Improving the Competitive Ratio of the Online OVSF Code Assignment Problem

Miyazaki, Shuichi; Okamoto, Kazuya

doi:10.3390/a2030953

Cited by 3 publications

(2 citation statements)

References 10 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They improved the lower bound of the competitive ratio to 5/3 ≈ 1.67 [2]. Very recently, Miyazaki and Okamoto [14] gave a 7-competitive algorithm and improved the lower bound of the competitive ratio to 2.…”

Section: Introductionmentioning

confidence: 99%

“…1 gives a valid tree node assignment. The tree node assignment problem can be considered as a general resource allocation problem, which can model the specific problems, such as the Orthogonal Variable Spreading Factor (OVSF) code assignment problem [2,3,7,12,13,14,15,16,17], the buddy memory allocation problem [1,5,10,11], and the hypercube subcube allocation problem [6]. The main difference between these problems is how the resource, the nodes at level i, for 0 ≤ i ≤ h, are interpreted.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Online Tree Node Assignment with Resource Augmentation

Chan

Chin

Ting

et al. 2009

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Given a complete binary tree of height h, the online tree node assignment problem is to serve a sequence of assignment/release requests, where an assignment request, with an integer parameter 0 ≤ i ≤ h, is served by assigning a (tree) node at level (or height) i and a release request is served by releasing a specified assigned node. The node assignments have to guarantee that no node is assigned to two assignment requests unreleased, and every leaf-to-root path of the tree contains at most one assigned node. With assigned node reassignments allowed, the target of the problem is to minimize the number of assignments/reassigments, i.e., the cost, to serve the whole sequence of requests. This online tree node assignment problem is fundamental to many applications, including OVSF code assignment in WCDMA networks, buddy memory allocation and hypercube subcube allocation.Most of the previous results focus on how to achieve good performance when the same amount of resource is given to both the online and the optimal offline algorithms, i.e., one tree. In this paper, we focus on resource augmentation, where the online algorithm is allowed to use more trees than the optimal offline algorithm. By using different approaches, we give (1) a 1-competitive online algorithm, which uses (h + 1)/2 trees, and is optimal because (h + 1)/2 trees are required by any online algorithm to match the cost of the optimal offline algorithm with one tree; (2) a 2-competitive algorithm with 3h/8 + 2 trees; (3) an amortized (4/3 + α)-competitive algorithm with (11/4 + 4/(3α)) trees, for any α where 0 < α ≤ 4/3.

show abstract

Section: Introductionmentioning

confidence: 99%