Mobile Edge Computing
2020-06-02 11:37:59 0 举报AI智能生成
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Mobile Edge Computing @Kwong
I. INTRODUCTION
brief introduction of it origin etc...
providing a survey of key research progress in this young field
containing an ensemble of promising research directions for MEC.
A. Mobile Computing for 5G: From Clouds to Edges\u00A0
\u00A0Mobile Cloud Computing (MCC)
\u00A0the long propagation
Mission of 5G
\u00A0require unprecedented high access speed and low latency
information is increasingly generated locally and consumed locally
MEC is implemented based on a virtualized platform that leverages advancements in
\u00A0network functions virtualization (NFV)
\u00A0information-centric networks (ICN)
softwaredefined networks (SDN)
A main focus of MEC research\u00A0
\u00A0develop these general network technologies
\u00A0mobile applications
\u00A0the face recognition
augmented reality (AR)
B. Mobile Edge Computing Versus Mobile Cloud Computing
Low Latency
Mobile Energy Savings
Context-Awareness
Privacy/Security Enhancement
C. Paper Motivation and Outline
a wide-range of issues related to MEC
system and network modeling
\u00A0optimal control
\u00A0multiuser resource allocation
implementation
\u00A0standardization
survey in [36]
potential directions for research and development
content scaling
\u00A0local connectivity
\u00A0augmentation
data aggregation \u00A0and analytics\u00A0
\u00A0lacks a survey article\u00A0
\u00A0providingcomprehensive and concrete discussions on specific MEC research results with a deep integration of mobile computing and wireless communications
providing relevant surveys on joint radio-and-computational resource allocation for MEC
II. MEC COMPUTATION AND COMMUNICATION MODELS\u00A0
A. Computation Task Models
\u00A0parameters that play critical roles
\u00A0latency
bandwidth utilization
\u00A0context awareness
\u00A0generality
\u00A0scalability\u00A0
1) Task Model for Binary Offloading
\u00A0A highly integrated or relatively simple task cannot be partitioned and has to be executed as a whole either locally at the mobile device or offloaded to the MEC server
2) Task Models for Partial Offloading
be partitioned into two parts with one executed at the mobile device and the other offoaded for edge execution.
affects the procedure of execution and computation offoading
\u00A0the execution order of functions or routines\u00A0
some can only be executed locally such as the image display function
task-call graph
can capture the inter-dependency among different computation functions and routines in an application
\u00A0a directed acyclic graph (DAG)
B. Communication Models
wireless channels differ from the wired counterparts
multipath fading in wireless channels
severe inter-symbol inference (ISI)
\u00A0a signal being interfered by other signals occupying the same spectrum
interference management becomes one of the most important design issues for wireless communication systems
considerations
joint design of offloading and wireless transmissions
\u00A0be adaptive to the time-varying channels based on the accurate channel-state information (CSI).\u00A0
\u00A0communications are typically between APs and mobile devices with the possibility of direct D2D communications.
D2D communications with neighboring devices provide the opportunity to forward the computation tasks to MEC servers.
\u00A0different types of commercialized technologies for mobile communications
\u00A0radio frequency identification (RFID)
Bluetooth
\u00A0WiFi
\u00A0cellular technologies\u00A0
\u00A0the key characteristics in Table II
C. Computation Models of Mobile Devices\u00A0
discuss methodologies of evaluating the computation performance.
CPU performance\u00A0
the execution latency
\u00A0energy consumption for local computation\u00A0
\u00A0other hardware components\u00A0
D. Computation Models of MEC Servers\u00A0
\u00A0Similar as the mobile devices
The server-computation latency is negligible\u00A0
consider the nonnegligible server execution time in the general design of MEC systems
Two possible models
\u00A0deterministic\u00A0
\u00A0stochastic\u00A0
The energy consumption\u00A0
the usage of the CPU
storage
\u00A0memory
network interfaces
III. RESOURCE MANAGEMENT IN MEC SYSTEMS\u00A0
focusing on the research of joint radio-and-computational resource management for different types of MEC systems
A. Single-User MEC Systems
1) Deterministic Task Model with Binary Offloading
the binary offloading decision is on whether a particular task should be offloaded for edge execution or local computation
the problem of transmission-energy minimization under a computation-deadline constraint
minimize the energy consumption for executing a task with a soft real-time requirement
2) Deterministic Task Model with Partial Offloading
3) Stochastic Task Model
by random task arrivals
B. Multiuser MEC Systems\u00A0
1) Joint Radio-and-Computational Resource Allocation
2) MEC Server Scheduling
based on the assumptions of user synchronization and the feasibility of parallel local-and-edge computation
requires relaxation of these assumptions in practical study
3) Multiuser Cooperative Edge Computing
two advantages
burdens on the servers can be lightened
sharing the computational resources among the users can balance the uneven distribution of the computation workloads and computation capabilities over users.\u00A0
C. MEC Systems with Heterogeneous Servers\u00A0
1) Server Selection
\u00A0a key design issue is to determine the destination of computation offloading
2) Server Cooperation
benefits
\u00A0improve the resource utilization andincrease the revenues of computing service providers
provide more resources for mobile users to enhance their user experience
components
resource allocation
revenue management
\u00A0service provider cooperation
3) Computation Migration
motivated by the mobility of offloading users
D. Challenges\u00A0
1) Two-Timescale Resource Management
2) Online Task Partitioning
3) Large-Scale Optimization
the increase of the network size renders the resource management a large-scale optimization problem with respect to a large number of offloading decision as well as radio-andcomputational resource allocation variables
IV. AN OUTLOOK FOR MEC RESEARCH\u00A0
a set of key research directions; analyze the design challenges for each research problem and provide several potential research approaches
A. Deployment of MEC Systems
1) Site Selection for MEC Servers
important factors
\u00A0site rentals
\u00A0computation demands
user experience cannot be guaranteed due to the poor signal quality and congestion
obstacles
\u00A0physical limitations
the computation capabilities are smaller
incur security problems as they are easy-to-reach and vulnerable to external attacks
\u00A0there exist no available communication infrastructures
need to deploy edge servers with wireless transceivers by properly choosing new locations.\u00A0
\u00A0dependent on the computational resource-allocation strategy
2) MEC Network Architecture
design the Het-MEC systems [heterogeneous networks (HetNets)]
3) Server Density Planning
to determine the number of edge nodes as well as the optimal combination of different types of MEC servers
challenges
The timescales of computation and wireless channel coherence time may be different
\u00A0The computation offloading policy will\u00A0affect the radio resource management policy
\u00A0The computation demands are normally non-uniformly distributed and clustered\u00A0
B. Cache-Enabled MEC
1) Service Caching for MEC Resource Allocation
\u00A0less resources
\u00A0different mobile services require different resources
\u00A0two possible approaches
\u00A0spatial popularity-driven service caching
\u00A0temporal popularity-driven service caching
2) Data Caching for MEC Data Analytics
should be supported by comprehensive database
imposes extremely heavy burden on the edge server storage
relieved by intelligent data caching that only reserves frequently-used database
\u00A0how to balance the tradeoff between massive\u00A0database and finite storage capacity
\u00A0to establish a practical database popularity distribution model
C. Mobility Management for MEC
challenges for\u00A0realizing ubiquitous and reliable computing
\u00A0users moving among different cells will incur severe interference and pilot contamination
frequent handovers will increase the computation latency and thus deteriorate users’ experience.\u00A0
1) Mobility-Aware Online Prefetching
issue
Conventional design for mobile computation offloading brings long fetching latency &causes heavy loads
solution
leverage the statistical information of the user trajectory and prefetch parts of future computation data to potential servers during the server-computation time
\u00A0the trajectory prediction
the selection of the prefetched computation data
2) Mobility-Aware Offloading Using D2D Communications
\u00A0handle the user mobility problems in MEC systems\u00A0
\u00A0the user mobility brings new design issues\u00A0
1.\u00A0how to exploit the advantages of both D2D and cellular communications
3.\u00A0massive D2D links will introduce severe interference for reliable communications
3) Mobility-Aware Fault-Tolerant MEC
three major and interesting problems\u00A0
fault prevention
fault detection
by setting intelligent timing checks or receiving feedbacks for MEC services
fault recovery
\u00A0can be switched to more reliable backup wireless links with adaptive power control for higher-speed offloading.\u00A0
4) Mobility-Aware Server Scheduling
\u00A0this static scheduling design cannot be directly applied for the multiuser MEC systems with mobility due to dynamic environments
D. Green MEC\u00A0
1) Dynamic Right-Sizing for Energy-Proportional MEC
\u00A0to switch off/slow down the processing speeds of some edge servers with light computation loads
the profile of computation workload at each edge server should be accurately forecasted
2) Geographical Load Balancing for MEC
MEC servers can coordinate together to serve a mobile user
helps to improve the energy efficiency of the lightly-loaded edge servers as well as user experience
\u00A0prolong the battery lives of mobile devices
factors
the network congestion state should be monitored and considered
the mutual interests of MEC operators and edge computing service subscribers
the existence of conventional Cloud Computing infrastructures endows the edge servers with an extra option of offloading the latency-critical and computation-intensive tasks to remote cloud data centers
3) Renewable Energy-Powered MEC Systems
Good news
it is reasonable and feasible to power the MEC infrastructures with the state-of-the-art EH techniques.
\u00A0the mobile devices can also get benefits from using renewable energy\u00A0
\u00A0eliminates the need of human intervention such as replacing/recharging the batteries
Problem
\u00A0the green energy-aware resource allocation and computation offloading
a major concern
The randomness of renewable energy may introduce the offloading unreliability and risks of failure
solutions
\u00A0can be densely deployed over the system to provide more offloading opportunities for the users.
the chance of energy shortage can be reduced by properly selecting the renewable energy sources
MEC servers can be powered by hybrid energy sources to improve reliability
E. Security and Privacy Issues in MEC\u00A0
1) Trust and Authentication Mechanisms
the conventional trust and authentication mechanisms designed for Cloud Computing systems inapplicable
there will be a large number of edge servers serving massive mobile devices
2) Networking Security
the difficulties in the distribution of credentials
techniques such as SDN and NFV are\u00A0\u00A0softwares by nature and thus vulnerable
3) Secure and Private Computation
V. STANDARDIZATION EFFORTS AND USE SCENARIOS OF MEC\u00A0
A. Referenced MEC Server Framework\u00A0
B. Technical Challenges and Requirements\u00A0
1) Network Integration
2) Application Portability
3) Security
4) Performance
5) Resilience
6) Operation
7) Regulatory and legal considerations
C. Use Scenarios\u00A0
1) Video Stream Analysis Service
2) Augmented Reality Service
3) IoT Applications
4) Connected Vehicles
VI. CONCLUSION\u00A0
key components of MEC systems
the computation tasks
communications
mobile devices
\u00A0MEC servers computation
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