An Optimal Content Caching Framework for Utility Maximization

For desirable quality of service, content providers aim at covering content requests by large network caches. Content caching has been considered as a fundamental module in network architecture. There exist few studies on the optimization of content caching. Most existing works focus on the design of content measurement, and the cached content is replaced by a new one based on the given metric. Therefore, the performance for service provision with multiple levels is decreased. This paper investigates the problem of finding optimal timer for each content. According to the given timer, the caching policies determine whether to cache a content and which existing content should be replaced, when a content miss occurs. Aiming to maximize the aggregate utility with capacity constraint, this problem is formalized as an integer optimization problem. A linear programming based approximation algorithm is proposed, and the approximation ratio is proved. Furthermore, the problem of content caching with relaxed constraints is given. A Lagrange multiplier based approximation algorithm with polynomial time complexity is proposed. Experimental results show that the proposed algorithms have better performance.

Intelligent Task Offloading and Collaborative Computation over D2D Communication

In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and collaborators.In the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation offloading.First,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high power.Second,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,respectively.Moreover,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.

Distributed rate allocation for elastic flows in concurrent multipath transfer

Concurrent multipath transfer（CMT） using stream control transmission protocol（SCTP） multihoming has become an appealing option to increase the throughput and improve the performance of increasingly bandwidth-hungry applications.To investigate the rate allocation for applications in CMT,this paper analyzes the capacities of paths shared by competing sources,then proposes the rate allocation model for elastic flows based on the framework of network utility maximization（NUM）.In order to obtain the global optimum of the model,a distributed algorithm is presented which depends only on local available information.Simulation results confirm that the proposed algorithm can achieve the global optimum within reasonable convergence times.

CALCULATION OF FUZZY RELIABILITY IN THE CASE OF RANDOM STRESS AND FUZZY FATIGUE STRENGTH

The fuzzy sets theory is introduced into the fatigue reliability analysis. The concepts of maximizing set and minimizing set are developed to decide the ordering value of each fuzzy number, and these values can be used to determine the order of the fuzzy numbers. On the basis of the works mentioned above, the membership function defining the fuzzy safety event can be calculated, and then the fuzzy reliability in the case of stress and fuzzy fatigue strength is deduced. An example is given to illustrate the method.

Decision Aid Model for Private-owned Electric Vehicles Participating in Frequency Regulation Ancillary Service Market

To reduce the difficulty and enhance the enthusiasm of private-owned electric vehicles(EVs)to participate in frequency regulation ancillary service market(FRASM),a decision aid model(DAM)is proposed.This paper presents three options for EV participating in FRASM,i.e.,the base mode(BM),unidirectional charging mode(UCM),and bidirectional charging/discharging mode(BCDM),based on a reasonable simplification of users'participating willingness.In BM,individual EVs will not be involved in FRASM,and DAM will assist users to set the optimal charging schemes based on travel plans under the time-of-use(TOU)price.UCM and BCDM are two modes in which EVs can take part in FRASM.DAM can assist EV users to create their quotation plan,which includes hourly upper and lower reserve capabilities and regulation market mileage prices.In UCM and BCDM,the difference is that only the charging rate can be adjusted in the UCM,and the EVs in BCDM can not only charge but also discharge if necessary.DAM can estimate the expected revenue of all three modes,and EV users can make the final decision based on their preferences.Simulation results indicate that all the three modes of DAM can reduce the cost,while BCDM can get the maximum expected revenue.

Risk and Potential:An Asset Allocation Framework with Applications to Robo-Advising

We propose a novel dynamic asset allocation framework based on a family of mean-variance-induced utility functions that alleviate the non-monotonicity and time-inconsistency problems of mean-variance optimization.The utility functions are motivated by the equivalence between the mean-variance objective and a quadratic utility function.Crucially,our framework differs from mean-variance analysis in that we allow different treatment of upside and downside deviations from a target wealth level.This naturally leads to a different characterization of possible investment outcomes below and above a target wealth as risk and potential.Our proposed asset allocation framework retains two attractive features of mean-variance optimization:an intuitive explanation of the investment objective and an easily computed optimal strategy.We establish a semi-analytical solution for the optimal trading strategy in our framework and provide numerical examples to illustrate its behavior.Finally,we discuss applications of this framework to robo-advisors.

The Convergence Rate from Discrete to Continuous Optimal Investment Stopping Problem

The author studies the optimal investment stopping problem in both continuous and discrete cases, where the investor needs to choose the optimal trading strategy and optimal stopping time concurrently to maximize the expected utility of terminal wealth.Based on the work of Hu et al.(2018) with an additional stochastic payoff function,the author characterizes the value function for the continuous problem via the theory of quadratic reflected backward stochastic differential equations(BSDEs for short) with unbounded terminal condition. In regard to the discrete problem, she gets the discretization form composed of piecewise quadratic BSDEs recursively under Markovian framework and the assumption of bounded obstacle, and provides some useful a priori estimates about the solutions with the help of an auxiliary forward-backward SDE system and Malliavin calculus. Finally, she obtains the uniform convergence and relevant rate from discretely to continuously quadratic reflected BSDE, which arise from corresponding optimal investment stopping problem through above characterization.

Information uncertainty related to marked random times and optimal investment

We study an optimal investment problem under default risk where related information such as loss or recovery at default is considered as an exogenous ran-dom mark added at default time.Two types of agents who have different levels of information are considered.We first make precise the insider’s information flow by using the theory of enlargement of filtrations and then obtain explicit logarith-mic utility maximization results to compare optimal wealth for the insider and the ordinary agent.

Joint Bandwidth Allocation and Path Selection in WANs with Path Cardinality Constraints

In this paper,we study the joint bandwidth allocation and path selection problem,which is an extension of the well-known network utility maximization(NUM)problem,via solving a multi-objective minimization problem under path cardinality constraints.Specifically,such a problem formulation captures various types of objectives including proportional fairness,average delay,as well as load balancing.In addition,in order to handle the"unsplittable flows",path cardinality constraints are added,making the resulting optimization problem quite challenging to solve due to intrinsic nonsmoothness and nonconvexity.Almost all existing works deal with such a problem using relaxation techniques to transform it into a convex optimization problem.However,we provide a novel solution framework based on the linearized alternating direction method of multipliers(LADMM)to split the original problem with coupling terms into several subproblems.We then derive that these subproblems,albeit nonconvex nonsmooth,are actually simple to solve and easy to implement,which can be of independent interest.Under some mild assumptions,we prove that any limiting point of the generated sequence of the proposed algorithm is a stationary point.Numerical simulations are performed to demonstrate the advantages of our proposed algorithm compared with various baselines.