Stream‐based live data replication approach of in‐memory cache

Ma, Kun; Yang, Bo

doi:10.1002/cpe.4052

Cited by 14 publications

(10 citation statements)

References 29 publications

(58 reference statements)

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“…Most of the proposed replication strategies in the literature focus on ensuring a specific tenant objective, e.g., availability (Wei et al, 2010), reliability (Li et al, 2017), low latency (Ma and Yang, 2017), data durability (Liu et al, 2018), security (Ali et al, 2018) and energy efficiency (Boru et al, 2015) when other strategies balance among different objectives, e.g., availability and load balancing (Long et al, 2014). On the other hand, there are a number of strategies (Liu et al, 2018;Bonvin et al, 2011;Xiong et al, 2011;Mansouri et al, 2017;Edwin et al, 2017;Mansouri and Javidi, 2018) and replication frameworks (Pu et al, 2015) that aim to satisfy the tenant's objectives while reducing the cost of replication, e. g., data storage and/or data transfer costs, between Data Centers (DCs).…”

Section: E-mail Addresses: Mokadem@iritjr (R Mokadem) Hameurlain@irmentioning

confidence: 99%

A data replication strategy with tenant performance and provider economic profit guarantees in Cloud data centers

Mokadem

Hameurlain

2020

Journal of Systems and Software

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Meeting tenant performance requirements through data replication while ensuring an economic profit is very challenging for cloud providers. For this purpose, we propose a data Replication Strategy that satisfies Performance tenant objective and provider profit in Cloud data centers (RSPC). Before the exe cution of e.ach tenant query Q. data replication is considered only if: (i) the estimated Response Time of Q (RT Q) exceeds a critical RT threshold (per-query replication), or (ii) more often, if RT Q exceeds another (lower) RT threshold for a given number of times (replication per set of queries). Then, a new replica is really created only if a suitable replica placement is heuristically found so that the RT requirement is satisfied again while ensuring an economic profit for the provider. Both the provider's revenues and expenditures are also estimated while penalties and replication costs are taken into account. Further more, the replica factor is dynamically adjusted in order to reduce the resource consumption. Compared to four other strategies, RSPC best satisfies the RT requirement under high loads, complex queries and strict RT thresholds. Moreover, penalty and data transfer costs are significantly reduced, which impacts the provider profit.

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Section: E-mail Addresses: Mokadem@iritjr (R Mokadem) Hameurlain@irmentioning

confidence: 99%

A data replication strategy with tenant performance and provider economic profit guarantees in Cloud data centers

Mokadem

Hameurlain

2020

Journal of Systems and Software

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“…Another real-time clustering technique is stream computing. Several researchers have improved current method using stream computing framework [Ma and Yang (2017)]. But few research focus on data sampling with stream processing.…”

Section: Clustering Optimizationmentioning

confidence: 99%

Stream-Based Data Sampling Mechanism for Process Object

Lin¹,

Liu²,

Chen³

et al. 2019

Computers, Materials &Amp; Continua

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Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. R ecently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. T here i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.

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“…Ma and Yang, in their article Stream ‐ based Live Data Replication Approach of In ‐ memory Cache , tackle the traditional data replication issue by investigating an approach from in‐memory data cache. More precisely, the authors propose a live data replication approach of in‐memory document stores using stream processing framework.…”

Section: Themes Of This Special Issuementioning

confidence: 99%

Cloud computing and big data: Technologies and applications

Zbakh

Bakhouya

Essaaidi

2017

Concurrency and Computation

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to cloud computing and big data technologies, applications, and services.Authors of the selected papers were invited to submit an expanded version to this special issue. These papers were reviewed in the same manner as regular submissions to this edition. After rigorous reviews, 7 papers were accepted for publication and are briefly described in the rest of this section. and the results show that the proposed algorithm outperforms 2 other task-scheduling algorithms in cost and throughput. | Mobile and cloud computing technologiesPrivacy and security in cloud computing is one of the most important issues, which still need to be tackled. As a contribution in this field, Souza et al, in their paper 9 Privacy-ensuring electronic health records on the cloud, present a solid access control framework in the context of Electronic Health Records (EHRs). This framework encompasses hybrid cryptography at the client side and an authentication technique to guarantee a secure key management protocol. They use the homomorphic and order-preserving encryption as a viable solution for the computation of regular searches over EHRs in the cloud, while preserving data confidentiality and patients' privacy. Furthermore, they introduce a trusted element, in the form of a browser extension that can prevent attacks from malicious providers. An HER-based prototype was developed to show the usefulness of the proposed framework. | Cloud computing applications and big dataThe | CONCLUSIONSThe papers presented in this special issue provide research works related to recent advances in cloud computing and big data. In particular, these research works focus on different topics including resources optimization, privacy and security, mobile cloud, cloud computing, and big data applications and services. We hope that the readers of this special issue will benefit from the research ideas and concepts presented in these research works.

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Stream‐based live data replication approach of in‐memory cache

Cited by 14 publications

References 29 publications

A data replication strategy with tenant performance and provider economic profit guarantees in Cloud data centers

A data replication strategy with tenant performance and provider economic profit guarantees in Cloud data centers

Stream-Based Data Sampling Mechanism for Process Object

Cloud computing and big data: Technologies and applications

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