arXiv subjects

Koji M. Kobayashi

Publications and source records attributed to Koji M. Kobayashi.

8 recordsLinked to original sources

arXiv2021

Connected domination in grid graphs

Given an undirected simple graph, a subset of the vertices of the graph is a {\em dominating set} if every vertex not in the subset is adjacent to at least one vertex in the subset. A subset of the vertices of the graph is a {\em connected dominating set} if the subset is a dominating set and the subgraph induced by the subset is connected. In this paper, we determine the minimum cardinality of a connected dominating set, called the {\em connected domination number}, of an $m \times n$ grid graph for any $m$ and $n$.

math.CO↗

arXiv2018

An optimal algorithm for 2-bounded delay buffer management with lookahead

The bounded delay buffer management problem, which was proposed by Kesselman et~al.\ (STOC 2001 and SIAM Journal on Computing 33(3), 2004), is an online problem focusing on buffer management of a switch supporting Quality of Service (QoS). The problem definition is as follows: Packets arrive to a buffer over time and each packet is specified by the {\em release time}, {\em deadline} and {\em value}. An algorithm can transmit at most one packet from the buffer at each integer time and can gain its value as the {\em profit} if transmitting a packet by its deadline after its release time. The objective of this problem is to maximize the gained profit. We say that an instance of the problem is $s$-bounded if for any packet, an algorithm has at most $s$ chances to transmit it. For any $s \geq 2$, Hajek (CISS 2001) showed that the competitive ratio of any deterministic algorithm is at least $(1 + \sqrt{5})/2 \approx 1.619$. It is conjectured that there exists an algorithm whose competitive ratio matching this lower bound for any $s$. However, it has not been shown yet. Then, when $s = 2$, B{ö}hm et al.~(ISAAC 2016) introduced the {\em lookahead} ability to an online algorithm, that is the algorithm can gain information about future arriving packets, and showed that the algorithm achieves the competitive ratio of $(-1 + \sqrt{13})/2 \approx 1.303$. Also, they showed that the competitive ratio of any deterministic algorithm is at least $(1 + \sqrt{17})/4 \approx 1.281$. In this paper, for the 2-bounded model with lookahead, we design an algorithm with a matching competitive ratio of $(1 + \sqrt{17})/4$.

cs.DS↗

arXiv2018

Online interval scheduling to maximize total satisfaction

The interval scheduling problem is one variant of the scheduling problem. In this paper, we propose a novel variant of the interval scheduling problem, whose definition is as follows: given jobs are specified by their {\em release times}, {\em deadlines} and {\em profits}. An algorithm must start a job at its release time on one of $m$ identical machines, and continue processing until its deadline on the machine to complete the job. All the jobs must be completed and the algorithm can obtain the profit of a completed job as a user's satisfaction. It is possible to process more than one job at a time on one machine. The profit of a job is distributed uniformly between its release time and deadline, that is its interval, and the profit gained from a subinterval of a job decreases in reverse proportion to the number of jobs whose intervals intersect with the subinterval on the same machine. The objective of our variant is to maximize the total profit of completed jobs. This formulation is naturally motivated by best-effort requests and responses to them, which appear in many situations. In best-effort requests and responses, the total amount of available resources for users is always invariant and the resources are equally shared with every user. We study online algorithms for this problem. Specifically, we show that for the case where the profits of jobs are arbitrary, there does not exist an algorithm whose competitive ratio is bounded. Then, we consider the case in which the profit of each job is equal to its length, that is, the time interval between its release time and deadline. For this case, we prove that for $m = 2$ and $m \geq 3$, the competitive ratios of a greedy algorithm are at most $4/3$ and at most $3$, respectively. Also, for each $m \geq 2$, we show a lower bound on the competitive ratio of any deterministic algorithm.

cs.DS↗

arXiv2017

Improved Bounds for Online Dominating Sets of Trees

The online dominating set problem is an online variant of the minimum dominating set problem, which is one of the most important NP-hard problems on graphs. This problem is defined as follows: Given an undirected graph $G = (V, E)$, in which $V$ is a set of vertices and $E$ is a set of edges. We say that a set $D \subseteq V$ of vertices is a {\em dominating set} of $G$ if for each $v \in V \setminus D$, there exists a vertex $u \in D$ such that $\{ u, v \} \in E$. The vertices are revealed to an online algorithm one by one over time. When a vertex is revealed, edges between the vertex and vertices revealed in the past are also revealed. A revelaed subtree is connected at any time. Immediately after the revelation of each vertex, an online algorithm can choose vertices which were already revealed irrevocably and must maintain a dominating set of a graph revealed so far. The cost of an algorithm on a given tree is the number of vertices chosen by it, and its objective is to minimize the cost. Eidenbenz (Technical report, Institute of Theoretical Computer Science, ETH Zürich, 2002) and Boyar et al.\ (SWAT 2016) studied the case in which given graphs are trees. They designed a deterministic online algorithm whose competitive ratio is at most three, and proved that a lower bound on the competitive ratio of any deterministic algorithm is two. In this paper, we also focus on trees. We establish a matching lower bound for any deterministic algorithm. Moreover, we design a randomized online algorithm whose competitive ratio is at most $5/2 = 2.5$, and show that the competitive ratio of any randomized algorithm is at least $4/3 \approx 1.333$.

cs.DS↗

arXiv2016

A tight analysis of Kierstead-Trotter algorithm for online unit interval coloring

Kierstead and Trotter (Congressus Numerantium 33, 1981) proved that their algorithm is an optimal online algorithm for the online interval coloring problem. In this paper, for online unit interval coloring, we show that the number of colors used by the Kierstead-Trotter algorithm is at most $3 ω(G) - 3$, where $ω(G)$ is the size of the maximum clique in a given graph $G$, and it is the best possible.

cs.DS↗

arXiv2015

An improved lower bound for one-dimensional online unit clustering

The online unit clustering problem was proposed by Chan and Zarrabi-Zadeh (WAOA2007 and Theory of Computing Systems 45(3), 2009), which is defined as follows: "Points" are given online in the $d$-dimensional Euclidean space one by one. An algorithm creates a "cluster," which is a $d$-dimensional rectangle. The initial length of each edge of a cluster is 0. An algorithm can extend an edge until it reaches unit length independently of other dimensions. The task of an algorithm is to cover a new given point either by creating a new cluster and assigning it to the point, or by extending edges of an existing cluster created in past times. The goal is to minimize the total number of created clusters. Chan and Zarrabi-Zadeh proposed some method to obtain a competitive algorithm for the $d$-dimensional case using an algorithm for the one-dimensional case, and thus the one-dimensional case has been extensively studied including some variants of the unit clustering problem. In this paper, we show a lower bound of $13/8 = 1.625$ on the competitive ratio of any deterministic online algorithm for the one-dimensional unit clustering, improving the previous lower bound $8/5 (=1.6)$ presented by Epstein and van Stee (WAOA2007 and ACM Transactions on Algorithms 7(1), 2010). Note that Ehmsen and Larsen (SWAT2010 and Theoretical Computer Science, 500, 2013) showed the current best upper bound of $5/3$, and conjectured that the exact competitive ratio in the one-dimensional case may be $13/8$.

cs.DS↗

arXiv2014

Tight Analysis of Priority Queuing Policy for Egress Traffic

Recently, the problems of evaluating performances of switches and routers have been formulated as online problems, and a great amount of results have been presented. In this paper, we focus on managing outgoing packets (called {\em egress traffic}) on switches that support Quality of Service (QoS), and analyze the performance of one of the most fundamental scheduling policies {\em Priority Queuing} ($PQ$) using competitive analysis. We formulate the problem of managing egress queues as follows: An output interface is equipped with $m$ queues, each of which has a buffer of size $B$. The size of a packet is unit, and each buffer can store up to $B$ packets simultaneously. Each packet is associated with one of $m$ priority values $α_{j}$ ($1 \leq j \leq m$), where $α_{1} \leq α_{2} \leq \cdots \leq α_{m}$, $α_{1} = 1$, and $α_{m} = α$ and the task of an online algorithm is to select one of $m$ queues at each scheduling step. The purpose of this problem is to maximize the sum of the values of the scheduled packets. For any $B$ and any $m$, we show that the competitive ratio of $PQ$ is exactly $2 - \min_{x \in [1, m-1] } \{ \frac{ α_{x+1} }{ \sum_{j = 1}^{x+1} α_{j} } \}$. That is, we conduct a complete analysis of the performance of $PQ$ using worst case analysis. Moreover, we show that no deterministic online algorithm can have a competitive ratio smaller than $1 + \frac{ α^3 + α^2 + α}{ α^4 + 4 α^3 + 3 α^2 + 4 α+ 1 }$.

cs.DS↗

arXiv2013

Better Bounds for Online $k$-Frame Throughput Maximization in Network Switches

We consider a variant of the online buffer management problem in network switches, called the $k$-frame throughput maximization problem ($k$-FTM). This problem models the situation where a large frame is fragmented into $k$ packets and transmitted through the Internet, and the receiver can reconstruct the frame only if he/she accepts all the $k$ packets. Kesselman et al.\ introduced this problem and showed that its competitive ratio is unbounded even when $k=2$. They also introduced an "order-respecting" variant of $k$-FTM, called $k$-OFTM, where inputs are restricted in some natural way. They proposed an online algorithm and showed that its competitive ratio is at most $\frac{2kB}{\lfloor B/k \rfloor} + k$ for any $B \ge k$, where $B$ is the size of the buffer. They also gave a lower bound of $\frac{B}{\lfloor 2B/k \rfloor}$ for deterministic online algorithms when $2B \geq k$ and $k$ is a power of 2. In this paper, we improve upper and lower bounds on the competitive ratio of $k$-OFTM. Our main result is to improve an upper bound of $O(k^{2})$ by Kesselman et al.\ to $\frac{5B + \lfloor B/k \rfloor - 4}{\lfloor B/2k \rfloor} = O(k)$ for $B\geq 2k$. Note that this upper bound is tight up to a multiplicative constant factor since the lower bound given by Kesselman et al.\ is $Ω(k)$. We also give two lower bounds. First we give a lower bound of $\frac{2B}{\lfloor {B/(k-1)} \rfloor} + 1$ on the competitive ratio of deterministic online algorithms for any $k \geq 2$ and any $B \geq k-1$, which improves the previous lower bound of $\frac{B}{\lfloor 2B/k \rfloor}$ by a factor of almost four. Next, we present the first nontrivial lower bound on the competitive ratio of randomized algorithms. Specifically, we give a lower bound of $k-1$ against an oblivious adversary for any $k \geq 3$ and any $B$.

cs.DS↗