19.優格Yogurt 20.醋Vinegar

中研院八年完成—排毒最強的食物



1.地瓜Sweet Potato

所含的纖維質鬆軟易消化，可促進腸胃蠕動，有助排便。最棒的吃法是烤地瓜，連皮一起烤，一起吃掉。



2.綠豆Mung Beens

具清熱解毒，除濕利尿，消暑解渴功效，多喝綠豆湯有利排毒消腫，煮的時間不宜過長，以免有機酸、維生素受到破壞而降低作用。



3.燕麥Oats

能滑順通便，促進糞便體積變大，水分增加，配合纖維促進腸胃蠕動，發揮通便排毒的作用，打成汁來喝，可加入蘋果、葡萄乾，是不錯的選擇。



4.薏仁Job’s Tears

可促進體內血液循環，水分代謝，發揮利尿效果，有助於改善水腫型肥胖，將薏仁煮熟後加少許的糖，是肌膚美白的天然保養品。



5.小米Millet

不含麩質，不會刺激腸道壁，容易被消化。小米粥很適合排毒，有清熱利尿的效果，營養豐富，也有助美白。



6.糙米Brown Rice

具吸水、吸脂作用，能整腸利便，有助於排毒。每天早餐來一杯糙米豆漿或糙米粥，是不錯的排毒方法。



7.紅豆Small Red Beans

紅豆可增加腸胃蠕動，減少便秘，促進排尿。在睡前將紅豆用電鍋墩煮浸泡一段時間，隔天將無糖的紅豆水當水喝，能有效促進排毒。



8.胡蘿蔔Carrot

對改善便秘很有效，也富含β-胡蘿蔔素，可中和毒素，能清熱解毒，潤腸通便。打成果汁+蜂蜜+檸檬汁等飲用有利排毒。



9.山藥Yam

可整頓消化系統，減少皮下脂肪沉積，避免肥胖而且增加免疫功能。以生食最好，將去皮山藥+鳳梨切小塊打汁，有健胃整腸之功能。



10.牛蒡Lapp

牛蒡可促進血液循環，新陳代謝並有調整腸胃功能的效果，所含的膳食纖維可以保有水分，軟化糞便，有助排毒，消除便秘。將其作成牛蒡茶，隨時飲用，長期服用。



11.蘆筍Asparagus

蘆筍含多種營養素，所含的天門冬與鉀有利尿作用，能排除體內多餘的水分，有利排毒。蘆筍的筍尖富含維生素Ａ，料理時可將尖端為漏水面，能保存最多營養素，滋味又好。



12.洋蔥Onion

洋蔥能促進腸胃蠕動，加強消化能力，含有豐富的硫，和蛋白質結合的情形最好，對肝臟特別有益，因此有助於排毒。煮一鍋洋蔥蔬菜湯，加入花椰菜、胡蘿蔔、芹菜等多種高纖蔬果，能分解體內累積的毒素，有助排便。



13.蓮藕Lotus Root

蓮藕的利尿作用，能促進體內廢物快速排出，藉此淨化血液。將其榨成汁+蜂蜜飲用，也可以小火加溫+糖，趁熱時喝。



14.白蘿蔔Radish

白蘿蔔有很好的利尿效果，所含的纖維素也可促進排便，利於減肥。如果想利用蘿蔔來排毒，則適合生食，打成汁或涼拌方式食用。



15.山茼蒿

含豐富的維生素Ａ，可維護肝臟，有助體內毒素排出，將山茼蒿和柳丁、蕃茄、胡蘿蔔、柚子、蘋果、綜合堅果等蔬果一起打成精力湯飲用，是很不錯的選擇。



16.地瓜葉Sweet Potato’s Left

地瓜葉先為質柔細，不苦澀，容易有飽足感，有能促進胃腸蠕動，預防便秘。將地瓜葉洗淨青燙+大蒜+少許鹽油拌勻就是一道美味餐。



17.蘿蔔葉Radish’s Left

含有豐富的維生素和纖維質，有促進食慾、活潑腸道的作用，也能改善便秘。將蘿蔔葉打成汁+少許蜂蜜一起食用，常喝可排毒和保健。



18.川七

葉片含有降血糖的成分，並能治療習慣性便秘，減少身體負擔。把川七、蕃茄、苜蓿芽、黃甜椒、奇異果等蔬果，加上綜合堅果，少許的百香果汁或蘋果醋，混合打成汁飲用。



19.優格Yogurt

含有大量豐富的乳酸菌，可改善便秘，穩定腸胃，原本積存在腸道的毒素，也會因為乳酸菌的作用而變得容易排出，最好早餐空腹前吃，利用優格增加飽足感，減少早餐的攝食量。



20.醋Vinegar

有利於人體的新陳代謝，可排出體內的酸性物質而消除疲勞，還有利尿通便的效果，每天早晚用過餐後，各喝一次稀釋過的醋，適量飲用，有助健康。

人到達一個年齡，清楚甚麼該要，甚麼不該要，是一種智慧。

A guy is 70 years old and loves to fish.
一個70歲的老先生喜歡釣魚。

He was sitting in his boat the other day when he heard a voice say, 'Pick me up.'
一天，他坐在船上釣魚的當下聽到一個聲音說：「把我拿起來。」

He looked around and couldn't see anyone.
他四處張望，卻四下無人。

He thought he was dreaming when he heard the voice say again, 'Pick me up.'
當他又聽到「把我拿起來」時，他以為是他的幻覺。

He looked in the water and there, floating on the top, was a frog.
他往水中定神一看，那裡正有一隻青蛙浮在水面上。

The man said, 'Are you talking to me?'
老先生問道：「你在跟我說話嗎？」

The frog said, 'Yes, I'm talking to you.'
青蛙回道：「對，就是我啊！」

Pick me up then, kiss me and I'll turn into the most beautiful woman you have ever seen.
將我拿起來，吻我，我就會變為你今生所見最漂亮的女人。

I'll make sure that all your friends are envious and jealous because I will be your bride!'
我確定你的朋友是既羨慕又嫉妒，因為我即將成為你的新娘。

The man looked at the frog for a short time, reached over, picked it up carefully, and placed it in his front pocket.
老先生凝視青蛙片刻後，伸出手，很小心地托起青蛙放入他前面的口袋裡。

The frog said, 'What, are you nuts? Didn't you hear what I said? I said kiss me and I will be your beautiful bride.'
青蛙說：「怎麼，你瘋啦？你沒聽我說嗎？我說，吻我，然後我就會成為你美麗的新娘。」

He opened his pocket, looked at the frog and said,
'Nah, at my age I'd rather have a talking frog.'


他撥開他的口袋，看著青蛙說：「算了，以我這樣的年紀，我寧願有一隻會說話的青蛙。」



"人到達一個年齡，清楚甚麼該要，甚麼不該要，是一種智慧。 "

最重要的是最後這句話~~發人省思~~

村落 三間 台塑

村落 三間 台塑

---

二次無理數的連分數

二次無理數的連分數\\

$\frac{\sqrt{N}+u}{d}=a+\frac{1}{a_1+\frac{\sqrt{N}+u_1}{d_1}}$

二次無理數的連分數

x^2

x^2

matrix

\[
\mathbf{A} =
\begin{pmatrix}
a + b + c & uv\\
a + b & u + v
\end{pmatrix}
\begin{vmatrix}
30 & 7\\
3 & 17
\end{vmatrix}
\]

Result, #, 計算

$
n=4,x_1=3,x_2=2,x_3=3,x_4=4, $ 求 \\
$ \sum_{i=1}^{n} x_{i}^{2} =38, \qquad
\prod_{i=1}^{n} x_{i}^{2}
=5184.
$
\\

級數, sum, product

試看看吧!
級數, sum, product

cnn.com Cardinal Newman

Cardinal Newman: Who was he?On Sunday Pope Benedict XVI will conduct an open-air beatification Mass for the English cardinal John Henry Newman. Yet the majority of British people know little about the cardinal or how significant he was in Catholicism. FULL STORY



原文是英文▼翻譯成中文(繁體)▼翻譯文字或網頁Cardinal Newman: Who was he?On Sunday Pope Benedict XVI will conduct an open-air beatification Mass for the English cardinal John Henry Newman. Yet the majority of British people know little about the cardinal or how significant he was in Catholicism. FULL STORY

請輸入文字、網址，您也可翻譯文件。
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紅衣主教紐曼：他是誰？星期天教皇本篤十六世將進行露天封聖彌撒英文紅衣主教紐曼。然而，大多數英國人知之甚少的大是大非或如何重要，他是在天主教。詳細內容

家庭消費力 全台第一

察目前新竹的推案狀況，不僅台中地區建商如豐邑、惠宇、富宇、長安等前仆後繼的搶進，就連上市建商華固與國揚，都已在新竹地區
圖／經濟日報提供

插旗，並大展身手。


高所得聚落 「錢」力無限

《住展》雜誌研發長倪子仁表示，如今大台北地區土地價格屢創新高，在土地成本過高的壓力下，許多重量級開發商，紛紛拓展土地開發區域，特別是擁有竹科高所得收入群的新竹市與竹北市，最受建商青睞。

據《住展》雜誌的統計，新
圖／經濟日報提供

竹地區近年的推案量持續放大，其中，新竹市的推案量從97年的383.7億元，上升至99年8月底的395.5億元，預估今年全年的推案量將可達到500億元以上。

至於竹北市的市況，則更火紅，推案量在97年為303.6億元，99年截止至8月底，推案量已高達610.1億元，預估全年推案量可望超過750億元，創下金融海嘯以來的新高量。


竹北市好夯 推案衝高

倪子仁指出，今年華固建設首度離開台北市推案，進軍的地區正是新竹科學園區，推出位在科學園區內的預售豪宅建案「天湖」，基地廣達千坪，規劃47戶獨棟雙併、坪數130坪的大戶住家，開價每坪48萬元、總價6,000萬元起跳，雙雙打破新竹房市的紀錄。

以造鎮計畫聞名的遠雄建設也看好新竹房市，目前正整合10多公頃土地、面積超過3萬坪，可能往造鎮模式作規劃。

另一重量級建商潤泰創新，也傳出將與新竹在地建商竹風建設共同合作，由潤泰創新負責產品規畫設計與工程承攬。

昌益及志嘉等新竹地區大型知名建商，在面對諸多外來兵團搶進新竹房市，更是不敢掉以輕心，紛紛在產品力、品牌知名度及售後服務上加強力道與強度，以鞏固既有的灘頭堡。

倪子仁指出，根據行政院主計處最新公布的台灣地區家庭收支調查結果，新竹市去年家庭每戶平均可支配所得為113.4萬元，每人平均可支配所得為32.3萬元，均僅次於台北市；在支出方面，新竹市家庭平均每戶消費支出95.6萬元，則超越台北市的95.3萬元，是全台第一。


家庭消費力 全台第一

新竹市政府主計處的統計也顯示，全台各縣市家庭可支配所得超越百萬元者，只有台北市、新竹市及新竹縣三個地區，新竹市去年家庭每戶平均可支配所得為113.4萬元，為台灣地區平均88.8萬元的1.28倍。

另外，新竹市家庭消費支出結構，包括平均每戶全年教育支出7萬元、醫療保健支出14.7萬元，均為全台第一，休閒與文化支出5.5萬元則為全台第二。房屋自有比率85.37%，平均每戶建坪48.58坪，多項家庭現代化設備每百戶擁有數量均名列前茅。

SAD HAPPY ANGRY LED LOGHT

SAD HAPPY ANGRY LED LOGHT

http://5i01.com/topicdetail.php?f=457&t=1746419&p=5

載入圖片 ▼ »41
有一次我聽蕭駿講過一個故事, 關於他當年失敗的人生, 那時候他剛畢業, 拿了爸爸的退休金在台北開了一個茶館, 剛開始附近這樣可以閒聊的餐廳少, 股市正熱著, 所以每每收盤後, 他的茶藝館充滿了剛看完盤的投資人, 所以生意不惡, 但是慢慢的餐廳多了, 優劣立分高下, 開始人潮都往那幾家氣氛好食物優的餐廳咖啡廳跑, 好險在那個景氣的年代, 雖然不像當初這樣天天滿座, 但是拜股民所賜, 每天還是有三成客人, 但已經開始有點入不敷出, 他自己的收銀檯下供奉著土地公神像, 他從天天上香希望客人回流, 到最後開始詛咒, 他開始跟著土地公說, 現在是股市這麼熱, 等到股市垮了, 對面那家投資巨額的餐廳肯定也會跟著垮了, 隔壁那家歐式庭園咖啡廳應該也會跟著倒閉, 到時候我的店就可以回到之前的榮景..就這樣沒幾個月, 果然股市崩盤了, 股市每天的成交量從原本的千億以上, 到了最後只有一百來億, 他講得沒錯, 對面投資巨額的餐廳最後真的撐不下去了, 隔壁的庭園餐廳也沒多久都結束營業了, 但這些都是在他連原來的三成的客人都不在, 賠光他父親的退休金, 收掉自己的茶藝館之後發生的事

園區垮了, 工程師可以去當外勞, 但是新竹未必變成公園, 就像是底特律的汽車業垮了, 底特律不是變成沒有工廠的公園, 反而是變成全美國治安最差, 吸毒人口最多, 街頭充斥著賣淫販毒的恐怖之城, 同樣的高雄, 從世界前三大的港口, 船運業興盛, 又有加工出口區, 變成今天失業率前三名, 當然也會造成治安最差, 犯罪率最高, 家裡往往幾口人只有一個人就業, 所以弄到連專櫃小姐, 幼教老師都要兼差了

CNN news

Tokyo intervened in the currency markets for the first time in more than six years to weaken the yen, after the currency broke through Y83 against the U.S. dollar and threatened exporter profits and business sentiment.

東京外匯市場干預，首次超過六年削弱日元，突破後 Y83貨幣兌美元，並威脅出口商利潤和企業信心。

Blog Test and list

Blog Test and list

各公民營行庫 房貸利率一覽表 (更新時間：2010/7/22)

各公民營行庫 房貸利率一覽表 (更新時間：2010/7/22)

行庫名稱 貸款利率
台灣銀行 「Easy Go」房屋輕鬆貸優惠專案前6個月依台銀定儲利率指數加碼0.512%起浮動計息(目前為1.5%起)，第7~12個月依台銀定儲利率指數加碼0.85%起浮動計息(目前為1.838%起)，第2年依台銀定儲指數加碼1.2%起浮動計息(目前為2.188%起)

台灣企銀 好家園房貸專案階段式：前6個月2%起，第7個月起依利率指數加碼，機動計息
不分段：依台灣企銀定儲利率指數加碼(目前最低為2.55%起)訂定，機動計息

兆豐國際商銀 「歡喜雙享炮」專案前六個月固定1.5%(IR+0.47%)，第七至十二個月1.70%(IR+0.67%) ，第2 年起1.99%(IR+0.96%) 。

「歡喜理財家房貸」不循環額度：前六個月1.50%(IR+0.47%)，第七至十二個月1.70%(IR+0.67%)，第2 年起1.99%(IR+0.96%)。
循環額度：依已動用餘額按 IR+1.47%( 現為2.50%) 為下限計息

華南銀行 三段式利率前6個月2%~4%機動計息，第7-24個月2.45%~ 4.45%機動計息，第3年起2.99% ~4.99%機動計息。

築巢優利貸-全國公教員工房屋貸款依臺灣郵政二年期定儲機動利率固定加碼0.265％機動計息。(目前為1.36%)

花旗銀行 花旗自由年貸(本利攤還型房貸)貸款年利率為2~3%起

土地銀行 菁英購屋貸款(圓夢計劃)第1-6個月 I+0.97%起(目前為2.06%)、第7-12個月 I + 1.07 ％ 起（目前為2.16％）、第2年起 I +1.27 ％ 起（目前為2.36％）

中華郵政 「安穩貸」專案菁英保戶首年固定利率2.65%、第二-五年3.20%，第六年起依指標利率固定加碼0.85%

「美利貸」專案依指標利率加碼1.1%~1.5%，現為2.135%~2.535%

板信商業銀行 指數型房貸前6個月1.87%~2.07%機動，第7-24個月2.35%~2.55%機動，第3年2.65%~2.85%機動

台北富邦銀行 「富邦優選房貸」指數利率指數利率一段式：貸款期間固定最低加碼0.83%起(目前為1.83%)，並按指數型房貸基準利率加碼機動計息。

理財家房貸利率最低2.74%起，並依指數型房貸基準利率加碼，浮動計息

台中商業銀行 指數型房貸最低依定儲利率指數加碼1.58%起機動計息，A級客戶可再享減1碼優惠

理財指數型房貸最低依指數房貸利率加碼3.38%起，A級客戶可再享減1碼優惠

第一銀行 「First Aviva 安家房貸保障專案」1.94%~6.08%，機動計息

「輕鬆還」房貸專案2.29%~6.08%，機動計息

玉山銀行 指數型房貸前二年2.03%~2.23%，第三年起2.53%~2.73%

理財型房貸利率2.58%~3.68%

彰化銀行 定儲利率指數房貸依本行定儲利率指數加碼機動計息，目前為1.5%-2.91%以上。

安心Go購Home貸款按本行定儲利率指數加年息0.84%(目前為1.75%)以上機動計收

遠東國際商銀 定儲指數房貸依本行定儲指數利率加碼浮動計息(目前為2%~3%浮動計息)

大眾銀行 一般房貸貸款利率2.44%~3.69%
理財型房貸貸款利率3.44%~5.33%

元大商業銀行 一段式利率(一心一意)房貸2.25%~2.85%
三段式利率房貸前6個月1.68%，第7-24個月起2.08%～2.18%，第3年起2.20%～2.40%

合作金庫 2%~3.80％(浮動計息)
星展銀行 指數型房貸第1~3月固定利率1.49％起，第4~12月固定利率1.69％起，第二年起以本行定儲利率指數I + 1.11％起，機動計息 (2009年09月21日本行定儲利率指數I (季調整) = 0.77％)

陽信銀行 指數型房貸1.98%~2.5%浮動計息

聯邦銀行 均利型利率指數房貸分段式：前半年1.7%起，第7~12個月2.1%起，第2年起2.5%起浮動計息
不分段式：利率2.6%~5.72%

yahoo 交友 停止

又一項網路功能停掉了

整理清洗浮額

假使散戶持續進入的市場，籌碼就會比較雜亂，因此！整理清洗浮額是必要的動作，我們並不是不看好傳產類股，只是漲多就需要拉回整理，對比目前在低檔的電子股，院長取捨過後，相信低基期的電子股會比較有爆發力。

一台1124元！ 印度推最便宜平板電腦

印度推出只要台幣三百五十元的筆電


http://www.libertytimes.com.tw/2010/new/jul/24/today-int4.htm

一台1124元！ 印度推最便宜平板電腦

花花

台西港

台西港

遙望

遙望 ---- 香榭大道

少林武術創自達摩,達摩所著《易筋經 》即為天下武學聖典,這是當今世人所共知 的。 姑且不論其考證結果為何, 「易筋經」 是中國歷代武術派別及拳種之源頭

夢工廠

Hello Kitty
玩偶
日本

bear

Psychology of advanced mathematical thinking

Psychology of advanced mathematical thinking
Title: Psychology of advanced mathematical thinking
Author: Tall D.
Read "Psychology of advanced mathematical thinking"
"Psychology of advanced mathematical thinking" pages map

60loop

http://bioinformatics.bravehost.com/

http://bioinformatics.bravehost.com/

在面對台西鄉顏嚴重人口外流,地方原有養殖產業面臨外來

工業<六輕，七輕，八輕>衝擊下，招集台西鄉當地有理想

有熱情的人，調理海口風味餐,有新的文化風貌。

台西漁港海堤，吹海風，看夕陽，涼庭和周邊綠美化，讓

老舊的的台西港變身成了不少遊客假日喜愛的休憩景點。

每到假日傍晚時分，不少民眾都會前往台西港邊木造的觀

景平台，可坐可躺，三五成群坐在涼庭上聊天，海風吹來格

外涼爽，相當悠閒，還有民眾帶著相機前來補捉夕陽美景。

柳丁花

柳丁

寒天白雲

寒天白雲
寒天白雲

海

貝殼圖

See

大林車站

大林車站

MG,阿里山,嘉義市

嘉北車站

嘉北車站

NComputing Brings Inverse Cloud Computing To Joe The Plumber

My apologies, but with the election over, I just couldn’t resist the urge to use ‘Joe the Plumber’. What a joke, but I’ll tell you what isn’t a joke, Ncomputing’s networked computer system. The tiny, fanless box contains no CPU or extreme hardware, but allows its user to perform their day-to-day PC tasks, such as Web surfring, document editing and more. It works by connecting to one central computer that does all the heavy lifting that is shared with other users. As a result, NComputing’s machines are ultra green using 95% less energy than the laptop I write this post on – about 1-4watts. Also, with no moving parts in the NComputers there’s less to breakdown and less heat dissipation, which means no cooling fans or costly, nonecofriendly A/C.

The NComputers already in use by over a million people in India, Bangladesh and Macedonia and are largely utilized by schools, business and public access areas.

Official product page here

Mathematical Proof of the Inevitability of Cloud Computing

http://cloudonomics.wordpress.com/2009/11/30/mathematical-proof-of-the-inevitability-of-cloud-computing/

http://cloudonomics.wordpress.com/2009/11/30/mathematical-proof-of-the-inevitability-of-cloud-computing/


In the emerging business model and technology known as cloud computing, there has been discussion regarding whether a private solution, a cloud-based utility service, or a mix of the two is optimal. My analysis examines the conditions under which dedicated capacity, on-demand capacity, or a hybrid of the two are lowest cost. The analysis applies not just to cloud computing, but also to similar decisions, e.g.: buy a house or rent it; rent a house or stay in a hotel; buy a car or rent it; rent a car or take a taxi; and so forth.

To jump right to the punchline(s), a pay-per-use solution obviously makes sense if the unit cost of cloud services is lower than dedicated, owned capacity. And, in many cases, clouds provide this cost advantage.

Counterintuitively, though, a pure cloud solution also makes sense even if its unit cost is higher, as long as the peak-to-average ratio of the demand curve is higher than the cost differential between on-demand and dedicated capacity. In other words, even if cloud services cost, say, twice as much, a pure cloud solution makes sense for those demand curves where the peak-to-average ratio is two-to-one or higher. This is very often the case across a variety of industries. The reason for this is that the fixed capacity dedicated solution must be built to peak, whereas the cost of the on-demand pay-per-use solution is proportional to the average.

Also important and not obvious, leveraging pay-per-use pricing, either in a wholly on-demand solution or a hybrid with dedicated capacity turns out to make sense any time there is a peak of “short enough” duration. Specifically, if the percentage of time spent at peak is less than the inverse of the utility premium, using a cloud or other pay-per-use utility for at least part of the solution makes sense. For example, even if the cost of cloud services were, say, four times as much as owned capacity, they still make sense as part of the solution if peak demand only occurs one-quarter of the time or less.

In practice, this means that cloud services should be widely adopted, since absolute peaks rarely last that long. For example, today, Cyber Monday, represents peak demand for many etailers. It is a peak who’s duration is only one-three hundred sixty-fifth of the time. Online flower services who reach peaks around Valentine’s Day and Mother’s day have a peak duration of only one one-hundred eightieth of the time. While retailers experience most of their business during one month of the year, there are busy days and slow days even during those peaks. “Peak” is actually a fractal concept, so if cloud resources can be provisioned, deprovisioned, and billed on an hourly basis or by the minute, then instead of peak month or peak day we need to look at peak hours or peak minutes, in which case the conclusions are even more compelling.

I look at the optimal cost solutions between dedicated capacity, which is paid for whether it is used or not, and pay-per-use utilities. My assumptions for this analysis are that pay-per-use capacity is 1) paid for when used and not paid for when not used; 2) the cost for such capacity does not depend on the time of request or use; 3) the unit cost for on-demand or dedicated capacity does not depend on the quantity of resources requested; 4) there are no additional relevant costs needed for the analysis; 5) all demand must be served without delay.

These are assumptions which may or may not correspond to reality. For example, with respect to assumption (1), most pay-per-use pricing mechanisms offered today are pure. However, in many domains there are membership fees, non-refundable deposits, option fees, or reservation fees where one may end up paying even if the capacity is not used. Assumption (2) may not hold due to the time value of money, or to the extent that dynamic pricing exists in the industry under consideration. A (pay-per-use) hotel room may cost $79 on Tuesday but $799 the subsequent Saturday night. Assumption (3) may not hold due to quantity discounts or, conversely, due to the service provider using yield management techniques to charge less when provider capacity is underutilized or more as provider capacity nears 100% utilization Assumption (4) may or may not apply based on the nature of the application and marginal costs to link the dedicated resources to on-demand resources vs. if they were all dedicated or all on-demand. As an example, there may be wide-area network bandwidth costs to link an enterprise data center to a cloud service provider’s location. Finally, assumption (5) actually says two things. One, that we must serve all demand, not just a limited portion, and two, that we don’t have the ability to defer demand until there is sufficient capacity available. Serving all demand makes sense, because presumably the cost to serve the demand is greatly exceeded by the revenue or value of serving it. Otherwise, the lowest cost solution is zero dedicated and zero utility resources; in other words, just shut down the business. In some cases we can defer demand, e.g., scheduling elective surgery or waiting for a restaurant table to open up. However, most tasks today seem to require nearly real-time response, whether it’s web search, streaming a video, buying or selling stocks, communicating, collaborating, or microblogging.

It is tempting to view this analysis as relating to “private enterprise data centers” vs. “cloud service providers,” but strictly speaking this is not true. For example, the dedicated capacity may be viewed as owned resources in a co-location facility, managed servers or storage with fixed capacity under a long term lease or managed services contract, or even “reserved instances.” By “dedicated” we really mean “fixed for the time period under consideration.” For this reason, I will use the terms “pay-per-use” or “utility” rather than “cloud” except when providing colloquial interpretations.

Let the demand D for resources during the interval 0 to T be a function of time D(t), 0 <= t <= T.

逆雲端運算

目前雲端運算的賣點 運算能力與儲存空間
基於網際網路n層伺服器架構的系統開發與瀏覽器終端
吳昇的Web 3.與Opera瀏覽器的先進功能
雲端運算與電子書互動閱讀 知識管理
雲端運算與直流電力供應系統
雲端運算對災情通報與災害治理系統的效益
台灣雲端運算的大型實驗計畫
台灣雲端運算的前端基礎建設
逆雲端運算

目前雲端運算的賣點 運算能力與儲存空間
基於網際網路n層伺服器架構的系統開發與瀏覽器終端
吳昇的Web 3.與Opera瀏覽器的先進功能
雲端運算與電子書互動閱讀 知識管理
雲端運算與直流電力供應系統
雲端運算對災情通報與災害治理系統的效益
台灣雲端運算的大型實驗計畫
台灣雲端運算的前端基礎建設

http://www.mobile01.com/topicdetail.php?f=488&t=817574&p=1&cache=0

http://www.mobile01.com/topicdetail.php?f=488&t=817574&p=1&cache=0

微積分 Laplace 微分方程 數學