Subtopic Deep Dive
Online Buffer Management Algorithms
Research Guide
What is Online Buffer Management Algorithms?
Online buffer management algorithms are competitive online algorithms for managing finite buffers in data streams and queuing systems to minimize evictions and overflows.
These algorithms process packet arrivals without future knowledge, evaluated via competitive ratio against optimal offline strategies. Goldwasser (2010) surveys buffer policies for packet switches with 121 citations. Research includes strict competitive analysis and randomized variants for network switches.
Why It Matters
Online buffer management algorithms optimize high-speed routers and switches, reducing packet drops in internet infrastructure (Goldwasser, 2010). They support caching in data centers and wireless sensor networks (Luong et al., 2016). Acharya and Muthukrishnan (1998) apply similar principles to on-demand broadcast scheduling with 232 citations.
Key Research Challenges
Tight Competitive Ratios
Achieving optimal competitive ratios remains open for many buffer models, especially with variable packet sizes. Goldwasser (2010) notes unresolved bounds for multi-queue settings. Randomized algorithms improve ratios but require probabilistic analysis.
Multi-Queue Coordination
Coordinating buffers across multiple input ports leads to complex interactions and evictions. Tay et al. (1985) model locking performance in databases, analogous to buffer contention with 146 citations. Scalability to high port counts challenges greedy strategies.
Adapting to Workload Changes
Algorithms must handle dynamic traffic patterns without preprocessing. Lampson (1968) discusses scheduling philosophy for multiprocessing, relevant to buffer adaptation with 102 citations. Setup costs complicate transitions (Allahverdi et al., 2006).
Essential Papers
A survey of scheduling problems with setup times or costs
Ali Allahverdi, C.T. Ng, T.C.E. Cheng et al. · 2006 · European Journal of Operational Research · 1.3K citations
Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey
Nguyen Cong Luong, Dinh Thai Hoang, Ping Wang et al. · 2016 · IEEE Communications Surveys & Tutorials · 349 citations
This paper provides a state-of-the-art literature review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT). Wireless sensor networks...
Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks
Roni Stern, Nathan Sturtevant, Ariel Felner et al. · 2021 · Proceedings of the International Symposium on Combinatorial Search · 275 citations
The multi-agent pathfinding problem (MAPF) is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurren...
Scheduling on-demand broadcasts
Swarup Acharya, S. Muthukrishnan · 1998 · 232 citations
Article Free Access Share on Scheduling on-demand broadcasts: new metrics and algorithms Authors: Swarup Acharya Information Sciences Research Center, Bell Laboratories, Lucent Technologies Murray ...
Trajectory planning for multi-robot systems: Methods and applications
Ángel Madridano, Abdulla Al-Kaff, David Martín et al. · 2021 · Expert Systems with Applications · 226 citations
Locking performance in centralized databases
Y. C. Tay, Nathan Goodman, R. Suri · 1985 · ACM Transactions on Database Systems · 146 citations
An analytic model is used to study the performance of dynamic locking. The analysis uses only the steady-state average values of the variables. The solution to the model is given by a cubic, which ...
Evaluating battery charging and swapping strategies in a robotic mobile fulfillment system
Bipan Zou, Xianhao Xu, Yeming Gong et al. · 2017 · European Journal of Operational Research · 138 citations
International audience
Reading Guide
Foundational Papers
Start with Goldwasser (2010) survey for packet switch models (121 citations), then Lampson (1968) scheduling philosophy (102 citations), and Tay et al. (1985) locking analysis (146 citations) for performance modeling.
Recent Advances
Luong et al. (2016, 349 citations) on IoT data collection extending buffer ideas; Chin et al. (2005, 94 citations) on weighted throughput algorithms.
Core Methods
Competitive analysis (deterministic/randomized), greedy heuristics (e.g., Largest Color First), simulation models with steady-state cubics (Tay et al., 1985).
How PapersFlow Helps You Research Online Buffer Management Algorithms
Discover & Search
Research Agent uses searchPapers and citationGraph to map core works like Goldwasser (2010)'s survey (121 citations), revealing connections to Acharya and Muthukrishnan (1998). exaSearch finds variants in packet switch models; findSimilarPapers expands to Tay et al. (1985) locking analysis.
Analyze & Verify
Analysis Agent applies readPaperContent to extract competitive ratios from Goldwasser (2010), verifies claims with CoVe chain-of-verification, and uses runPythonAnalysis for simulating buffer eviction metrics with NumPy/pandas. GRADE grading scores algorithm performance evidence across surveys.
Synthesize & Write
Synthesis Agent detects gaps in multi-queue analysis from Goldwasser (2010) and Allahverdi et al. (2006); Writing Agent uses latexEditText, latexSyncCitations for competitive analysis proofs, and latexCompile for publication-ready surveys with exportMermaid for eviction diagrams.
Use Cases
"Simulate competitive ratio of greedy buffer eviction on 1000-packet trace."
Research Agent → searchPapers('buffer management competitive') → Analysis Agent → runPythonAnalysis (pandas simulation of Goldwasser (2010) model) → matplotlib plot of ratio vs buffer size.
"Write LaTeX survey section on packet switch buffer algorithms."
Synthesis Agent → gap detection (Allahverdi et al., 2006) → Writing Agent → latexEditText + latexSyncCitations (Acharya 1998, Goldwasser 2010) → latexCompile → PDF with eviction flow diagram.
"Find GitHub repos implementing online buffer algorithms."
Research Agent → searchPapers('online buffer management') → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect → verified code for Goldwasser-style simulations.
Automated Workflows
Deep Research workflow conducts systematic review: searchPapers on 'buffer management packet switches' → citationGraph → DeepScan 7-step analysis of Goldwasser (2010) with GRADE checkpoints → structured report on competitive ratios. Theorizer generates new eviction heuristics from Acharya and Muthukrishnan (1998) patterns. DeepScan verifies randomized variants via CoVe on Tay et al. (1985).
Frequently Asked Questions
What defines online buffer management algorithms?
Algorithms make irrevocable decisions on packet admission/eviction to finite buffers without future knowledge, measured by competitive ratio to offline optimum (Goldwasser, 2010).
What are main methods in buffer management?
Greedy strategies like Largest-First, randomized smoothing, and Longest-in-System; Goldwasser (2010) reviews policies for packet switches with color/size constraints.
What are key papers on the topic?
Goldwasser (2010, 121 citations) surveys packet switch buffers; Acharya and Muthukrishnan (1998, 232 citations) on broadcast scheduling; Tay et al. (1985, 146 citations) on locking models.
What open problems exist?
Optimal ratios for multi-queue with priorities and dynamic workloads; extending to IoT data streams (Luong et al., 2016).
Research Optimization and Search Problems with AI
PapersFlow provides specialized AI tools for Computer Science researchers. Here are the most relevant for this topic:
AI Literature Review
Automate paper discovery and synthesis across 474M+ papers
Code & Data Discovery
Find datasets, code repositories, and computational tools
Deep Research Reports
Multi-source evidence synthesis with counter-evidence
AI Academic Writing
Write research papers with AI assistance and LaTeX support
See how researchers in Computer Science & AI use PapersFlow
Field-specific workflows, example queries, and use cases.
Start Researching Online Buffer Management Algorithms with AI
Search 474M+ papers, run AI-powered literature reviews, and write with integrated citations — all in one workspace.
See how PapersFlow works for Computer Science researchers
Part of the Optimization and Search Problems Research Guide