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Online Algorithms Advanced Seminar A Supervisor: Matya Katz Ran Taig, Achiya Elyasaf December, 2009
2 Overview-part A Introduction What is Online Algorithm? Evaluating the Online Algorithm The Online Paging Problem & Algorithm Deterministic Online Algorithms For Paging CA ≥ k Theorem Adversary Models Randomize Online Paging Algorithm Oblivious Adversary Adaptive Adversary
3 Introduction What is Online Algorithm? So far, all algorithms received their entire inputs at one time Online algorithms (OA), receive and process the input in partial amounts. A sequence of requests are received and the OA must service each request before it receives the next one.
4 Introduction What is Online Algorithm? (Cont.) In servicing each request, a several alternatives with an associated cost are possible. The alternative chosen may influence the costs of alternatives on future requests. Examples: data-structuring resource-allocation in operating systems finance distributed computing
5 Introduction Evaluating the Online Algorithm IIt is often meaningless to have an absolute performance measure for an algorithm. The algorithm can be forced to incur an unbounded cost by appropriately choosing the input sequence IIt is difficult, if not impossible, to perform a comparison of competing strategies. QQ: How to evaluate the algorithm? AA: We compare the total cost of the OA on a sequence of requests, to the total cost of an offline algorithm WWe refer to such an analysis as a competitive analysis
6 Introduction The Paging Problem Some definitions: Memory item - a page of virtual memory Cache memory – a fast memory of size of k memory items Main memory - a slower memory that can potentially hold an infinite number of items A paging algorithm - decides which k items to retain in the cache at each point in time We have a sequence of requests, each of which specifies a memory item. A hit – the requested item is in the cache. There is no cost A miss – the item must be fetched from the main memory. There is a 'unit' cost and one of the k items in the cache must be evicted Paging – The replacement of one page with another in the cache is called paging or page fault
7 Introduction The Paging Algorithm The cost measure is the number of misses on a sequence of requests This cost depends on the algorithm that decides which k items to retain in the cache at each point in time When a page fault happens, the paging algorithm invoke an eviction rule for deciding which item currently in the cache should be evicted to make room for the new item Intuitively, items that will be requested again in the near future should not be evicted
8 Introduction Offline Algorithm The offline algorithm (aka the MIN algorithm): on a miss, evict that item whose next request occurs furthest in the future The worst-case number of misses on a request sequence of length N is N/k.
9 Introduction Deterministic Online Algorithms For Paging Least Recently Used (LRU): evict the item in the cache whose most recent request occurred furthest in the past. First-in, First-out (FIFO): evict the item that has been in the cache for the longest period Least Frequently Used (LFU): evict the item in the cache that has been requested least often
10 Introduction Deterministic Online Algorithms For Paging (Cont.) Let p 1, p 2,...,p n be a request sequence presented to an online paging algorithm A Let f A (p 1, p 2,...,p n ) denote the number of times A misses on p 1, p 2,...,p n Let f 0 (p 1, p 2,...,p n ) denote the minimum number of misses on p 1, p 2,...,p n
11 A deterministic online paging algorithm A is said to be C-Competitive if there exists a constant b such that on every sequence of requests p 1, p 2,...,p n : f A (p 1, p 2,...,p n ) - C∙f 0 (p 1, p 2,...,p n ) ≤ b where the constant b must be independent of N but may depend on k Competitiveness measures the performance of an OA in terms of the worst-case ratio of its cost to that of the optimal offline algorithm Introduction Deterministic Online Algorithms For Paging (Cont.)
12 Introduction Deterministic Online Algorithms For Paging (Cont.) The competitiveness coefficient of A, denoted C A, is the infimum of C such that A is C-competitive Since the worst case of offline algorithm is N/k, no deterministic online paging algorithm has competitiveness coefficient smaller than k LRU, FIFO are known to be k-competitive No deterministic online paging algorithm has competitiveness coefficient smaller than k (proof will follow soon…) Thereby, LRU and FIFO are optimal deterministic OA
13 Introduction Paging Algorithm - Formally A paging algorithm consists of an automaton with a finite set S of states The automaton response is defined by function The cache after the request is serviced must include the requested item curr. statecurr. cachenew itemcurr. statecurr. cache
14 Introduction C A ≥ k Theorem Let A be a deterministic online algorithm for paging then C A ≥ k Proof: Initialization - Both A & the offline algorithm are managing different caches for the same request sequence They both has the same k-items in the cache First request is to an item not in either cache, and the algorithms incur a miss Let S be the set of k+1 items consisting of the initially k items together with the new item From then on, every request is for the unique item in S not in A's cache Thus A misses on every request
15 Introduction C A ≥ k Theorem (cont.) A round is a maximal sequence of requests in which at most k distinct items are requested; each of these items may be requested any number of times and in any order A round ends when, after k distinct items have been requested, a new item p is requested, and p then becomes the first request of the next round Since the round contains at least k requests and A misses on every one of them, it misses at least k times during the round
16 Introduction C A ≥ k Theorem (cont.) There is an offline algorithm that misses only once during a round, in fact on the first request of the round We denote p as first request of the following round When the offline algorithm misses on the first request, it evicts p and thereby ensures that there are no further misses in that round (as expected from a MIN algorithm)
17 The offline algorithm knows A's initial cache, the entire request sequence in advance and the identity of p for every round At the end of each round, both the online algorithm and the offline algorithm have the same set of items in their caches This construction can be repeated many times, proving that there are arbitrarily long sequences on which A has k times as many misses as the offline algorithm. Introduction C A ≥ k Theorem (cont.)
18 Introduction Conclusions The proof uses only the fact that the OA doesn't know future requests Thus the lower bound applies to any deterministic OA without any regard for its use of computational resources such as time or space This is a negative result for the online algorithms The offline algorithm may be view as an adversary who is not only managing a cache, but is also generating the request sequence
19 Overview-part A Introduction What is Online Algorithm? Evaluating the Online Algorithm The Online Paging Problem & Algorithm Deterministic Online Algorithms For Paging CA ≥ k Theorem Adversary Models Randomize Online Paging Algorithm Oblivious Adversary Adaptive Adversary
20 Adversary Models Randomize Online Paging Algorithm The Adversary Models, where in collusion with a reference algorithm that is the yardstick against which the competitiveness of the given online algorithm is being measured The adversary's goal is to increase the cost to the given online algorithm, while keeping it down for the reference algorithm
21 Adversary Models Randomize Online Paging Algorithm 2 definitions: R- a randomize online paging algorithm (=ROA) f R (p 1, p 2,...,p n ) – a random variable, denotes the number of times that R misses on the sequence Evaluating the ROA We still study the behavior of R when the sequence of requests is generated by an adversary However, there is no longer a unique notion of an "adversary" for a randomized online algorithm
22 Adversary Models Oblivious Adversary The weakest adversary knows R in advance, but has no knowledge of the random choices made by R This adversary calculates the "worst case" request sequence for R, regardless of the actual execution of R The fixed cost of this sequence is not a random variable and is denoted by f 0 (p 1, p 2,...,p n ) We call such an adversary an oblivious adversary, reflecting that the adversary is oblivious to the random choices made by R
23 R is C-competitive against the oblivious adversary if for every sequence of requests p 1, p 2,...,p n : for a constant b independent of N. The oblivious competitiveness coefficient of R, denoted by, is the infimum of C such that R is C-competitive Adversary Models Oblivious Adversary
24 The Adaptive Adversary chooses p i+1 after observing the responses of the ROA to p 1, p 2,...,p i To define the cost of the optimal algorithm it might help to think of the adaptive adversary and the optimal algorithm as working in collusion Adversary Models Adaptive Adversary
25 First scenario: The adversary generates the optimal strategy adaptively by learning p 1, p 2,...,p i We refer to this as the adaptive offline adversary The request sequence is a random sequence. Thus, f 0 (p 1, p 2,...,p n ) and f R (p 1, p 2,...,p n ) are random variables Adversary Models Adaptive Adversary
26 Second scenario: The adversary works as before, but also required to concurrently manage a cache online Meaning, the adversary generates p i+1 based on the responses of R to p 1,p 2,...,p i, and immediately exhibits its own response to p i+1 Again both f 0 (p 1, p 2,...,p n ) and f R (p 1, p 2,...,p n ) are random variables We refer to such an adversary as an adaptive online adversary Adversary Models Adaptive Adversary
27 We say that R is C-competitive against the adaptive offline adversary if for a constant b independent of N: The adaptive offline competitiveness coefficient of R,, is the infimum of C such that R is C-competitive Likewise, is the adaptive online competitiveness coefficient of R Adversary Models Adaptive Adversary
28 Clearly, we can define the following proportion – Let be the lowest oblivious competitive coefficient of any randomized paging algorithm Similarly we define Finally we define to be the lowest competitive coefficient of any deterministic paging algorithm Then we have Adversary Models Adaptive Adversary
29 Overview-part B Introduction Paging against an oblivious – Definitions. The players on this section – What is the goal? Yao's minimax theorem. Proof of a Theorem on C R : Preparations. Behavior of the offline algorithm. Behavior of a deterministic algorithm. Results. The marker algorithm Description. Proof of it's competitiveness coefficient. Summary
30 Paging against an oblivious adversary Remember that in the proof of the result : C A ≥ k we based our analysis on the fact that the request sequence was determined at each step according to the behavior (the cache contents) of the deterministic online algorithm When talking about randomized algorithm it's intuitive that this ability of the offline algorithm won't be so helpful
31 Our goal now We now want to prove that against an oblivious adversary a randomized online algorithm can do much better in terms of competitiveness against the result (K) we saw when we talked about deterministic online algorithm. We will see that under the above assumptions. where is the k'th harmonic number which is clearly smaller then K.
32 So… who against who? Our players now are an oblivious adversary which specifies all the request sequence in advance and never changing it later on Then, an optimal offline algorithm is activated on this sequence and announcing it's result Only then – the sequence is given to the random online algorithm we want to test – each request at a time.
33 A reminder : Yao's MiniMax principle Yao's minimax principle states that given any arbitrarily chosen input distribution P: the expected cost of the optimal deterministic algorithm under this distribution is a lower bound on the expected cost of the optimal randomized (Las Vegas) algorithm.
34 How to use this principle for our purposes? The principle gives us the ability to deal only with deterministic algorithms in order to give a lower bound for the randomized algorithm. All we need to do is to choose a probability distribution for the inputs and then give a lower bound for the best (in terms of expected cost) deterministic online algorithm under this distribution.
35 Overview-part B Introduction Paging against an oblivious – Definitions. The players on this section – What is the goal? Yao's minimax theorem. Proof of a Theorem on C R : Preparations. Behavior of the offline algorithm. Behavior of a deterministic algorithm. Results. The marker algorithm Description. Proof of it's competitiveness coefficient. Summary
36 Some preparations: The probability distribution on the inputs can be taught as the probability to choose the i'th memory item on the sequence – this probability can depend in the older requests on the chosen sequence; Given a deterministic online paging algorithm A we define it's competitiveness under a distribution ρ to be the infimum of C such that:
37 Cont. Formally – the MiniMax principle gives us: Meaning of the left member: The competitiveness co efficient of the best random algorithm (Vs. an oblivious adversary). Meaning of the Right member: The competitiveness coefficient of the best deterministic online Alg. Under a "worst-case" input distribution probability.
38 Since we are looking for a lower bound we can look at a very simple case We will look on a world where there are only K+1 memory items: I = {I 1, ……….,I k+1 } Since the cache can consist at most K items at once only one of the k + 1 items is out at each step So, we can say the algorithm decides which of the items will be out at each step. Theorem: Let R be a randomized algorithm for paging then
39 Choosing a "worst-case" distribution First we should give P – an input probability distribution : - The sequences to be chosen will be of length N – we assume N>>K. 1. Choose the first request p 1 uniformly from all the items in I. 2. Choose the i'th request p i uniformly from the set : I\{p i-1 }. i>1 Intuitive explanation: We never request an item that we are sure about it's status – this is why this is a worst-case distribution.
40 Demonstration I4I4 12345678910 I = {I 1,I 2,I 3, I 4,I 5,I 6,I 7,I 8,I 9,I 10,I 11 } current choice set = {I 1,I 2,I 3, I 5,I 6,I 7,I 8,I 9,I 10,I 11 } I4I4 I5I5 12345678910
41 Now we need to find a lower bound for A – an optimal det' online algorithm We will use again the notion of rounds that Achiya has mentioned: The first round starts with the first request and ends when, in the first time all the k+1 items in I has been requested at least once. The next round starts with the next request and ends just before the request to the (k+1)th distinct item since the start of the current round. All next rounds acts the same.
42 How the offline algorithm behaves on each round? The offline algorithm will use MIN on each round – the result will be that the item left out will be the one requested only at the last request of the round This means that the offline algorithm will miss only once during a round – at the last request of a round when it adds the missed item to the cache it puts out the last requested item of the next round
43 Demonstration I = {I 1,I 2,I 3,I4} Requests= {I1,I2,I1,I4,I3,I2,I4,I1,I3,I1,I3,I2,I4,I2,I4,I3} I1I2I4 123 321 I2I3 321 I4I2 321 I1I2I3 321 I 321 I4I1I2 321 I4I
44 How often does the offline algorithm miss? We saw that the answer to that question depends the length of each round ( since it misses once in a round). We can ask an equivalent question: what is the expected number of random choices we need to do until we choose all items in a group of order K+1? Let's look at it as a random walk on a complete graph with k+1 vertices – the expected number of steps to achieve full cover of that graph was approved to be : K*H k
45 How a deterministic online algorithm behaves on each round? Since the request sequence is random we can't expect any regularity in the sequence. Since the policy of the known deterministic algorithms is based on some kind of regularity we can't expect any of these policies to be better then the others. So – the probability for a miss is totally random.
46 Cont. the probability that any request falls on the item that A leaves out at same time-step is exactly 1/k explanation: remember there is no item that will be requested twice consecutively so the next request is for one of the other k items that A might have choose to leave out. Given the expected length of a round we conclude that the expected number of misses by A during a round will be: (K*H k )*(1/k) = H k
47 Let's finish the proof We found that the any of the deterministic online algorithms misses (expected) H k times the number of (expected) misses by an optimal offline algorithm This result is under the worst case input distribution we defined So we found H k to be the competitiveness coefficient of A and by the MinMax principle we conclude this is also the competitiveness coefficient of an optimal randomized algorithm for our problem
48 Overview-part B Introduction Paging against an oblivious – Definitions. The players on this section – What is the goal? Yao's minimax theorem. Proof of a Theorem on C R : Preparations. Behavior of the offline algorithm. Behavior of a deterministic algorithm. Results. The marker algorithm Description. Proof of it's competitiveness coefficient. Summary
49 The Marker Algorithm After we proved that there are randomized online algorithms with a competitiveness coefficient of H k we will now see an example for a randomized online algorithm with a very close achievement. The algorithm require K bits (marker bits) – one for each cache location At The beginning all these bits are set to 0.
50 The Algorithm 1. Given a memory request: 1.1 if the requested Item is currently in cache – mark it's location (set mark bit to 1). 1.2 else bring the Item into memory and evict an item by the following policy: 1.2.1 choose uniformly at random an unmarked memory location. 1.2.2 mark this location, replace the item in it with the currently requested item. 2. If all memory locations are marked - unmark all locations when the next request is given.
51 Theorem : The Marker Algorithm is 2*H k - competitive We will again use the notion of rounds – about the same definition as earlier : A round starts with Pi that is not in the cache and ends with Pj where : P i,P i+1,……….P j,P j+1 where j+1 is the smallest integer s.t the above list consists k+1 distinct items. As we will demonstrate each round ends with all the locations marked. Each round begins with an item that is not currently in the cache.
52 Demonstration I = {I 1,I 2,I 3,I 4 } Requests= {I1,I2,I1,I4,I3,I2,I4,….I1,I3,I1,I4,I2,I4,I2,I3} I1 123 321 I4I1 321 I4I3 321 I2I3I1 321321 I2I3I4 321 I 321 I2I1 321 I2 321 I4I2I1 321 I4I2 321 I4I2I1 321 I4I2 321 I3I4 321 I I1 123 321 I4I1 321 I4I3 321 I4I3I2 321321 I4I3I2 321 I 321 I3I4 321 I
53 Some definitions We will divide all the items requested during a round to 2 groups: Stale item is an item that is currently unmarked but was marked during the last round – meaning: he was requested during the last round but not during this round clean item is an item that is not stale and/or marked – meaning it wasn't requested during the last and current rounds Let l be the number of clean items requested during each round.
54 Some more definitions : S 0 is the set of k items currently in the offline algorithm cache S m is the set of k items currently in the Marker algorithm cache D I = |S 0 /S M | at the beginning of a round D F = |S 0 /S M | at the end of a round M 0 = the number of misses incurred by the offline algorithm cache during a round.
55 Observetions: Note that each of the distinct items is either clean or stale at the beginning of a round. M0≥ l – D I the number of misses by the offline algorithm is at least as the number of the items that wasn't requested during the last round (l) and are not in it's cache at the beginning of the round. on the other end, all The k items in S m at the end of a round are items that were requested during the last round so we can conclude that the offline algorithm missed at least D F misses in the last round since these D F items are not in Sm!
56 How many time the offline algorithm miss during all rounds? By the last observation we have: Note that D F of the k'th round equals D I of the (k+1)'th round so they are telescoping when summing the above lower bound on all rounds. We can use amortization and we charge each round with l/2 misses for the offline algorithm. Our mistake range is ±2k – size of the 2 cache memories.
57 How many time the Marker algorithm miss during all rounds? First of all by definition, all the clean items are missed during each round For the k-l requests to stale items we should consider the probability that the item requested is not in the cache and this depends directly on the number of clean items requested before him in the round See the board for calculation of this probability and the expected number of misses.
58 So, let's finish the proof: We saw the Marker algorithm misses at most twice the best result possible (achieved by the offline algorithm: Note that these of you interested can find a more sophisticated algorithm that actually achieves Hk competitiveness coefficient.
59 Overview-part B Introduction Paging against an oblivious – Definitions. The players on this section – What is the goal? Yao's minimax theorem. Proof of a Theorem on C R : Preparations. Behavior of the offline algorithm. Behavior of a deterministic algorithm. Results. The marker algorithm Description. Proof of it's competitiveness coefficient. Summary
60 Some more interesting insights/summary One should ask himself if it was a fair game since we gave the randomized algorithm some more comfortable conditions comparing to these of the deterministic algorithm Well the all advantage of the randomized algorithm lies on the fact that it's actions are unexpected This way we prevent the ability to predict the behavior of the algorithm and affect it.
61 Cont. This is why it's intuitive to think on an oblivious adversary when talking on randomized online algorithms which hides the status of memory and the eviction policy at each step At the same time, it's intuitive that on application where it is important to prevent outsiders from affecting the algorithm we won't use a deterministic algorithm which is totally expectable Application for these kind of algorithms with changing demands can be found in finance, robot navigation, short paths in graph, task systems etc.
62 Further results For these of you interested: Further results shows much less impressive achievements of the randomized algorithms against the smarter adversaries presented by Achiya. competitive coefficient against the online adaptive algorithm : k competitive coefficient against the offine adaptive algorithm : k*H k
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Indica :
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This clone, named XVIII, is an outstanding representation of the Pakistani workability. Growing such a true, distinctive hash plant implies dealing with one of the main characters from the Hindu Kush. In few words, X-18 simply works. Though the yield is average at best, the plants show a typical Paki stretch during the first month of flowering.
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Pet Lovers Blog Money Making Avenues
Are you thinking of starting your own pet site? Do you already have a blog that you would like to market?
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Déor Continues Expansion
Related Articles
- Décor by Beader's Touch Unveils Dec Pillow Line. // Home Textiles Today;9/29/2008, Vol. 29 Issue 25, p14
The article reports that Decor by Beader's Touch has launched a line of decorative pillows in linen and linen matte constructions. Several features of the 100 percent linen pillows include embroideries, metallic embroideries and mother of pearl buttons. The company's signature bed, Platina is a...
- COMFORTERS/BEDSPREADS. // Home Textiles Today;01/7/2002, Vol. 23 Issue 18, p38
Ranks the top vendors of comforters and bedspreads in the United States in 2001. Top position taken by Springs Industries; Sales in 2000 and 2001; Success of Dan River's assortment of juvenile licenses; WestPoint Stevens' addition of Ralph Lauren and Disney Home licenses.
- SHEETS & PILLOWCASES. // Home Textiles Today;01/7/2002, Vol. 23 Issue 18, p42
Ranks the top vendors of sheets and pillowcases in the United States in 2001. Top position taken by Springs Industries; Sales in 2000 and 2001; WestPoint Stevens' agreements with the Designers Guild and Disney Home; Sales generated by Dan river's juvenile licenses.
- DOWN COMFORTERS. // Home Textiles Today;01/7/2002, Vol. 23 Issue 18, p39
Ranks the top vendors of down comforters in the United States in 2001. Top position taken by Pacific Coast Feather; Sales in 2000 and 2001; Reorganization of Pillowtex; High expectations for Hollander Home Fashions in 2002; Additional products launched by Down Life International.
- Dakotah goes contemporary. SanFilippo, Michele // Home Textiles Today;4/12/2004, Vol. 25 Issue 31, p2
Reports on domestric decorative pillow and bedding manufacturer Dakotah Inc.'s entry into the bedspreads market. Designs of the company's bedspreads; Reason behind the firm's move.
- Calvin Klein Broadens Home Lines. // Home Textiles Today;8/13/2007, Vol. 28 Issue 21, p1
The article reports that Calvin Klein Home Inc. bed and bath company has completed transition of its step-down Khaki program to Calvin Klein White Label with different assortments and price points. The first White Label Collection introductions include six beds with a younger point of view and...
- New Intros Make the Most of Market. // Home Textiles Today;4/25/2005, Vol. 26 Issue 32, p15
Introduces various household textiles. Physical description of the Perfect Boyfriend pillow, manufactured by Arlee Home Fashions; Specifications of the new comforter sets from Baltic Linen Co.; Design features of the kitchen rugs from Glenoit Rugs.
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Introduces the new collection of fabrics, bedding and pillows from the bedding and fabric manufacturer, House. Features of the products; Designs; Sizes.
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Reports the growth of the household textiles market in Great Britain. Statistical figures gathered from AMA Research; Composition of the bedlinen sector; Value of the filled products market.


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Info Letter No. 3 of the 2nd International Miners Coordination
Dear colleagues, dear comrades,
the 2nd meeting of the "International Coordination Group" (ICG) was from the 15th to the 18th of May, 2018 and we would like to inform you about the most important results. The meeting took place in a turbulent political situation worldwide. The Main Coordinator reported about a successful strike by Australian miners and everybody was pleased that contact with them has finally been established.


text:
Hotel | Lavon LakeResidence Inn Dallas Park Central
United States > Texas > Dallas > Residence Inn Dallas Park Central
Residence Inn Dallas Park Central
7642 LBJ Freeway
Dallas, TX 75251
Dallas, TX 75251
Nightly Rates: 122.55 - 139.00Currency: USD
Hotel Description
The Residence Inn by Marriott Dallas Park Central hotel is very close to the offices of Raytheon, Texas Instruments, Medical City Dallas Hospital, Galleria Mall and minutes to downtown Dallas. Enjoy our spacious suites or catch up on your reading in our comfortable lobby. Mix and mingle at our complimentary social hours or hit the treadmill and go a few laps in the pool. Stay connected with FREE wired or wireless internet and get the job done in your own in-room work space. Sink into luxury bedding at the end of it all. Wake-up energized with our complimentary hot breakfast buffet or whip up your favorite meal in your own full-size kitchen. Stretch out, unwind, work smart, sleep tight, eat right and Thrive at Residence Inn Dallas Park Central.
Check In: 4:00 PMCheck Out: 12:00 PMTime Zone:
Number of Floors: 3Number of Rooms: 139Number of Suites: 139
Photo Gallery
Hotel Amenities
• 24-hour front desk • ATM/Cash machine • Accessible facilities • Accessible parking • BBQ/Picnic area • Barber shop • Beauty shop/salon • Boarding pass print-out available • Bookstore • Buffet breakfast • Business center • Business services • Car rental desk • Check cashing policy • Coin operated laundry • Complimentary breakfast • Complimentary buffet breakfast • Complimentary cocktails • Complimentary coffee in lobby • Complimentary full american breakfast • Complimentary in-room coffee or tea • Complimentary newspaper in lobby • Dinner delivery service from local restaurant • Drugstore/pharmacy • Dry cleaning • Efolio available to company • Elevators • Exercise gym • Express check-out • Florist • Free high speed internet connection • Free parking • Front desk • Full service housekeeping • Grocery shopping service available • Hair dryer • Health club • High speed internet access • High speed wireless • Hot breakfast • Housekeeping - daily • Individual Efolio available • Interior corridors • Ironing board • Laundry/Valet service • Managers reception • Meeting rooms • Motorcycle parking • Non-smoking rooms (generic) • On-Site parking • Onsite laundry • Outdoor pool • Parking • Pets allowed • Pool • Public area air conditioned • Safe deposit box • Security • Tennis court • Travel agency • Valet cleaning • Valet same day dry cleaning • Wakeup service • Wedding services • Wireless internet connection in public areas
Room Info
Guest Room: High speed internet connection, Internet access, Wireless internet connection, High speed wireless, Meal included - breakfast, Non-smoking, Coffee/Tea maker, Alarm clock, Ceiling fan, Air conditioning, Air conditioning individually controlled in room, Fireplace, First nighters' kit, Bathtub/shower combination, Private bathroom, Hairdryer, Upgraded bathroom amenities, Separate closet, Closets in room, Iron, Bathroom amenities, Down/feather pillows, Foam pillows, Pillow top mattress, Sofa bed, Cribs, Local calls, Free toll free calls, Full kitchen, Pantry, Tables and chairs, Refrigerator, Refrigerator with ice maker, Stove, Oven, Microwave, Toaster, Silverware/utensils, Pots and pans, Plates and bowls, Dishwasher, Dish-cleaning supplies, Color television, Cable television, Premium movie channels, List of movie channels available, CNN availaible, HBO available, ESPN available, All news channel, AM/FM radio, Remote control television, Large desk, Desk with electrical outlet, Spare electrical outlet available at desk, Electrical current voltage, Separate modem line available, Telephone, Voice mail, Two-line phone, Multi-line phone, Chair provided with desk
1 queen sofabed: TV
1 queen sofabed: TV
bedroom 1 has 1 queen bedroom 2 has 1 queen: TV
Hotel Map
Area Attractions
Downtown Dallas
Union Station
Greyhound Bus Lines
Dallas Theater Center
The Black Academy of Arts and Letters
Dallas Hub Theater
Majestic Theatre
Shakespeare in the Park
Dave & Buster's
Lower Greenville
Dallas Arboretum & Botanical Gardens
Celebration Station
Superpages.com Center
Nokia Live Theatre
Bass Performance
Billy Bob's
Richardson Telecom Corridor
Dallas Love Field
Dallas/Fort Worth International Airport
Cowtown Segway Tours
Dallas Segway Tours
All in One Tour Service
Dallas Highlights
McKinney Avenue Trolley Tours
Dallas Convention Center
State Fair of Texas
Texas State Fair Ground - Fair Park
Galleria Mall
High Five Exchange
Cavanaugh Flight Museum
Dallas Museum of Art
Sixth Floor Museum at Dealey Plaza
Museum of Nature & Science
Kimbell Art Museum
Cotton Bowl Stadium
Dallas Cowboys Stadium
Devry University
Southern Methodist University
Dallas Theological Seminary
Texas Tech University
University of North Texas
Dallas Baptist University
Texas Women's University
University of Texas Southwestern
Texas Wesleyan University
Galleria Mall
State Fair Grounds
American Airlines Center
Texas Stadium
Six Flags Over Texas
The Ballpark @ Arlington
Dallas Museum of Art
Sherrill Park Golf Courses
NorthPark Center
Texas Motor Speedway
Six Flags Hurrican Harbor
Cotton Bowl
Malibu SpeedZone
Myerson Symphony Center
Lone Star Park
Mesquite Rodeo
Dallas Zoo
Ft. Worth Stockyard
Brookhaven Community College
El Centro Community College
Dallas Christian College
Richland Community College
North Lake Community College
Mountain View Community College
Everest College
Dave & Busters
Mercy, a Wine Bar
Sambuca Jazz Cafe
Gilley's Dallas
NorthPark Center
Grapevine Mills Mall
Tanger Outlet Center
Northeast Mall
Highland Park Village
Dallas Mavericks
Texas Rangers
Dallas Stars
Grand Prairie AirHogs
SMU Mustangs
Texas Instruments
Global Knowledge - Nortel Training Center
Interstate Batteries
Medical City Dallas Hospital
Brinker International
Blue Cross Blue Shield
Greatwide Dedicated Transport
Exco Resources
CH2M Hill
KR Wind
Quick Links
Lavon Lake News
Lavon Lake Photos
Lavon Lake Videos


text:
A Resume Killer – Way Too Much Information!
It is an usual false impression that if you have an excellent resume it will win you the job you want. While that is a fantastic, hopeful overview, it is not always the case. Your resume merely serves the purpose of getting your means of access for a meeting.
To that, you only have 10 to 30 seconds to get a hiring supervisor's attention prior to they carry on. So, it makes good sense to produce the most concise, captivating piece of literature they pick up that day.
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Among the very best methods to achieve this is to offer a high-level summary of abilities, experiences, and accomplishments, in contrast to an extensive, super-detailed testimonial of your work as well as education and learning history. Success are too often hidden in unneeded information. Giving much less information creates the possibility to focus to achievements and makes the hiring manager interested in learning more concerning you. Then, when you score the interview, you can expand on your information.
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Prior to: Handled top-selling team to perform inbound as well as outgoing calls, deal with customer service requests, update stock system, fill orders, issue invoices, develop records, and also follow up on consumer grievances.
After: Led group of 8 to business's leading sales setting by implementing outstanding client service deliverables from order gratification to issue resolution.
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When you have such a short amount of time to obtain a hiring manager's attention, these details make all the distinction. Give your resume some punch and you will be one action better to the job you desire.
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Editorial from Andrea Wood, Newsletter Editor
This month there are invitations galore in the Parish! Residents of the Parish are invited to throw their hat into the ring for the Parish Council Elections which will be held later this year. To find out more please see the newsletter or contact the clerk to the parish council on email@example.com. Nomination packs can be obtained from Mid Devon District Council at www.middevon.gov.uk
An invite to the Annual Parish Council Meeting
Also on this month is the Annual Parish Council meeting. This gives residents an opportunity to meet our Councillors in an informal setting and discover what they have been doing on our behalf. There is also the possibility of passing on our thanks for all their selfless endeavours in maintaining our community and making it a great place to live. It will be held at the Spalding Hall on the 11th March `at 7.00pm.
Options for administering the Pre-School
The successful and well attended pre-school is run by a committee of volunteers who are overstretched and under staffed and the time has come to review the way it is run. There are a number of options to be reviewed and some decisions to be made. Please see page 17 of the newsletter for more details.
Former Headmaster, Mr Alan Conridge, has passed away
Long time residents of Cheriton Bishop will be saddened to learn that Mr Alan Conridge, former Headmaster of the Primary School, has passed away. By all accounts he was a much loved and respected Head who created a wonderful, welcoming atmosphere in the school.
Local Plan Review Hearing
As many of you will know a section of land between Hescane Park and The Old Rectory has been earmarked as a potential site for new housing. The Hearing to review the Local Plan was held on Friday 15th February at the Phoenix Chambers in Tiverton. I will update you on what was discussed and agreed as soon as I have some further information.
If you own a dog, do the right thing!
Finally I have been asked to say a few words about Dog Dirt. If your dog defecates on the pavement, please pick it up! This helps keep our streets clean and minimises the risk of inadvertently taking it home!
Well that's all for now folks! Please continue to contact us with your suggestions and comments at firstname.lastname@example.com
I look forward to hearing from you.
Andrea Wood, Newsletter Editor


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The card for tonight's (Sunday, June 17, 2018 ) WWE Money in the Bank event has been finalized.
The sports entertainment company has yet to officially announce the main event of this event that takes place in Chicago, Illinois at the Allstate Arena and will air on the WWE Network.
Luke Harper & Erick Rowan defending the SmackDown Tag Team Titles against Karl Anderson & Luke Gallows will take place on the pre-show.
Former UFC Women's Bantamweight Champion Ronda Rousey will take part in not only her first title match but also her first singles match in the WWE when she challenges Nia Jax for the RAW Women's Title.
As a reminder, We will be providing live play-by-play coverage of this special later tonight.
The Card
– Men's Money In The Bank Ladder Match: Braun Strowman vs. Finn Balor vs. The Miz vs. Rusev vs. Kevin Owens vs. Bobby Roode vs. Samoa Joe
– Women's Money In The Bank Ladder Match: Ember Moon vs. Charlotte Flair vs. Alexa Bliss vs. Becky Lynch vs. Natalya vs. Lana vs. Naomi vs. Sasha Banks
– WWE Title Last Man Standing Match: AJ Styles © vs. Shinsuke Nakamura
– WWE Raw Women's Title Match: Nia Jax © vs. Ronda Rousey
– WWE Intercontinental Title Match: Seth Rollins © vs. Elias
– WWE SmackDown Women's Title Match: Carmella © vs. Asuka
– Singles Match: Roman Reigns vs. Jinder Mahal
– Singles Match: Daniel Bryan vs. Big Cass
– Singles Match: Bobby Lashley vs. Sami Zayn
– WWE SmackDown Tag Team Title Match (Pre-Show): Luke Harper & Erick Rowan © vs. Karl Anderson & Luke Gallows
What are your thoughts on this card? Sound off in the comment section.


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Posted by Kate Gerogianis on May 10, 2007, 6:25 pm
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Have a wonderful day all !
Message Thread:
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Well, its now official. Many of you have known for a while, but we are moving! The Middleton family is moving up to Washington D.C. for one year. We applied and got accepted to an internship program at National Community Church pastored by Mark Batterson. We will be filling the church planter in residence position in their internship program. Amy and I are very excited and very sad at the same time.
We are leaving behind our best friends on the planet, an amazing church and campus ministry. To keep this from getting way to long on my blog, here is an incredibly abbreviated version, call me if you want the long version.
A little over a year ago, Amy told me she felt like there was transition coming for us. I should have known better but I disagreed:) Around January, after praying for a few months about it, I also started to feel that there was transition coming. At this point there was a lot of dialogue among our church staff about what the future would look like at Every Nation Tallahassee. For a while we thought we were going to stay in Tallahassee for a while longer and had talked pretty extensively with our Pastor about what that could potentially look like. In March P. Ron (the senior pastor of Every Nation Tallahassee) and I sat down and he told me that around October he also felt like it was time for us to transition. This was huge for both Amy and I. It was very important that our spiritual authority was on the same page as us.
At this point we weren't 100% sure if that meant staying in Tallahassee or moving away but we were leaning towards moving for several reasons. One, if we are going to do any moving , we want to do it while our boys are young, so the effects are minimal on them. Secondly, we've been talking about planting a church for years now and always knew it was in the future, but were waiting to discern the times and seasons for our family. After talking with many people and praying, we felt like it was better to do it sooner rather than later. We also felt it would be a little early for us to go out and plant now. The failure rate in church planting is so high we wanted to get some other tools in our belt before we actually left and planted. We believe the internship at NCC will help prepare us and expose us to some different ways and ideas of doing church than we have had and felt that experience would be very valuable. Thirdly, God confirmed to us supernaturally 3 additional times that this was the right move for us.
After our year is up in D.C., our plan is to go plant a church with Every Nation. We both absolutely love our ministry. The vision, and values of our ministry is what beats in our hearts and we can't wait to be apart of Every Nation in a different role. We have loved being campus ministers and we can't thank the leadership of our ministry enough, they have truly made it an amazing ministry to work for. While up in D.C. in our spare time we are hoping to have enough time to serve at our ENCM chapters up in D.C. We look forward to connecting with some of our Every Nation family up in D.C. in addition to making lots of great new friends at NCC.
We are leaving our ministry in better hands at FSU. Donny and Janna Fisher have taken over and are now serving as the campus directors at FSU for ENCM. There is not a better couple for the job. They are some of our closest friends in the world and will do an amazing job. We pray they, along with Susan Reed and our future staff, Bella Chung and Derek Kasdorf will take our campus ministry farther than we ever did. We also are leaving behind our amazing students and student leaders. Trust me, we already miss you. I've already written several blogs that will be published in the next few days about transition and some of the lessons we've learned.
Amy and I are so excited to be living the adventure that God has put us on. Ministry has taken us places we may not have otherwise gone. We are so thankful to have spent the last 10 years of our life in Tallahassee at our church and with our staff, its been an amazing 10 years and we could not have asked for more.
Lastly, we are not sure where we are going to plant a church, and are currently praying through that. We are open to suggestions:) We would love for many of you to come with us to relocate to that city when we figure out where it is. If you are interested in supporting us financially or through prayer, we would love to talk to you about this next season of our lives and how you could be apart. God bless and stay classy Tallahassee.


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Florida Agricultural and Mechanical University has the following programs available online:
Degree details for these programs are included in our database.
Additional information about Florida Agricultural and Mechanical University may be found on the following websites:
View the main website at: www.famu.edu
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Monarchs Announce 2018-19 Season-Opening Roster
October 11, 2018 - ECHL (ECHL) - Manchester Monarchs News Release
MANCHESTER, N.H. - The Manchester Monarchs, proud ECHL affiliate of the National Hockey League's Los Angeles Kings, announced today the team's season-opening roster for the 2018-19 season.
The roster consists of 13 forwards, seven defenseman and two goaltenders, and is highlighted by 13 returning players from the 2017-18 Monarchs roster.
Manchester Monarchs 2018-19 Season-Opening Roster
Goaltenders - 2:
Charles Williams, Edwin Minney.
Defensemen - 7:
Colton Saucerman, David Kolomatis, Eric Schurhamer, Chris Carlisle, Rob Hamilton, Craig Wyszomirski, Stepan Falkovsky.
Forwards - 13:
Nic Pierog, Cory Ward, Michael Doherty, Joe Sullivan, Gasper Kopitar, Tony Cameranesi, Sam Kurker, Zeb Knutson, Nick Moutrey, Brendan Collier, Jared Fiegl, Matt Marcinew, Spencer Watson.
The Monarchs open the season on the road against the Brampton Beast on Oct. 13, before returning home on Oct. 19 at 7 p.m. to face the Newfoundland Growlers. For tickets to Opening Night, Season Memberships and group tickets, please contact the Monarchs front office at 603-626-7825.
Individual game tickets are on sale now through Ticketmaster.
• Discuss this story on the ECHL message board...
ECHL Stories from October 11, 2018
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- Glads to Hold Player Meet and Greet at Uncle Jack's Meat House - Atlanta Gladiators
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- ECHL Transactions - October 11 - ECHL
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- Stingrays Announce Season-Opening Roster for 2018-19 - South Carolina Stingrays
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- Grizzlies Release Opening Day Roster - Utah Grizzlies
- Cool Insuring Arena Announces Upstate Chevy Dealers Box Office - Adirondack Thunder
- Everblades Announce 2018-19 Season-Opening Roster - Florida Everblades
- Cyclones Unveil Season-Opening Roster - Cincinnati Cyclones
- Fuel Announce 2018-19 Opening Night Roster - Indy Fuel
- Komets Open Season at Indy Friday - Fort Wayne Komets
- Luukko Named Captain, Royals Announce Season-Opening Roster - Reading Royals
- ECHL Announces Season-Opening Rosters - ECHL
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- Gladiators Announce Opening Night Roster - Atlanta Gladiators
- Monarchs Announce 2018-19 Season-Opening Roster - Manchester Monarchs
- Rocco Carzo Named Team Captain - Kansas City Mavericks
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- K-Wings Release Opening Night Roster - Kalamazoo Wings
- Swamp Rabbits Announce Season Opening Roster - Greenville Swamp Rabbits
- Americans Announce 2018-2019 Regular Season Roster - Allen Americans
- 2018-19 Allen Americans Season Preview - Allen Americans
- IceMen Announce Season-Opening Roster - Jacksonville IceMen
- Steelheads Announce 2018-19 Opening Night Roster - Idaho Steelheads
- Worcester Railers HC Announce 2018-19 Opening Night Roster - Worcester Railers HC
- Joey Leach Named Stingrays Team Captain - South Carolina Stingrays
- Oilers Reveal Season-Opening Roster - Tulsa Oilers
- Mavericks Announce Opening Day Roster - Kansas City Mavericks
The opinions expressed in this release are those of the organization issuing it, and do not necessarily reflect the thoughts or opinions of OurSports Central or its staff.


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