of objects which are partitioned across a set of machines. It can be rebuilt if a partition is lost. Spark is able to outperform Hadoop in iterative machine learning jobs and can be used to interactively query around and above 35 GB dataset with sub-second response time. This paper presents an approach cluster computing framework named Spark, which supports working sets while providing similar scalability and fault tolerance properties to MapReduce III. Proposed method An Objective of proposed System is to the underutilization of CPU processes, the growing importance of MapReduce performance and to establish an efficient data analysis framework for handling the large data Drift in the workloads from enterprise through the exploration of data handling mechanism like parallel database such as Hadoop. Figure 1: Provision of Cache Manager III.A.Provision of Dataset To Map Phase : Cache refers to the intermediate data that is produced by worker nodes/processes during the execution of a Map Reduce task. A piece of cached data is stored in a Distributed File System (DFS). The content of a cache item is described by the original data and the operations applied. A cache item is explained by a 2-tuple: Origin, Operation. The name of a file is denoted by Origin in the DFS. Linear list of available operations performed on the Origin file is denoted by Operaion. Example, consider in the word count application, each mapper node or process emits a list of word, counting tuples that record the count of each word in the file that the mapper processes. Cache manager stores this list to a file. This file becomes a cache item. Here, item refers to white-space-separated character strings. Note that the new line character is also considered as one of the whitespaces, so item precisely captures the word in a text file and item count directly corresponds to the word count operat ion performed on the data file. The input data are get selected by the user in the cloud. The input files are splitted. And then that is given as the input to the map phase. The input to the map phase are very important. These input are processed by the map phase. III.B.Analyze in Cache Manager: Mapper and reducer nodes/processes record cache items into their local storage space. On the completion of these operations , the cache items are directed towards the cache manager, which acts like an inter-mediator in the publish/subscribe model. Then recording of the description and the file name of the cache item in the DFS is performed by cache manager. The cache item should be placed on the same machine as the worker process that generates it. So data locality will be improved by this requirement. The cache manager maintains a copy of the mapping between the cache descriptions and the file names of the cache items in its main memory to accelerate queries. Permanently to avoid the data loss, it also flushes the mapping file into the disk periodically. Before beginning the processing of an input data file, the cache manager is contacted by a worker node/process. The file name and the operations are send by the worker process that it plans to apply to the file to the cache manager. Upon receiving this message, the cache manager compares it with the stored mapping data. If an exact match to a cache item is found, i.e., its origin is the same as the file name of the request and its operations are the same as the proposed operations that will be performed on the data file, then a reply containing the tentative description of the cache item is sent by the cache manager to the worker process.On receiving the tentative description,the worker node will fetch the cache item. For processing further, the worker has to send the file to the next-stage worker processes. The mapper has to inform the cache manager that it already processed the input file splits for this job. These results are then reported by the cache manager to the next phase reducers. If the cache service is not utilized by the reducers then the output in the map phase can be directly shuffled to form the input for the reducers. Otherwise, a more complex process is performed to get the required cache ite ms. If the proposed operations are different from the cache items in the manager’s records, there are situations where the origin of the cache item is the same as the requested file, and the operations of the cache item are a strict subset of the proposed operations. On applying some additional operations on the subset item, the item is obtained. This fact is the concept of a strict super set. For example, an item count operation is a strict subset operation of an item count followed by a selection operation. This fact means that if the system have a cache item for the first operation, then the selection operation can be included, that guarantees the correctness of the operation. To perform a previous operation on this new input data is troublesome in conventional MapReduce, because MapReduce does not have the tools for readily expressing such incremental operations. Either the operation has to be performed again on the new input data, or the developers of application need to manually cache the stored intermediate data and pick them up in the incremental processing. Application developers have the ability to express their intentions and operations by using cache description and to request intermediate results through the dispatching service of the cache manager.The request is transferred to the cache manager. The request is analyzed in the cache manager. If the data is present in the cache manager means then that is transferred to the map phase. If the data is not present in the cache manager means then there is no response to the map phase. IV.Conclusion Map reduce framework generates large amount of intermediate data. But, this framework is unable to use the intermediate data. This system stores the task intermediate data in the cache manager. It uses the intermediate data in the cache manager before executing the actual computing work.It can eliminate all the duplicate tasks in incremental Map Reduce jobs. V. Future work In the current system the data are not deleted at certain time period. It decreases the efficiency of the memory. The cache manager stores the intermediate files. In future, these intermediate files can be deleted based on time period will be proposed. New datasets can be saved. So the memory management of the proposed system can be highly improved. VI. References [1] Asha, T., U. M. Shravanthi, N. Nagashree, and M. Monika. Building Machine Learning Algorithms on Hadoop for Bigdata. International Journal of Engineering and Technology 3, no. 2 (2013). [2] Begoli, Edmon, and James Horey. Design Principles for Effective Knowledge Discovery from Big Data. In Software Architecture (WICSA) and European Conference on Software Architecture (ECSA), 2012 Joint Working IEEE/IFIP Conference on, pp. 215-218. IEEE, 2012. [3] Zhang, Junbo, Jian-Syuan Wong, Tianrui Li, and Yi Pan. A comparison of parallel large-scale knowledge acquisition using rough set theory ondifferent MapReduce runtime systems. International Journal of Approximate Reasoning (2013) [4] Vaidya, Madhavi. Parallel Processing of cluster by Map Reduce. International Journal of Distributed Parallel Systems 3, no. 1 (2012). [5] Apache HBase. Available at http://hbase.apache.org [6] Verma, Abhishek, Ludmila Cherkasova, and R. Campbell. Orchestrating an Ensemble of MapReduce Jobs for Minimizing Their Makespan. (2013): 1-1. [7] L. Popa, M. Budiu, Y. Yu, and M. Isard, Dryadinc:Reusing work in large-scale computations, in Proc. ofHotCloud’09, Berkeley, CA, USA, 2009 [8] T. Karagiannis, C. Gkantsidis, D. Narayanan, and A.Rowstron, Hermes: Clustering users in large-scale e-mailservices, in Proc. of SoCC ’10, New York, NY, USA, 2010. [9] P. Scheuermann, G. Weikum, and P. Zabback, Datapartitioning and load balancing in parallel disk systems,The VLDB Journal, vol. 7, no. 1, pp. 48-66, 1998. [10] Parmeshwari P. Sabnis, Chaitali A.Laulkar , â€Å"SURVEY OF MAPREDUCE OPTIMIZATION METHODS†, ISSN (Print): 2319- 2526, Volume -3, Issue -1, 2014 [11] Puneet Singh Duggal ,Sanchita Paul ,â€Å" Big Data Analysis:Challenges and Solutions†, International Conference on Cloud, Big Data and Trust 2013, Nov 13-15, RGPV [12] D. Peng and F. Dabek, Largescale incremental processingusing distributed transactions and notifications, in Proc. ofOSDI’ 2010, Berkeley, CA, USA, 2010 [13] Shvachko, Konstantin, Hairong Kuang, Sanjay Radia, and Robert Chansler. The hadoop distributed file system. In Mass Storage Systems and Technologies (MSST), 2010 IEEE 26th Symposium on, pp. 1-10. IEEE, 2010. [14] â€Å"Spark: Cluster Computing withWorking Sets â€Å"Matei Zaharia, Mosharaf Chowdhury, Michael J. Franklin, Scott Shenker, Ion Stoica University of California, Berkeley

History Repeats Itself Essay -- History Historical Research Papers

History Repeats Itself   Ã‚  Ã‚  Ã‚  Ã‚  History repeats itself. This concept applies not only within the realm of a single nation's history but throughout and between nations. That is to say, that what one nation endures, throughout its economic and political history, may be compared to and be strikingly similar to that of many other nations. As we analyze social change thought the world we have noticed a cyclical pattern of histories, both economic and political, in the countries of Spain, Holland, Britain, and the United States. I.  Ã‚  Ã‚  Ã‚  Ã‚  Historical Periodization:   Ã‚  Ã‚  Ã‚  Ã‚  Throughout history and during alternating time periods, countries have grown from feeble entities, defeated by or ruled by the governing structures of foreign nations, to powerful nations. Between the fifteenth and the sixteenth century, SPAIN ruled as a great power among other nations. Its empire began when, in 1492, Spain financed Columbus's expeditions and explorations to conquer territory in the New World. Once it held its new established territory, Spain relied on the influx of gold and silver from the New World. Spain was the first country to start an empire and consequently started a trend. Once HOLLAND gained their independence from Spanish rule, at the beginning of the seventeenth century, it moved on to become a great power. Holland had relied on seafaring and the economic success of Amsterdam until around 1620. "By mid-century, however, they had used their technical sophistication and control of vital raw commodities to build successful industries . . . and supported by Holland's bourgeois virtues, trading preeminence and credit, Dutch manufactures soon dominated a number of European markets" (BP 198). Holland remained in power until its decline began in the middle of the eighteenth century. In 1750, the Dutch started losing European markets but continued as the number one market country in Europe. The British moved in where the Dutch had been. GREAT BRITAIN reached great heights in the middle of the eighteenth century. Starting out as the home of the Industrial Revolution, Britain was considered the workshop of the world. However, by the 1890's Britain was losing ground in the global market of manufacturing, specifically to the United States and Germany. The UNITED STATES, is the youngest of the nations studied in this essay, which became a major power a... ... decline again. In Great Britain polarization was reversed by redistribution of income, socialism, and welfarism. This benefited the middle and lower--middle class citizens but hurt the elite. In the UNITED STATES at the end of the "Roaring Twenties", when the stock market crashed, the major financial institutions were left to fail and die out. When the bubble of the 1980's burst, however, the United States government bailed out the companies and caused the country to go into economic decline, deficit, and ruin the budget. The "Roaring Twenties", and the "Anxious 1980's" are examples of rises and later declines of economic and political prosperity and power. Decline in the United States is occurring on both an economic and social level. America has witnessed a rapid centralization at the seat of federal power and a capital more influenced by interest groups than by voters. "Imperial capitals don't become notorious until they display wealth and develop serious, parasitic elites, not true of Washington until it came of age in the late 1960's and 1970's" (AC 29). "There is no point in mincing words. Aging great-power capitals often become parasitic cultures"(AC xix).

How are relationships presented in romeo and juliet compared to the machine stops Essay

EXPLORE THE WAYS RELATIPNSHIPS ARE PRESENTED IN THE TEXTS YOU HAVE STUDIED Romeo and Juliet is a tragic play, written by William Shakespeare. Trailing the catastrophic events occurring in the lives to two teenage â€Å"star-cross’d lovers† whose premature deaths unite their quarrelling families. The Machine Stops however, is a futuristic novella written by E.M Forster following a mother and son in an attempt to keep their relationship in a society run by a machine which controls the humans. Prince Escalus of Romeo and Juliet is the authority of Verona; he controls everybody in his city. We can see this from the first time we see him; in act I scene I. â€Å"rebellious subjects, enemies to peace/profaners of this neighbour- stained steel,† . â€Å"subjects†- instead of referring to them as the people of the city, making them seem worthless, portraying a higher power, since ‘subject’ suggests someone being discussed or dealt with, the gentry would be thinking about the princes language and recognise the depth in his la nguage whereas the groundlings would be entertained by the previous brawl which provided them with action, the audience then recognise that he is on higher social status than the other characters. â€Å"steel†-speaking of their swords, the prince is fustraghted because these swords are meant for the purpose of protecting and defending the city, but they are being used to spill the blood of its residents; †stained†-meaning blood, the audience would then look forward to the punishments that the prince will provide. Shakespeare portrays the prince as majestic and royal through his language, he speaks very formally, much more than the other characters. â€Å"but I’ll amerce you with a strong fine/that you shall all repent the loss of mine:†. The prince speaks using rhyming couplets making him seem more superior and authorative. The audience, especially the gentry, will realise how he is very imperious and lordly. In the Machine stops, the machine controls the underground dystopian society; it is relatively like the prince in the sense that it holds control over inhibitors. Since the Machine doesn’t literally speak, we have the words of the humans which know the characteristics of the Machine. When Kuno is persuading Vashti to visit the surface of the earth, she replies â€Å"it is contrary to the spirit of the age† Kuno exclaims â€Å"do you mean by that, contrary to the machine?†Vashti cautiously replies â€Å"in a sense, but†¦Ã¢â‚¬  she is acting as if the machine has developed feelings or a sense of betrayal. The reader would find this a perqullia society because the humans are worshiping  a machine, made by men. The machine and prince Escalus are presented in a similar way, this is unusual since the machine stops is set in the future and Romeo and Juliet was written in 1500’s yet they possess similar qualities, it is also intriguing how E.M Forster came up with this idea since he wrote this in early 1900’s in England, a democratic society, in a time of great inventions but thinks of a society which is almost like a dictatorship, and having to respect a ruler. But this was orthodox in the Shakespeare’s age to respect the authority to your town. Hate relationships are strong and frequent in romeo and juliet, romeo and tybalt have a hate relationship.however, as much as tybalts vulgur, impulsive, violent attitude riles romeo, he attempts to refrain from vocalising his loave because tybalt is juliets cousin and, unbeknown to tybalt, romeos kinsman.in act 3 scene 1 tybalt is roaming the streets in search of a brawl, romeo appears and tybalt begins to provoke him, mercutio, who never fails to rise mutiny, begins a quarrel with tybalt, ending with mercutio fatally wounded romeo then fills with resentment and vexation, then once tybalt returns, they fight;resulting in tybalts death. whilst tybalt is dying, he says to romeo â€Å"thou, wrecked boy, that dids’t consort him here,/ shalt with him hence† tybalts insult towards romeo, â€Å"boy† is the same as what he said at the capulet feast. romeo is usually presented as civil and caring, not violent, but he shows the audience that he is protective of people that he loves. most of the hateful relationships are presented by shakespeare through physical actions, which may be because battles were a conventional part of society in the 1500’s and it is also more interesting for the audience, since the plays were preformed live, not read. despite that, forster presents hate through words because it makes for a more interesting read. since its hard to translate physical actions into words and still keep the effectiveness on the reader. for example, kuno’s hate for the machine is never portrayed in actions, but words. â€Å"the machine is much, but it is not everything. i see something like you in this plate, but i do not see you. i hear something like you through this telephone, but i do not hear you† he tells vashti that the machine has excluded the sense of personal touch and communication. juno is fustraighted by the fact that inhibitors praise so highly of the machine, yet they forget that it was made by man, they treat it as a god and follow it like a religion, his hate grows further for the  machine because the machine causes vashti and juno to have an argument where they completely disown each other, and kuno sees this as down to the machine. he feels resentment to wards the machine also, because of how it makes people act. â€Å"Thrice she felt the delirium of aquiesance.† â€Å"Delirium† by that meaning vashti is gibing in to the machine, showing the reader how compelling the machine is, since it takes vashti out of her normal state and conscious mind. kuno never directly says to the machine † i hate you†, but we imply this from his words, different from shakespeare plays, where feelings are relationships are vocalised clearly. love is presented rather strange, by e.m forster. vashrti loves the machine, she installs all of her faith in it, we see this when the machine begins to malfunction, â€Å"she continued to whirl† she is going crazy and it is almost like now that the machine is going, she is developing a sense of separation anxiety . ‘whirled’ she is beginning to possess machine like’ properties, displaying how she has a bond that runs extremely deep inside of her. However, her passion for the machine is seen as unconventional by the reader since the machine isn’t a person, but forster may have done this to help emphasis that this is a futuristic novella which would mean that society will conceits of different elements than today. the reader may respond frightened, since this is written in 1909 and technology has evolved extensively, compelling the reader that this may come of society in years to come. another factor that makes vashti’s love for the machine unconventional, is that her passion resembles that of a cult follower â€Å"you must not say anything against the machine.† and no matter how hard kuno tries to erach her because he recognises her state, she can’t pull away, she still follows. shakespeare presents relationships differently however, romeo and juliets love is sudden, impulsive and very swift. since the original story of romeo and julliet is spread over 9 months, whereas this quicker paced play is sq ueezed into 5 days. romeos love for juliet is very sudden, â€Å"so shows a snowy dove tropping with crows,† his first sight of juliet, he falls deeply in love with her, forgetting about his â€Å"love† he was depressed over, showing the audience romeos infatuation. because by ‘crow’ he is comparing juliet to rosaline, implied as the frow. declaring that he has never felt love until this night, showing the audience how romeo is very  indecisive and impulsive. their love is also presented to the audience through sonnets, indicating that their love is so passionate, that their dialogue is spoken through love poems, displaying a higher romance to the audience because sonnets are often used to write about love.there is a dark underling meaning behind these rods, however. as these are a foreshadow of romeo and juliets coming death, we recognise this because these 14 line sonnets match the 14 line prologue with the same rhyming scheme as the sonnets, this prol ogue mentions their deaths, linking the teens love to their tragic fate.the foreshadowing of romeo and juliets death are frequent thouout the play. â€Å"methinks i see there, now thou art below / as one dead in the bottom of a tomb:†- an omen of when romeo is in the bottom of a tomb, the gentry would recognise these hints of whats coming, but mostlikly at the end of the play. The love presented in romeo and juliet is much more conventional than presented in the machine stops, since romeo is a typical; lovesick: self-pityful; impulsive teenager,but vashtis relationship with the machine is interesting since it is much more unorthodox and surreal. shakespeare presents capulet and juliets relationship as very influx, because we first see capulet as a loving father, whilst he is speaking with paris (a potential candidate to marry juliet) and says â€Å"let two more summers wither in their pride†¦ but woo her† capulet doesn’t want his daughter to leave him for another two years, however, he contrasts this, â€Å"an she agree within her scope of choice / lies my concent and fair according voice† ‘ scope’ suggesting that within her choice of men, if she doesn’t comply, ‘according voice’ then he will force her. his voice will always be there to influence her, and he will use this parental influence as a to ol of fate. to shakespeare’s audience, this wouldn’t seem queer, but to the modern day audience, this would seem bizarre. but a father had lots of control of the life their daughter led. but this is also partly to do with hoe capulet prides himself on the place he has in society and doesn’t want his name, which he has accumulates such power to, not to be carried through to descendants. juliets power against her father is weakened because she is his heir, but she is weakened further due to her social standards as a woman, and women being dominated by males in the 1500’s. Capulets underling anger becomes clearer in act 3 scene 5, it was expected that noble women would marry rich men, on par with their status, but when capulet hears that romeo  is who uliet wants to marry, he becomes very riled, dramatically contrasting his earlier behaviour when she was obeying him, â€Å"disobedient wretch † suggesting to the audience that his previous love was superficial. he is saying all of this in front of juliet, yet he speaks of her in the third person, asif she isn’t there, â€Å"we have a curse in having her†¦rid of her† referring to someone in the third person is deemed more rude and shakespeare gave capulet the words â€Å"her† to make juliet feel worthless and put distance between her and capulet, also in attempt to belittle her and make her feel bad., therefore portraying more anger to the audience. His rage relates back to capulet priding his statue in society and wanting his image to remain in tact. however, once he hears of juliets death, his feelings turn suddenly very remorseful and flu of self-pity. he feels bad because his last words to his daughter were very harsh, the audience were full of excitement but now they are very sorrowful and shocked, contratig their earlier thoughts and feelings, of an otherwise action-packed scene. â€Å"ready to go, but never to return,† capulet say says she is going for the wrong reasons, she should have been going to marry paris, but rather, going to be buried. he mentions how le lost the successor to his name, reputation, legacy, and empire which he has built, which sould have been passed down by juliet, heir to the capulet fortune, reinforcing the social standards of an heir in elizabeths reign.

Physical And Mental Requirements Of Zoology - 1155 Words

One of the most interesting jobs of today is a Zoologist. Not only is this job very easy to acquirer it can also benefit someone. The main factors to look up in zoology are the job requirements like physical and mental requirements, the education you acquire, job advancement, the salary, the responsibilities it comes with, and the travel opportunities. This job can help a person by giving them knowledge and adventure in their life. It can provide him or her with essentials they will need in an average person’s life. The first thing in this job that is not required but preferred is that you must be in shape if you do field work. Unless you a put in the lab there you just study tissue and the blood of animals stuff like that. But†¦show more content†¦These are just a few of what can benefit someone if they are planning to become a zoologist. By doing those things it will not only prepare that person for just zoology it can help them acquire different careers. For instan ce if you wanted to study animal behaviors in general you can it’s called ethologists. Or if you want to study a specific animal like a whale you can it’s called a cetologist. Another thing is if you want to specialize in mammals it’s called a mammalogists. Zoology is a good preparation for a number of other careers. Like a veterinarian, animal caretaker, or park ranger something that involves interaction with animals. With that degree it could also lead you to very different careers like pharmacy, dentistry, or journalism. This job has multiple routes you can take who knows maybe you find out this job is not for you and you want to change it would not be that hard. In www.environmentalscience.org or www.bls.gov it says that the salary for a Zoologist can average from 50,000 to 80,000. To get up to that amount you could do what I said before and specialize in a certain type of branch in zoology by doing this you could get better pay. A zoologist could also get m ore money if they work for the right people. For instance if they got on board with the government they would be getting up to 56,000 to 60,000 or a University which pays up to 60,000 depending on how good ofShow MoreRelatedAnalysis Of The Novel Life Of Pi 1400 Words   |  6 Pagesstory. Pi is a willing, cordial, and volatile kid, reliant on his family for assurance and direction. In school, his essential concerns include keeping his classmates from misspeaking his name and adapting to the extent that he can about religion and zoology. However when the boat sinks, Pi is torn from his family and left alone on a raft with wild creatures. The catastrophe serves as the impetus in his enthusiastic development; he should now get to be independent. Despite the fact that he grieves theRead MoreMontessori: Preparing a Child for the Futur8416 Words   |  34 Pagesacademically for the benefit of the learning child. 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