Stochastic resource allocation

Funaro, Liran; Ben-Yehuda, Orna Agmon; Schuster, Assaf

doi:10.1145/3313808.3313815

Cited by 10 publications

(9 citation statements)

References 68 publications

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“…A recent survey [16] discusses a wide range of cache-partitioning approaches for different optimization objectives, such as improving system throughput [10], [13] or fairness [3], [15]. Notably, some recent proposals attempt to enforce QoS constraints for latency critical workloads [14], [17], [24].…”

Section: Related Workmentioning

confidence: 99%

“…Our work explores how to efficiently leverage OS-level cache-clustering to improve fairness on commodity multicores. Our research stands in contrast with recent work on cache-partitioning, which has proposed mostly userlevel partitioning approaches that deliver fairness [3], or pursue other objectives, such as system throughput optimization [10], [13] or improving client satisfaction on virtual environments [14]. In this article we build on our prior work [15] to advance the state-of-the-art in fairness-aware cache-clustering.…”

Section: Introductionmentioning

confidence: 99%

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LFOC+: A Fair OS-level Cache-Clustering Policy for Commodity Multicore Systems

Sáez¹,

Castro²,

Fanizzi³

et al. 2021

IEEE Trans. Comput.

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Commodity multicore systems are increasingly adopting hardware support that enables the system software to partition the last-level cache (LLC). This support makes it possible for the operating system (OS) or the Virtual Machine Monitor (VMM) to mitigate shared-resource contention effects on multicores by assigning different co-running applications to various cache partitions. Recently cache-clustering (or partition-sharing) strategies have emerged as a way to improve system throughput and fairness on new platforms with cache-partitioning support. As opposed to strict cache-partitioning, which allocates separate cache partitions to each application, cache-clustering allows partitions to be shared by a group of applications.In this article we propose LFOC+, a fairness-aware OS-level cache-clustering policy for commodity multicore systems. LFOC+ tries to mimic the behavior of the optimal cache-clustering solution for fairness, which we could obtain for different workload scenarios by using a simulation tool. Our dynamic cache-clustering strategy continuously gathers data from performance monitoring counters to classify applications at runtime based on the degree of cache sensitivity and contentiousness, and effectively separates cache-sensitive applications from aggressor programs to improve fairness, while providing acceptable system throughput. We implemented LFOC+ in the Linux kernel and evaluated it on a real system featuring an Intel Skylake processor, where we compare its effectiveness to that of four previously proposed cache-clustering policies. Our experimental analysis reveals that LFOC+ constitutes a lightweight OS-level policy and improves fairness relative to two other state-of-the-art fairness-aware strategies -Dunn and LFOC-, by up to 22% and up to 20.6%, respectively, and by 9% and 4.9% on average.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LFOC+: A Fair OS-level Cache-Clustering Policy for Commodity Multicore Systems

Sáez¹,

Castro²,

Fanizzi³

et al. 2021

IEEE Trans. Comput.

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“…The increasing datasets include weakly increasing valuation functions that might not be concave. This is our main test case as real-life valuation functions may have multiple inflection points [11,20,39,56,60,62]. Valuation functions, however, are not expected to decrease when more resources are offered, if these resources can be freely discarded.…”

Section: Benchmark Datasetmentioning

confidence: 99%

“…Others solve the problem for a single resource when only one function is not concave but is monotonically increasing [9]. Concave valuation functions are an unrealistic requirement for cloud clients as their valuation functions have multiple inflection points [11,20,39,56,60,62].…”

Section: Introductionmentioning

confidence: 99%

Efficient Multi-resource, Multi-unit VCG Auction

Funaro

Ben-Yehuda

Schuster

2019

Economics of Grids, Clouds, Systems, and Services

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We consider the optimization problem of a multi-resource, multi-unit VCG auction that produces an optimal, i.e., non-approximated, social welfare. We present an algorithm that solves this optimization problem with pseudo-polynomial complexity and demonstrate its efficiency via our implementation. Our implementation is efficient enough to be deployed in real systems to allocate computing resources in fine time-granularity. Our algorithm has a pseudo-near-linear time complexity on average (over all possible realistic inputs) with respect to the number of clients and the number of possible unit allocations. In the worst case, it is quadratic with respect to the number of possible allocations. Our experiments validate our analysis and show near-linear complexity. This is in contrast to the unbounded, nonpolynomial complexity of known solutions, which do not scale well for a large number of agents. For a single resource and concave valuations, our algorithm reproduces the results of a well-known algorithm. It does so, however, without subjecting the valuations to any restrictions and supports a multiple resource auction, which improves the social welfare over a combination of single-resource auctions by a factor of 2.5-50. This makes our algorithm applicable to real clients in a real system.

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“…Water resource evaluation is a prerequisite and basis for comprehensive water conservancy planning [1][2][3], rational allocation of water resources, and scientific management, and has important significance for the safety of water resources and water ecology in river basins or regions. Due to the impact of global climate change and human activities on watershed or regional water resources, water resources face complex and diverse problems.…”

Section: Introductionmentioning

confidence: 99%

Research on Hydrogeology and Water Resources Evaluation Based on Big Data Technology

Xu,

Sun

2024

Frontiers in Artificial Intelligence and Applications

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With the development of science and technology, the amount of hydrological information data has undergone tremendous growth. How to fully utilize these large-scale data to support decision-making and water resource evaluation is currently a problem faced by scientists and society. In the traditional process of water resource allocation, the calculation of reservoir water balance results in a huge amount of abandoned water, resulting in the waste of a large amount of available water resources. By training Big data, the model self-learning can quickly analyze the characteristics of the river water level discharge relationship, fit the water level discharge relationship curve, and obtain the water balance, which reduces the number of flow measurements required to fit the water level discharge relationship curve by traditional methods, thus reducing errors, improving accuracy, and reducing time and economic costs.

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Stochastic resource allocation

Cited by 10 publications

References 68 publications

LFOC+: A Fair OS-level Cache-Clustering Policy for Commodity Multicore Systems

LFOC+: A Fair OS-level Cache-Clustering Policy for Commodity Multicore Systems

Efficient Multi-resource, Multi-unit VCG Auction

Research on Hydrogeology and Water Resources Evaluation Based on Big Data Technology

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