Global Warming Cause and Effect Essay

Global Warming Cause and Effect Essay Global Warming Cause and Effect Essay It is believed peoples careless use of fossil fuels are responsible for causing Global warming. Environmentalists say people do not realize the serious effects of their own actions. They continue to waste resources and pollute the air despite all the evidence pointing to the effects of such behavior. There are numerous, well known things we do that perpetuate Global Warming. Who would have known that the invention of the car/automobile would eventually contribute to the leading cause of global warming? For years society have been driving cars emitting fossil fuels, using coal powered electricity or heating homes with natural gas and oil. This has caused carbon dioxide and other harmful gasses to be released into Earths atmosphere and environment. Our tree population, for example, has decreased tremendously throughout the years because of increased tree cutting. Has also contributed to more polluted air due to lack of oxygen convertors. (http://environment.about.com/od/faqglobalwarming/f/globalwarming.htm) Peoples lack of concern for conserving its resources will only make Global warming worse. In 2007, scientists met and determined that society has â€Å"very likely† been the main cause of global warming since the 1950s. (http://environment.about.com/od/globalwarming/a/ipcc_report.htm) According to EnvironmentalGraffti.com in 2007 it was determined that the spread of diseases, warming waters resulting in more hurricanes, the probability of increase heat waves,drought, economic consequences, and polar ice caps melting are the top â€Å"5 Deadliest Effects of Global Warming†. (http://www.environmentalgraffiti.com/sciencetech/5-deadliest-effects-of-global-warming/276) Rising sea levels will cause millions who live in coastal regions to lose their homes. Insects are migrating to more warming climates and carrying diseases. It is believed that Malaria still exists in some countries as a direct cause of global warming. In addition, with the continually rising temperature of the ocean there is a threat of an increased chance of more powerful and frequent hurricanes. We all remember hurricane Katrina and the devastation it brought to New Orleans as well as to several other towns and cities throughout the Southeast. With Katrinas aftermath came many economical struggles. People were trying to rebuild their homes, relocate and even find work as many businesses were destroyed. Unfortunately, people are still struggling to rebuild their lives today.Why not try to make changes in our daily lives to prevent devastation such as Katrina? Just as important, the droughts and heat waves that are occurring as a result of global warming are phenomenal. Even here in Arizona, during the monsoon season, we have experienced drought. Though I do not think our drought is as severe as places such as Africa, where they already are experiencing dangerously low water supplies and extreme heat waves. As a result, some speculate that this could cause people to start fighting for water- a necessary resource many take advantage of and assume will be available forever. Half way around the world, however, Sea levels are rising with the increased melting of polar ice caps. When fresh water ice caps melt into the ocean these changes dillute the salty waters. Of course this then affects the ocean and acquatic life which cannot survive without a homeostatic balance of salt content of the ocean. You are probably thinking that losing a little ocean life is no big deal, right? Did you know that the increased global temperature is because the su n can no longer reflect off the tops of the ice caps? When the sun reflects off the ice caps it projects into space. And why is that important? It helps to decrease the temperature in the earths atmosphere which in turn keeps us cooler. So what can we do to help slow down the process of losing our planet? First we need to start by caring. I see people everyday that could walk the 4 blocks to school or work. They choose not to, however. Imagine how much vehicle pollution we could eliminate if everyone were to ask 20 people they know if they could walk instead of driving. If they cant walk them maybe we can use technology wisely and find ways to create vehicles that are environmentally friendly. Another step is to increase recycling. Next time you go to throw that soda can or paper away, remember it is recyclable (and make money from recycling cans). What about disposable diapers? I know they are convenient, but why not give cloths a try? It may take a little extra work and many places around the country have diaper laundry services(but would also provides work and business for others.) Finally, when your kids ask to watch television or play a video game why not limit the amount of time of their use? This will cut bac k on electricity use and theyll benefit doing other, healthier activities. Encourage them to read a book or play outside like we or our parents did. In conclusion, evidence shows that peoples choices in everyday life and their use of technology and resources has undoubtedly increased global warming. We can slow down the negative effects of such choices if everyone does their part. It does not matter how small or big the effort every little bit helps in helping reverse the potential, devastating effects of global warming. Think of the earth as a team and our team wants to win. Help to slow Global warming! Go Green! Works Cited: http://environment.about.com/od/faqglobalwarming/f/globalwarming.htm http://environment.about.com/od/globalwarming/a/ipcc_report.htm http://www.environmentalgraffiti.com/sciencetech/5-deadliest-effects-of-global-warming/276

Data Processing in Big Data Centres Cost Reduction Approach

Data Processing in Big Data Centres Cost Reduction Approach A Cost Reduction Approach for Data Processing in Big Data Centers R. Reni Hena Helan ABSTRACT- The tremendous development in cloud data processing leads to the high load on computation, storage and communication in the data storage centers, which influence the data center providers to spend a considerable expenditure in data processing. There are three features leading to this increased expenditure, ie., job allotment, data positioning and data movement. In this paper, these three features are taken into consideration and an approach for cost reduction for cloud data processing is proposed. I propose a Markov Chain Model to analyze the task completion considering the data transmission and its computation. Keywords: Markov Chain Model, Data Center, Cloud data, Data Positioning, Data Processing. INTRODUCTION In recent years, the outburst of data all over the world has led to the demand of data processing in the data storage centers. This demand further leads to the increase in the cost incurred in the computation and the communication resources. As predicted by Gartner, by 2015, 71% of the data storage center hardware utilization would be from the cloud data processing which will cross around $126.2 billion. So, it is of vital importance to analyze the cost reduction problem in cloud data processing in the data storage centers. Data Center resizing (DCR) has been proposed to reduce the cost involved in data processing by adjusting the number of activated servers through task placement[1]. The Cloud Data Service Architecture mainly consists of distributed file systems which is helpful in distributing the data and its copies all over the data centers for an efficient load balancing and high performance. Some studies focused on reducing the communication cost by taking steps to place data on the servers where the input data exist to solve the remote data loading problem. Even though there were many solutions proposed to solve the above issues, none of the solutions were helpful in providing a cost efficient big data processing due to few disadvantages. First one, being the wastage of resources for the data that is not often accessed. Second, being the transmission costs involved depending on the distances and the type of communication used between the data centers. Not all the data could be stored on the same server because of its high volume; it is a mandatory one to store few data into remote servers that would incur transmission cost. Transmission costs get increased proportionally with the number of communication links involved. To get rid of the above disadvantages, I consider the cost reduction for cloud data processing through a joint optimization approach of task placement and data positioning in the data centers. Every server may have only a few resources needed for each piece of data residing on it. The data will need more resources to carry out with its big data processing tasks. The main aim of this paper is to optimize the data positioning, task allocation, routing and DCR to minimize the overall computation cost involved. The contributions are briefed as follows, 1.This paper considers the cost reduction problem involved with the cloud data processing in the data centers by the joint optimization of data positioning, task allocation and routing. To explain the computation and the transmission involved with the data centers, the Markov Chain model has been used and the task completion time has been derived. 2. For cost reduction, three factors are taken into consideration. The first one is how to place data in servers and the second one is how to distribute the data and the third one is how to resize the data centers to achieve minimum cost operation. II. OTHER RELATED WORKS Cost Minimization in Server The data centers are distributed throughout the world to store huge volumes of data that are accessible to thousands of users. A data center consists of a large number of servers that consume much power. Few Million dollars were to be spent on electricity cost that is a rising problem leading to the increased operation cost. The best known mechanisms proposed that grabbed attention was the DCR that focused on energy management by the data centers. Liu et al.[2] examined the same issue by considering the delay with the network. Fan et. al [3] analyses on how much computing equipments can be hosted within a fixed power budget in a safer and an efficient manner. Data Management The main aspect of data management is the reliability and effective data positioning. Sathiamoorthy et al. [4] proposed a solution based on erasure codes that offered high reliability in comparison with the Reed-Solomon codes. Yazd et al[5] proposed a scheduling algorithm to improve energy efficiency in data centers considering the data locality properties. Data Placement Agarwal et al[6] gave a data placement approach for the geographically distributed cloud services by considering the bandwidth cost, data center capacity, etc. It analyzes the logs based on the data access types and the client locations. All the existing works either focus on the task allotment or on the data placement or on the data management. But this paper takes into consideration, the data positioning, the task allotment and the routing of data systematically. SYSTEM MODEL The geographically distributed data center topology is shown in Fig. 1. with all the data centers containing the same data are connected via switches. There are a set of data centers(D), and each data center d à Ã‚ µ D that consists of a set of servers Sd connected to the switch md à Ã‚ µ M having a local transmission cost of Cl . The local transmission cost Cl will be less than the data center transmission cost Cr. Le the whole system be modeled as a Graph denoted by G=(N,E) where, N is the vertex set that includes all the switches(M) and the servers(Sd) E is the edge set. The weight involved with the edges are represented as, w(u,v)= Cr , if u,v à Ã‚ µ M Cl, otherwise The data stored in geographically distributed data centers are divided into a set of chunks C. Each data chunk c à Ã‚ µ C has a size and its is normalized to the server storage capacity. For each chunk of data, there will be P copies available in the distributed system for the fault tolerance. ÃŽ »c be the average task arrival rate requesting for chunk c. Fig. 1. Data Center topology The task arrival in each server is considered as a Poisson Process. If the task is distributed to a data center where the data chunk does not reside, it will take some amount of time till the data chunk gets transferred to that data center. Each task should be replied with a response time of R. PROBLEM FORMULATION Data Placement and Task allocation constraints The binary variable ysc is used to refer to whether the data chunk c is placed on the server s. ysc takes the value 1 if the chunk c is placed in the server s and it takes the value 0 if the chunk c is not placed in the server s. In any distributed file system for each data, there are P copies of data chunks stored and the data stored in each server cannot go beyond the storage capacity. Any server is termed as an activated one(as), only if there are data chunks stored onto it or else tasks assigned to it. Data Loading Constraints For every data chunk c required by the server s, there are few external or internal data transmissions involved for which a routing procedure is devised. The Graph containing the servers and the switches is divided into three categories, 1. Source Nodes: These are the servers consisting of the data chunks 2. Relay Nodes : These nodes receive data from the source nodes and forward them to the  other nodes based on some routing technique. 3. Destination Nodes: These are the nodes that are receiving the data chunks. Each and every destination node will receive the data chunks only if does not have a copy of it. Cost Reduction The cost involved with the transmission of the data chunks could be minimized by choosing the parameters such as the ysc ,as , ÃŽ »c etc. PERFORMANCE EVALUATION The performance analysis of the joint optimization approach describes that the communication costs decreased if more tasks and data chunks were placed in the same data center. Further increase in the number of servers will not affect the data chunk distribution among them. Increased requests lead to more activated servers and more computation resources and the joint optimization approach tries to lower the server cost. This approach balances between the server cost and the communication cost. When the delay requirement is very small, many servers are activated to provide quality of service. And the server costs decrease as the delay constraints increases. CONCLUSION This paper explains the joint optimization approach of data positioning, task allotment and routing of  data to reduce the overall operational cost involved with the data centers that are geographically distributed. This approach reduced the computational complexity considerably. REFERENCES [1] L. Rao, X. Liu, L. Xie, and W. Liu , â€Å"Minimizing Electricity Cost: Optimization of Distributed Internet Data Centers in a Multi-Electricity –Market Environment,† in Proceedings of the 29th International Conference on Computer Communications (INFOCOM).IEEE,2010, pp. 1-9. [2] Z. Liu, M. Lin, A. Wierman, S.H. Low, and L.L. Andrew, â€Å"Greening Geographical Load Balancing ,†in Proceedings of International Conference on Measurement an Modeling of Computer Systems(SIGMETRICS. ACM, 2011,pp.233-244. [3] X. Fan, W. D. Weber, and L. A. Barroso, â€Å"Power Provisioning for a Warehouse-sized Computer,† in Proceedings of the 34th Annual International Symposium on Computer Architecture (ICA).ACM, 2007, pp.13-23. [4] M. Sathiamoorthy, M. Asteris, D. Papailiopoulos, A. G. Dimakis, R. Vadali, S. Chen, and D. Borthakur, â€Å"Xoring elephants: novel erasure codes for big data,† in Proceedings of the 39th International Conference on Very Large Data Bases, ser. PVLDB’13. VLDB Endowment, 2013, pp.325-336. [5] S. A. Yazd, S.Venkatesan, and N. Mittal, â€Å"Boosting energy efficiency with mirrored data block replication policy and energy scheduler,† SIGOPS Oper. Syst. Rev., vol.47, no.2, pp.33-40, 2013. [6] S. Agarwal, J. Dunagan, N. Jain, S. Saroiu, A. Wolman, and H. Bhogan, â€Å"Volley: Automated Data Placement for Geo-Distributed Cloud Services,† in the 7th USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2010,pp.17-32. [7] S. Govindan, A. Sivasubramaniam, and B. Urgaonkar, â€Å"Benefits and Limitations of Tapping Into Stored Energy for Datacenters,† in Proceedings of the 38th Annual International Symposium on Computer Architecture (ISCA). ACM.,pp.341-352. [8] P. X. Gao, A. R. Curtis, B. Wong, and S. Keshav, â€Å"It’s Not Easy Being Green, â€Å" in Proceedings of the ACM Special Interest Group on Data Communication(SIGCOMM), ACM,2012.pp.211-222. [9] J. Cohen, B. Dolan, M. Dunlap, J. M. Hellerstein, and C. Welton, â€Å"Mad Skills : new analysis practices for big data,† Proc. VLDB Endow. Vol.2, no.2, pp. 1481-1492, 2009. [10] H. Sachnai, G. Tamir, and T. Tamir, â€Å"Minimal cost reconfiguration of data placement in a storage area network, â€Å"Theoretical Computer Science, vol. 460.pp.42-53, 2012.

Tinnitus - Have You Experienced It Yet? Essay -- Biology Essays Resear

Tinnitus - Have You Experienced It Yet? Can you ever recall hearing a strange annoying noise in your ears that remained constant for days or seemed recurrent? If you answered yes to this question then you may be one of the 50,000,000 individuals in the U.S. who suffer from tinnitus. Almost everyone at one time or another has experienced brief periods of mild ringing or other sounds in the ear and it is estimated that one out of every five people experience some degree of tinnitus (1). The presence of tinnitus is a very common and annoying occurrence that affects about 17% of the general population and 33% of the elderly (2). With such statistics, could it be that we are all most likely destined to become a victim of tinnitus? Tinnitus is the internal perception of sound when there actually is no external sound present. It is a symptom that can occur in either or both ears or can seem as if it is coming from somewhere in the head. Tinnitus can sound like a bell, whistle, roar, screech, hum, crickets, tone, something else, or any combination of the above. It can be continuous, pulsatile, or can fluctuate in character or loudness (3). Tinnitus is classified into two forms: objective and subjective. Objective tinnitus, the rarer form, consists of a sound that may be audible to people other than the sufferer. The noises are usually caused by vascular diseases or abnomalies, repetitive muscle contractions, or inner ear structural defects. The sounds are heard by the sufferer and are generally external to the auditory system. Benign causes, such as noise from the jaw joint, openings of the Eustachian tubes, or repetitive muscle contractions may be the cause of objective tinnitus. It can be an early sign of increased intracrania... ...on sufferers are so seriously debilitated that they cannot function on a day-to-day basis. The upsetting notion of tinnitus is that it can strike people of all ages and, for most, it does not go away. Tinnitus is just a nuisance for some, but for others it is a stressful, life-altering condition (7). WWW Sources 1) Tinnitus FAQ- Discovering and Understanding http://www.cccd.edu/faq/tinnitus/discover.html 2) Tinnitus Information Network http://members.aol.com/MyTinnitus/English/definition.html 3) Tinnitus & Loudness Sensitivity Center http://www.earaces.com/tinnitus.htm 4)Tinnitus Relief Center: FAQ http://www.tinnitusrelief.com/faq3.html 5) Tinnitus Relief Center: About Tinnitus http://www.tinnitusrelief.com/abouttinnitus.html 6) ASHA Brochure - Tinnitus 7) American Tinnitus Association http://home.teleport.com/~ata/FAQS~RC.HTM

Abortion in Hemingways Hills Like White Elephants Essay -- Hills Like

Abortion in Hemingway's Hills Like White Elephants The story "Hills Like White Elephants" is a conversation between a young woman `Jig' and an American man waiting for a train at a station in Spain. The author never names the topic of their discussion but as their dialogue progresses; it becomes evident that Jig is pregnant. The man wants Jig to abort the unborn child but she is unconvinced and wants to become a mother. Hemingway has brilliantly written the story's dialogue which "captures the feel of a private conversation while at the same time communicating the necessary narrative background" (O'Brien 19). At the end of the story, it is unclear as to what decision has been made; however, Hemingway gives the reader several clues regarding what Jig feels, and what she wants to do. Jig's private thoughts are illuminated by Hemingway's description of the setting, the character, and the conflict. Stanley Renner suggests that, as a result of the couple's discussion, "Jig has become able to make a more clear-sighted estimation, and perha ps a better choice, of men" Wyche(59). The couple's inability to communicate effectively their true thoughts and emotions makes their dialogue very appealing. The story examines the gender differences and miscommunications as they influence the decision whether to abort the unborn child or not (Smiley). In his book on Hemingway, published in 1999, Carl P. Eby points out that "[f]or the past two decades, Hemingway criticism has been dominated by a reconsideration of the role of gender in his work" (Bauer 125). Hemingway's characters in the story represent the stereotypical male and female in the real world, to some extent. The American is the typical masculine, testosterone-crazed male who just ... ...s'. The Hemingway Review, 22 (1) (Fall 2002): 56-71. EBSCOhost. Renner, Stanley "Moving to the Girl's Side of `Hills Like White Elephants'." The Hemingway Review, 15 (1) (Fall 1995): 27-41. As Rpt. in Wyche, David "Letting the Air into a Relationship: Metaphorical Abortion in `Hills Like White Elephants'. The Hemingway Review, 22 (1) (Fall 2002): 56-71. EBSCOhost. Eby, Carl P. "Hemingway's Fetishism: Psychoanalysis and the Mirror of Manhood. Albany: State University of New York Press. As Rpt. in Bauer, Margaret D. "Forget the Legend and Read the Work: Teaching Two Stories by Ernest Hemingway. College Literature, 30 (3) (Summer 2003): 124-37. EBSCOhost. Burroway, Janet. Writing Fiction: A Guide to Narrative Craft. 6th ed. New York: Longman, 2003. As Rpt. in Rankin, Paul "Hemingway's `Hills Like White Elephants'." Explicator, 63 (4) (Summer 2005): 234-37.