Joint Allocation of Computing and Connectivity Resources in Survivable Inter-Datacenter Elastic Optical Networks

Inter-datacenter elastic optical networks(EON)need to provide the service for the requests of cloud computing that require not only connectivity and computing resources but also network survivability.In this paper,to realize joint allocation of computing and connectivity resources in survivable inter-datacenter EONs,a survivable routing,modulation level,spectrum,and computing resource allocation algorithm(SRMLSCRA)algorithm and three datacenter selection strategies,i.e.Computing Resource First(CRF),Shortest Path First(SPF)and Random Destination(RD),are proposed for different scenarios.Unicast and manycast are applied to the communication of computing requests,and the routing strategies are calculated respectively.Simulation results show that SRMLCRA-CRF can serve the largest amount of protected computing tasks,and the requested calculation blocking probability is reduced by 29.2%,28.3%and 30.5%compared with SRMLSCRA-SPF,SRMLSCRA-RD and the benchmark EPS-RMSA algorithms respectively.Therefore,it is more applicable to the networks with huge calculations.Besides,SRMLSCRA-SPF consumes the least spectrum,thereby exhibiting its suitability for scenarios where the amount of calculation is small and communication resources are scarce.The results demonstrate that the proposed methods realize the joint allocation of computing and connectivity resources,and could provide efficient protection for services under single-link failure and occupy less spectrum.

Joint wireless and cloud resource allocation based on parallel auction for mobile edge computing

A joint resource allocation algorithm based on parallel auction(JRAPA)is proposed for mobile edge computing(MEC).In JRAPA,the joint allocation of wireless and cloud resources is modeled as an auction process,aiming at maximizing the utilities of service providers(SPs)and satisfying the delay requirements of mobile terminals(MTs).The auction process consists of the bidding submission,winner determination and pricing stages.At the bidding submission stage,the MTs take available resources from SPs and distance factors into account to decide the bidding priority,thereby reducing the processing delay and improving the successful trades rate.A resource constrained utility ranking(RCUR)algorithm is put forward at the winner determination stage to determine the winners and losers so as to maximize the utilities of SPs.At the pricing stage,the sealed second-price rule is adopted to ensure the independence between the price paid by the buyer and its own bid.The simulation results show that the proposed JRAPA algorithm outperforms other existing algorithms in terms of the convergence rate and the number of successful trades rate.Moreover,it can not only achieve a larger average utility of SPs but also significantly reduce the average delay of MTs.

Joint resource allocation scheme based on evolutionary game for mobile edge computing

To satisfy mobile terminals ’( MTs) offloading requirements and reduce MTs’ cost,a joint cloud and wireless resource allocation scheme based on the evolutionary game( JRA-EG) is proposed for overlapping heterogeneous networks in mobile edge computing environments. MTs that have tasks offloading requirements in the same service area form a population. MTs in one population acquire different wireless and computation resources by selecting different service providers( SPs). An evolutionary game is formulated to model the SP selection and resource allocation of the MTs. The cost function of the game consists of energy consumption,time delay and monetary cost. The solutions of evolutionary equilibrium( EE) include the centralized algorithm based on replicator dynamics and the distributed algorithm based on Q-learning.Simulation results show that both algorithms can converge to the EE rapidly. The differences between them are the convergence speed and trajectory stability. Compared with the existing schemes,the JRA-EG scheme can save more energy and have a smaller time delay when the data size becomes larger. The proposed scheme can schedule the wireless and computation resources reasonably so that the offloading cost is reduced efficiently.

STUDY ON STRATEGY OF DYNAMIC JOINT ROUTING AND RESOURCE ALLOCATION IN LAYERED OPTICAL TRANSPORT NETWORKS

A layered network model for optical transport networks is proposed in this paper,which involves Internet Protocol(IP) ,Synchronous Digital Hierarchy(SDH) and Wavelength Division Mul-tiplexing(WDM) layers. The strategy of Dynamic Joint Routing and Resource Allocation(DJRRA) and its algorithm description are also presented for the proposed layered network model. DJRRA op-timizes the bandwidth usage of interface links between different layers and the logic links inside all layers. The simulation results show that DJRRA can reduce the blocking probability and increase network throughput effectively,which is in contrast to the classical separate sequential routing and resource allocation solutions.

An autonomic joint radio resource management algorithm in end-to-end reconfigurable system

This paper presents the multi-step Q-learning(MQL)algorithm as an autonomic approach to thejoint radio resource management(JRRM)among heterogeneous radio access technologies(RATs)in theB3G environment.Through the'trial-and-error'on-line learning process,the JRRM controller can con-verge to the optimized admission control policy.The JRRM controller learns to give the best allocation foreach session in terms of both the access RAT and the service bandwidth.Simulation results show that theproposed algorithm realizes the autonomy of JRRM and achieves well trade-off between the spectrum utilityand the blocking probability comparing to the load-balancing algorithm and the utility-maximizing algo-rithm.Besides,the proposed algorithm has better online performances and convergence speed than theone-step Q-learning(QL)algorithm.Therefore,the user statisfaction degree could be improved also.

Joint Access Point Selection and Resource Allocation in MEC-Assisted Network:A Reinforcement Learning Based Approach

A distributed reinforcement learning(RL)based resource management framework is proposed for a mobile edge computing(MEC)system with both latency-sensitive and latency-insensitive services.We investigate joint optimization of both computing and radio resources to achieve efficient on-demand matches of multi-dimensional resources and diverse requirements of users.A multi-objective integer programming problem is formulated by two subproblems,i.e.,access point(AP)selection and subcarrier allocation,which can be solved jointly by our proposed distributed RL-based approach with a heuristic iteration algorithm.The proposed algorithm allows for the reduction in complexity since each user needs to consider only its own selection of AP without knowing full global information.Simulation results show that our algorithm can achieve near-optimal performance while reducing computational complexity significantly.Compared with other algorithms that only optimize either of the two sub-problems,the proposed algorithm can serve more users with much less power consumption and content delivery latency.

Resource allocation for relay assisted cognitive radio network

Different schemes, which performed channel, power and time allocation to enhance the network performance of overall end-to-end throughput for cooperative cognitive radio network, were investigated. Interference temperature limit of corresponding primary users was considered. Due to the constraints caused by multiple dual channels, the power allocation problem is non-convex and NP-hard. Based on geometric programming (GP), a novel and general algorithm, which turned the problem into a series of GP problems by logarithm approximation (LASGP), was proposed to efficiently solve it. Numerical results verify the efficiency and availability of the LASGP algorithm. Solutions of LASGP are provably convergent and globally optimal point can be observed as well as the channel allocation always outperforms power or timeslot allocation from simulations. Compared with schemes without any allocation, the scheme with joint channel, power and timeslot allocation significantly increases the overall end-to-end throughput by no less than 70% under same simulation conditions. This scheme can not only maximize the throughput by increasing total maximum power of relay node, but also outperform other resource allocation schemes when lower total maximum power of source and relay nodes is restricted. As the total maximum power of source node increases, the scheme with joint channel and timeslot allocation performs best in all schemes.

Genetic algorithm for autonomic joint radio resource management in end-to-end reconfigurable systems

This article presents the genetic algorithm （GA） as an autonomic approach for the joint radio resource management （JRRM） amongst heterogeneous radio access technologies （RATs） in the end-to-end reconfigurable systems. The joint session admission control （JOSAC） and the bandwidth allocation are combined as a specific decision made by the operations of the genetic algorithm with certain advisable modifications. The proposed algorithm is triggered on the following two conditions When a session is initiated, it is triggered for the session to camp on the most appropriate RAT and select the most suitable bandwidth for the desired service. When a session terminates, it is also used to adjust the distribution of the ongoing sessions through the handovers. This will increase the adjustment frequency of the JRRM controller for the best system performance. Simulation results indicate that the proposed autonomic JRRM scheme not only effectively reduces the handover times, but also achieves well trade-off between the spectrum utility and the blocking probability.

Energy efficiency enhancement in heterogeneous networks:a joint resource allocation approach

To support the drastic growth of wireless multimedia services and the requirements of ubiquitous access, numerous wireless infrastructures which consume enormous energy, such as macrocell, small cell, distributed antenna systems and wireless sensor networks, have been deployed. Under the background of environmental protection, improving the energy efficiency（EE） in wireless networks is becoming more and more important. In this paper, an EE enhancement scheme in heterogeneous networks（Het Nets） by using a joint resource allocation approach is proposed. The Het Nets consists of a mix of macrocell and small cells. Firstly, we model this strategic coexistence as a multi-agent system in which decentralized resource management inspired from Reinforcement Learning are devised. Secondly, a Q-learning based joint resource allocation algorithm is designed. Meanwhile, with the consideration of the time-varying channel characteristics, we take the long-term learning reward into account. At last, simulation results show that the proposed decentralized algorithm can approximate to centralized algorithm with low-complexity and obtain high spectral efficiency（SE） in the meantime.

Multi-radio access based bandwidth allocation strategy for video communication in heterogeneous wireless networks

In order to make full use of the radio resource of heterogeneous wireless networks（HWNs） and promote the quality of service（Qo S） of multi-homing users for video communication, a bandwidth allocation algorithm based on multi-radio access is proposed in this paper. The proposed algorithm adopts an improved distributed common radio resource management（DCRRM） model which can reduce the signaling overhead sufficiently. This scheme can be divided into two phases. In the first phase, candidate network set of each user is obtained according to the received signal strength（RSS）. And the simple additive weighted（SAW） method is employed to determine the active network set. In the second phase, the utility optimization problem is formulated by linear combining of the video communication satisfaction model, cost model and energy efficiency model. And finding the optimal bandwidth allocation scheme with Lagrange multiplier method and Karush-Kuhn-Tucker（KKT） conditions. Simulation results show that the proposed algorithm promotes the network load performances and guarantees that users obtain the best joint utility under current situation.