Online Collaborative Data Caching in Edge Computing

Xia, Xiaoyu; Chen, Feifei; He, Qiang; Grundy, John; Abdelrazek, Mohamed; Jin, Hai

doi:10.1109/tpds.2020.3016344

Cited by 186 publications

(49 citation statements)

References 41 publications

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“…However, there are two critical limitations to this solution. First, it costs Microsoft tremendously to save 10 data replicas (10GB in total) in the ESS over a long time due to the expensive storage resources on the edge servers [19], [23], [26]. Second, it does not take advantage of the collaboration of edge servers to transmit data to each other to deliver data for users [8], [18], [24].…”

Section: Motivating Examplementioning

confidence: 99%

“…Compared with replica-based storage solutions, EC-based solutions are more flexible because they require less storage occupation on individual edge servers. This is a critical advantage in the edge computing environment where the storage resources of edge servers are highly constrained and expensive [19], [23], [26].…”

Section: Motivating Examplementioning

confidence: 99%

“…A user can only access coded blocks from edge servers that cover the user. This is the proximity constraint [22], [23]. A storage approach based on erasure code (referred to as EC-based storage approach hereafter) must ensure that every user in the area can retrieve enough coded blocks to construct data X .…”

Section: Introductionmentioning

confidence: 99%

“…Compared with traditional cloud storage systems, coded data blocks cannot be stored in different storage nodes arbitrarily in ESSs. This is the transmission constraint [6], [23], [25].…”

Section: Introductionmentioning

confidence: 99%

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Cost-Effective Data Placement in Edge Storage Systems With Erasure Code

Jin

Luo

et al. 2023

IEEE Trans. Serv. Comput.

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Edge computing, as a new computing paradigm, brings cloud computing's computing and storage capacities to network edge for providing low latency services for users. The networked edge servers in a specific area constitute edge storage systems (ESSs), where popular data can be stored to serve the users in the area. The novel ESSs raise many new opportunities as well as unprecedented challenges. Most existing studies of ESSs focus on the storage of data replicas in the system to ensure low data retrieval latency for users. However, replica-based edge storage strategies can easily incur high storage costs. It is not cost-effective to store massive replicas of large-size data, especially those that do not require real-time access at the edge, e.g., system upgrade files, popular app installation files, videos in online games. It may not even be possible due to the constrained storage resources on edge servers. In this paper, we make the first attempt to investigate the use of erasure codes in cost-effective data storage at the edge. The focus is to find the optimal strategy for placing coded data blocks on the edge servers in an ESS, aiming to minimize the storage cost while serving all the users in the system. We first model this novel Erasure Coding based Edge Data Placement (EC-EDP) problem as an integer linear programming problem and prove its N P-hardness. Then, we propose an optimal approach named EC-EDP-O based on integer programming. Another approximation algorithm named EC-EDP-V is proposed to address the high computation complexity of large-scale EC-EDP scenarios efficiently. The extensive experimental results demonstrate that EC-EDP-O and EC-EDP-V can save an average of 68.58% (and up to 81.16% in large-scale scenarios) storage cost compared with replica-based storage approaches.

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Section: Motivating Examplementioning

confidence: 99%

Section: Motivating Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Compared with traditional cloud storage systems, coded data blocks cannot be stored in different storage nodes arbitrarily in ESSs. This is the transmission constraint [6], [23], [25].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cost-Effective Data Placement in Edge Storage Systems With Erasure Code

Jin

Luo

et al. 2023

IEEE Trans. Serv. Comput.

Self Cite

View full text Add to dashboard Cite

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“…However, despite the numerous possibilities and advantages introduced by EC-in contrast with the Cloud where large-scale computational and communication infrastructures are the norm-the resources at the Edge are limited to micro data-centers, consisting of only few servers [6]. Thus, an efficient resource allocation technique is required for both users and infrastructure providers.…”

Section: Motivation and Challengesmentioning

confidence: 99%

ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Avgeris

Spatharakis

Dechouniotis

et al. 2022

Sensors

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Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one.

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QoS prediction of cloud services by selective ensemble learning on prefilling‐based matrix factorizations

Mao,

Chen,

Towey

et al. 2024

Concurrency and Computation

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SummaryWhen selecting services from a cloud center to build applications, the quality of service (QoS) is an important nonfunctional attribute to be considered. However, in actual application scenarios, the QoS details for many services may not be available. This has led to a situation where prediction of the missing QoS records for services has become a key problem for service selection. This article presents a selective ensemble learning (SEL) framework for prefilling‐based matrix factorization (PFMF) predictors. In each PFMF predictor, the improved collaborative filtering is defined by examining the stability of the QoS records when measuring the similarity of users (or services), and then used to prefill empty records in the initial QoS matrix. To ensure the diversity of the basic PFMF predictors, various prefilled QoS matrices are constructed for the matrix factorization. In this process, different reference weights are assigned to the original and the prefilled QoS records. Finally, particle swarm optimization is used to set the ensemble weights for the basic PFMF predictors. The proposed SEL on PFMF (SEL‐PFMF) algorithm is validated on a public dataset, where its prediction performance outperforms the state‐of‐the‐art algorithms, and also shows good stability.

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Online Collaborative Data Caching in Edge Computing

Cited by 186 publications

References 41 publications

Cost-Effective Data Placement in Edge Storage Systems With Erasure Code

Cost-Effective Data Placement in Edge Storage Systems With Erasure Code

ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

QoS prediction of cloud services by selective ensemble learning on prefilling‐based matrix factorizations

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