Data caching as a cloud service

Chockler, Gregory; Laden, Guy; Vigfússon, Ýmir

doi:10.1145/1859184.1859190

Cited by 23 publications

(12 citation statements)

References 11 publications

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“…They experimentally proved that when the percentage of cached-entries is 20% of total number of objects, then the disk page accesses is almost negligible. Chockler et al [8] preferred to set the cache size equal to the total memory allocated for data cache by the service provider. Ilayaraja et al [10] set the cache-size to 10% of the database size, which was 50 in number of objects.…”

Section: Prior Related Work On Cachingmentioning

confidence: 99%

Determining Appropriate Cache-size for Cost-effective Cloud Database Queries

Nanda¹,

Chande²,

Sharma³

2017

IJCA

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Retrieving results from the cache is one of the prominent techniques to improve the query response time and reducing load on the back-end database servers. One of the important factors that influences the performance of cache system the most, is the size of the cache. In cloud-based systems, memory is scalable and hence, size of the cache is not a critical issue. However, when the cache is overpopulated with queries and their results, in that case, the query response time increases. This is due to the fact that time for searching the cache for the desired results increases. In this paper, an appropriate cache size is calculated in terms of the number of queries, for the database-size under consideration. This paper also describes the set-up of Virtual Machine Creation (VMC) cloud, using Cloud Virtual Machine Creation (CVMC) algorithm. This facilitates the deployment of database in cloud-based systems. An appropriate cache-size for cloud-based system is determined through experimentation using Apache HBase.

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Section: Prior Related Work On Cachingmentioning

confidence: 99%

Determining Appropriate Cache-size for Cost-effective Cloud Database Queries

Nanda¹,

Chande²,

Sharma³

2017

IJCA

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“…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%

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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“…To eliminate effects of factor dimensions, all the input are standardized by x P V . As a result, (5) uses an iteration to predict weight for each evicted datum when cycle t begins. In (5), wei ij (t-1) represents the last cycle datum weight.…”

Section: B Data Weightingmentioning

confidence: 99%

“…Cloud distributed cache service plays a vital role in improving cloud application performance by expediting data fetching, and thus it reduces latency and improves user experience greatly [5] [6]. Likewise, Forrest Research's Mike Gualtieri noted cloud cache importance as "Elastic caching and cloud computing are a match made in heaven for app scaling in the cloud" [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

An SLA-Driven Cache Optimization Approach for Multi-tenant Application on PaaS

Chen

et al. 2014

2014 IEEE 38th Annual Computer Software and Applications Conference

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as multi-tenant applications spring up in clouds, more and more people advocate using Service Level Agreement (SLA) in service delivery to fit tenants' non-functional needs e.g. response time and budget limit. However, most of the present application optimizations based on SLA focuses on virtual machine-based (VM-based) computing service, while other services such as storage and cache are often neglected. In this paper, we propose an SLA-driven application optimization for cache service to help to meet tenants' needs better and improve cost-effectiveness, which can be taken as complementary to the existing work. The proposed approach, built on top of Platform-as-a-Service (PaaS), pays attention to evicted data. It considers both tenant SLA-evaluated status and data performance when weighting the evicted data with re-cache likelihoods, and then adjusts their re-cache priorities. At the beginning of every cycle it predicts tenant status and evicted data performance for the coming cycle by Holt-Winters double exponential smoothing. Our simulation experiments demonstrate the optimization effectiveness in improving cache cost-effectiveness and satisfying tenant SLAs.

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Data caching as a cloud service

Cited by 23 publications

References 11 publications

Determining Appropriate Cache-size for Cost-effective Cloud Database Queries

Determining Appropriate Cache-size for Cost-effective Cloud Database Queries

Dynamic Performance Profiling of Cloud Caches

An SLA-Driven Cache Optimization Approach for Multi-tenant Application on PaaS

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