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.

Cumberland Metal Industries Essay

In its initial evaluation, the company believes that the new product, curled metal cushion pads, has a potential for increasing the company’s sales and profit and for diversifying the products it offers. Early demands for the product appear to be good, however an in-depth analysis of the marketing aspect is necessary before the company can introduce the product to the market. Situational Analysis Based on the two trials made, it is clear that the company is in possession of a product that is better than any of those currently available in the market. The question only is how much better this product is than the others already available. Based on the first trial performed, the product is 25% more efficient than its closest competitor in terms of feet of piles driven per hour. It also increases the efficiency of the operations by requiring less time for set changes of about 80%. Each set of the product also performs nineteen times better than said competitor. In the second trial, the product maintained its previous performance as far as feet driven per hour time for set changes is concerned. It surpassed its previous performance by performing forty-nine times better than its competitor. The efficiency and effectivity of the product translates to other forms of savings on the part of the user. First, the contractor or builder will decrease the cost of rental of the equipment used in the construction by at least 25%. If the workers’ compensation is computed based on the number of hours worked, the contractor or builder will incur savings amounting to 25% of the cost of labor. Aside from its efficiency the product can also decrease, on the part of the workers, the health risk usually associated with the use of asbestos and decrease employers’ expenses relating to workers’ health insurance in the long run. Actual figures of demand for the product are hard to calculate. Based on the assumptions made by Mr. Simpson that the construction industry will need to drive 290 million to 390 million feet of piles annually, the total demand for the product annually is around 174,000 (290,000,000/10,000 x 6) to 324,000 (390,000,000/10,000 x 6) pads annually. To penetrate the market, the company will need to convince the key players of the construction industry that it should use the product. The approval of the architecture/ consulting firms, engineering/construction contractors and independent contractors are a must since they are the ones that will actually use the products. Independent contractors could be targeted since they are more willing to try new products that can increase their profits. A positive review from this group can convince bigger firms to use the product. The approval of pile hammer manufacturers and pile hammer distributing/ renting companies will be good but not necessary. These two groups know that the use of the product will decrease their profits so they have motivation to not recommend the products to their customers. Pile hammer distributing/ renting companies can provide an efficient distribution channel and reduce the company’s distribution cost since the mechanism is already in place, but not having the approval of this group is not essential since other methods of distribution are available to the company. Once amount of demand for the product is large enough this group can later decide to offer the product to their customers. Recommendations Initially, the pricing used by the company should be based on the data provided by the first test since it is more conservative. It can gradually increase its prices if the performance of the second trial is confirmed by actual practice. Based on the first trial, the company should charge $167 per pad. This price is computed by multiplying the cost of each set with the number of sets required ($50 x 20 pads) and dividing the result with the no. f pads of curled metal cushion required (1000/6). By using this price, the consumer will incur the same amount of cost for cushion pads in a project whether it uses asbestos or the curled metal cushions; it is only the amount of cushion pads used that will vary. However, this price does not charge consumer for the additional benefits provided by the product, such as 25% decrease in the cost of renting equipment and wages of laborers. This will be the selling point of the product upon its introduction. It will be important for the company to concentrate on building the confidence of the key players of the industry, instead of maximizing the profits in the first few years of introducing the product. Marketing campaigns should be focused towards convincing the architecture/ consulting firms, engineering/construction contractors and independent contractors to use the products. It could try to market the products directly to these groups since renting companies may be hesitant to distribute products that will decrease the profits generated by the services they offered. Moreover, most contractors used their own pile hammers instead of renting, so distribution through these renting companies has limited reach. Independent contractors should be given particular attention, as far as efforts at direct selling is concerned, since these groups are more likely to try new methods that decreases their cost and increases their bottom line than the more established firms. When the reputation of the product is established, it can decide whether it will continue its direct approach or use the services of pile hammer distributing/ renting companies. For the reasons stated above, the demands of the Colerick Foundation Company should be accepted by the company since this would be the first of the company’s direct sales. In summary, I would like to recommend that the company sell its curled metal cushion pads for $167 each. The company should first focus in building the reputation of its product because of its innovative quality. One way of doing so is convincing the experts that it is better than anything else present in the market.

Corporate Finance - 69408 Words

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Genetically Modified Crops Hope For Developing Countries

Luis Herrera-Estrella and Ariel Alvarez-Morales, authors of â€Å"Genetically modified crops: hope for developing countries?†(2001) argues for genetically modified food to be grown in developing countries in an attempt to alleviate starvation and assist those countries in joining the developed world. The author supports this by immediately identifying and addressing prominent counter-arguments, displaying a localized need of the technology (soon to be globalized), and examining how harshly GMOs are criticized with specific comparisons to deforestation and other climate change variables. The authors intended audience is educated voter populations and politicians, in order to make an impact on the GMO debate. Herrera-Estrella s and Alvarez-Morales article serves its purpose, to educate and persuade those involved in the politically charged topic with a researched and defended opinion, one they hope will carry into the polling place and be spread further than its scientific pu blishing s beginnings. The article manages to persuade by structuring the discussion in order to narrow the reader s thoughts, citing research supporting their thought process, using the reader s sense of global community, and a both humanitarian and scientific mindset on the issue. The structure of the article is truly well thought out. 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The author supports this by immediately identifying and addressing prominent counter-arguments, displaying a localized need of the technology (soon to be globalized), and examining how harshly GMOs are criticized with specific comparisons to deforestation and other climate change variables. The authors intended audience is educated voter populations and politicians, in order to make an impact on the GMO debate. Herrera-Estrella s and†¦show more content†¦This ordering of arguments allows the reader to understand the current situation from which the authors speak, and further grasp the concepts the authors distill into their argument. The article uses the previously discuss ed introduction to create a strong background of the debate for the foreground that is the argument presented therein. This lead-in is taken advantage of by creating a sense of urgency for the decision. â€Å"Over the next 50 years, humankind s greatest challenge will be to ensure sufficient food production on a global scale.† and â€Å" †¦ project the world s population to be 9.3 billion in 2050 – 400 million more than previously estimated.† These statistics promote the need of GM crops and technology in developing countries, facilitating the article s