The Scheduler is Very Powerful in Competitive Analysis of Distributed List Accessing
July 18, 2018 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Joan Boyar, Faith Ellen, Kim S. Larsen
arXiv ID
1807.06820
Category
cs.DS: Data Structures & Algorithms
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
This work is a continuation of efforts to define and understand competitive analysis of algorithms in a distributed shared memory setting, which is surprisingly different from the classical online setting. In fact, in a distributed shared memory setting, we find a counter-example to the theorem concerning classical randomized online algorithms which shows that, if there is a $c$-competitive randomized algorithm against an adaptive offline adversary, then there is a $c$-competitive deterministic algorithm [Ben-David, Borodin, Karp, Tardos, Wigderson, 1994]. In a distributed setting, there is additional lack of knowledge concerning what the other processes have done. There is also additional power for the adversary, having control of the scheduler which decides when each process is allowed to take steps. We consider the list accessing problem, which is a benchmark problem for sequential online algorithms. In the distributed version of this problem, each process has its own finite sequence of requests to a shared list. The scheduler arises as a major issue in its competitive analysis. We introduce two different adversaries, which differ in how they are allowed to schedule processes, and use them to perform competitive analysis of distributed list accessing. We prove tight upper and lower bounds on combinatorial properties of merges of the request sequences, which we use in the analysis. Our analysis shows that the effects of the adversarial scheduler can be quite significant, dominating the usual quality loss due to lack of information about the future.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Data Structures & Algorithms
π
π
The Cartographer
R.I.P.
π»
Ghosted
Route Planning in Transportation Networks
R.I.P.
π»
Ghosted
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
R.I.P.
π»
Ghosted
Hierarchical Clustering: Objective Functions and Algorithms
R.I.P.
π»
Ghosted
Graph Isomorphism in Quasipolynomial Time
π
π
The Cartographer
Simulation optimization: A review of algorithms and applications
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted