DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems

By Jeongho Kwak, Yeongjin Kim, Joohyung Lee, Song Chong. IEEE Journal on Selected Areas in Communications, 2015

402 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/712817fbf4adfec8bb283ec83f2a9cb91bebfadf

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Citations in the corpus

Latency and Reliability-Aware Task Offloading and Resource Allocation for Mobile Edge Computing (2017, methodology): “, the processing density, which is application-dependent [5].”
Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks (2019, neutral): “where κi ≥ 0 and νi ≥ 1 are positive coefficients specified in the CPU model [45].”
EcoMobiFog–Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications (2019, methodology): “A second (substantial) research branch focuses on the so-called task migration/allocation problem, where two physical computing nodes (like, for example, aMobile device and a cloud orMEC node) are still involved [13]–[15].”
Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices (2016, methodology): “, a high-speed multi-core CPU, is available a t the MEC server, and thus ignore its execution delay [16], [18], [26].”
Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems (2019, background): “The tradeoff problem with different kinds of computing tasks in MEC systems has been discussed in [10] and [11].”
A Survey on Mobile Edge Computing: The Communication Perspective (2017, methodology): “i-MDP and propose a one-dimensional heuristic search algorithm Energy and Latency [106] Jointly control the local CPU frequency, modulation scheme as well as the data rates under a semi-MDP framework [107] Propose a Lyapunov optimization-based algorithm to decide the ofﬂoading policy, task allocatio”
Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems With Min-Max Fairness Guarantee (2018, methodology): “W ITH smart mobile user equipments (UEs) gains enormous popularity, people expect to run more and more computation-intensive mobile applications [1].”
Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing (2018, methodology): “1For example, the six-queen puzzle, 400-frame video game, seven-queen puzzle, face recognition, and virus scanning require the processing densities of 1760 cycle/bit, 2640 cycle/bit, 8250 cycle/bit, 31680 cycle/bit, and 36992 cycle/bit, respectively [7].”
Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study (2017, background): “Numerically measured processing densities of some test applications [118].”
Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing (2018, methodology): “unchanged. The request arrival process in the interval Rk(t) for each user k is uniformly distributed within [0.6,1] Mbps and its processing density is 2640 cycle/bit (such as 400 frame video game in [41]). To simplify the problem, we assume that the maximum computing capacity of all MEC nodes is th”
Edge-Computing-Enabled Smart Cities: A Comprehensive Survey (2019, neutral): “How does one effectively manage computation, communication, and storage resources between different players in smart cities to optimize the overall system performance? To answer this question, we must devise resource management schemes that consider energy [156]–[159], latency [160], traffic handlin”
Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems (2017, background): “We assume that the mobile devices are running independent and fine-grained tasks [16], [25], [26], [29]: At the beginning of the t th time slot, Ai (t) (bits) of computation tasks arrive at the i th mobile device, which can be processed starting from the (t + 1)th time slot.”