2019
DOI: 10.3390/sym11010058
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Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing

Abstract: The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource l… Show more

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Cited by 57 publications
(28 citation statements)
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“…In the case of multi-user multi-server MEC architectures, most studies focus on offloading onto single-tier multi-server MEC architectures [34][35][36][37][38][39][40]. In SBS and MBS systems equipped with MEC servers, the SBS and MBS work independently and considered equally important (the SBS is not connected to the MBS), enabling MDs to choose either the SBS or MBS for offload.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…In the case of multi-user multi-server MEC architectures, most studies focus on offloading onto single-tier multi-server MEC architectures [34][35][36][37][38][39][40]. In SBS and MBS systems equipped with MEC servers, the SBS and MBS work independently and considered equally important (the SBS is not connected to the MBS), enabling MDs to choose either the SBS or MBS for offload.…”
Section: Related Workmentioning
confidence: 99%
“…In one study [37], the authors developed a game-theoretic algorithm to solve the optimization problem for overall computing overhead considering various types of computational tasks through computation-offloading decisions, whereas in the other study [38], they optimized the overall cost of the computing overhead according to power consumption and completion time by combining the optimal offloading decision and resource allocation (CPU cycles and power transmission) by MDs. Qui et al [39] studied task offloading to a MEC server in order to extend resource capacity by hiring resources from cloud computing resources and vehicular nodes in order to minimize the total computing overhead, including latency and monetary cost, of using computer resources. Yang et al [40] considered a two-tier small cell network comprising a set of relay base stations connected to a set of micro-base stations, where only the micro-base stations are integrated with the MEC servers in order to perform computational tasks from the user.…”
Section: Related Workmentioning
confidence: 99%
“…The problem of edge workload scheduling or computation offloading is an attractive and challenging topic in MEC, which decides whether/how to schedule/offload users' tasks (requests) from their devices to the appropriate computing nodes in the MEC or the cloud. Various aspects of this problem were investigated in the literature, considering different objectives (e.g., minimizing the makespan [16] or the energy [17] or the cost [18]), workload models (e.g., independent atomic tasks [19], composite applications/workflows [20]), and MEC architectures and offloading schemes (e.g., non-collaborative [18] versus collaborative [14], flat versus hierarchical [15]). However, in these works, MEC nodes are implicitly assumed to execute any types of computation tasks without considering whether the corresponding services are available on the MEC nodes.…”
Section: Related Workmentioning
confidence: 99%
“…It naturally raises the question of determining the appropriate computing node (i.e., local MEC, a nearby MEC, or the central cloud) for each service request originated from each MEC. Many previous studies solved this problem under the theme of computation offloading [13][14][15][16][17][18][19][20]. However, most of them focused on effectively improving the use of computing resources while neglecting non-trivial amounts of data, which need to be pre-stored to enable service execution, especially for data-intensive services.…”
Section: Introductionmentioning
confidence: 99%
“…Typically, MEC servers are smaller with moderate computing resources and deployed at aggregation points such as base stations (BSs). Moreover, MEC is highly scalable as the number of MEC servers is expected to increase significantly and is able to support low-latency applications, real-time mobility, and location awareness [3,4]. Among considered problems in MEC, computation offloading is of great importance and presents in many network use cases.…”
Section: Introductionmentioning
confidence: 99%