Rochester Elastic Cache Utility (RECU): Unequal Cache Sharing is Good Economics

Ye, Chencheng; Brock, Jacob; Ding, Chen; Jin, Hai

doi:10.1007/s10766-015-0384-3

Cited by 10 publications

(5 citation statements)

References 10 publications

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“…A possible solution extends EMBA to be QoS-aware, such that the performance of any individual program is guaranteed to be not less than certain percentage of the baseline, for example, when the performance is measured in bandwidth, 20% QoS means the bandwidth reduction of any individual program in an application mix should not exceed 20% of its bandwidth in baseline. A similar QoS metric was used by Ye et al [39] for cache partition.…”

Section: Discussionmentioning

confidence: 99%

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Xiang

Wang

et al. 2019

Proceedings of the 48th International Conference on Parallel Processing

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On multi-core processors, contention on shared resources such as the last level cache (LLC) and memory bandwidth may cause serious performance degradation, which makes efficient resource allocation a critical issue in data centers. Intel recently introduces Memory Bandwidth Allocation (MBA) technology on its Xeon scalable processors, which makes it possible to allocate memory bandwidth in a real system. However, how to make the most of MBA to improve system performance remains an open question. In this work, (1) we formulate a quantitative relationship between a program's performance and its LLC occupancy and memory request rate on commodity processors. (2) Guided by the performance formula, we propose a heuristic bound-aware throttling algorithm to improve system performance and (3) we further develop a hierarchical clustering method to improve the algorithm's efficiency. (4) We implement these algorithms in EMBA, a low-overhead dynamic memory bandwidth scheduling system to improve performance on Intel commodity processors. The results show that, when multiple programs run simultaneously on a multi-core processor whose memory bandwidth is saturated, the programs with high memory bandwidth demand usually use bandwidth inefficiently compared with programs with medium memory bandwidth demand from the perspective of CPU performance. By slightly throttling the former's bandwidth, we can significantly improve the performance of the latter. On average, we improve system performance by 36.9% at the expense of 8.6% bandwidth utilization rate.

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Section: Discussionmentioning

confidence: 99%

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Xiang

Wang

et al. 2019

Proceedings of the 48th International Conference on Parallel Processing

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“…If fairness is a concern, then we can amend the cost function to discard an unfair scheme and optimize the performance under the fairness constraint. A recent solution is the baseline optimization by Brock et al [24] and Ye et al [52].…”

Section: Algorithm 3: Dynamic Programming For Penalty Class Allocationmentioning

confidence: 99%

Penalty- and Locality-aware Memory Allocation in Redis Using Enhanced AET

et al. 2021

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Due to large data volume and low latency requirements of modern web services, the use of an in-memory key-value (KV) cache often becomes an inevitable choice (e.g., Redis and Memcached). The in-memory cache holds hot data, reduces request latency, and alleviates the load on background databases. Inheriting from the traditional hardware cache design, many existing KV cache systems still use recency-based cache replacement algorithms, e.g., least recently used or its approximations. However, the diversity of miss penalty distinguishes a KV cache from a hardware cache. Inadequate consideration of penalty can substantially compromise space utilization and request service time. KV accesses also demonstrate locality, which needs to be coordinated with miss penalty to guide cache management. In this article, we first discuss how to enhance the existing cache model, the Average Eviction Time model, so that it can adapt to modeling a KV cache. After that, we apply the model to Redis and propose pRedis, Penalty- and Locality-aware Memory Allocation in Redis, which synthesizes data locality and miss penalty, in a quantitative manner, to guide memory allocation and replacement in Redis. At the same time, we also explore the diurnal behavior of a KV store and exploit long-term reuse. We replace the original passive eviction mechanism with an automatic dump/load mechanism, to smooth the transition between access peaks and valleys. Our evaluation shows that pRedis effectively reduces the average and tail access latency with minimal time and space overhead. For both real-world and synthetic workloads, our approach delivers an average of 14.0%∼52.3% latency reduction over a state-of-the-art penalty-aware cache management scheme, Hyperbolic Caching (HC), and shows more quantitative predictability of performance. Moreover, we can obtain even lower average latency (1.1%∼5.5%) when dynamically switching policies between pRedis and HC.

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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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Rochester Elastic Cache Utility (RECU): Unequal Cache Sharing is Good Economics

Cited by 10 publications

References 10 publications

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Penalty- and Locality-aware Memory Allocation in Redis Using Enhanced AET

Efficient Multi-resource, Multi-unit VCG Auction

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