A developing trend in the IT environment is mobile cloud computing (MCC) with colossal infrastructural and resource requirements. In the cloud computing environment, load balancing -a way of distributing workloads across numerous computing resources, is a vital aspect. A proficient load balancing guarantees an effective resource usage through the supply of network resources based on the user demands. It can also organize the network clients using the fitting planning criteria. This paper sets forth an advanced load balancing and energy/cost aware technique for a demand-based network resource allocation in cloud computing. The load balancing process in the proposed strategy utilizes a Krill load balancer (Krill LB) which is expected to achieve a well-balanced load over virtual machines. The aim of using the Krill LB as the load balancer is to increase the throughput of the network as much as possible. The speed, task cost, and weight of the tasks were first determined, after which, the Krill herd optimization algorithm was for the load balancing based on the measured parameters. Furthermore, a modified dynamic energy-aware cloudlet-based mobile cloud computing model (MDECM) was introduced for energy cost awareness in load balancing based on the service rate and energy of the mobile users. The proposed work was aimed at optimizing resource allocation in MCC in an energy-efficient manner. The performance of the suggested Krill-LB was benchmarked against that of Honey Bee Behavior Load Balancing (HBB-LB), Kill Herd, and Round Robin algorithms. or applications to servers in cloud domain in order to execute them, and after that, recover the outcome of the execution from these servers [8]. It utilizes communication technology to share information and assets and incorporates locationaware technologies, mobile access to IT, and energy sparing technology specifically designed for mobile devices [15]. As of late, mobile applications have been noticeably copious with different classes, such as entertainment, health, games, business, social networking, travel, andIn the cloud, the load assigned to every node in the network is similarly distributed with an even quantity of resources over time. This enhances the scheme performance by moving the workloads among various nodes [4] [26]. The primary goal is to expedite the implementation of applications on resources whose workload changes at the runtime in an unpredictable way. These are generally discussed in homogeneous conditions such as grids. Fundamentally, there are two ways of load balancing procedures: (i) Static and (ii) dynamic [3]. The load balancing plan and a migration policy are aimed at virtual machine (VM) clustering to brilliantly choose a VM from an over-burdened Studies in Informatics and Control, 26(4) 413-424,

Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers' information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.

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Vehicular Ad-hoc Networks (VANETs) are ad hoc wireless mobile networks which is important role in commercial applications and safety of public vehicular communications. In VANETs, data routing is a major problem because of its quick change in topology and vehicular high mobility. The routing protocols that base on vehicular position are attracting much interest because of the availability and advancement of devices equipped with GPS. A major problem with VANETs is the frequent disruptions in the path of vehicles due to the high mobility of the participating vehicles. This can lead to the breaking of established links and result in low throughput, delay in service delivery, less overhead as well as low rate of delivery. In this paper, the Civil Defiance Vehicle (CDV) approach is proposed. The proposed approach (CDV-VANET) compared to VANET, incorporates a potential score-based strategy which is traditionally better in performance and might achieve less delay in service delivery with a higher rate of delivery.

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