Cloud Computing – “Whats the Buzz? - Ask, Find, Discuss about ...

[ THE GOLDEN CLOUD ]“From CapEx to OpEx”Parul SaxenaCloud Computing transforms the financialparadigm of an enterprise. Cloud Computingshifts the CAPEX (Capital Expenditure) to anOPEX (Operating expenditure) model by useof operational expenses rather than capitalassets and the treatment of operatingstatements rather than balance sheetmanagement.Adopting the Cloud Computing paradigmseeks to make more money (increaserevenues) while driving capital costs downthrough greater efficiencies of workingcapital and OPEX changes. As per IBM's CTOfor Cloud Computing, Kristof Kloeckner, cloudcomputing reduces IT labor costs by up to50%, improves capital utilization by 75% andreduces provisioning from weeks to minutes.However what is the primary motivation ofany firm’s financial resource? It is costreduction.How does cloud computing efficientlyachieve cost reduction?An article written by Michael Amburst, ”TheView of Cloud Computing” answers thisquestion through the following three cases:Case One‐ Demand for a service varies withtime. If a conventional data center isprovisioned for its peak then it would beunderutilized on other days. Instead, cloudcomputing lets an organization pay by thehour for computing resources, potentiallyleading to cost savings, even if the hourly rateto rent a machine from a cloud provider ishigher than the rate to own one. As per theexample taken by Michael Amburst, if anenterprise’ service has a predictable demandwhere at peak the requirement is 500 serverswhile at trough only 100 are required and theaverage utilization over a whole day is 300servers, the actual cost per day is 300 × 24 =7,200 server hours; but since it must beprovisioned for the peak of 500 servers, wepay for 500 ×24 = 12,000 server‐hours, afactor of 1.7 more. Therefore, as long as the“pay‐as‐you‐go cost” per server‐hour overthree years (usual amortization time) is lessthan 1.7 times the cost of buying the server,utility computing is cheaper thanconventional data centers.Case Two‐ Forecasting of unknown demand.For example a startup meets invariabledemand. Initially there might be a highdemand but it might gradually wean away. Asecond scenario could be unforeseendemand. To understand that by taking ahypothetical example, we can divide theusers in two categories: active users anddefectors (those who abandon the site or areturned away from the site due to poorperformance).Assuming that 10% of users are lostpermanently due to under‐provisioning inpeak hours.If we have 400 servers with each havingcapacity of 1000 users, total capacity that canbe handled is 400000 users .But with duepopularity if it has 500000 hits in 1st hourthen as per our assumption 10000 users arelost (permanently) with 4,90,000 users left.If in next hour, we have 2,50,000 new andunique users then as 10,000 can still beaccommodated rest 2,40,000 are23