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001450978 003__ OCoLC
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001450978 019__ $$a1349563245
001450978 020__ $$a9783031168222$$q(electronic bk.)
001450978 020__ $$a3031168224$$q(electronic bk.)
001450978 020__ $$z9783031168215
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001450978 0247_ $$a10.1007/978-3-031-16822-2$$2doi
001450978 035__ $$aSP(OCoLC)1350381178
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001450978 049__ $$aISEA
001450978 050_4 $$aQA76.583
001450978 08204 $$a005.25$$223/eng/20221107
001450978 1001_ $$aChen, Ying,$$eauthor.
001450978 24510 $$aEnergy efficient computation offloading in mobile edge computing /$$cYing Chen, Ning Zhang, Yuan Wu, Sherman Shen.
001450978 264_1 $$aCham :$$bSpringer,$$c2022.
001450978 300__ $$a1 online resource :$$billustrations (black and white).
001450978 336__ $$atext$$btxt$$2rdacontent
001450978 337__ $$acomputer$$bc$$2rdamedia
001450978 338__ $$aonline resource$$bcr$$2rdacarrier
001450978 4901_ $$aWireless networks
001450978 5050_ $$aIntroduction -- 1.1 Background -- 1.1.1 Mobile Cloud Computing -- 1.1.2 Mobile Edge Computing -- 1.1.3 Computation Offloading -- 1.2 Challenges -- 1.3 Contributions -- 1.4 Book Outline -- References -- 2 Dynamic Computation Offloading for Energy Efficiency in Mobile -- Edge Computing -- 2.1 System Model and Problem Statement -- 2.1.1 Network Model -- 2.1.2 Task Offloading Model -- 2.1.3 Task Queuing Model -- 2.1.4 Energy Consumption Model -- 2.1.5 Problem Statement -- 2.2 EEDCO: Energy Efficient Dynamic Computing Offloading for -- Mobile Edge Computing -- 2.2.1 Joint Optimization of Energy and Queue -- 2.2.2 Dynamic Computation Offloading for Mobile Edge -- Computing -- 2.2.3 Trade-off Between Queue Backlog and Energy Efficiency -- 2.2.4 Convergence and Complexity Analysis -- 2.3 Performance Evaluation -- 2.3.1 Impacts of Parameters -- 2.3.2 Performance Comparison with EA and QW Schemes -- 2.4 Literature Review -- 2.5 Summary -- References -- ix -- x Contents -- 3 Energy Efficient Offloading and Frequency Scaling for Internet of -- Things Devices -- 3.1 System Model and Problem Formulation -- 3.1.1 Network Model -- 3.1.2 Task Model -- 3.1.3 Queuing Model -- 3.1.4 Energy Consumption Model -- 3.1.5 Problem Formulation -- 3.2 COFSEE:Computation Offloading and Frequency Scaling for -- Energy Efficiency of Internet of Things Devices -- 3.2.1 Problem Transformation -- 3.2.2 Optimal Frequency Scaling -- 3.2.3 Local Computation Allocation -- 3.2.4 MEC Computation Allocation -- 3.2.5 Theoretical Analysis -- 3.3 Performance Evaluation -- 3.3.1 Impacts of System Parameters -- 3.3.2 Performance Comparison with RLE,RME and TS Schemes -- 3.4 Literature Review -- 3.5 Summary -- References -- 4 Deep Reinforcement Learning for Delay-aware and Energy-Efficient -- Computation Offloading -- 4.1 System Model and Problem formulation -- 4.1.1 System Mode -- 4.1.2 Problem Formulation -- 4.2 Proposed DRL Method -- 4.2.1 Data prepossessing -- 4.2.2 DRL Model -- 4.2.3 Training -- 4.3 Performance Evaluation -- 4.4 Literature Review -- 4.5 Summary -- References -- 5 Energy-Efficient Multi-task Multi-access Computation Offloading -- via NOMA -- 5.1 System Model and Problem Formulation -- 5.1.1 Motivation -- 5.1.2 System Model -- 5.1.3 Problem Formulation -- 5.2 LEEMMO: Layered Energy-efficient Multi-task Multi-access -- Algorithm -- 5.2.1 Layered Decomposition of Joint Optimization Problem -- Contents xi -- 5.2.2 Proposed Subroutine for Solving Problem (TEM-E-Sub) -- 5.2.3 A Layered Algorithm for Solving Problem (TEM-E-Top) -- 5.2.4 DRL-based Online Algorithm -- 5.3 Performance Evaluation -- 5.3.1 Impacts of Parameters -- 5.3.2 Performance Comparison with FDMA based Offloading -- Schemes -- 5.4 Literature Review -- 5.5 Summary -- Reference -- 6 Conclusion -- 6.1 Concluding Remarks -- 6.2 Future Directions -- References.
001450978 506__ $$aAccess limited to authorized users.
001450978 520__ $$aThis book provides a comprehensive review and in-depth discussion of the state-of-the-art research literature and propose energy-efficient computation offloading and resources management for mobile edge computing (MEC), covering task offloading, channel allocation, frequency scaling and resource scheduling. Since the task arrival process and channel conditions are stochastic and dynamic, the authors first propose an energy efficient dynamic computing offloading scheme to minimize energy consumption and guarantee end devices delay performance. To further improve energy efficiency combined with tail energy, the authors present a computation offloading and frequency scaling scheme to jointly deal with the stochastic task allocation and CPU-cycle frequency scaling for minimal energy consumption while guaranteeing the system stability. They also investigate delay-aware and energy-efficient computation offloading in a dynamic MEC system with multiple edge servers, and introduce an end-to-end deep reinforcement learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. Finally, the authors study the multi-task computation offloading in multi-access MEC via non-orthogonal multiple access (NOMA) and accounting for the time-varying channel conditions. An online algorithm based on DRL is proposed to efficiently learn the near-optimal offloading solutions. Researchers working in mobile edge computing, task offloading and resource management, as well as advanced level students in electrical and computer engineering, telecommunications, computer science or other related disciplines will find this book useful as a reference. Professionals working within these related fields will also benefit from this book.
001450978 588__ $$aDescription based on print version record.
001450978 650_0 $$aEdge computing.
001450978 650_0 $$aEdge computing$$xEnergy consumption.
001450978 650_0 $$aMobile computing.
001450978 650_0 $$aMobile computing$$xEnergy consumption.
001450978 655_0 $$aElectronic books.
001450978 7001_ $$aZhang, Ning$$c(Computer scientist),$$eauthor.
001450978 7001_ $$aWu, Yuan,$$eauthor.
001450978 7001_ $$aShen, X.$$q(Xuemin),$$d1958-$$eauthor.$$1https://isni.org/isni/0000000113128394
001450978 77608 $$iPrint version:$$aChen, Ying.$$tEnergy efficient computation offloading in mobile edge computing.$$dCham : Springer, 2022$$z9783031168215$$w(OCoLC)1346945077
001450978 830_0 $$aWireless networks (Springer (Firm))
001450978 852__ $$bebk
001450978 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-16822-2$$zOnline Access$$91397441.1
001450978 909CO $$ooai:library.usi.edu:1450978$$pGLOBAL_SET
001450978 980__ $$aBIB
001450978 980__ $$aEBOOK
001450978 982__ $$aEbook
001450978 983__ $$aOnline
001450978 994__ $$a92$$bISE