43,788 research outputs found
Cache policies for cloud-based systems: To keep or not to keep
In this paper, we study cache policies for cloud-based caching. Cloud-based
caching uses cloud storage services such as Amazon S3 as a cache for data items
that would have been recomputed otherwise. Cloud-based caching departs from
classical caching: cloud resources are potentially infinite and only paid when
used, while classical caching relies on a fixed storage capacity and its main
monetary cost comes from the initial investment. To deal with this new context,
we design and evaluate a new caching policy that minimizes the overall cost of
a cloud-based system. The policy takes into account the frequency of
consumption of an item and the cloud cost model. We show that this policy is
easier to operate, that it scales with the demand and that it outperforms
classical policies managing a fixed capacity.Comment: Proceedings of IEEE International Conference on Cloud Computing 2014
(CLOUD 14
A Community-based Cloud Computing Caching Service
Caching has become an important technology in the development of cloud computing-based high-performance web services. Caches reduce the request to response latency experienced by users, and reduce workload on backend databases. They need a high cache-hit rate to be fit for purpose, and this rate is dependent on the cache management policy used. Existing cache management policies are not designed to prevent cache pollution or cache monopoly problems, which impacts negatively on the cache-hit rate. This paper proposes a community-based caching approach (CC) to address these two problems. CC was evaluated for performance against thirteen commercially available cache management policies, and results demonstrate that the cache-hit rate achieved by CC was between 0.7% and 55% better than the alternate cache management policies
Defending cache memory against cold-boot attacks boosted by power or EM radiation analysis
Some algorithms running with compromised data select cache memory as a type of secure memory where data is confined and not transferred to main memory. However, cold-boot attacks that target cache memories exploit the data remanence. Thus, a sudden power shutdown may not delete data entirely, giving the opportunity to steal data. The biggest challenge for any technique aiming to secure the cache memory is performance penalty. Techniques based on data scrambling have demonstrated that security can be improved with a limited reduction in performance. However, they still cannot resist side-channel attacks like power or electromagnetic analysis. This paper presents a review of known attacks on memories and countermeasures proposed so far and an improved scrambling technique named random masking interleaved scrambling technique (RM-ISTe). This method is designed to protect the cache memory against cold-boot attacks, even if these are boosted by side-channel techniques like power or electromagnetic analysis.Postprint (author's final draft
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
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