337 research outputs found
Cooperative Local Caching under Heterogeneous File Preferences
Local caching is an effective scheme for leveraging the memory of the mobile
terminal (MT) and short range communications to save the bandwidth usage and
reduce the download delay in the cellular communication system. Specifically,
the MTs first cache in their local memories in off-peak hours and then exchange
the requested files with each other in the vicinity during peak hours. However,
prior works largely overlook MTs' heterogeneity in file preferences and their
selfish behaviours. In this paper, we practically categorize the MTs into
different interest groups according to the MTs' preferences. Each group of MTs
aims to increase the probability of successful file discovery from the
neighbouring MTs (from the same or different groups). Hence, we define the
groups' utilities as the probability of successfully discovering the file in
the neighbouring MTs, which should be maximized by deciding the caching
strategies of different groups. By modelling MTs' mobilities as homogeneous
Poisson point processes (HPPPs), we analytically characterize MTs' utilities in
closed-form. We first consider the fully cooperative case where a centralizer
helps all groups to make caching decisions. We formulate the problem as a
weighted-sum utility maximization problem, through which the maximum utility
trade-offs of different groups are characterized. Next, we study two benchmark
cases under selfish caching, namely, partial and no cooperation, with and
without inter-group file sharing, respectively. The optimal caching
distributions for these two cases are derived. Finally, numerical examples are
presented to compare the utilities under different cases and show the
effectiveness of the fully cooperative local caching compared to the two
benchmark cases
LERC: Coordinated Cache Management for Data-Parallel Systems
Memory caches are being aggressively used in today's data-parallel frameworks
such as Spark, Tez and Storm. By caching input and intermediate data in memory,
compute tasks can witness speedup by orders of magnitude. To maximize the
chance of in-memory data access, existing cache algorithms, be it recency- or
frequency-based, settle on cache hit ratio as the optimization objective.
However, unlike the conventional belief, we show in this paper that simply
pursuing a higher cache hit ratio of individual data blocks does not
necessarily translate into faster task completion in data-parallel
environments. A data-parallel task typically depends on multiple input data
blocks. Unless all of these blocks are cached in memory, no speedup will
result. To capture this all-or-nothing property, we propose a more relevant
metric, called effective cache hit ratio. Specifically, a cache hit of a data
block is said to be effective if it can speed up a compute task. In order to
optimize the effective cache hit ratio, we propose the Least Effective
Reference Count (LERC) policy that persists the dependent blocks of a compute
task as a whole in memory. We have implemented the LERC policy as a memory
manager in Spark and evaluated its performance through Amazon EC2 deployment.
Evaluation results demonstrate that LERC helps speed up data-parallel jobs by
up to 37% compared with the widely employed least-recently-used (LRU) policy
Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study
Large Language Models (LLMs) have shown remarkable proficiency in language
understanding and have been successfully applied to a variety of real-world
tasks through task-specific fine-tuning or prompt engineering. Despite these
advancements, it remains an open question whether LLMs are fundamentally
capable of reasoning and planning, or if they primarily rely on recalling and
synthesizing information from their training data. In our research, we
introduce a novel task -- Minesweeper -- specifically designed in a format
unfamiliar to LLMs and absent from their training datasets. This task
challenges LLMs to identify the locations of mines based on numerical clues
provided by adjacent opened cells. Successfully completing this task requires
an understanding of each cell's state, discerning spatial relationships between
the clues and mines, and strategizing actions based on logical deductions drawn
from the arrangement of the cells. Our experiments, including trials with the
advanced GPT-4 model, indicate that while LLMs possess the foundational
abilities required for this task, they struggle to integrate these into a
coherent, multi-step logical reasoning process needed to solve Minesweeper.
These findings highlight the need for further research to understand and nature
of reasoning capabilities in LLMs under similar circumstances, and to explore
pathways towards more sophisticated AI reasoning and planning models.Comment: 24 pages, 5 figures, 3 table
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