Design and implementation of caching services in the cloud

Chockler, Gregory; Laden, Guy; Vigfússon, Ýmir

doi:10.1147/jrd.2011.2171649

Cited by 20 publications

(13 citation statements)

References 16 publications

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“…The average accuracy over the traces in Table 1 ranged from 98.9% for B = 8 to 99.3% for B = 128 for both aging policies. Although our methods were not designed for CLOCK, they approximated the HRC with significantly higher fidelity than the previous method SC2 [11], whose prediction accuracy averaged 96.4% on the traces.…”

Section: Simulationsmentioning

confidence: 93%

“…When requested by an operator, MIMIR can generate an up-to-date hit rate curve showing the current estimate of cache hit rate vs. memory size as output. The resulting curves can inform decisions on cache provisioning, either by operators manually or automatically adjusting or partitioning resources [10,11,[39][40][41], raise alerts for developers about unexpected cache behavior, and so forth.…”

Section: Architecturementioning

confidence: 99%

“…The HRC estimator periodically calls an aging routine to keep the estimate current ( §3.3). Finally, as illustrated on the right, MIMIR can generate an up-to-date hit rate curve through the export-HRC function when desired by an operator or by a framework using MIMIR as input into a resource profiler or cache partitioning framework [11,[39][40][41].…”

Section: Aging Policymentioning

confidence: 99%

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Dynamic Performance Profiling of Cloud Caches

Saemundsson

Björnsson

Chockler

et al. 2014

Proceedings of the ACM Symposium on Cloud Computing

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Large-scale in-memory object caches such as memcached are widely used to accelerate popular web sites and to reduce burden on backend databases. Yet current cache systems give cache operators limited information on what resources are required to optimally accommodate the present workload. This paper focuses on a key question for cache operators: how much total memory should be allocated to the in-memory cache tier to achieve desired performance?We present our MIMIR system: a lightweight online profiler that hooks into the replacement policy of each cache server and produces graphs of the overall cache hit rate as a function of memory size. The profiler enables cache operators to dynamically project the cost and performance impact from adding or removing memory resources within a distributed in-memory cache, allowing "what-if" questions about cache performance to be answered without laborious offline tuning. Internally, MIMIR uses a novel lock-free algorithm and lookup filters for quickly and dynamically estimating hit rate of LRU caches.Running MIMIR as a profiler requires minimal changes to the cache server, thanks to a lean API. Our experiments show that MIMIR produces dynamic hit rate curves with over 98% accuracy and 2 − 5% overhead on request latency and throughput when MIMIR is run in tandem with memcached, suggesting online cache profiling can be a practical tool for improving provisioning of large caches.

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Section: Simulationsmentioning

confidence: 93%

Section: Architecturementioning

confidence: 99%

Section: Aging Policymentioning

confidence: 99%

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Dynamic Performance Profiling of Cloud Caches

Saemundsson

Björnsson

Chockler

et al. 2014

Proceedings of the ACM Symposium on Cloud Computing

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“…The cost model considers all queries and necessary infrastructure expenditure such as bandwidth, network, disk space and CPU times. Chockler, Laden and Vigfusson [5] proposed the economic model of cloud cache service for charge balancing between customer benefit and cost of the service. Kantere et al [6] proposed an economic model and dynamic pricing scheme designed for a cloud cache, which offers querying services.…”

Section: Related Workmentioning

confidence: 99%

An economic model for client-side cloud caching service

Sriwiroj

Banditwattanawong

2015

2015 7th International Conference on Knowledge and Smart Technology (KST)

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Cache-as-a-Service (CaaS) benefits cloud computing users from personal to corporate levels. One of the important factors contributing to the success of CaaS at the corporation level is much lower expense compared to performance received. However, careful consideration must be given when choosing CaaS to achieve cost-effectiveness. Thus, the service model of CaaS allowing custom-made SLA will be extremely useful. Currently, almost all available CaaSes are server-side. This paper presents the flexible economic model of client-side CaaS as an early attempt in the field. The model considers both CapEx and OpEx as well as fixed costs and variable costs of the clientside CaaS.

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“…Moreover, the paper adopts the storage caching [12] of the sub-cloud servers for popular (frequently) requested files [13] by the connected users, and the quality scalability [14] and bitstream division [15] of the media files, so as to give the capability to the users to access them in quality of Class A, B, C and D, as they are presented in the Scalable HEVC software SHM provided by JCT-VC [16], due to their preferences or the quality restrictions of their internet navigation devices. In addition, the media files which are stored in the MCS will be compressed with the new generation compression technology, H.265 or High-Efficiency Video Coding (HEVC) standard, as it is well-known.…”

Section: Introductionmentioning

confidence: 99%

Efficient Multimedia Transmission over Scalable IoT Architecture

Memos¹

2018

IJCNIS

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Abstract-Today the new technology concept, called "Internet of Things" (IoT), presents a significant field of research, due to the fact that it promises many advantages to the users worldwide. Besides this, multimedia file streaming tends to be a daily user habit, as a result of the evolution of the internet and the capabilities of devices, such as mobile phones and computers. This tendency has established Cloud Computing (CC) as a vital technology due to the fact that it reduces economic costs, improves accessibility and flexibility, while it increases the total network performance too. This paper demonstrates a new technological approach for IoT architecture, where a layered implementation with the use of many cloud servers can offer optimized streaming and download, because of the significant reduction of the transfer time costs and the storage space of the Main Cloud Server (MCS). Moreover, users will have the ability to choose or adapt to their devices' restrictions, between low -up toUltra-High Definition (UHD) quality for multimedia access and downloading, thanks to a new quality scalability feature which is introduced and is based on the new High-Efficiency Video Coding (HEVC) compression standard. Experimental results show the efficiency of the proposed approach with math formulas and graphs.

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Design and implementation of caching services in the cloud

Cited by 20 publications

References 16 publications

Dynamic Performance Profiling of Cloud Caches

Dynamic Performance Profiling of Cloud Caches

An economic model for client-side cloud caching service

Efficient Multimedia Transmission over Scalable IoT Architecture

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