# The Relative Worst Order Ratio for On-Line Algorithms

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1 The Relative Worst Order Ratio for On-Line Algorithms Joan Boyar 1 and Lene M. Favrholdt 2 1 Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark, 2 Department of Computer Science, University of Copenhagen, Denmark, Abstract. We consider a new measure for the quality of on-line algorithms, the relative worst order ratio, using ideas from the Max/Max ratio [2] and from the random order ratio [8]. The new ratio is used to compare on-line algorithms directly by taking the ratio of their performances on their respective worst orderings of a worst-case sequence. Two variants of the bin packing problem are considered: the Classical Bin Packing Problem and the Dual Bin Packing Problem. Standard algorithms are compared using this new measure. Many of the results obtained here are consistent with those previously obtained with the competitive ratio or the competitive ratio on accommodating sequences, but new separations and easier results are also shown to be possible with the relative worst order ratio. 1 Introduction The standard measure for the quality of on-line algorithms is the competitive ratio [5, 10, 7], which is, roughly speaking, the worst-case ratio, over all possible input sequences, of the on-line performance to the optimal off-line performance. There have been many attempts to provide alternative measures which either give more realistic results than the competitive ratio or do better at distinguishing between algorithms which have very different behaviors in practice. Two very interesting attempts at this are the Max/Max ratio [2] and the random order ratio [8]. The Max/Max Ratio. The Max/Max ratio allows direct comparison of two online algorithms for an optimization problem, without the intermediate comparison to OPT, as is necessary with the competitive ratio. Rather than comparing Supported in part by the Danish Natural Science Research Council (SNF) and in part by the Future and Emerging Technologies program of the EU under contract number IST (alcom-ft). Part of this work was done while visiting the Computer Science Department, University of Toronto. Part of this work was done while working at the Department of Mathematics and Computer Science, University of Southern Denmark.

2 two algorithms on the same sequence, they are compared on their respective worst case sequences of the same length. Ben-David and Borodin [2] demonstrate that for the k-server problem the Max/Max ratio can provide more optimistic and detailed results than the competitive ratio. The Random Order Ratio. The random order ratio gives the possibility of considering some randomness of the request sequences without specifying a complete probability distribution. For an on-line algorithm A for a minimization problem, the random order ratio is the maximum ratio over all multi-sets of requests of the expected performance of A compared with OPT on a random permutation of the multi-set. If, for all possible multi-sets of requests, any ordering of these requests is equally likely, this ratio gives a meaningful worst-case measure of how well an algorithm can do. Kenyon [8] has shown that for the Classical Bin Packing Problem, the random order ratio of Best-Fit lies between 1.08 and 1.5. In contrast, the competitive ratio of Best-Fit is 1.7. The Relative Worst Order Ratio. Attempting to combine the desirable properties of both the Max/Max ratio and the random order ratio, we define the relative worst order ratio, where when comparing two on-line algorithms, we consider a worst-case sequence and take the ratio of how the two algorithms do on their worst orderings of that sequence. Note that the two algorithms may have different worst orderings for the same sequence. The Worst Order Ratio. Although one of the goals in defining the relative worst order ratio was to avoid the intermediate comparison of any on-line algorithm to the optimal off-line algorithm OPT, it is still possible to compare on-line algorithms to OPT. In this case, the measure is called the worst order ratio. Note that for many problems, the worst order ratio is the same as the competitive ratio, since the order in which requests arrive does not matter for an optimal off-line algorithm. However, for the Fair Bin Packing Problem mentioned below, the order does matter, even for OPT. The same is true for bounded space bin packing [6] where only a limited number of bins are allowed open at one time. The Bin Packing Problems. In the Classical Bin Packing Problem we are given an unlimited number of unit sized bins and a sequence of items each with a non-negative size, and the goal is to minimize the number of bins used to pack all the items. In contrast, in the Dual Bin Packing Problem, we are given a fixed number n of unit sized bins, and the goal is to maximize the number of items packed in the n bins. A variant of Dual Bin Packing is the Fair Bin Packing Problem, where the algorithms have to be fair, i.e., to reject items only when they do not fit in any bin. Our Results. Many results obtained are consistent with those previously obtained with the competitive ratio or the competitive ratio on accommodating sequences [4], but new separations and easier proofs are also shown to be possible with the relative worst order ratio. For the Classical Bin Packing Problem, First-Fit and Best-Fit are better than Worst-Fit, which is better than Next-Fit. This latter result is in contrast to the

3 competitive ratio, where there appears to be no advantage to Worst-Fit being able to use empty space in earlier bins, since both have a competitive ratio of 2 [6]. First-Fit is still the best Any-Fit algorithm and Next-Fit is no better than any Any-Fit algorithm. The worst order ratio for any fair deterministic algorithm for the Dual Bin Packing Problem is not bounded below by any constant. In contrast, with the relative worst order ratio, one gets constant ratios. We find that First-Fit does at least as well as any Any-Fit algorithm and is better than Best-Fit, which is better than Worst-Fit. Worst-Fit is at least as bad as any fair on-line algorithm and strictly worse than First-Fit. This contrasts favorably with the competitive ratio, where Worst-Fit is better than First-Fit [4]. Unfair-First-Fit is an algorithm for Dual Bin Packing which is not fair and does better than First-Fit when using the competitive ratio on accommodating sequences. Under the relative worst order ratio, Unfair-First-Fit is incomparable to all Any-Fit algorithms, i.e., for any Any-Fit algorithm A there are sequences where A does better and sequences where Unfair-First-Fit does better. 2 The (Relative) Worst Order Ratio This paper considers the worst order ratio as well as the relative worst order ratio. The relative worst order ratio appears to be the more interesting measure for the two variants of bin packing studied here. The definition of the relative worst order ratio uses A W (I), the performance of an on-line algorithm A on the worst ordering of the multi-set I of requests, formally defined in the following way. Definition 1. Consider an on-line problem P and let I be any request sequence of length n. If σ is a permutation on n elements, then σ(i) denotes I permuted by σ. For a maximization problem, A(I) is the value of running the on-line algorithm A on I, and A W (I) = min σ A(σ(I)). For a minimization problem, A(I) is a cost, and A W (I) = max σ A(σ(I)). Definition 2. Let S 1 (c) be the statement There exists a constant b such that A W (I) c B W (I) + b for all I and let S 2 (c) be the statement There exists a constant b such that A W (I) c B W (I) b for all I. The relative worst order ratio WR A,B of on-line algorithm A to algorithm B is defined if S 1 (1) or S 2 (1) holds. Otherwise the ratio is undefined and the algorithms are said to be incomparable. If S 1 (1) holds, then WR A,B = sup {r S 2 (r)}. If S 2 (1) holds, then WR A,B = inf {r S 1 (r)}. Note that if S 1 (1) holds, the supremum involves S 2 rather than S 1, and vice versa. A ratio of 1 means that the two algorithms perform identically with

4 minimization maximization A better than B < 1 > 1 B better than A > 1 < 1 Table 1. Ratio values for minimization and maximization problems respect to this quality measure; the further away from 1 the greater the difference in performance. The ratio may be greater than or less than one, depending on whether the problem is a minimization or a maximization problem and on which of the two algorithms is better. These possibilities are illustrated in Table 1. Although not all pairs of algorithms are comparable with the relative worst order ratio, for algorithms which are comparable, the measure is transitive. Theorem 1. The ordering of algorithms for a specific problem is transitive. Proof. Suppose that three algorithms A, B, and C for some on-line problem, P, are such that A is at least as good as B and B is at least as good as C, as measured by the relative worst order ratio. If P is a minimization problem there exists a constant b such that for all I, A W (I) B W (I) + b, and there exists a constant d such that for all I, B W (I) C W (I) + d, so there exist constants b and d such that for all I, A W (I) C W (I) + b + d, Thus A also performs at least as well as C, according to the relative worst order ratio. The argument for a maximization problem is essentially the same. Finally we define the worst order ratio formally: Definition 3. The worst order ratio WR A of an on-line algorithm A is the relative worst order ratio of A to an optimal off-line algorithm OPT, i.e., WR A = WR A,OPT. As mentioned in the introduction, if there is no restriction on the behavior of OPT, the worst order ratio is the same as the competitive ratio. This does not necessarily hold for the Fair Bin Packing Problem or bounded space bin packing, because of the restrictions on OPT s behavior. Clearly, the worst order ratio is never worse than the competitive ratio. 3 Classical Bin Packing The Classical Bin Packing Problem is a minimization problem, so algorithms which do well compared to others have relative worst order ratios of less than 1 to the poorer algorithms. We consider Any-Fit algorithms, a class of fair algorithms defined by Johnson [6], which use an empty bin, only if there is not enough space in any partially full bins. Three examples of Any-Fit algorithms are First-Fit (FF), which places an item in the first bin in which it fits, Best-Fit (BF), which places an item in one of the fullest bins in which it fits, and Worst-Fit (WF), which will place an item in a least full open bin. We also consider an algorithm, Next-Fit (NF), which is

5 not an Any-Fit algorithm. Next-Fit is the algorithm which first attempts to fit an item in the current bin, places it there if it fits, or opens a new bin if it does not. Most of the results we obtain with the relative worst order ratio for the Classical Bin Packing Problem are very similar to those obtained for the competitive ratio and use similar techniques. However, the relative worst order ratio separates Worst-Fit and Next-Fit, which the competitive ratio cannot. We first present those results consistent with the competitive ratio. According to the competitive ratio for the Classical Bin Packing Problem, First-Fit is the best Any-Fit algorithm [6]. Using the same proof, one can show that this also holds for the relative worst order ratio. The idea is to consider the First-Fit packing of an arbitrary sequence. If the items are given bin by bin, any Any-Fit algorithm will produce exactly the same packing. Theorem 2. For any Any-Fit algorithm A, WR FF,A 1. Not all Any-Fit algorithms perform as well. Worst-Fit is the worst possible among the Any-Fit algorithms, and it is significantly worse than First-Fit and Best-Fit. Theorem 3. For any Any-Fit algorithm A, WR WF,A 1. Proof. Consider a request sequence I and its packing by A. Call the bins used by A, b 1, b 2,..., b n, numbered according to the order in which they were opened by A. Let l A (b j ) be the level of b j, i.e., the sum of the sizes of the items packed in b j. Let l min A (j) = min 1 i j {l(b i )}. Furthermore, let E j be the set of items packed in b j, 1 j n. Let I be a permutation of I, where each item e E i appears before each item e E j, for 1 i < j n. Consider the packing of I produced by Worst- Fit. Let l WF (b j ) be the level of b j in this packing. We prove by induction on j that, for 1 j n, l WF (b j ) l min A (j) and all items in j i=1 E i are packed in b 1,..., b j by Worst-Fit. The base case j = 1 is trivial: all items in E 1 are clearly packed in b 1, since Worst-Fit is an Any-Fit algorithm. For j > 1, the induction hypothesis says that l WF (b i ) l min A (i), 1 i j 1, and that before giving the items of E j, b j is empty. If Worst-Fit packs all items of E j in b j, the result trivially follows. Assume now, that some item in E j is packed in some bin b i b j. Since b j is empty before giving the items of E j and Worst-Fit is an Any-Fit algorithm, we conclude that i < j. By the Worst-Fit packing rule, l WF (b j ) l WF (b i ) l min A (i) lmin A (j). Now, let e j be the first item packed by A in b j, 1 j n. Since A is an Any-Fit algorithm, e j does not fit in b i, 1 i < j. This means that e j is larger than 1 l min A (j 1). Since, in the Worst-Fit packing, b i has a level of at least l min A (i), this means that for each b i and each e j, 1 i < j n, e j does not fit in b i. In words, for each bin b i, the bottommost item in each bin b j, 1 i < j n, in the packing of A does not fit in b i in the Worst-Fit packing. Hence, for each e j, 1 j n, Worst-Fit must open a new bin, i.e., Worst-Fit uses the same number of bins as A.

6 Using Johnson s results and techniques [6], one can show that the relative worst order ratio of Worst-Fit to either First-Fit or Best-Fit is 2. Theorem 4. WR WF,FF = WR WF,BF = 2. Proof. Since FF W (I) BF W (I) for all I, by the proof of Theorem 2, it is only necessary to compare Worst-Fit and Best-Fit. The above theorem shows that WR WF,BF 1, so in order to prove a lower bound of 2, it is sufficient to find a family of sequences I n, with lim n WF W (I n ) =, where there exists a constant b such that for all I n, WF W (I n ) 2BF W (I n ) b. The family of sequences used in [6] to bound Worst-Fit s competitive ratio works here. Let 0 < ε 1 2n. Consider the sequence I n with pairs ( 1 2, ε), for i = 1...n. In this order, Worst-Fit will pack all of the pairs, one per bin, using n bins. Best-Fit will pack the small items all in one bin, regardless of the ordering, using only n+1 2 bins. Thus, WF W(I n ) = n 2 n = 2BF W(I n ) 2, so the relative worst order ratio is at least 2. The relative worst order ratio of Worst-Fit to either First-Fit or Best-Fit is at most 2, since Worst-Fit s competitive ratio is 2 [6]. Now we consider Next-Fit, which is not an Any-Fit algorithm. Next-Fit is strictly worse than Worst-Fit and all other Any-Fit algorithms. This result is in contrast to the competitive ratio where Next-Fit and Worst-Fit both have ratios of 2 [6]. Theorem 5. For any Any-Fit algorithm A, WR NF,A = 2. Proof. To see that WR NF,A 1, consider any request sequence I and its packing by A. Create a new sequence I from I by taking the items bin by bin from A s packing, starting with the first bin, in the order they were placed in the bins, and concatenate the contents together to form the sequence I. Next-Fit also has to open a new bin for the first item put in each bin, so it ends up with the same configuration. Hence, for all I, NF W (I) A W (I), giving a ratio of at least one. Since WR NF,A 1, to prove the lower bound of 2 it is sufficient to find a family of sequences I n, with lim n NF W (I n ) =, where there exists a constant b such that for all I n, NF W (I n ) 2A W (I n ) b. Let 0 < ε 1 n 1. Consider the sequence I n with n 1 pairs (ε, 1). In this order, Next-Fit will pack each item in a new bin, whereas A will pack all of the small items in the same bin. Thus, NF W (I n ) = 2(n 1) = 2A W (I n ) 2, so WR NF,A 2. For the upper bound, note that the relative worst order ratio of Next-Fit to any Any-Fit algorithm is at most 2, since Next-Fit s competitive ratio is 2 [6]. 4 Dual Bin Packing When switching to the Dual Bin Packing Problem, which is a maximization problem, algorithms which do well compared to others have relative worst order ratios greater than 1, instead of less than 1, to the poorer algorithms.

7 First-Fit and Best-Fit are defined in the same manner for the Dual Bin Packing Problem as for the Classical Bin Packing Problem, though clearly they reject items which do not fit in any of the n bins. If one uses the same definition for Worst-Fit for Dual Bin Packing as for Classical Bin Packing, one can again show that it is the worst Any-Fit algorithm. However, we use a more natural definition for the fixed number of bins, where Worst-Fit always places an item in a least full bin. For the first n items, the least full bin will be empty, so the Worst-Fit we consider here is not an Any-Fit algorithm. (Note that this definition is not at all natural for the Classical Bin Packing Problem, since a new bin would be opened for every item. This variant of Worst-Fit is clearly the worst possible algorithm for the classical problem.) The result we obtain for the worst order ratio for the Dual Bin Packing Problem is similar to that obtained previously with the competitive ratio, while most of the results we obtain for the relative worst order ratio are similar to those for the competitive ratio on accommodating sequences. The proof that First-Fit and Best-Fit are strictly better than Worst-Fit, which is true with the relative worst order ratio and the competitive ratio on accommodating sequences, but not with the competitive ratio [4], is much easier using the relative worst order ratio. Computing the worst order ratio for deterministic algorithms for Fair Bin Packing, not surprisingly, gives similar results to the competitive ratio [4] very pessimistic results. Theorem 6. The worst order ratio for any deterministic algorithm for the Fair Bin Packing Problem is not bounded below by any constant. Proof. Consider any fair, deterministic on-line algorithm, A. For the following sequence, I, defined on the basis of A s performance, A will accept all of the larger items and reject all of the small, while, for any permutation of I, OPT will be able to arrange to reject some of the larger items and accept many of the small ones. Let 0 < ε < The sequence I begins with n items of size 1 3 +ε, called items of type A. Suppose A places these items in the bins, leaving q bins empty. Then, exactly q bins have two items and n 2q have one. The sequence continues with 1 Type B items: n + q items of size 3 Type C items: n 2q items of size 1 3 ε Type D items: q items of size 1 3 2ε Type E items: n 12ε n 4 items of size ε Items of types A, B, C, and D are the large items, and items of type E are the small items. Since it is fair, A is forced to accept all of the large items, completely filling up the bins, so that it must reject all the small items. A W (I) = 3n. In the worst ordering of I for OPT, OPT will be able to reject the least number of large items and thus accept the least number of small items. Consider such a worst-case ordering I = σ(i). Without loss of generality, we may assume that all of the large items in I come before all of the small. In such a sequence,

8 all items of type D are accepted since there are only 3n large items in all, and an item of type D will fit in any bin which only contains two large items. In addition, we may assume that all of the items of type D come after all of the larger items. To see this, suppose there exists an item x of type D, which occurs before some larger item y. Choose these items so that x occurs immediately before y. Now consider the sequence I, which is identical to I, except that the order of x and y is inverted. OPT accepts x in both orderings, since it is of type D. If it also accepts y in I, it will accept it in I too. If it rejects y in I, and also in I, I is also a worst ordering. If it rejects y in I, but accepts it in I, then its decision to reject y in I was because that was better, so I is at least as bad as I. One can continue in this manner moving all items of type D after the larger ones. A similar argument shows that all of the items of type A can be assumed to come before all of the others. If the items of type A are placed one per bin, then the items of type B will also have to be placed with at most one per bin, so at least q are rejected. If the items of type A are placed so that r bins are empty, then those r bins will each get three large items, and r bins will get exactly two items of type A, plus possibly one of type D. The remaining n 2r bins can each hold at most two items of type B or C. Thus, there is space for at most 3r +2(n 2r) = 2n r items of types B or C. This is smallest when r = n 2, its largest possible value. There are 2n q items of types B and C, so n 2 q are rejected. OPT can choose whether to place the items of type A one per bin or two per bin, whichever leads to more rejected large items. If q n 4, it will choose the first; otherwise it will choose the second. In any case, it rejects s n 4 large items and accepts at least s( 1 3 ε)/ε small items. Thus, the worst order ratio is at most WR A = 3n (3n s) + s( 1 3 ε)/ε = 3nε (3n s)ε + s( 1 3 ε) 3nε 3nε (3n 2s)ε + s 3 (3n n 2 )ε + n = 36ε 12 30ε + 1 < 36ε. Note that for the Dual Bin Packing Problem, the competitive ratio on accommodating sequences [4] can be used to get results concerning the relative worst order ratio, but the competitive ratio cannot necessarily. The problem with using the competitive ratio directly is that we are comparing to OPT which may be more able to take advantage of a fairness restriction with some orderings than with others. When the sequences are not accommodating sequences, then we may be looking at sequences where there is some order where OPT also does poorly. This cannot happen with accommodating sequences. For example, if algorithm A has a competitive ratio on accommodating sequences of at least p and B has a competitive ratio of at most r < p, then there is an accommodating sequence I where A W (I) p I > r I B W (I). This can help give a result in the case where one has already shown that A is at least as good as B on all sequences.

9 For Dual Bin Packing, one can again show that First-Fit is the best Any-Fit algorithm, also using the proof by Johnson [6], the only difference being that now any items First-Fit rejects are concatenated to the end of the sequence created for the Any-Fit algorithm A and will also be rejected by A. Theorem 7. For any Any-Fit algorithm A, WR FF,A 1. Theorem 8. WR FF,BF 7 6. Proof. The above theorem shows that WR FF,BF 1, so it is sufficient to find a family of sequences I n, with lim n FF W (I n ) =, where there exists a constant b such that for all I n, FF W (I n ) 7 6 BF W(I n ) b. Let 0 < ε < 1 8n. 2 Consider the sequence I n starting with pairs, ( niε, ε), for i = 0...n 1 and followed by 1 2 n(2i + 1)ε, for i = 0,..n 1 and n 1 of size nε. Best-Fit will reject the last n 1 items when they are given in this order. The worst order for First-Fit would be such that First-Fit paired together the items of size just less than 1 2, so that it could only accept n 2 of those larger than 1 2. Thus, FF W (I n ) 3n 1 + n 1 2 = 7 6 (3n) 3 2 = 7 6 BF W(I n ) 3 2, as required. Recall that for the Dual Bin Packing Problem, Worst-Fit is the algorithm which places an item in one of the bins which are least full; we assume it chooses the first such bin. Worst-Fit is a fair algorithm. Its relative worst order ratio to any other fair algorithm is less than or equal to one, so it is the worst such algorithm. To prove this we consider the packing of an arbitrary sequence done by the Any-Fit algorithm under consideration and define the height of an item e to be the total size of the items packed before e in the same bins as e. If the items are given in order of non-decreasing height and then the rejected items at the end, Worst-Fit will produce essentially the same packing, and reject exactly the same items the Any-Fit algorithm does. Theorem 9. For any fair algorithm A, WR WF,A 1. According to the relative worst order ratio, First-Fit and Best-Fit are strictly better than Worst-Fit. This is in contrast to the competitive ratio, where, for the restricted problem where all item sizes are multiples of some constant f, First- Fit and Best-Fit actually have worse ratios than Worst-Fit [4]. The relative worst order ratio corresponds more to the competitive ratio on accommodating sequences, where First-Fit and Best-Fit can be shown to perform better than Worst-Fit [4]. The result concerning the relative worst order ratio is, however, much easier to prove. Theorem 10. WR WF,FF = WR WF,BF = 1 2. Proof. The proof of the above theorem shows that there is no sequence I where WF W (I) > FF W (I) or WF W (I) > BF W (I), so to prove the upper bound it is sufficient to find a family of sequences I n, with lim n FF W (I n ) =, where there exists a constant b such that for all I n, WF W (I n ) 1 2 BF W(I n ) + b. Let 0 < ε 1 n, and let I n consist of n items of size ε, followed by n 1 of size 1.

10 Worst-Fit will accept only the n items of size ε when they are given in this order. First-Fit or Best-Fit will accept all of these items, regardless of their order. This n gives a ratio of 2n 1. Consider now the lower bound. Since Worst-Fit cannot have a lower ratio to Best-Fit than to First-Fit, it is sufficient to show that it holds for First-Fit. Consider any sequence I and the worst ordering of I for Worst-Fit. Without loss of generality, assume that all the items Worst-Fit accepts appear in I before those it rejects. Consider First-Fit s performance on this ordering of I, and suppose it accepts m items, but Worst-Fit only accepts m < m. Reorder the first m items so that First-Fit gets them bin by bin, according to Worst-Fit s packing. Since no item will be packed in a later bin by First-Fit than by Worst-Fit, for each item Worst-Fit accepts, First-Fit will have room for it in the same bin. Thus, First-Fit will accept all the items Worst-Fit accepts. First-Fit accepts at most n 1 more items than Worst-Fit, since each of the m m items which Worst-Fit rejects must be larger than the empty space in any of Worst-Fit s bins. Thus, the total size of any n rejected items (if there are that many) would be more than the total empty space in the n bins after packing the items accepted by Worst-Fit. Since Worst-Fit is fair, it must accept at least n items if it rejects any at all. Thus, the relative worst order ratio of Worst-Fit to either First-Fit or Best-Fit is at least n 2n 1. An example of an algorithm for the Dual Bin Packing Problem which is not fair is Unfair-First-Fit [1]. It behaves as First-Fit, except that when given a request of size greater than 1/2, it automatically rejects that item if it has already accepted at least 2/3 of the items it has received so far. The intuition is that by rejecting some large items, it may have more room for more small items. The algorithm is defined in Figure 1. Input: S = o 1, o 2,..., o n Output: A, R, and a packing for those items in A A:= {}; R:= {} while S o:= hd(s); S:= tail(s) if size(o) > 1 and A 2 2 A + R +1 3 R:= R {o} else if there is space for o in some bin pack o according to the First-Fit rule A:= A {o} else R:= R {o} Fig. 1. The algorithm Unfair-First-Fit

11 Theorem 11. Under the relative worst order ratio, Unfair-First-Fit is incomparable to all Any-Fit algorithms. Proof. It is easy to see that there exist sequences where Unfair-First-Fit does worse than any Any-Fit algorithm. Consider, for example, the request sequence containing n items of size 1. Unfair-First-Fit will only accept 2/3 of them, while any fair algorithm (and thus all Any-Fit algorithms) will accept all of them. Hence, on such a sequence, any Any-Fit algorithm accepts 3/2 times as many items as Unfair-First-Fit. To show the other direction, it suffices to compare Unfair-First-Fit to First- Fit, the best among the Any-Fit algorithms. Since the competitive ratio on accommodating sequences for First-Fit is bounded above by O( 1 n ), and the competitive ratio on accommodating sequences for Unfair-First-Fit is 2 3 ± Θ( 1 n ), for large enough n [1], there exists an accommodating sequence where Unfair-First-Fit outperforms First-Fit. Asymptotically, Unfair-First-Fit accepts times as many items as First-Fit Conclusion and Open Problems This new performance measure gives the advantages that one can compare two on-line algorithms directly, that it is intuitively suitable for some natural problems where any ordering of the input is equally likely, and that it is easier to compute than the random order ratio. It is also better than the competitive ratio at distinguishing between algorithms for Classical and Dual Bin Packing. Although the competitive ratio on accommodating sequences can also be used to show that Worst-Fit is better than First-Fit for Dual Bin Packing, the proof is easier with the relative worst order ratio. The definition of the competitive ratio has been taken rather directly from that of the approximation ratio. This seems natural in that on-line algorithms can be viewed as a special class of approximation algorithms. However, for approximation algorithms, the comparison to OPT is very natural, since one is comparing to another algorithm of the same general type, just with more computing power, while for on-line algorithms, the comparison to OPT is to a different type of algorithm. Although the competitive ratio has been an extremely useful notion, in many cases it has appeared inadequate at differentiating between on-line algorithms. When this is the goal, doing a direct comparison between the algorithms, instead of involving an intermediate comparison to OPT, seems the obvious choice. A direct comparison on exactly the same sequences will produce the result that many algorithms are incomparable because one algorithm does well on one type of ordering, while the other does well on another type. With the relative worst order ratio, on-line algorithms are compared directly to each other on their respective worst orderings of multisets. This first study of this new measure seems very promising in that most results obtained are consistent with those obtained with the competitive ratio, but new separations are found.

12 Work in progress [3] shows that for the paging problem, the relative worst order ratio of LRU (FIFO) to LRU (FIFO) with lookahead l is min(k, l+1) when there are k pages in fast memory. This compares well with the competitive ratio, where these algorithms have the same competitive ratio. This result is similar to that which Koutsoupias and Papadimitriou obtained using comparative analysis [9], and stronger than that obtained with the Max/Max ratio [2]. The relative worst order ratio should be applied to other on-line problems to see if it is also useful for those problems. For the Classical Bin Packing Problem, there exist multi-sets where First- Fit s worst ordering uses one less bin than Best-Fit s worst ordering. One example of this is the following sequence: 1 4, 1 4, ε, 3 4, ε, 1 4, 1 4, where Best-Fit uses three bins for its worst ordering, while First-Fit only uses two. However this seems to be hard to extend to an asymptotic result. Determining if WR FF,BF < 1 is an open problem. 6 Acknowledgments We would like to thank Kim Skak Larsen for helpful discussions. References 1. Y. Azar, J. Boyar, L. Epstein, L. M. Favrholdt, K. S. Larsen, and M. N. Nielsen. Fair versus Unrestricted Bin Packing. Algorithmica, 34(2): , S. Ben-David and A. Borodin. A New Measure for the Study of On-Line Algorithms. Algorithmica, 11(1):73 91, J. Boyar, L. M. Favrholdt, and K. S. Larsen. Work in progress. 4. J. Boyar, K. S. Larsen, and M. N. Nielsen. The Accommodating Function a Generalization of the Competitive Ratio. SIAM Journal of Computation, 31(1): , Also in WADS 99, pages R. L. Graham. Bounds for Certain Multiprocessing Anomalies. Bell Systems Technical Journal, 45: , D. S. Johnson. Fast Algorithms for Bin Packing. Journal of Computer and System Sciences, 8: , A. R. Karlin, M. S. Manasse, L. Rudolph, and D. D. Sleator. Competitive Snoopy Caching. Algorithmica, 3(1):79 119, C. Kenyon. Best-Fit Bin-Packing with Random Order. In 7th Annual ACM-SIAM Symposium on Discrete Algorithms, pages , E. Koutsoupias and C. H. Papadimitriou. Beyond Competitive Analysis. In 35th Annual Symposium on Foundations of Computer Science, pages , D. D. Sleator and R. E. Tarjan. Amortized Efficiency of List Update and Paging Rules. Communications of the ACM, 28(2): , 1985.

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