A Cooperative Defense Framework against Application-level DDoS Attacks on Mobile Edge Computing Services

Mobile edge computing (MEC) provides cloud-computing services for mobile devices to offload intensive computation tasks to the physically proximal MEC servers. In this paper, we consider a multiserver system where a single mobile device asks for computation offloading to multiple nearby servers. We formulate this offloading problem as the joint optimization of computation task assignment and CPU frequency scaling, in order to minimize a tradeoff between task execution time and mobile energy consumption. The resulting optimization problem is combinatorial in essence, and the optimal solution generally can only be obtained by exhaustive search with extremely high complexity. Leveraging the Markov approximation technique, we propose a light-weight algorithm that can provably converge to a bounded near-optimal solution. The simulation results show that the proposed algorithm is able to generate near-optimal solutions and outperform other benchmark algorithms.

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An Energy Efficient Design of Computation Offloading Enabled by UAV

Sensors ◽

10.3390/s20123363 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3363

Author(s):

Linpei Li ◽

Xiangming Wen ◽

Zhaoming Lu ◽

Wenpeng Jing

Keyword(s):

Unmanned Aerial Vehicle ◽

Energy Efficient ◽

Computing System ◽

Edge Computing ◽

Mobile Edge Computing ◽

Efficient Design ◽

Scheme Design ◽

Computing Services ◽

Efficient Scheme ◽

Aerial Vehicle

The data volume is exploding due to various newly-developing applications that call for stringent communication requirements towards 5th generation wireless systems. Fortunately, mobile edge computing makes it possible to relieve the heavy computation pressure of ground users and decrease the latency and energy consumption. What is more, the unmanned aerial vehicle has the advantages of agility and easy deployment, which gives the unmanned aerial vehicle enabled mobile edge computing system opportunities to fly towards areas with communication demand, such as hotspot areas. However, the limited endurance time of unmanned aerial vehicle affects the performance of mobile edge computing services, which results in the incomplete mobile edge computing services under the time limit. Consequently, this paper concerns the energy-efficient scheme design of the unmanned aerial vehicle while providing high-quality offloading services for ground users, particularly in the regions where the ground communication infrastructures are overloaded or damaged after natural disasters. Firstly, the model of energy-efficient design of the unmanned aerial vehicle is set up taking the constraints of the energy limitation of the unmanned aerial vehicle, the data causality, and the speed of the unmanned aerial vehicle into account. Subsequently, aiming at maximizing the energy efficiency of the unmanned aerial vehicle in the unmanned aerial vehicle enabled mobile edge computing system, the bits allocation in each time slot and the trajectory of the unmanned aerial vehicle are jointly optimized. Secondly, a successive convex approximation based alternating algorithm is brought forward to deal with the non-convex energy efficiency maximization problem. Finally, it is proved that the proposed energy efficient scheme design of the unmanned aerial vehicle is superior to other benchmark schemes by the simulation results. Besides, how the performance of proposed scheme design change under different parameters is discussed.

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Mobile edge computing services for dynamic quality of service control

2017 20th Conference of Open Innovations Association (FRUCT) ◽

10.23919/fruct.2017.8071335 ◽

2017 ◽

Author(s):

Evelina Pencheva ◽

Ivaylo Atanasov

Keyword(s):

Quality Of Service ◽

Edge Computing ◽

Mobile Edge Computing ◽

Computing Services ◽

Service Control ◽

Dynamic Quality

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Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market

Future Internet ◽

10.3390/fi11050118 ◽

2019 ◽

Vol 11 (5) ◽

pp. 118 ◽

Cited By ~ 10

Author(s):

Giorgos Mitsis ◽

Pavlos Athanasios Apostolopoulos ◽

Eirini Eleni Tsiropoulou ◽

Symeon Papavassiliou

Keyword(s):

Selection Process ◽

Learning Automata ◽

Low Complexity ◽

End Users ◽

Edge Computing ◽

Mobile Edge Computing ◽

Data Offloading ◽

Learning Framework ◽

Computing Services ◽

Key Enabling Technologies

Software Defined Networks (SDN) and Mobile Edge Computing (MEC), capable of dynamically managing and satisfying the end-users computing demands, have emerged as key enabling technologies of 5G networks. In this paper, the joint problem of MEC server selection by the end-users and their optimal data offloading, as well as the optimal price setting by the MEC servers is studied in a multiple MEC servers and multiple end-users environment. The flexibility and programmability offered by the SDN technology enables the realistic implementation of the proposed framework. Initially, an SDN controller executes a reinforcement learning framework based on the theory of stochastic learning automata towards enabling the end-users to select a MEC server to offload their data. The discount offered by the MEC server, its congestion and its penetration in terms of serving end-users’ computing tasks, and its announced pricing for its computing services are considered in the overall MEC selection process. To determine the end-users’ data offloading portion to the selected MEC server, a non-cooperative game among the end-users of each server is formulated and the existence and uniqueness of the corresponding Nash Equilibrium is shown. An optimization problem of maximizing the MEC servers’ profit is formulated and solved to determine the MEC servers’ optimal pricing with respect to their offered computing services and the received offloaded data. To realize the proposed framework, an iterative and low-complexity algorithm is introduced and designed. The performance of the proposed approach was evaluated through modeling and simulation under several scenarios, with both homogeneous and heterogeneous end-users.

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Hierarchical Mobile Edge Computing Architecture Based on Context Awareness

Applied Sciences ◽

10.3390/app8071160 ◽

2018 ◽

Vol 8 (7) ◽

pp. 1160 ◽

Cited By ~ 15

Author(s):

Juyong Lee ◽

Jihoon Lee

Keyword(s):

Mobile Network ◽

Content Delivery ◽

Edge Computing ◽

Multimedia Streaming ◽

Mobile Edge Computing ◽

Computing Power ◽

Computing Services ◽

Recent Developments ◽

Computing Performance ◽

Fast Access

Due to the recent developments in mobile network technology and the supply of mobile devices, services that require high computing power and fast access speed, such as machine learning and multimedia streaming, are attracting attention. Mobile Edge Computing (MEC) has emerged. MEC allows servers to be located close to users to efficiently handle these services and provides users with ultra-low latency content delivery and powerful computing services. However, there has been a lack of research into the architecture required to efficiently use the computing power and resources of MEC. So, this paper proposes hierarchical MEC architecture in which MEC servers (MECS) are arranged in a hierarchical scheme to provide users with rapid content delivery, high computing performance, and efficient use of server resources.

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