Use of Distributed Computing in Processing Big Data

Use of Distributed Computing in Processing Big Data Distributed Systems is an upcoming area in computer science and has the ability to have a large impact on the many aspects in[G1] the medical, scientific, financial and commercial sector. This document will provide an overview of distributed systems along with their current applications and application in big data. The most commonly used definition for a distributed system is, a system comprised of geographically dispersed computing components interacting on a hardware or software level [1].The rise in interest for distributed computing can be attributed to two major factors. The first factor is the creation and advancements in local and wide area networks which allow for large amounts of data to be transmitted over great distances in a short period of time [2]. The second factor is the new craze of the Internet of Things (IoT), where nearly every physical device manufacture having some sort of internet connectivity allowing for the possibility of tens of billions of devices that are able to interact. This large network of interconnected devices can be utilised to compute large amounts of data in a fraction of the time it would currently take to process. Characteristics of a Distributed System Heterogeneity Heterogeneity refers to the ability for the system to operate on a variety of different hardware and software components. This is achieved through the implementation of middleware in the software layer. The goal of the middleware is to abstract and interpret the programming procedural calls such that the distributed processing can be achieved on a variety of differing nodes [3]. Openness The openness of a distributed system is defined as the difficulty involved to extend or improve an existing system. This characteristic allows us to reuse a distributed system for multiple functions or to process varying sets of data. Concurrency Concurrency refers to the systems ability to handle the access and use of shared recourses. This is important because if there is no measure implemented it is possible for data to get corrupted or lost by two nodes making different changes to the same resource such that the system can carry this error through different processes causing an incorrect result. One way to counteract these errors is to implement a locking mechanism making a node unable to access a resource whilst it is being used by another node.[G2][G3] Scalability Scalability is one of the major characteristics that effectiveness of a distributed system, it refers to how easily the system can adapt to a changing size. This is due to the volatile nature of computers, such that a device is prone to leaving and joining the system at will. This volatility is caused by computers powering down, or unstable networks causing connectivity issues.[G4][G5] One factor that affects scalability is the degree at which the system is centralised. This is due to if a system relies on a centralised component or process (e.g. a central [G6]server), the more nodes that try to communicate or use this component, the more likely it is that there will be a bottleneck at this point in the system.[G7] Fault Tolerance Due to a distributed system having many computers comprised of different aged hardware, it is very likely for a part to fail in such a way that a node can no longer operate. Fault Tolerance is the ability for the system to handle such failures, this is achieved by using recovery and redundancy. Recovery is [G8]where a component will act in a predictable, controlled way if it relies on a component. Redundancy is where crucial systems and processes will have a backup that takes over if a system fails.[G9][G10] Transparency Transparency in a distributed system refers to the idea that the user perceives that they are interacting with a whole quantity rather than a collection of cooperating components. Transparency can be split into the following 8 sub-characteristics defined in Table 1. Table 1 Different forms of transparency in a distributed system [2]. Transparency Description Access Hide differences in data representation and how an object is accessed Location Hide where an object is located Relocation Hide that an object may be moved to another location while in use Migration Hide that an object may move to another location Replication Hide that an object is replicated Concurrency Hide that an object may be shared by several independent users Failure Hide the failure and recovery of an object The Internet The internet is the largest and most well-known decentralised distributed system ever created. It is currently comprised of millions of geographically distributed interconnected web servers that can communicate autonomously with each other and the billions of endpoint nodes [4]. The internet is constantly growing with more website and nodes added every day. One of the major factors contributing to the growth of nodes is the boost in IoT or smart devices. ATM Machines ATM machines are an example of a centralised distributed system that has been implemented globally. This is a centralised system because each ATM machine will [G11]only communicate with its bank central server. Centralisation is enforced as a measure to increase the security of the sensitive information stored on the banks databases[G12]. Each banks ATM network has the ability to communicate with another banks server [G13]such that a user can withdraw money from any ATM around the world. Botnets Botnets are an example of a malicious distributed system. They are can either be operated by a central server or based off a peer-[G14]to-peer network. A botnet is comprised of a collection of zombie machines which have been infected with malware allowing the bot master to control it and a command and control server whose role is to control the zombie computers allowing the zombie machines to execute any command that the botmaster desires. Data is any accumulation of facts and statistics to be analysed or referenced. Big data is most commonly defined as extremely large sets of data, both structured and unstructured, [G15]that can be analysed to reveal patterns and trends. This data is sufficiently complex or large enough that conventional data processing processes and applications are unable to deal with it [5]. Crowdsourcing is not a new idea in the software world, it is not an uncommon sight to see a developer pose a task to the masses and have someone else complete the task. This is mostly done free of charge. A similar concept is starting to be applied to big data, where researchers and institutes have started to crowdsource data for people to process[G16]. Currently, most data that has been crowdsourced is [G17]for scientific or medical research. A factor that contributes to the success of data processing on distributed systems is the relatively low cost of [G18]transferring data compared the cost incurred from doing the data processing internally [6].[G19] Play to Cure: Genes in Space Play to Cure: Genes in Space is a mobile gaming application developed by Cancer Research UK. Its main purpose is to allow the general public to process large amounts of data for the scientist at Cambridge University.[G20] The data is processed by the user controlling a spaceship to try and collect as much Element Alpha as possible. What the user is not aware that the placement of Element Alpha directly correlates to a singular piece of plotted data [7]. In the first month alone the application has managed to analyse 1.5 million data sample. To process a similar number of samples the research team achieve a similar amount of samples processed, it would take the research team 125,000 man hours [7]. Whilst it is a rudimentary implementation of a distributed system, Play to Cure: Genes in Space is a successful implementation and can show how important large distributed systems can processing big data. [emailprotected] [emailprotected] is currently the largest distributed computing program and was created by the SETI (Search for Extraterrestrial Intelligence) Institute and hosted out at UC Berkeley. It currently has approximately 3 million active users donating their computers[G21][G22] spare processing power to process data obtained from SETIs radio telescopes [8]. Since [emailprotected] is a voluntary program, each node needs to be able to process data in a way that the user is not negatively affected and choose to leave the program. This is achieved through the application processing data when it is detected that a machines CPU is [G23]idling [9]. As of the 10 March 2017, the [G24][emailprotected] program has come close to processing 18 years worth of data from the Arecibo Observatory radio telescope [10]. This achievement displays how easily large amounts of data can be processed by large distributed systems. There are endless possibilities when it comes to the potential applications[G25] for distributed systems. Processing big data is a lucrative market, this might cause a lot of large multinational organisation to try and utilise their own hardware to implement their own personal distributed system to process the terabytes of data that they can extrapolate from their Enterprise resource planning (ERP) software and from data obtained from the media and other sources. Stock trading is a cut throat industry, and the ability to predict market trends faster than a competitor can allow a particular firm to make millions of dollars. It is plausible for large firms to implement their own distributed system to analyse previous market trends and current global and local affairs to predict the upcoming state of the market. In the future, distributed systems will allow for big data to be processed potentially at a near real-time timeframe. This document has outlined how distributed systems can assist in the faster and more effective processing of big data. References [1]H. Karatza and G. Theodoropoulos, Distributed Systems Simulation, Simulation Modelling Practice and Theory, vol. 14, no. 6, pp. 677-678, 2006. [2]M. van Steen and A. Tanenbaum, A brief introduction to distributed systems, Computing, vol. 98, no. 10, pp. 967-1009, 2016. [3]G. Coulouris, J. Dollimore, T. Kindberg and G. Blair, Distributed systems, 1st ed. Harlow, England: Addison-Wesley, 2012, pp. 16-25. [4]G. Coulouris, J. Dollimore, T. Kindberg and G. Blair, Distributed systems, 1st ed. Harlow, England: Addison-Wesley, 2012, pp. 8-9. [5]P. Grover and R. Johari, BCD: BigData, cloud computing and distributed computing, 2015 Global Conference on Communication Technologies (GCCT), 2015. [6]J. Gray, Distributed Computing Economics, Queue, vol. 6, no. 3, pp. 63-68, 2008. [7]O. Childs, Download our revolutionary mobile game to help speed up cancer research, Cancer Research UK Science blog, 2017. [Online]. Available: http://scienceblog.cancerresearchuk.org/2014/02/04/download-our-revolutionary-mobile-game-to-help-speed-up-cancer-research/. [Accessed: 24- Mar- 2017]. [8]B. Marr, Big Data: Using SMART Big Data; Analytics and Metrics To Make Better Decisions and Improve Performance, 1st ed. Wiley, 2015, pp. 208-209. [9]E. Korpela, D. Werthimer, D. Anderson, J. Cobb and M. Leboisky, [emailprotected] distributed computing for SETI, Computing in Science Engineering, vol. 3, no. 1, pp. 78-83, 2001. [10][emailprotected], Setiathome.berkeley.edu, 2017. [Online]. Available: https://setiathome.berkeley.edu/. [Accessed: 24- Mar- 2017]. [11]D. Anderson, J. Cobb, E. Korpela, M. Lebofsky and D. Werthimer, [emailprotected]: an experiment in public-resource computing, Communications of the ACM, vol. 45, no. 11, pp. 56-61, 2002. [12]S. Khan, The Curious Case of Distributed Systems and Continuous Computing, IT Professional, vol. 18, no. 2, pp. 4-7, 2016. [13]E. Albert, J. Correas, G. Puebla and G. Romà ¡n-Dà ­ez, Quantified abstract configurations of distributed systems, Formal Aspects of Computing, vol. 27, no. 4, pp. 665-699, 2014. [14]S. Vinoski, Rediscovering Distributed Systems, IEEE Internet Computing, vol. 18, no. 2, pp. 3-6, 2014. [15]I. Foster, C. Kesselman, J. Nick and S. Tuecke, Grid services for distributed system integration, Computer, vol. 35, no. 6, pp. 37-46, 2002. 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Estimating the % purity of marble by back titration method Essay

The values for the % purity of marble that I have calculated lie in a close range, however there is a significant difference, of 4.74% in the purity estimated between the 2 individual values. Nonetheless, the deviation is covered by the uncertainty of à ¯Ã‚ ¿Ã‚ ½4.84%. However, certain errors have occurred that have caused this deviation in the final results. REASONS FOR DEVIATION Random Errors 1. Loss of marble after weighing: After weighing out the marble, there is a small loss in mass in transferring the mass from the butter paper used for weighing as a small quantity of the fine powdered marble sticks to the butter paper and does not react with the HCl at all. Also, while transferring the crushed CaCO3 some particles could have also been blown away by the wind. Thus the disparity in the masses used could account for the different values obtained. 2. HCl left in the pipette: being a manual instrument, there is no definite way of making sure that each and every drop of the acid that was measured has been transferred to the conical flask, this reduces the volume of acid used and such a change in any one of the reading will directly affect the value of the % purity. 3. Air bubbles in pipette dispenser: For this experiment, due to the corrosive nature of the concentrated acid, a pipette dispenser was used to measure out the HCl. However there were some air bubbles that were trapped that cause an error in the volume of HCl that is not accounted for by the absolute uncertainty of the pipette. Systematic Errors 1. Unevenly crushed marble: The CaCO3 provided was not evenly crushed; some was still in larger chunks while the rest was finer granules. This non-uniformity in the texture of CaCO3 also creates a different surface area for the acid to react with and this can be the cause of the differing values of purity. 2. Excess NaOH added in titration: While titrating the HCl and the HCl + CaCO3 solutions the usage of the phenolphthalein indicator causes an error in the amount of alkali added as the titration is only stopped once the colour changes to a pale pink, this firstly is a very qualitative and subjective instruction and also the indicator on turning pink indicate a slight alkalinity. Hence, the actual neutralization point was left behind 1-2 drops back. 3. Unevenly distributed impurities: CaCO3 is found naturally as marble in the earth. Thus, being a natural stone the impurity that it will contain will be randomly distributed and due to this unevenness, the purity will vary with every sample. IMPROVEMENTS TO PREVENT AFOREMENTIONED ERRORS 1. Keep fans and windows shut and cover CaCO3 while transporting: By doing this one can minimize the loss of particles and thus reducing the uncertainty. 2. Carefully using apparatus to avoid human errors: By practice and careful usage one can eliminate the errors caused by the air bubbles in the pipette, errors in transfer and parallax. 3. Marble should be crushed evenly: the CaCO3 should all be of the same texture so that the surface area is kept controlled and this will reduce the disparity in the purity values that have been caused by the differing surface areas. 4. Using another indicator that prevents uncertainty in the exact point of neutralization: If a solution like Universal Indicator is used then the exact point when the titration is complete can be easily identified and thus by eradicating this error, one can aim to reduce the disparity observed in the readings.

The Poverty Of The United States - 1727 Words

Poverty defined by the American Heritage Dictionary is â€Å"lack of the means of providing material needs or comforts† (Hirokazu Yoshikawa, 2012). Poverty in the United States is an issue that is often times overlooked because the focus of poverty is on developing and struggling countries. People often think America does not experience poverty because it is such a thriving country. The problem with this is that America is indeed struggling with poverty: â€Å"there are currently 488 counties in America where twenty percent of the population has lived below the poverty line for the past thirty years or more† (Clyburn, 2014, p. 1). Utilitarian ethics supports the idea to do the greatest good for the greatest number of people. In this case, the greatest good for the greatest number of people would be to stop poverty in the United States. The causes of poverty include: lack of jobs, lack of education, and overpopulation. The reasons are linked to government policies and th e way the officials handle situations. Poverty in America may not be as bad as some countries, yet it is time people focus on solving this before it gets out of hand. It is necessary to look at utilitarian ethics when talking about poverty because the greatest good is that poverty is fixed. Solving poverty would relieve many issues within the United States. Crime rates will lower, diseases and illnesses will decrease, and the world will be a better place. To solve poverty it is important to look at the causes. SomeShow MoreRelatedThe Poverty Of The United States1548 Words   |  7 Pagescitizens in poverty has risen. Several organizations have been set up to help those who suffer from poverty and provide their everyday needs. There are always ways where a community can help eliminate the amount of people suffering poverty. Government has an influence on how much money flow there is in the United States such as the FED, which was created to help maintain a stable monetary and financial system and control the money supp ly. People themselves can also help from falling into poverty, butRead MorePoverty Of The United States1408 Words   |  6 PagesWhen people hear the word poverty many people think of the bad connotations that come with it like, smelly homeless people that are crackheads and disease holders. Some people may even think they are uneducated or not hard working enough and rather ask for money instead of trying to get a job. Although a small portion of that may be true to some homeless people due to addictions on drugs and the toll it takes on their lives. The majority of homeless people are either veterans or immigrants, who findRead MorePoverty in the United States755 Words   |  4 PagesPoverty in the United States is getting in inferior quality every day and nothing is being done about it. Many people who want to help the poor, but no one knows exactly how to help them. A primary reason for people not taking action is because of lack of information that is provided about issues o n poverty. Poverty is defined as the state of one who lacks a usual or socially acceptable amount of money or material possessions. According to the U.S. Census Bureau data released Tuesday September 13thRead MorePoverty Of The United States Essay1369 Words   |  6 PagesPoverty within the United States is defined as â€Å"having an income below a federally determined poverty threshold. † Poverty thresholds were developed by the United States government in the 60s. Over time these thresholds are adjusted to account for inflation; it is typical to adjust the poverty threshold levels annually. They represent the government’s estimate of the point below which a family has insufficient resources to meet their basic needs. Any family with less income than that establishedRead MoreThe Poverty Of The United States1531 Words   |  7 Pagessuch dialog, topics on the increasing and rather consistent levels of poverty in some regions in America are touched on as well. Pover ty is defined as a condition where one’s basics needs for food, clothing, and shelter are not being met (What Is Poverty? â€Å"). From sea to shining sea, more than 15 percent of the American population live in poverty, a total of people over 46 million. Many who live in poverty within the United States live in areas that were once thriving from the country’s economic growthRead MoreThe Poverty Of The United States Essay1385 Words   |  6 Pages The Character of Poverty in America Poverty has always been a key factor in United States History. Ever sense Americas birth there have been groups affected by poverty, but the forms of the poverty that affected these groups have changed as well as the nature of poverty itself in the USA. The abolition of slavery, the forced assimilation of native Americans, and mass immigration changed character of poverty within the united states change due to an evolution from agriculture to industry and a changeRead MoreThe Poverty Of The United States1746 Words   |  7 PagesWhat is poverty? A question most Americans will not have to think twice before answering. Poverty is, of course, simply a lack of money. The views of a specific person will defer when politics or morals are introduced, however, the idea stays the same. Those in poverty are there because they have less money than what has been decided to be livable. Poverty has changed significantly over the last two hundred years in the United States, and yet, the measuremen t has hardly changed since it was createdRead MorePoverty in the United States1061 Words   |  5 PagesThe Background of Poverty in America In the United States, there are about more than forty-six million people living in impoverished conditions today. Poverty is a major conflict issue in this country amongst people who are part of the lower class because American families always had a hard time making ends meet, even before the Great Recession began. Living in poverty puts them at a disadvantage because they have to choose between necessitates like health care, child care, and food in order toRead MorePoverty Of The United States1475 Words   |  6 Pages â€Æ' Poverty in the United States is defined as a social problem. As outlined in the text, a social problem is â€Å"a condition that undermines the well-being of some or all members of a society and is usually a matter of public controversy†. It is easy to see that there is a large economic divide in the United States, but with only a small percentage of people in the highest income stratification and the vast majority struggling to get by, the majority of United States citizens agree that there is tooRead MorePoverty Of The United States1529 Words   |  7 PagesPoverty is an important issue in the United States. In fact, child poverty in the US is at its highest point in 20 years. [Flores Lesley, 2014] The poor are at a disadvantage, because they have an unfulfilled right to a good education. A majority of children attending public schools come from low-income families. It is hypothesized that a low household income correlates with poor achievement in school. A solution to poverty is for everyone to have a good education so everyone can be equally

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