关键词： mobile edge computing   application partition   directed acyclic graph   offloading   Internet of Vehicles  

摘要： Mobile edge computing(MEC)is a promising technology for the Internet of Vehicles,especially in terms of application offloading and resource *** existing offloading schemes are sub-optimal,since these offloading strategies consider an application as a *** comparison,in this paper we propose an application-centric framework and build a finer-grained offloading scheme based on application *** our framework,each application is modelled as a directed acyclic graph,where each node represents a subtask and each edge represents the data flow dependency between a pair of *** vehicles and MEC server within the communication range can be used as candidate offloading ***,the offloading involves assigning these computing nodes to *** addition,the proposed offloading scheme deal with the delay constraint of each *** experimental evaluation show that,compared to existing non-partitioning offloading schemes,this proposed one effectively improves the performance of the application in terms of execution time and throughput.

关键词： Wireless body area networks   mobile edge computing   Green computing   computation offloading   healthcare  

摘要： To meet the demands of new healthcare applications with high computation complexity, multi-access edge computing (MEC) is emerging as a key component of modern healthcare systems which provides rich computing services to the users. With the increase in the number of wireless body area network (WBAN) users requesting computing services, the computational load on the MEC servers increases. A major issue related to the operation of these MEC servers is their sustainability in terms of energy consumption and heavy carbon emission. Therefore, in this work, we propose a resource management scheme, which minimizes the energy consumption of the MEC server without compromising on the quality-of-experience (QoE) of the WBAN users. For that, we propose a cooperative framework between the MEC server and WBAN users, where the MEC server motivates WBAN users to opt for partial offloading instead of full offloading of the computing services. More specifically, the MEC server bargains with each WBAN user for the amount of the task it compute locally and the corresponding reimbursement. We model this economic interaction between the MEC server and all the participating WBAN users using the Nash bargaining theory. Thereafter, we derive the closed-form Nash bargaining solutions (NBS) for two different bargaining protocols. Finally, numerical results show the proposed bargaining scheme is capable of improving the MEC server payoff by 44.3 , 51.4, and 56.1, respectively, compared to the state-of-the art schemes.

关键词： Mobile edge computing   resource allocation   profit maximization  

摘要： Mobile edge computing (MEC) can significantly reduce latency by pushing resources away from remote clouds to distributed base stations (BSs) equipped with MEC servers, which are closer to users and deployed by service providers (SPs) at the edge of cellular networks. To improve user experience and increase their own revenue, SPs tend to use resources in their deployed BSs to provide services instead of using resources in BSs deployed by other SPs. We envision a densely-deployed multi-SP MEC network where a user equipment (UE) is covered by multiple BSs from different SPs. As the resource in BSs and MEC servers is limited, it is a challenging problem for SPs to reasonably allocate resources in the edge computing (EC) layer to improve the quality of service. In this article, we propose a novel resource allocation scheme, Decentralized Multi-SP Resource Allocation (DMRA), which aims to maximize the total profit of all SPs at the EC layer and provide high-quality services. Then, we extend our design to the online scheme. The algorithm Decentralized Multi-SP Online Resource Allocation (DMORA) is proposed to fit the dynamic network environment. Simulation results indicate that our proposed schemes can effectively maximize the total profit of all SPs at the EC layer while improving user experience.

关键词： Mobile edge computing   joint cooperative placement and scheduling   user deadline preference   ECs' strategic behaviors  

摘要： Mobile edge computing enables resource-limited edge clouds (ECs) in federation to help each other with resource-hungry yet delay-sensitive service requests. Contrary to common practice, we acknowledge that mobile services are heterogeneous and the limited storage resources of ECs allow only a subset of services to be placed at the same time. This paper presents a jointly optimized design of cooperative placement and scheduling framework, named JCPS, that pursues social cost minimization over time while ensuring diverse user demands. Our main contribution is a novel perspective on cost reduction by exploiting the spatial-temporal diversities in workload and resource cost among federated ECs. To build a practical edge cloud federation system, we have to consider two major challenges: user deadline preference and ECs' strategic behaviors. We first formulate and solve the problem of spatially strategic optimization without deadline awareness, which is proved ArP-hard. By leveraging user deadline tolerance, we develop a Lyapunov-based deadline-driven joint cooperative mechanism under the scenario where the workload and resource information of ECs are known for one-shot global cost minimization. The service priority imposed by deadline urgency drives time-critical placement and scheduling, which, combined with cooperative control, enables workloads migrated across different times and ECs. Given selfishness of individual ECs, we further design an auction-based cooperative mechanism to elicit truthful bids on workload and resource cost. Rigorous theoretical analysis and extensive simulations are performed, validating the efficiency of JCPS in realizing cost reduction and user satisfaction.

关键词： Mobile edge computing   application placement   heuristic algorithm  

摘要： In this article, we address the Multi-Component Application Placement Problem (${\sf MCAPP}$MCAPP) in Mobile Edge Computing (MEC) systems. We formulate this problem as a Mixed Integer Non-Linear Program (MINLP) with the objective of minimizing the total cost of running the applications. In our formulation, we take into account two important and challenging characteristics of MEC systems, the mobility of users and the network capabilities. We analyze the complexity of ${\sf MCAPP}$MCAPP and prove that it is $NP$NP-hard, that is, finding the optimal solution in reasonable amount of time is infeasible. We design two algorithms, one based on matching and local search and one based on a greedy approach, and evaluate their performance by conducting an extensive experimental analysis driven by two types of user mobility models, real-life mobility traces and random-walk. The results show that the proposed algorithms obtain near-optimal solutions and require small execution times for reasonably large problem instances.

关键词： Mobile edge computing   edge server network   network density   optimization algorithm  

摘要： Mobile edge computing (MEC) deploys edge servers at the base station in the proximity of users to provide cloud computing-like computing and storage functionalities, which can achieve applications' low latency requirement at the network edge. The edge server network (ESN), constituted by edge servers in an area and the links between them, can host app vendors' services for serving nearby users. Many existing studies have demonstrated that a high ESN density allows for high service performance because edge servers can communicate and share resources with each other effectively over the ESN. However, in the real-world MEC environment, constructing a high-density ESN may incur high construction costs. The trade-off between construction cost and network density plays a vital role in the design of an ESN. Unfortunately, existing studies of MEC have commonly and simply assumed the densities of the ESNs in their experiments. In this paper, we make the first attempt to study the design of cost-effective ESNs with the aim to trade off between the network construction cost and the network density. We model this novel Edge Server Network Design (ESND) problem as a constrained optimization problem and prove its $\mathcal {NP}$NP-hardness. ESND-O as an optimal approach is proposed based on integer programming to solve small-scale ESND problems. Another approximation approach named ESND-A is designed to solve large-scale ESND problems efficiently. We conduct extensive experiments to test the performance of ESND-O and ESND-A on a real-world dataset, and the experimental results demonstrate their effectiveness and efficiency against four representative approaches.

关键词： Computation offloading   mobile edge computing   energy optimization   resource allocation   particle swarm optimization   genetic algorithm  

摘要： Mobile Devices (MDs) support various delay-sensitive and computation-intensive applications. Yet they only have limited battery energy and computing resources, thereby failing to totally run all applications. A mobile edge computing (MEC) paradigm has been proposed to provide additional computation, storage, and networking resources for MDs. Servers in MEC are often deployed in both macro base stations (MBSs) and small base stations (SBSs). Thus, it is highly challenging to associate resource-limited MDs to them with high performance, and realize partial computation offloading among them for minimizing total energy consumption of an MEC system. To tackle these challenges, this work proposes a novel computation offloading approach for delay-sensitive applications with multiple separable tasks in hybrid networks including MDs, SBSs, and an MBS. To achieve it, this work formulates total energy consumption minimization as a constrained mixed integer non-linear program. To solve it, this work designs an improved meta-heuristic optimization algorithm called Particle swarm optimization based on Genetic Learning (PGL), which integrates strong local search capacity of a particle swarm optimizer, and genetic operations of a genetic algorithm. PGL jointly optimizes task offloading among MDs, SBSs, and MBS, users' connection to SBSs, MDs' CPU speeds and transmission power, SBSs and MBS, and bandwidth allocation of available channels. Simulations with real-world data collected from Google cluster trace demonstrate that PGL significantly outperforms other existing methods in total energy consumption of an entire system.

关键词： Mobile edge computing   Computation offloading   Resource allocation   Lyapunov optimization  

摘要： The proposed mobile edge computing can transfer the computing tasks in mobile applications to the nearby edge devices, effectively reducing the processing pressure of local servers and avoiding delays in backhaul and core networks, thus better solving the problem that cloud computing cannot effectively handle resource allocation. However, the complexity of the wireless environment during the communication process leads to the fact that the computing tasks are easily caused by the increase in the number of traffic and packet loss when they are uploaded to the MEC side through the wireless link, which cannot guarantee a lower total system energy con-sumption and shorter total delay. To address the problem that mobile devices cannot handle many computa-tionally intensive tasks in a timely manner, this paper proposes a task offloading optimization scheme for SDN-enabled MEC environments. Modeling the computation offloading problem based on Lyapunov optimization, and then analyzes the offloading delay with respect to the offloading gain. In order to guarantee application re-quirements, minimize energy consumption and latency, and better satisfy user QoS requests, this paper proposes a resource allocation strategy based on deep reinforcement learning. The strategy designs a DQN-based resource allocation algorithm to deploy a joint optimal offloading decision and resource allocation scheme in a mobile edge computing environment under the limited computational resources and the latency constraints of the computational tasks. Based on the experimental results, it is shown that the proposed task offloading strategy can reduce the overall latency;the proposed resource allocation strategy can reduce the total energy consumption and total latency of the system and improve the successful execution rate of tasks.

关键词： Approximation algorithms   Resilience   Process control   Hardware   Energy consumption   Edge computing   Cloud computing   Resource allocation   service placement   Lagrangian relaxation   mobile edge computing   availability  

摘要： Mobile edge computing (MEC) is an emerging architecture for accommodating latency sensitive virtualized services (VSs). Many of these VSs are expected to be safety critical, and will have some form of reliability requirements. In order to support provisioning reliability to such VSs in MEC in an efficient and confidentiality preserving manner, in this paper we consider the joint resource dimensioning and placement problem for VSs with diverse reliability requirements, with the objective of minimizing the energy consumption. We formulate the problem as an integer programming problem, and prove that it is NP-hard. We propose a two-step approximation algorithm with bounded approximation ratio based on Lagrangian relaxation. We benchmark our algorithm against two greedy algorithms in realistic scenarios. The results show that the proposed solution is computationally efficient, scalable and can provide up to 30 percent reduction in energy consumption compared to greedy algorithms.

关键词： Resource management   Task analysis   Servers   Optimization   Energy consumption   Collaboration   Collaborative work   Mobile edge computing   task offloading   resource allocation   dependency   collaborative computing  

摘要： In mobile edge computing (MEC) systems, mobile users (MUs) are capable of allocating local resources (CPU frequency and transmission power) and offloading tasks to edge servers in the vicinity in order to enhance their computation capabilities and reduce back-and-forth transmission over backhaul link. Nevertheless, mobile environment makes it hard to draw offloading and resource allocation decisions under dynamical wireless channel state and users' locations. In real life, social relationship is also provably a significant factor affecting integral performance in collaborative work, which results in MUs decisions strongly coupled and renders this problem further intractable. Most of previous works ignore the impact of inter-user dependency (or data dependency among IoT devices). To bridge this gap, we study the service collaboration with master-slave dependency among service chains of MUs and formulate this combinational optimization problem as a mixed integer non-linear programming (MINLP) problem. To this end, we derive the closed-form expression of resource allocation solution by convex optimization and transform it to integer linear programming (ILP) problem. Subsequently, we propose a distributed algorithm based on Markov approximation which has polynomial computation complexity. Experimental result on real-world dataset substantiates the usefulness and superiority of our scheme, in terms of reducing latency and energy consumption.