Classification of Request-Based Mobility Load Balancing in Fog Computing

Every day,more and more data is being produced by the Internet of Things(IoT)applications.IoT data differ in amount,diversity,veracity,and velocity.Because of latency,various types of data handling in cloud computing are not suitable for many time-sensitive applications.When users move from one site to another,mobility also adds to the latency.By placing computing close to IoT devices with mobility support,fog computing addresses these problems.An efficient Load Balancing Algorithm(LBA)improves user experience and Quality of Service(QoS).Classification of Request(CoR)based Resource Adaptive LBA is suggested in this research.This technique clusters fog nodes using an efficient K-means clustering algorithm and then uses a Decision Tree approach to categorize the request.The decision-making process for time-sensitive and delay-tolerable requests is facilitated by the classification of requests.LBA does the operation based on these classifications.The MobFogSim simulation program is utilized to assess how well the algorithm with mobility features performs.The outcome demonstrates that the LBA algorithm’s performance enhances the total system performance,which was attained by(90.8%).Using LBA,several metrics may be examined,including Response Time(RT),delay(d),Energy Consumption(EC),and latency.Through the on-demand provisioning of necessary resources to IoT users,our suggested LBA assures effective resource usage.

Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises,as well as by patients from their mobile devices through communication interfaces.These systems promote reliable and remote interactions between patients and healthcare professionals.However,there are several limitations to these innovative cloud computing-based systems,namely network availability,latency,battery life and resource availability.We propose a hybrid mobile cloud computing(HMCC)architecture to address these challenges.Furthermore,we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture.We compare them,to identify the strengths and weaknesses of each algorithm;and provide their comparative results,to show latency and energy consumption performance.Challenging issues for cloudbased healthcare systems are discussed in detail.

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing inMobile Cloud Computing

This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit.

BE-RPL:Balanced-load and Energy-efficient RPL

Internet of Things(IoT)empowers imaginative applications and permits new services when mobile nodes are included.For IoT-enabled low-power and lossy networks(LLN),the Routing Protocol for Low-power and Lossy Networks(RPL)has become an established standard routing protocol.Mobility under standard RPL remains a difficult issue as it causes continuous path disturbance,energy loss,and increases the end-to-end delay in the network.In this unique circumstance,a Balanced-load and Energy-efficient RPL(BE-RPL)is proposed.It is a routing technique that is both energy-efficient and mobility-aware.It responds quicker to link breakage through received signal strength-based mobility monitoring and selecting a new preferred parent reactively.The proposed system also implements load balancing among stationary nodes for leaf node allocation.Static nodes with more leaf nodes are restricted from participating in the election for a new preferred parent.The performance of BE-RPL is assessed using the COOJA simulator.It improves the energy use,network control overhead,frame acknowledgment ratio,and packet delivery ratio of the network.

AN ADVANCED DYNAMIC FEEDBACK AND RANDOM DISPATCHING LOAD-BALANCING ALGORITHM FOR GMLC IN 3G

Based on the system architecture and software structure of GMLC (Gateway Mobile Location Center) in 3G (third generation), a new dynamic load-balancing algorithm is proposed. It bases on dynamic feedback and imports the increment for admitting new request into the load forecast. It dynamically adjusts the dispatching probability according to the remainder process capability of each node. Experiments on the per- formance of algorithm have been carried out in GMLC and the algorithm is compared with Pick-KX algorithm and DFB (Dynamic FeedBack) algorithm in average throughput and average response time. Experiments re- sults show that the average throughput of the proposed algorithm is about five percents higher than that of the other two algorithms and the average response time is four percents higher under high system loading condi- tion.

移动边缘计算不确定性任务持续卸载及资源分配方法

移动边缘计算场景中任务的不确定性增加了任务卸载及资源分配的复杂性和难度.鉴于此,提出一种移动边缘计算不确定性任务持续卸载及资源分配方法.首先,构建一种移动边缘计算不确定性任务持续卸载模型,通过基于持续时间片划分的任务多批次处理技术应对任务的不确定性,并设计多设备计算资源协同机制提升对计算密集型任务的承载能力.其次,提出一种基于负载均衡的自适应策略选择算法,避免计算资源过度分配导致信道拥堵进而产生额外能耗.最后,基于泊松分布实现了对不确定任务场景模型的仿真,大量实验结果表明时间片长度减小能够降低系统总能耗.此外,所提算法能够更有效地实现任务卸载及资源分配,相较于对比算法,最大可降低能耗11.8%.

基于带Mobile Agent的无线传感器网络的分簇算法

相对于传统方式的无线传感器网络结构,带Mobile Agent(MA)的无线传感器网络(sensornetwork with mobile agent,SENMA)具有更高的能量效率和更长的网络生存时间.设计了一种针对SENMA的分簇算法:依据节点之间的位置关系将节点分为多个簇并选举出簇头节点,每个簇中,簇成员不与簇头进行通信,由簇头将监测数据回传至MA.实验证明这种算法能较好地平衡节点负载,缓解因节点失效导致的网络性能衰减.

基于Mobile Agent的协作式反垃圾邮件系统设计

提出一种基于MobileAgent技术的协作式反垃圾邮件体系结构,并对由代理服务器、反垃圾邮件客户端、动态负载均衡层、MobileAgent层及反垃圾邮件服务器组成的五层结构进行阐述;代理服务器用来解决对邮件服务器的统一访问,降低由邮件服务器及客户端的多样性带来的系统复杂度;使用Nilsimsa算法实现相似邮件Hash过滤;最后对协作式反垃圾系统进行测试。

基于Mobile Agent技术的负载平衡模型研究

为更好地实现分布式系统中的负载平衡,结合自主设计的分布式数据库服务器系统DDSS(distributeddatabaseserversystem),抽象出分布式冗余系统的3层管理框架,从对负载索引的客观描述与定义着眼,引入服务请求消化率、阈值负载贴近度等概念及主动负载平衡的思想,提出并实现了基于移动代理技术的负载平衡模型MMA,并对其性能进行了分析.通过分析表明,基于移动代理技术的负载平衡模型MMA具有代价小、智能性好、效率高及具有对负载索引的刻画客观准确等优点.

Mobile-Agent的空洞避免路由算法

为了解决空洞恢复路由算法在能耗、路由开销及可扩展性等方面的问题,提出了使用移动代理(mobile-agent,MA)进行网间处理来避免"路由空洞"的算法(hole avoiding routing algorithm with mobile-agent,HAMA)。HAMA将网络中的节点分为"空洞相关节点"或"空洞无关节点",通过采用MA方式,对"空洞相关节点"进行路由处理,为其找到一个最优下一跳节点,最终成功建立到Sink节点的路由,从而达到避免"路由空洞"的目的。HAMA使节点提前意识到"路由空洞"的存在,尽可能绕开路由空洞,从而降低网络能耗,均衡网络负载。与经典空洞路由算法GPSR(greedy perimeter stateless routing)相比,HAMA能够成功避免"路由空洞",即使遇到"空洞相关节点",也能较为成功地启用各种恢复机制绕过空洞,具有低成本、高性能的特点。

基于负载预测的多目标优化任务卸载策略

在车联网中随着智能网联汽车的升级,新兴车载应用对计算能力提出更高要求,车载单元本身的计算能力已远远不够,移动边缘计算(MEC)的出现可以为车辆提供更加可靠的服务。为解决车联网边缘计算中的任务卸载问题,提出基于负载预测的多目标优化卸载策略算法,降低任务时延并实现边缘服务器负载均衡。通过基于自适应优化神经网络的负载预测算法预测MEC服务器的负载,提前感知MEC服务器的负载变化,解决任务卸载滞后问题。以最小化时延及MEC服务器负载均衡为目标,综合考虑通信环境、计算资源、任务量等因素构建多目标优化模型。通过非支配排序遗传算法-Ⅲ(NSGA-Ⅲ)获得最优任务卸载策略。仿真实验结果表明,该算法能对MEC服务器的负载进行较精确的预测。相比MTUOA、NSGA2、QTD和AOS,该算法的任务时延分别降低1.7%、7.3%、12.4%、17.5%,并在MEC服务器负载均衡中取得显著优势,解决MEC服务器负载不均衡的问题。此外,该算法可以根据不同通信小区的通信环境及车辆数等制定差异化的任务卸载方案。

移动性负载均衡(MLB)在校园优化中的应用分析

随着LTE用户数的飞速发展和用户潮汐效应,部分场景下忙时小区用户数或PRB利用率已接近饱和,而周边小区资源利用率却很低,如何均衡特殊场景下同覆盖或存在重叠覆盖区域的小区间的负载是一个极有意义的课题。文章以武汉校园场景为例,探讨LTE小区移动负载均衡~[1]参数优化。

A Decision-Based Hybrid Proxy Mobile IPv6 Scheme for Better Resources Utilization

Seamless mobility is always one of the major requirements of modern-day communication.In a heterogeneous and massive IoT environment,efficient network-based mobility protocol such as proxy mobile IPv6(PMIPv6),is potentially a good candidate for efficient mobility as well as resource utilization efficiency.Several extensions are devised for performance in the research domain.However,a multi-criterion decision-based resourceefficient PMIPv6 extension is required to achieve efficiency when network resources are overloaded.In this research,a multi-criterion decision-based PMIPv6 scheme is devised that provides better performance when the Local Mobility Anchor(LMA)or Mobile Access Gateway(MAG)is overloaded.The objective is achieved by monitoring the load status of MAG or LMA and based on their status,the proposed scheme adapts itself to provide seamless mobility in addition to optimal efficiency.The proposed scheme is compared with the existing LMA and MAG-based mobility management protocol extensions.Based on the analysis of the comparison,the obtained results prove that providing a decision-based PMIPv6 scheme is better for service continuity as well as optimal performance in the context of required buffering,handover efficiency,and necessary signaling cost.

基于IPv6的Mobile Ad Hoc网络接入技术研究

在移动自组网(MANET)与Internet互联的网络结构中,采用IPv6协议,针对传统单网关接入的不足,提出一种基于多网关负载均衡的接入技术.此技术由网关发现、网关注册和数据传输三个步骤实现.进行NS2软件仿真实验,结果表明,此接入算法在网络重负荷的情况下能有效地均衡网络中MANET节点与网关节点的负载,有效延长了网络寿命.

面向时延和能耗联合优化的MEC计算卸载策略

在移动边缘计算(MEC)中,计算卸载可以有效缓解资源受限和提高网络服务质量。以任务执行时延、终端能耗和边缘服务器负载率的联合优化为目标,提出面向时延和能耗联合优化的MEC计算卸载策略。构建多目标约束的成本优化模型,引入多变异算子,以迭代关联概率更新变异算子,设计多变异差分进化(MDE)算法求解,实现计算卸载成本最优。为验证MDE算法的有效性,基于Autonomous Systems by Skitter公开数据集构建3个不同规模的实验网络,将MDE算法与随机计算卸载算法、能量优化计算卸载算法、多目标贪婪计算卸载等算法进行对比分析,MDE算法的执行成功率、卸载成功率、服务器负载均衡性分别平均提升了13.23%,12.96%,29.37%,MDE算法能实现MEC中高效、稳定的计算卸载。

车联网中基于麻雀搜索算法的计算卸载策略

针对车联网场景下的边缘计算系统中MEC服务器负载不均衡,紧急任务无法得到优先处理的问题,提出一种基于麻雀搜索算法的计算卸载策略(COSSA)。以最小化VEC系统的任务计算时延和MEC资源服务费为目标建立数学模型,利用层次分析法根据任务的属性为每个需要卸载任务分配优先级,运用麻雀搜索算法根据目标函数找出最优的卸载决策,实现服务器负载均衡。实验结果表明,与Random、ALP和OMP策略相比,COSSA策略可以有效地降低系统开销、均衡MEC服务器负载。

面向边缘增强分布式电力无线传感网的资源分配

针对目前传统电力无线传感网络中出现的访问时延高、能耗大以及用户服务质量恶化等问题,引入移动边缘计算(mobile edge computing,MEC),建立访问时延和网络能耗联合优化模型,目标为最小化时延和能耗的联合函数;随后在射频拉远单元的剩余资源量、负载排队等待时延和中心单元/分布单元的剩余资源量的共同约束下,通过控制负载转移过程中的等待时延,从而确定中心单元/分布单元的总资源处理量;接着根据MEC服务器的资源利用率,通过减少活跃的MEC服务器数量,来减少传感器设备的能耗。仿真分析了权重系数大小对目标函数的影响,以及算法在不同业务流量和时刻下的对比。结果表明:提出的分配机制在降低访问时延和设备能耗方面,效果优于其他对比算法。该机制能有效提高传感器的通信质量和系统工作效率,延长传感器设备寿命,降低网络成本开销。

基于博弈论的多边缘服务器负载均衡策略

移动边缘计算(Mobile Edge Computing,MEC)作为一种新兴的计算范式,旨在弥补物联网中移动设备的计算、存储和带宽等资源的不足。由于地域、时间等因素,边缘服务器间的负载差异大,因此边缘服务器的负载均衡至关重要。文中提出了一种基于博弈论的边缘服务器负载均衡策略,其满足边缘服务器间的负载均衡需求。首先,将MEC服务器负载均衡问题建模为非合作博弈,引入基于近端分解算法(Proximal Decomposition Algorithm,PDA)的正则化方法来得到唯一的纳什均衡解。然后,根据建立的博弈模型,提出了一种分布式边缘服务器负载均衡算法(Distributed Load Balancing Algorithm,DLBA),优化系统响应时间与能耗。实验结果表明,DLBA能够通过较少的迭代次数快速达到纳什均衡点,且DLBA得到的策略在平均响应时延方面较本地计算策略、基于计算能力分配策略降低了18.39%和9.91%;在平均能耗方面较本地计算策略、基于计算能力分配策略降低了2.42%和7.33%;与粒子群遗传算法得到的最优策略差距较小,但计算时间仅为粒子群遗传算法的1.81%。因此,该策略可以有效降低系统响应时间和能量消耗,且执行时间较短,适用于真实场景。

Load balancing routing for single-layered satellite networks

The varying population density leads to imbalanced utilization rate of satellites. To ensure an intelligent engineering of traffic over satellite networks, a distributed routing scheme for single-layered satellite network, load balancing routing protocol based on mobile agent （LBRP-MA） is proposed. For LBRP-MA, mobile agents explore route by migrating autonomously. Upon arriving at destination, mobile agents migrate back. On each intermediate satellite, mobile agents evaluate path cost considering satellite geographical position as well as inter-satellite link （ISL） cost, and finally take ISL congestion index into account to update routing tables. Through simulations on the Courier-like constellation, the proposed approach is shown to achieve guaranteed end-to-end delay bound and decrease packet loss ratio with better throughput, which is especially suitable for data transferring in case of high traffic load. Moreover, results of the complexity analysis demonstrate that LBRP-MA can have low onboard signaling, storage and computation requirements. Furthermore, issues of LBRP-MA such as ISL congestion index and cost modification factor are discussed.