Recently, the edge data caching (EDC) problem has received much attention. It aims to appropriately cache data on edge servers. Existing EDC approaches suffer from a series of limitations. First, they often overlook t...
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ISBN:
(纸本)9798350368567;9798350368550
Recently, the edge data caching (EDC) problem has received much attention. It aims to appropriately cache data on edge servers. Existing EDC approaches suffer from a series of limitations. First, they often overlook the diverse characteristics of data, including caching costs and latency preferences. In reality, different types of data vary in size and require different storage resources for caching. The impact of specified latency preferences of edge users for different data on the quality of experience should be considered in the EDC problem. Second, the temporal dynamics of edge users' data requests and distributions have been insufficiently addressed. To overcome these limitations systematically, this paper focuses on the problem of temporal-aware edge data caching with specified latency preference (TEDC). We first formulate the TEDC problem and transform it into an optimization problem with multiple objectives and global constraints and prove its NP-hardness. Then, we propose an optimal approach named TEDC-IP to solve this TEDC problem with the Integer Programming technique and a heuristic algorithm named TEDC-A for finding approximate solutions to large-scale TEDC problems efficiently. Extensive experiments are conducted on two widely-used real-world datasets to evaluate the performance of our approach. The results demonstrate that TEDC-IP and TEDC-A significantly outperform state-of-the-art approaches in finding approximate solutions in terms of the trade-off among multiple metrics.
Mobile edge computing (MEC) provides a new computing paradigm that can overcome the inability of the traditional cloud computing paradigm to ensure low service latency by pushing computing power and resources to the n...
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Mobile edge computing (MEC) provides a new computing paradigm that can overcome the inability of the traditional cloud computing paradigm to ensure low service latency by pushing computing power and resources to the network edge. Many studies have attempted to formulate edge data caching strategies for app vendors to optimize caching performance by caching the right data on the right edge servers. However, existing edge data caching approaches have unfortunately ignored fairness, which is an important issue from the app vendor's perspective. In general, an app vendor needs to cache data on edge servers to serve its users with insignificant latency differences at a minimum caching cost. In this paper, we make the first attempt to tackle the fair edge data caching (FEDC) problem. Specifically, we formulate the FEDC problem as a constraint optimization problem (COP) and prove its $\mathcal {NP}$NP-hardness. An optimal approach named FEDC-OPT is proposed to find optimal solutions to small-scale FEDC problems with integer programming technique. In addition, an approximate algorithm named FEDC-APX is proposed to find approximate solutions in large-scale FEDC problems. The performance of the proposed approaches is analyzed theoretically, and evaluated experimentally on a widely-used real-world data set against four representative approaches. The experimental results show that the proposed approaches can solve the FEDC problem efficiently and effectively.
edge computing has emerged as a new computing paradigm that allows computation and storage resources in the cloud to be distributed to edge servers. Those edge servers are deployed at base stations to provide nearby u...
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edge computing has emerged as a new computing paradigm that allows computation and storage resources in the cloud to be distributed to edge servers. Those edge servers are deployed at base stations to provide nearby users with high-quality services. Thus, datacaching is extremely important in ensuring low latency for service delivery in the edge computing environment. To minimize the datacaching cost and maximize the reduction in service latency, we formulate this edge data caching (EDC) problem as a constrained optimization problem in this paper. We prove the NP-completeness of this EDC problem and provide an optimal solution named IPEDC to solve this problem based on Integer Programming. Then, we propose an approximation algorithm named AEDC to find approximate solutions with a limited bound. We conduct intensive experiments on a real-world data set and a synthesized data set to evaluate our approaches. Our results demonstrate that IPEDC and AEDC significantly outperform the four representative baseline approaches. (C) 2020 Elsevier B.V. All rights reserved.
In the edge computing environment, app vendors can distribute popular data from the cloud to edge servers to provide low-latency data retrieval. A key problem is how to distribute these data from the cloud to edge ser...
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In the edge computing environment, app vendors can distribute popular data from the cloud to edge servers to provide low-latency data retrieval. A key problem is how to distribute these data from the cloud to edge servers cost-effectively. Under current schemes, a file is divided into some data blocks for parallel transmissions from the cloud to target edge servers. edge servers can then combine received data blocks to reconstruct the file. While this method expedites the data distribution process, it presents potential drawbacks. It is sensitive to transmission delays and transmission failures caused by runtime exceptions like network fluctuations and server failures. This paper presents edgeHydra, the first edgedata distribution scheme that tackles this challenge through fault tolerance based on erasure coding. Under edgeHydra, a file is encoded into data blocks and parity blocks for parallel transmission from the cloud to target edge servers. An edge server can reconstruct the file upon the receipt of a sufficient number of these blocks without having to wait for all the blocks in transmission. It also innovatively employs a leaderless block supplement mechanism to ensure the receipt of sufficient blocks for individual target edge servers. These improve the robustness of the data distribution process significantly. Extensive experiments show that edgeHydra can tolerate delays and failures in individual transmission links effectively, outperforming the state-of-the-art scheme by up to 50.54% in distribution time.
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