Data Caching Optimization in the Edge Computing Environment

Ying, Liu; He, Qiang; Zheng, Dequan; Xia, Xiaoyu; Chen, Feifei; Zhang, Bin

doi:10.1109/tsc.2020.3032724

Cited by 34 publications

(28 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A set of works considers the data caching problem in the edge computing environment, proposing schemes that maxi-mize the data caching revenue of the operator [181], or that improve content delivery speeds, network traffic congestion, cache resource utilization efficiency, and users' quality of experience in highly populated cities [182]. Referring to the VNF architecture, the SFC and VNF placement can be performed reducing the execution time and the resource utilization [183], taking into account both service requirements and the resource capacity in the edge [184], maximizing revenue at the network level while matching demand [185], minimizing both energy consumption and resource utilization [186], or maximizing the number of user request admissions while minimizing their admission cost (i.e., computing cost on instantiations of requested VNF instances and the data packet traffic processing of requests in their VNF instances, and the communication cost of routing data packet traffic of requests between users and the MEH hosting their requested VNF instances) [187].…”

Section: Yesmentioning

confidence: 99%

5G Multi-access Edge Computing: Security, Dependability, and Performance

Nencioni,

Garroppo,

Olimid

2021

Preprint

View full text Add to dashboard Cite

The main innovation of the Fifth Generation (5G) of mobile networks is the ability to provide novel services with new and stricter requirements. One of the technologies that enable the new 5G services is the Multi-access Edge Computing (MEC). MEC is a system composed of multiple devices with computing and storage capabilities that are deployed at the edge of the network, i.e., close to the end users. MEC reduces latency and enables contextual information and real-time awareness of the local environment. MEC also allows cloud offloading and the reduction of traffic congestion. Performance is not the only requirement that the new 5G services have. New missioncritical applications also require high security and dependability. These three aspects (security, dependability, and performance) are rarely addressed together. This survey fills this gap and presents 5G MEC by addressing all these three aspects. First, we overview the background knowledge on MEC by referring to the current standardization efforts. Second, we individually present each aspect by introducing the related taxonomy (important for the not expert on the aspect), the state of the art, and the challenges on 5G MEC. Finally, we discuss the challenges of jointly addressing the three aspects.

show abstract

Section: Yesmentioning

confidence: 99%

5G Multi-access Edge Computing: Security, Dependability, and Performance

Nencioni,

Garroppo,

Olimid

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…where "k + 1" represent the cloud data center, further, the constraint presented in Equation (12) depicts that each data block can be placed into multiple numbers of edge servers. Furthermore, the constraint portrayed in Equation ( 13) highlights that the complete collection of data blocks can be placed into the same edge server based on the storage space size available with that edge server.…”

Section: Data Blocks Placement Problem Formulationmentioning

confidence: 99%

“…11 In edge computing, storing data into the network edge server is proving to be favorable as it stores data blocks into it for satisfying the requirement of the user tasks or neighboring edge servers which are closely located to the user devices. 12,13 When requests are generated by the user devices and propagated to the edge server, the data blocks required for processing the tasks are obtained from the edge server that exists locally; else, they are obtained from the neighboring edge servers. 14 Besides, the data blocks required by the edge server will be fetched from the remote server when the needed data blocks do not exist in both the local or remote data center.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Harris Hawk‐Salp swarm optimization algorithm‐based integrated optimal data placement and task scheduling for improving the user experience in edge computing

Vivekanandan¹,

Gnansekaran²

2021

Concurrency and Computation

View full text Add to dashboard Cite

Edge computing permits the computation process at the edge of the network for enhancing the efficiency of the data access. The problem of optimal data block placement entirely depends on the maximization of interdependent factors, which corresponds to an NP-hard problem. In this paper, the Hybrid Harris Hawk-Salp Swarm-based Optimization, Data Placement and Task Scheduling (HHHSS-ODPTS) scheme is proposed for improving the user experience in edge computing. This data placement is devised considering the popularity and user preference of data blocks, storage capacity of edge server, and the ratio of replacement associated with the edge servers. This proposed scheme utilized a 2/3-approximation method for essential mapping of tasks with generated containers of the edge server. The simulation results confirmed that the proposed HHHSS-ODPTS scheme is better at data response time, response time of tasks, number of the replaced data blocks, and hit rate of tasks with different capacity of data storage, number of required data blocks, and data popularity.

show abstract

“…Generally, since there is a high probability that the required data of computation tasks from diverse SDVs have the similarities, the transmission of such duplicated data to the ESs results in a huge waste of computing and communication resources [6]. To minimize the redundant data transmission and guarantee resource utilization, content caching is introduced by mobile network operators (MNOs) that enables the contents to be prefetched [7]. Caching reusable contents of computation tasks on the ESs is feasible for SDVs to cut down the repetitive computation offloading and execution, which reduces the network delay and improves the quality of service (QoS) of SDVs.…”

mentioning

confidence: 99%

CoPace: Edge Computation Offloading and Caching for Self-Driving With Deep Reinforcement Learning

Huang

et al. 2021

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Currently, self-driving, emerging as a key automatic application, has brought a huge potential for the provision of in-vehicle services (e.g., automatic path planning) to mitigate urban traffic congestion and enhance travel safety. To provide high-quality vehicular services with stringent delay constraints, edge computing (EC) enables resource-hungry self-driving vehicles (SDVs) to offload computation-intensive tasks to the edge servers (ESs). In addition, caching highly reusable contents decreases the redundant transmission time and improves the quality of services (QoS) of SDVs, which is envisioned as a supplement to the computation offloading. However, the high mobility and time-varying requests of SDVs make it challenging to provide reliable offloading decisions while guaranteeing the resource utilization of content caching. To this end, in this paper we propose a collaborative computation offloading and content caching method, named CoPace, by leveraging deep reinforcement learning (DRL) in EC for self-driving system. Specifically, we first introduce OSTP to predict the future timevarying content popularity, taking into account the temporalspatial attributes of requests. Moreover, a DRL-based algorithm is developed to jointly optimize the offloading and caching decisions, as well as the resource allocation (i.e., computing and communication resources) strategies. Extensive experiments with real-world datasets in Shanghai, China, are conducted to evaluate the performance, which demonstrates that CoPace is both effective and well-performed.

show abstract

Data Caching Optimization in the Edge Computing Environment

Cited by 34 publications

References 33 publications

5G Multi-access Edge Computing: Security, Dependability, and Performance

5G Multi-access Edge Computing: Security, Dependability, and Performance

Hybrid Harris Hawk‐Salp swarm optimization algorithm‐based integrated optimal data placement and task scheduling for improving the user experience in edge computing

CoPace: Edge Computation Offloading and Caching for Self-Driving With Deep Reinforcement Learning

Contact Info

Product

Resources

About