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As the volume of data and data-generating equipment in healthcare settings grows, so do issues like latency and inefficient processing inside healthmonitoring systems. The Internet of Things (IoT) has been used to create a wide variety of health monitoring systems. Most modern health monitoring solutions are based on cloud computing. However, large-scale deployment of latency-sensitive healthcare applications is hampered by the cloud’s design, which introduces significant delays during the processing of vast data volumes. By strategically positioning servers close to end users, fog computing mitigates latency issues and dramatically improves scaling on demand, resource accessibility, and security. In this work, we describe a new load-balancing strategy based on the Strength Pareto Evolutionary Algorithm 2 (SPEA2) for distributing work among fog nodes in a large-scale health monitoring system. We ran comprehensive simulations in the iFogSim toolkit to verify the efficacy of the proposed method, comparing the results to the cloud-only implementation, the Fog Node Placement Algorithm (FNPA), the Load Balancing (LAB) scheme, and the Load Balancing Scheme (LBS) in terms of latency and network utilization. The proposed health monitoring system deployment drastically decreases latency and network use in comparison to cloud-only, FNPA, LAB, and LBS schemes. [ABSTRACT FROM AUTHOR]

Considering several fundamental needs of network performance, the Routing and Media Access Control (MAC) layers are crucially checked out for their operations. Sometimes, MAC protocol faces difficulties in modifying the situations over underwater communication which are specifically considered to be dynamic. The nodes in water tend to remain unstable and network fluctuations are made depending upon the number of competitors. The existing hybrid MAC protocol does not possess dynamic network loads. It also involves a lack of modification in access control approaches which provide variations in network loads. Finally, it leads to delayed network performance. The routing layer focus on some difficult parameters like increased packet loss, restricted bandwidth with substantial energy incursion, reduced network lifetime, increased propagation delay, less remaining energy, consistent data transfer, and energy organization which are considered to be the most important research issues in Underwater Acoustic Sensor Networks (UWASNs). In MAC and routing layers, a Cross-layer approach involves some techniques which are discussed and implemented using Ns3 simulation to obtain the performance report. It is also proposed to compare the algorithm with existing techniques to provide accurate results. Reinforcement Learning (RL) in the MAC layer for SINR-based time slot allocation and Integrated Artificial Fish Swarm with Bacterial Foraging Algorithm (IAFSBFA) for energy-aware QoS reliable routing in the network layer are used in this system. In this cross-layer approach, load balancing in the MAC layer and efficient routing using a hybrid algorithm gives out better performance in the underwater network. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UWASNs) aim to find varied offshore ocean monitoring and exploration applications. In most of these applications, the network is composed of several sensor nodes deployed at different depths in the water. Sensor nodes located at depth on the seafloor cannot invariably communicate with nodes close to the surface level; these nodes need multihop communication facilitated by a suitable routing scheme. In this research work, a Cluster-based Cooperative Energy Efficient Routing (CEER) mechanism for UWSNs is proposed to overcome the shortcomings of the Co-UWSN and LEACH mechanisms. The optimal role of clustering and cooperation provides load balancing and improves the network profoundly. The simulation results using MATLAB show better performance of CEER routing protocol in terms of various parameters as compared to Co-UWSN routing protocol, i.e., the average end-to-end delay of CEER was 17.39, Co-UWSN was 55.819 and LEACH was 70.08. In addition, the average total energy consumption of CEER was 9.273, Co-UWSN was 12.198, and LEACH was 45.33. The packet delivery ratio of CEER was 53.955, CO-UWSN was 42.047, and LEACH was 30.31. The stability period CEER was 130.9, CO-UWSN was 129.3, and LEACH was 119.1. The obtained results maximized the lifetime and improved the overall performance of the CEER routing protocol. [ABSTRACT FROM AUTHOR]

A Mobile Ad hoc NETwork (MANET) is a self-configuring network that is not reliant on infrastructure. This paper introduces a new multipath routing method based on the Multi-Hop Routing (MHR) technique. MHR is the consecutive selection of suitable relay nodes to send information across nodes that are not within direct range of each other. Failing to ensure good MHR leads to several negative consequences, ultimately causing unsuccessful data transmission in a MANET. This research work consists of three portions. The first to attempt to propose an efficient MHR protocol is the design of Priority Based Dynamic Routing (PBDR) to adapt to the dynamic MANET environment by reducing Node Link Failures (NLF) in the network. This is achieved by dynamically considering a node's mobility parameters like relative velocity and link duration, which enable the next-hop selection. This method works more efficiently than the traditional protocols. Then the second stage is the Improved Multi-Path Dynamic Routing (IMPDR). The enhancement is mainly focused on further improving the Quality of Service (QoS) in MANETs by introducing a QoS timer at every node to help in the QoS routing of MANETs. Since QoS is the most vital metric that assesses a protocol, its dynamic estimation has improved network performance considerably. This method uses distance, linkability, trust, and QoS as the four parameters for the next-hop selection. IMPDR is compared against traditional routing protocols. The Network Simulator-2 (NS2) is used to conduct a simulation analysis of the protocols under consideration. The proposed tests are assessed for the Packet Delivery Ratio (PDR), Packet Loss Rate (PLR), End-to-End Delay (EED), and Network Throughput (NT). [ABSTRACT FROM AUTHOR]

Heterogeneous networks (HetNets) are a promising solution for managing the exponential increase in the number of mobile users while maintaining high data rates and coverage. HetNets consist of various cell types with different cell coverage and system capacities. However, the traffic load in HetNets is variable, uneven and random over time, leading to unequal cell loads. Some cells have excessive user presence where the competition for system resources is high, while other cells have low user presence where system resources are not fully utilised. This paper proposes a mobility load balancing algorithm that prioritises millimetre-wave (mmWave) cells in target cell selection and user association to provide load balancing in 5G HetNets and improve overall system performance. A two-step target cell selection method that considers the load level of cells and reference signal received power (RSRP) is proposed. This method prioritises mmWave cells that meet cell load level and RSRP conditions in target cell selection, taking full advantage of the unique properties of mmWave cells (enhanced network throughput, spectral efficiency and enormous bandwidth) to balance traffic loads. The HO triggering and decision-making process is independently performed for each user. In case the serving cell is overloaded, different HO procedures are applied for load balancing depending on the target cell type (macro base station (BS) or mmWave BS). The scope of the study is further expanded by applying different HO procedures according to the serving and target cell types to maintain mobility robustness in case the serving cell is not overloaded. This research proposes various system scenarios with fixed HO margin (HOM) values and fixed time-to-trigger (TTT) durations to examine the effects of HCP settings on the proposed algorithm's performance in terms of average load level of the serving cell, throughput, spectral efficiency and call dropping ratio (CDR). The system that provides the highest overall performance is applied to the proposed algorithm. To further assess and validate the performance of the proposed algorithm, it is compared with other load balancing algorithms from the literature. The simulation results reveal that the proposed algorithm does provide noticeable enhancements in network performance in terms of load level, throughput, spectral efficiency and CDR for various mobile speed scenarios as compared to existing load balancing algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recently, the fundamental problem with Hybrid Mobile Ad-hoc Networks (H-MANETs) is to find a suitable and secure way of balancing the load through Internet gateways. Moreover, the selection of the gateway and overload of the network results in packet loss and Delay (DL). For optimal performance, it is important to load balance between different gateways. As a result, a stable load balancing procedure is implemented, which selects gateways based on Fuzzy Logic (FL) and increases the efficiency of the network. In this case, since gateways are selected based on the number of nodes, the Energy Consumption (EC) was high. This paper presents a novel Node Quality-based Clustering Algorithm (NQCA) based on Fuzzy-Genetic for Cluster Head and Gateway Selection (FGCHGS). This algorithm combines NQCA with the Improved Weighted Clustering Algorithm (IWCA). The NQCA algorithm divides the network into clusters based upon node priority, transmission range, and neighbour fidelity. In addition, the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC, packet loss rate (PLR), etc. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) technology is the real-time application that is growing rapidly as the result of smart environments. Battery power is one of the most significant resources in WSN. For enhancing a power factor, the clustering techniques are used. During the forward of data in WSN, more power is consumed. In the existing system, it works with Load Balanced Clustering Method (LBCM) and provides the lifespan of the network with scalability and reliability. In the existing system, it does not deal with end-to-end delay and delivery of packets. For overcoming these issues in WSN, the proposed Genetic Algorithm based on Chicken Swarm Optimization (GA-CSO) with Load Balanced Clustering Method (LBCM) is used. Genetic Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness Function. Chicken Swarm Optimization (CSO) helps to solve the complex optimization problems. Also, it consists of chickens, hens, and rooster. It divides the chicken into clusters. Load Balanced Clustering Method (LBCM) maintains the energy during communication among the sensor nodes and also it balances the load in the gateways. The proposed GA-CSO with LBCM improves the lifespan of the network. Moreover, it minimizes the energy consumption and also balances the load over the network. The proposed method outperforms by using the following metrics such as energy efficiency, ratio of packet delivery, throughput of the network, lifetime of the sensor nodes. Therefore, the evaluation result shows the energy efficiency that has achieved 83.56% and the delivery ratio of the packet has reached 99.12%. Also, it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms. [ABSTRACT FROM AUTHOR]

In a non-static information exchange network, routing is an overly complex task to perform, which has to satisfy all the needs of the network. Software Defined Network (SDN) is the latest and widely used technology in the future communication networks, which would provide smart routing that is visible universally. The various features of routing are supported by the information centric network, which minimizes the congestion in the dataflow in a network and provides the content awareness through its mined mastery. Due to the advantages of the information centric network, the concepts of the information-centric network has been used in the paper to enable an optimal routing in the softwaredefined networks. Although there are many advantages in the information-centric network, there are some disadvantages due to the non-static communication properties, which affects the routing in SDN. In this regard, artificial intelligence methodology has been used in the proposed approach to solve these difficulties. A detailed analysis has been conducted to map the content awareness with deep learning and deep reinforcement learning with routing. The novel aligned internet investigation technique has been proposed to process the deep reinforcement learning. The performance evaluation of the proposed systems has been conducted among various existing approaches and results in optimal load balancing, usage of the bandwidth, and maximization in the throughput of the network. [ABSTRACT FROM AUTHOR]

A computer network system in a company or agency generally uses more than one Internet Service Provider (ISP) to maintain the quality of its internet services. When one of the ISP's services that are used is interrupted and the internet connection is lost, then the techniques that can be used are load balancing and failover. This study aims to establish a network of two ISPs using load balancing methods and failover techniques with MikroTik. Load balancing to equalize traffic loads and stabilize internet connections for each client and apply failover techniques with 2 ISPs. The method used is the Network Development Life Cycle which redesignates the new network topology by analyzing Quality of Service (QoS) based on the THIPON standard. Based on the test results, the QoS value obtained is 3.75 during busy internet usage hours and the QoS value is 4 at normal hours. Overall, the implementation of computer networks using load balancing techniques with the PCC method showed good changes, this was concluded based on the results of distributing questionnaires before and after implementation from the previous score of 0.4 rising to 0.89. [ABSTRACT FROM AUTHOR]

Cloud computing distributes task-parallel among the various resources. Applications with self-service supported and on-demand service have rapid growth. For these applications, cloud computing allocates the resources dynamically via the internet according to user requirements. Proper resource allocation is vital for fulfilling user requirements. In contrast, improper resource allocations result to load imbalance, which leads to severe service issues. The cloud resources implement internet-connected devices using the protocols for storing, communicating, and computations. The extensive needs and lack of optimal resource allocating scheme make cloud computing more complex. This paper proposes an NMDS (Network Manager based Dynamic Scheduling) for achieving a prominent resource allocation scheme for the users. The proposed system mainly focuses on dimensionality problems, where the conventional methods fail to address them. The proposed system introduced three -threshold mode of task based on its size STT, MTT, LTT (small, medium, large task thresholding). Along with it, task merging enables minimum energy consumption and response time. The proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme (EDS) and Decentralized Virtual Machine Migration (DVM). With a Network Manager-based Dynamic Scheduling, the proposed model achieves excellence in resource allocation compared to the other existing models. The obtained results shows the proposed system effectively allocate the resources and achieves about 94% of energy efficient than the other models. The evaluation metrics taken for comparison are energy consumption, mean response time, percentage of resource utilization, and migration. [ABSTRACT FROM AUTHOR]

Sensors are considered as important elements of electronic devices. In many applications and service, Wireless Sensor Networks (WSNs) are involved in significant data sharing that are delivered to the sink node in energy efficient manner using multi-hop communications. But, the major challenge in WSN is the nodes are having limited battery resources, it is important to monitor the consumption rate of energy is very much needed. However, reducing energy consumption can increase the network lifetime in effective manner. For that, clustering methods are widely used for optimizing the rate of energy consumption among the sensor nodes. In that concern, this paper involves in deriving a novel model called Improved Load-Balanced Clustering for Energy-Aware Routing (ILBC-EAR), which mainly concentrates on optimal energy utilization with load-balanced process among cluster heads and member nodes. For providing equal rate of energy consumption among nodes, the dimensions of framed clusters are measured. Moreover, the model develops a Finest Routing Scheme based on Load-Balanced Clustering to transmit the sensed information to the sink or base station. The evaluation results depict that the derived energy aware model attains higher rate of life time than other works and also achieves balanced energy rate among head node. Additionally, the model also provides higher throughput and minimal delay in delivering data packets. [ABSTRACT FROM AUTHOR]

Infrastructure-as-a-service cloud environment provides infrastructure services per the user's workflow requirements using the pay-you-go model on demand. Workflow applications are highly used in IT industries, project management, supply chain management, large distributed management, and many more. Load-balanced workflow allocation to solve all the requirements of the cloud users is already proven NP-hard. In this scenario, meta-heuristic algorithms are one of the best candidates to solve the load balancing problem in the cloud to achieve an optimal allocation schedule. In this paper, we proposed a load-balanced workflow allocation method using stochastic fractal search (SFS) algorithms to maximize the resource utilization of the virtualized cloud resources. In this way, the load imbalance on virtual machines can be minimized. SFS is an evolutionary optimization method that uses the natural growth behavior in the search process. The performance analysis is conducted by implementing the workflow load balancer in MATLAB. The study shows that the solutions achieved by the proposed SFS strategy are far better than those obtained by particle swarm optimization (PSO). [ABSTRACT FROM AUTHOR]

Cloud computing has shown noteworthy evolution in information technology. Users can enjoy various services of cloud technology only if internet connection is available. In cloud computing, load balancing considered as a fundamental issue that has confronted researchers in this domain. Load balancing basically works by allotting fair and efficient work among computing resources which ultimately achieve high user satisfaction and raises systems productivity. Many load-balancing techniques made efforts to resolve this problem using metaheuristics algorithm, and amplify the operation and efficiency of systems. In this paper, existing load balancing techniques has been discussed and research gaps in existing lirture hasbeeb discussed. Also a new technique has been proposed called IG-GWO - Inquisitive Genetic Grey Wolf Optimization algorithm using combination of Grey wolf optimization (GWO) algorithm and Genetic algorithm. [ABSTRACT FROM AUTHOR]

Task scheduling in the cloud is a difficult optimization challenge. The cloud system is assigned with a specific load based on the cloud architecture and user demands. However, both underloaded and overloaded situations result in a variety of system failures in terms of power consumption, machine failure, and so on. As a result, task-load balancing on virtual machines (VMs) is considered as an important part of cloud task scheduling. The present study proposes a dynamic load balancing algorithm based on the hybrid optimization algorithms named as Mantaray modified multi-objective Harris hawk optimization (MMHHO). The hybridization process updates the search space of Harris Hawk Optimization (HHO) by utilizing the Manta Ray Forging Optimization (MRFO) algorithm by considering the cost, response time, and resource utilization etc. The hybrid scheme, proposed in the present study, improves the system performance by enhancing the VMs throughput, balancing the load between the VMs, and sustaining the balance among priorities of tasks by adjusting the waiting time of the involved tasks. The proposed MMHHO based load balancing algorithm is implemented in CloudSim tool. The effectiveness of the suggested algorithm has been analyzed in terms of various parameters and compared with other existing algorithms. The simulation results show that the suggested MMHHO load balancing scheme outperforms other algorithms. [ABSTRACT FROM AUTHOR]

Task scheduling in the cloud is a difficult optimization challenge. The cloud system is assigned with a specific load based on the cloud architecture and user demands. However, both underloaded and overloaded situations result in a variety of system failures in terms of power consumption, machine failure, and so on. As a result, task-load balancing on virtual machines (VMs) is considered as an important part of cloud task scheduling. The present study proposes a dynamic load balancing algorithm based on the hybrid optimization algorithms named as Mantaray modified multi-objective Harris hawk optimization (MMHHO). The hybridization process updates the search space of Harris Hawk Optimization (HHO) by utilizing the Manta Ray Forging Optimization (MRFO) algorithm by considering the cost, response time, and resource utilization etc. The hybrid scheme, proposed in the present study, improves the system performance by enhancing the VMs throughput, balancing the load between the VMs, and sustaining the balance among priorities of tasks by adjusting the waiting time of the involved tasks. The proposed MMHHO based load balancing algorithm is implemented in CloudSim tool. The effectiveness of the suggested algorithm has been analyzed in terms of various parameters and compared with other existing algorithms. The simulation results show that the suggested MMHHO load balancing scheme outperforms other algorithms. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) allows for the coordination and orchestration of a large number of physical and virtual objects connected to the Internet. Sensors used in IoT applications may be in remote locations with an unstable network infrastructure or with limited management of their functions due to processing overload on devices responsible for performing their management (i.e. IoT Gateways) through IoT services. A failure or overload on a network link associated with an IoT Gateway, or failure on the IoT Gateway itself due to processing overload, may result in temporary or even permanent unavailability in IoT applications (Web, Mobile, and Desktop). To address these problems, this paper presents a solution for the need to execute load balancing for the Fog of Things Platforms (in the Fog, and between Fog and Cloud) via the programmability through the use of Software-Defined Networks (SDN). The IoT environment proposed in this work is based on the Fog of Things (FoT) paradigm, which proposes the provision of processing, storage and IoT services at edge devices (local network), optimizing, when possible, the use of IoT services in the Cloud. [ABSTRACT FROM AUTHOR]

Mobile cloud computing is used to define and determine computing services with a structure model. The data and resource of any service will be retrieved from cloud computing through internet service, some tools, and user interface (web-based or application). Multimedia information could be represented by Audio information, Video information, image information and text information. Mobile Cloud Computing (MCC) is a hybrid of cloud computing and mobile computing. Multimedia Information is the core of Mobile Cloud information because of the sizable information of multimedia particularly video streaming. Mobile Cloud mostly handles and processes that information. MCC is one of the business expressions with the real environment in the IT world. The concept of the MCC is still in the beginner stage of advancement. So, the handle of the innovation in a careful way especially in the bearing of future research should be provide. In this paper, an algorithm is throttled load balancing for mobile clouds has been presented within an example of Multimedia information. The results has shown that the load balancing of cloud computing environment. In this scenario, load balancing techniques in mobile cloud computing can be employed and can successfully manage time through the cloud. [ABSTRACT FROM AUTHOR]

Cloud computing provides hosted services (i.e., servers, storage, bandwidth, and software) over the internet. The key benefits of cloud computing are scalability, efficiency, and cost reduction. The key challenge in cloud computing is the even distribution of workload across numerous heterogeneous servers. Several Cloud scheduling and load-balancing techniques have been proposed in the literature. These techniques include heuristic-based, meta-heuristics-based, and hybrid algorithms. However, most of the current cloud scheduling and load balancing schemes are not content-aware (i.e., they are not considering the content-type of user tasks). The literature studies show that the content type of tasks can significantly improve the balanced distribution of workload. In this paper, a novel hybrid approach named Particle Swarm Optimization based Content-Aware Load Balancing Algorithm (PSO-CALBA) is proposed. PSO-CALBA scheduling scheme combines machine learning and meta-heuristic algorithm that performs classification utilizing file content type. The SVM classifier is used to classify users' tasks into different content types like video, audio, image, and text. Particle Swarm Optimization (PSO) based meta-heuristic algorithm is used to map user's tasks on Cloud. The proposed approach has been implemented and evaluated using a renowned Cloudsim simulation kit and compared with ACOFTF and DFTF. The proposed study shows significant improvement in terms of makespan, degree of imbalance (DI). [ABSTRACT FROM AUTHOR]

The design of controllers for robots is a complex system that is to be dealt with several tasks in real time for enabling the robots to function independently. The distributed robotic control system can be used in real time for resolving various challenges such as localization, motion controlling, mapping, route planning, etc. The distributed robotic control system can manage different kinds of heterogenous devices. Designing a distributed robotic control system is a challenging process as it needs to operate effectually under different hardware configurations and varying computational requirements. For instance, scheduling of resources (such as communication channel, computation unit, robot chassis, or sensor input) to the various system components turns out to be an essential requirement for completing the tasks on time. Therefore, resource scheduling is necessary for ensuring effective execution. In this regard, this paper introduces a novel chaotic shell game optimization algorithm (CSGOA) for resource scheduling, known as the CSGOA-RS technique for the distributed robotic control system environment. The CSGOA technique is based on the integration of the chaotic maps concept to the SGO algorithm for enhancing the overall performance. The CSGOA-RS technique is designed for allocating the resources in such a way that the transfer time is minimized and the resource utilization is increased. The CSGOA-RS technique is applicable even for the unpredicted environment where the resources are to be allotted dynamically based on the early estimations. For validating the enhanced performance of the CSGOA-RS technique, a series of simulations have been carried out and the obtained results have been examined with respect to a selected set of measures. The resultant outcomes highlighted the promising performance of the CSGOA-RS technique over the other resource scheduling techniques. [ABSTRACT FROM AUTHOR]

Data packet drop can happen in Optical Burst-Switched (OBS) when two data bursts are competing on the same wavelength. Recently, many techniques have been developed to solve this problem but they do not consider the congestion. Also, it is necessary to balance the load system in the OBS network. The Ant Colony Optimization (ACO) technique can be applied to determine the straight and the safest route. However, the ACO technique raises both power utilization as well as the execution time. In this study, Cuckoo Search (CS) and ACO methods based approach is proposed to avoid the congestion and load balancing in the OBS network. This strategy evaluates the intensity of hotspot data and then launches the congestion rate optimization process that depends on their load situations. The congestion rate optimization represents the available bandwidth, data distribution rate, queue size, and access rate, and also these factors are optimized through the CS technique. The fitness utility in the CS technique adjusts the distribution rate in the OBS network, and the proposed ACO technique solves the energy utilization problem. The simulation results proved that the presented strategy evades both the end-to-end delay as well as the possibility of packet drop. [ABSTRACT FROM AUTHOR]

Scalability is one of the most important quality attribute of softwareintensive systems, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) (which is also known as executor service) has been used extensively as a middleware service in software-intensive systems. TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime. In case of distributed-TPS (DTPS), another issue is the load balancing b/w available set of TPSs running at backend servers. Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload. Consequently, the performance of software-intensive system is suffered. Thus, in this paper, we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword. On the one hand, it effectively performs the load balancing (in case of overload situation) among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs. And on the other hand, its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size, because the pool size in each TPS is kept equal to the request rate on it, hence dynamically optimizes TPS. We evaluated the results of the proposed system against state of the art DTPSs by a clientserver based simulator and found that our system outperformed by sustaining smaller response times. [ABSTRACT FROM AUTHOR]

In cloud computing environment tasks are allocated among virtual machines (VMs) having different length, starting time and execution time. Therefore, balancing these loads among VM is a key factor. Load balancing has to be carried out in such a manner that all VMs should have balanced to achieve optimal utilization of its capabilities and improve the system performance. In this paper, we proposed a load balancing technique by using modified PSO task scheduling (LBMPSO) to schedule tasks over the available cloud resources that minimizes the makespan and maximizes resource utilization. This is achieved by having proper information among the tasks and resources within the datacenter. Our proposed scheduling algorithm is implemented by using CloudSim simulator. Simulation results clearly shows that proposed scheduling algorithm performs better in reducing makespan and increases the resource utilization than other existing techniques. [ABSTRACT FROM AUTHOR]

Web service applications are increasing tremendously in support of high-level businesses. There must be a need of better server load balancing mechanism for improving the performance of web services in business. Though many load balancing methods exist, there is still a need for sophisticated load balancing mechanism for not letting the clients to get frustrated. In this work, the server with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests. The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low, medium and high load by the load balancing application. Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system. Many Load Balancing schemes are based on the graded thresholds, because the exact information about the network flux is difficult to obtain. Using two thresholds L and U, it is possible to indicate the load on particular server as low, medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L, between L and U or above U respectively. However, the existing works of load balancing in the server farm incorporate fixed time to measure real time response time, which in general are not optimal for all traffic conditions. Therefore, an algorithm based on Proportional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal performance. The emulation results has shown a significant gain in the performance by tuning the threshold time. In addition to that, tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune the fixed time slots. [ABSTRACT FROM AUTHOR]

This paper discusses the design and implementation of a Robust Shrine Affine Projection Sign (RSAPS) adaptive control algorithm for power quality improvement. Various limitations of conventional 2-level inverters can be overcome using a 5-level Cascaded H-Bridge multilevel inverter (CHB-MLI). Such a CHB-MLI is realized and controlled as a distribution static compensator (DSTATCOM) unit in this paper. The DC link of capacitors have been maintained to reference value by using proportional-integral (PI) controllers without using complex balancing circuits. Extensive simulation has been performed and a scaled -down experimental prototype model has been developed in the laboratory to test the system under steady state and dynamic load conditions using dSPACE-1104 digital signal processor. The simulation and experimental results show the source current harmonics, DC voltage regulation, power factor correction, weight updation etc. under varying load. The proposed algorithm gives satisfactory performance in terms of weight convergence, computational burden, mean square error (MSE) etc. under dynamic load conditions and in addition the THD obtained in source voltage and current is within acceptable IEEE-519 limits. [ABSTRACT FROM AUTHOR]

Load balancing among virtual machines (VMs) is significant for delivering the cloud services in optimized way with minimum cost paid and total time acquired to deliver the services. In this paper, the various research gaps for load balancing optimization in the past literature have been presented, which need to be addressed for solving the load balancing problem in cloud environment. In present work, Hybrid approach based resource provisioning and load balancing framework for workflows execution has been proposed to optimize the utilization of VMs with uniform load distribution. The proposed framework is based on the hybridization of heuristic techniques with metaheuristic algorithm to achieve its optimal performance in terms of makespan and cost. Two hybrid approaches have been proposed for HDD-PLB framework-Hybrid Predict Earliest Finish Time (PEFT) Heuristic with Ant Colony Optimization (ACO) metaheuristic (HPA) and Hybrid Heterogeneous Earliest Finish Time (HEFT) heuristic with ACO (HHA). The two proposed approaches for load balancing have been analyzed and compared to determine which is superior for proposed HDD-PLB framework. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) has been already employed for fault detection of air conditioning systems. The sliding window, which is composed of some parameters satisfying with thermal load balance, can select the target historical fault-free reference data as the template which is similar to the current snapshot data. The size of sliding window is usually given according to empirical values, while the influence of different sizes of sliding windows on fault detection of an air conditioning system is not further studied. The air conditioning system is a dynamic response process, and the operating parameters change with the change of the load, while the response of the controller is delayed. In a variable air volume(VAV) air conditioning system controlled by the total air volume method, in order to ensure sufficient response time, 30 data points are selected first, and then their multiples are selected. Three different sizes of sliding windows with 30, 60 and 90 data points are applied to compare the fault detection effect in this paper. The results show that if the size of the sliding window is 60 data points, the average fault-free detection ratio is 80. 17% in fault-free testing days, and the average fault detection ratio is 88. 47% in faulty testing days. [ABSTRACT FROM AUTHOR]

Wireless Sensor Actor Networks (WSANs) have contributed to the development of pervasive computing wherein time consideration to perform the tasks of pervasive applications is necessary. Hence, time constraint is one of the major challenges of WSANs. In this paper, we propose an analytical approach based on queuing theory to minimize the total time taken for completion of tasks, i.e., make-span, in WSANs with hybrid architecture. The best allocation rates of tasks to actor nodes are figured out through solving inequities and qualities resulting from a steady state analysis of the proposed model. Applying the calculated tasks arrival rates at each of the actors, the make-span could be minimized. To assess the accuracy of the tasks assignment rates to each of the actors attained from the suggested analytical approach and to provide a graphical representation of the WSAN a formal model in terms of the generalized stochastic Petri net (GSPN) is presented. The proposed GSPN model is analyzed, tasks distribution weights to the actors are determined, and then tasks allocation rates can be computed. Comparing the results achieved from the analytical approach and the GSPN model demonstrates that allocation rates and hence, the make-span figured out from proposed approach and the formal model are the same. Experimental results in typical scenarios show shorter make-span and longer network lifetime compared to when one of the two popular traditional task allocation algorithms, namely, opportunistic load balancing (OLB), and stochastic allocation (SA) algorithms, is used. [ABSTRACT FROM AUTHOR]

Classical Vehicle Routing Problems (VRP) involve the supply of goods/services from a central depot to geographically scattered customers. Besides the classical objective of minimizing the total traveled distance, the present work also considers simultaneous optimization of two additional objectives namely minimizing makespan and minimizing distance imbalance. A mathematical model has been developed to deal with this multiobjective version of VRP (MO-VRPTW). A meta-heuristic based on multiple ant colony systems for solving this MO-VRPTW has also been proposed. Fire fly optimization Algorithm (FA) has also been applied to avoid local optima. Two new migration operators named Migration-I and Migration-II and multi-pheromone matrices have been developed to further improve the solution quality. The proposed algorithm has been tested on a number of benchmark problems and its superiority over other state-of-the-art approaches and Non Dominated Sorting Algorithm-II (NSGA-II) is demonstrated. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things technology has developed rapidly, and smart Internet of Things devices have also been widely popularized. A large amount of data is generated every moment. Now we are in the era of big data in the Internet of Things. The rapid growth of massive data has brought great challenges to storage technology, which cannot be well coped with by traditional storage technology. The demand for massive data storage has given birth to cloud storage technology. Load balancing technology plays an important role in improving the performance and resource utilization of cloud storage systems. Therefore, it is of great practical significance to study how to improve the performance and resource utilization of cloud storage systems through load balancing technology. On the basis of studying the read strategy of Swift, this article proposes a reread strategy based on load balancing of storage resources to solve the problem of unbalanced read load between interruptions caused by random data copying in Swift. The storage asynchronously tracks the I/O conversion to select the storage with the smallest load for asynchronous reading. The experimental results indicate that the proposed strategy can achieve a better load balancing state in terms of storage I/O utilization and CPU utilization than the random read strategy index of Swift. [ABSTRACT FROM AUTHOR]

This article presents the stress and deformation shape of joint plate in two commonly used constructive designs of truss joints of a rotating excavator's load-carrying structure. A local analysis of stresses and deformations of the joint plate have been conducted taking into consideration the most loaded truss joint of the working wheel's load-carrying structure on specific rotating excavator, having a riveted design, as well as the theoretically determined forces affecting the joining trusses of the truss joint. These activities are performed employing FEM (Finite Element Method) modelling of the truss joint and using a software package for this purpose, in view of defined characteristic static and dynamic loads. Nowadays, the load-carrying structures are more often made by welding, so it is in our interest as it would affect the stress and deformation shape of the subject truss joint in welded design, bearing the same load conditions. [ABSTRACT FROM AUTHOR]

Few studies were focused on the load-sharing characteristics of double-row planetary gear (DRPG) systems with bearings. Meanwhile, the supported bearing has an important influence on the transmission characteristics of the entire system. To overcome this problem, a multi-body dynamic (MBD) model of the DRPG system fully considering the influences of bearing parameters is established. Dynamic loads among contacting gear pairs have been obtained to calculate the load-sharing coefficient (LSC) of the system. The LSC of each gear tooth pair has been compared to study the effect of the supported bearing clearance on the load-sharing characteristics. These methods are based on Hertz contact theory. The liner stiffness and damping are used in the model. The results show that the supported bearing clearance has a greatly effect on the LSC of the DRPG system. Choosing appropriate clearance parameters of supported bearing can help suppress the uneven load distribution of the DRPG system. The results can provide some guidance to find new method to study the LSC and increase the service life of planetary gear systems. [ABSTRACT FROM AUTHOR]

Task scheduling in cloud computing is responsible for serving the user requirements. The scheduling strategy must handle the problems of high load over virtual machines (VMs), high-cost consumption and lengthier scheduling time effectively. The greatest challenge in the cloud computing environment is achieving the intended outcome of task scheduling under the uncertain user request demands as it is responsible for assigning specific resources to requests for achieving effective task completion. However, most of the task scheduling approaches contributed to the literature mainly focused on the design and development of scheduling algorithms but ignored to explore the impact of uncertain factors such as millions of instructions per second (MIPS) and network bandwidth during the scheduling process. In this paper, A Chameleon and Remora Search Optimization Algorithm (CRSOA) is proposed for achieving efficient scheduling process by exploring the impact of MIPS and network bandwidth which directly affects the virtual machine (VM) performance. Further the work includes the uncertainty factors of task completion rate, load balance, scheduling cost and makespan in a simultaneous manner during the process of scheduling. It is formulated a multi-objective cloud task scheduling optimization model by integrating the merits of Chameleon Search Algorithm (CSA) and Remora Search Optimization Algorithm (RSOA) using a greedy methodology for simulating the real cloud computing task scheduling process. The simulation results evidently confirmed that the proposed CRSOA approach is minimizing the completion time and effective in handling the load balancing between the available VMs against other competitive metaheuristic task scheduling algorithms. The experimental investigation of this CRSOA confirmed its predominance in minimizing the makespan by 18.96%, cost by 22.18%, and degree of imbalance by 20.54%, compared to the baseline approaches with different number of tasks and VMs. ● Proposed task scheduling optimization model considers MIPS and network bandwidth uncertainties, which prior works ignored.. ● The four optimization objectives are task completion rate, degree of load balancing, scheduling cost, and job makespan. ● Proposed using CSOA and REOA to balance the rate of exploration and exploitation under resource allocations to user requests.. ● Proposed to maximize diversity and convergence during cloud task scheduling of incoming user requests.. [ABSTRACT FROM AUTHOR]

Cloud computing is on-demand Internet-based computing, which is a highly scalable service adopted by different working and non-working classes of people around the globe. Task scheduling one of the critical applications used by end-users and cloud service providers. The significant challenging in the task scheduler is to find an optimal resource for the given input task. In this paper, we proposed Hybrid Electro Search with a genetic algorithm (HESGA) to improve the behavior of task scheduling by considering parameters such as makespan, load balancing, utilization of resources, and cost of the multi-cloud. The proposed method combined the advantage of a genetic algorithm and an electro search algorithm. The genetic algorithm provides the best local optimal solutions, whereas the Electro search algorithm provides the best global optima solutions. The proposed algorithm outperforms than existing scheduling algorithms such as Hybrid Particle Swarm Optimization Genetic Algorithm (HPSOGA), GA, ES, and ACO. [ABSTRACT FROM AUTHOR]

In order to solve the problems of resource storage, resource delivery delay and other issues in the online learning process, it is studied and analyzed from the perspective of process goals. Firstly, a measurable e-learning process capability maturity model (EPCMM) is established. Then, based on the analysis of workflows in EPCMM's five process areas, a mapping relationship between major activities and key objectives is established in the process area. Finally, the mapping algorithm of online learning process features to process goals and the mapping algorithm of online learning metrics to process features are given. The practical results show that compared with the classical algorithm, this algorithm can effectively reduce the average transmission delay of online learning resources and improve the load balance of the system. [ABSTRACT FROM AUTHOR]

Abstract Total Knee Replacement (TKR) is a common procedure that is gaining importance with the aging American population. Although TKR is common, about 20% of patients report being unhappy with their results. Previous research has pointed to misalignment and loosening as contributing factors to negative outcomes. What is lacking in the field of TKR is a sensory system that can determine the internal loads of the knee in a direct manner. Implant bearings embedded with piezoelectric transducers have already shown promise in providing accurate sensing data. To perform further experimentation, prototype implant bearings that can be accurately and efficiently produced are needed. This work investigates various fabrication processes and possible materials to provide a foundation for developing surrogate biomechanical implants, especially those with integrated smart sensors. In this study, an original knee bearing is scanned and the resulting geometries used to generate prototypes. The prototypes are fabricated using a variety of methods including CNC machining and additive manufacturing. The prototypes are then tested to determine load distribution, active sensor performance, as well as kinematic performance under loading. The results of this study show that FDM printing provides quick and affordable results but is not ideal for rigorous experimentation. SLA printed prototypes are improved in final quality with an increase in fabrication time. Lastly, CNC machined processes are more labor intensive but can provide the best material characteristics. The findings from this study aim to have an impact not only on researchers studying biomedical sensing, but on the field of biomechanical implants. [ABSTRACT FROM AUTHOR]

Abstract This paper proposed a new method for detecting islanding of distributed generation (DG), using Multi-gene Genetic Programming (MGP). Islanding has been a serious concern among power distribution utilities and distributed generation owners, because it poses risks to the safety of utilities' workers and consumers, and can cause damage to power distribution systems' equipment. Therefore, a DG must be disconnected as soon as an islanding is detected. In addition, an islanding detection method must have high degree of dependability to correctly discriminate islanding from other events, such as load switching, in order to avoid unnecessary disconnection of the distributed generator. In this context, the novelty of the proposed method is that the MGP is capable of obtaining a set of mathematical and logic functions employed to detect and classify islanding correctly. This is a new approach among the computational intelligent methods proposed for DG islanding detection. The main idea was to use local voltage measurements as input of the method, eliminating the need of complex and expensive communication infrastructure. The method has been trained with several islanding and non-islanding cases, by using a power distribution system comprising five concentrated loads, a synchronous distributed generator and a wind power plant. The results showed that the proposed method was successful in differentiating the islanding events from other disturbances, revealing its great potential to be applied in anti-islanding protection schemes for distributed generation. Highlights • Multi-gene genetic programming (MGP) is used to classify islanding of DG. • As far as we know, MGP has not been used yet for islanding detection. • Proposed method has superior performance than an artificial neural ntowrk method. • The proposed method has high potential for DG islanding detection purposes. • Proposed method has better performance than traditional islanding detection methods. [ABSTRACT FROM AUTHOR]

Abstract The advanced metering infrastructure (AMI) in a smart grid contains hardware, software, and other electronic components connected through a communication infrastructure. AMI transfers meter-reading data between a group of smart meters and a utility centre. Herein, a wireless mesh network (WMN) with a random mesh topology is used to deploy the AMI communication network. In a WMN, paths are identified using a hybrid wireless mesh routing protocol (HWMP) with a load balancing feature called load aware-HWMP (LA-HWMP). These paths reduce the demand on links with a minimal air time metric; however, the delay in the data transmission of certain smart meters is high, given the large number of retransmissions caused by packet drop. To avert this problem and enhance the end-to-end delay, a genetic algorithm is applied on the LA-HWMP to obtain the optimal path. The optimisation process will result in the selection of paths with minimal delay. The genetic algorithm is developed with a rank-based selection, a two-point crossover, and a random reset mutation with a repair function to eliminate duplicate entries. The proposed method is compared with the HWMP, the LA-HWMP, and a state-of-the-art method that uses a combination of the ant colony algorithm and simulated annealing (ACA-SA) for AMI networks of different sizes. The obtained results show that the path identified by the proposed method yields a shorter delay and higher throughput than paths identified using the other methods. Highlights • A new optimised load balanced algorithm for AMI networks. • It is proven for scalability with different count of smart meters. • The proposed method is more efficient than HWMP and Load aware-HWMP. • The method is compared with a recent state-of-the art method for the same AMI application. [ABSTRACT FROM AUTHOR]

Abstract In Wireless Sensor Networks (WSN) there are scenarios with conflicting objectives. This needs to be modeled as multi-objective optimization formulation. The optimization problem changes with the nature of application, network scenario and input/output parameters. Depending upon the underlying application requirements, there is a need of an appropriate Multi-Objective Optimization technique to manage these multiple conflicting objectives simultaneously and to yield an overall optimal solution. In this paper, a novel Multi-objective Load Balancing Clustering (MLBC) technique is proposed by utilizing Multi Objective Particle Swarm Optimization (MOPSO). Two objective functions are defined: Energy Efficiency and Reliability. The energy efficiency is based on the average residual energy of Cluster Heads (CHs), whereas the reliability is based on the communication cost of inter-cluster routing. The nodes transmit their information to Cluster Head or Base Station in single-hop or multi-hop manner. A healing function is utilized to avoid loops in the generated path. The load balancing is performed by shuffling the roles of next hop node and Cluster Head in each iteration. The best compromise solution is selected through the fuzzy decision-based approach. The performance of the proposed and existing approaches has been evaluated in terms of energy efficiency, network lifetime, packet delivery ratio, data accuracy and number of active nodes. Apart from these parameters, coverage and scalability are also considered to evaluate the quality of solutions provided by multi-objective optimization approach. Highlights • The problem of clustering and load balancing in WSN is formulated as a multi objective optimization problem, aiming at determining an energy efficient and reliable clustering solution. • Two objective functions are considered to be achieved simultaneously: minimize the average energy consumption and minimize the inter-cluster transmission cost of the network. • The load of data transmission is minimized and balanced by shuffling the nodes responsible according the weight assigned. • The average network lifetime of MLBC is 11.93%, 12.91% and 18.57% more as compared to existing techniques JPSO, MOPSO-DE and MOOCTC respectively. [ABSTRACT FROM AUTHOR]

In order to achieve faster content distribution speed and stronger fault tolerance, a P2P peer can connect to multiple peers in parallel and receive chunks of the data simultaneously. A critical issue in this environment is selecting a set of nodes participating in swarming sessions. Previous related researches only focus on performance metrics, such as downloading time or the round-trip time, but in this paper, we consider a new performance metric which is closely related to the network and propose a peer selection algorithm that produces the set of peers generating optimal worst link stress. We prove that the optimal algorithm is practicable and has the advantages with the experiments on PlanetLab. The algorithm optimizes the congestion level of the bottleneck link. It means the algorithm can maximize the affordable throughput. Second, the network load is well balanced. A balanced network improves the utilization of resources and leads to the fast content distribution. We also notice that if every client follows our algorithm in selecting peers, the probability is high that all sessions could benefit. We expect that the algorithm in this paper can be used complementary to existing methods to derive new and valuable insights in peer-to-peer networking. [ABSTRACT FROM AUTHOR]

Abstract Hybrid wireless sensor networks (WSNs) contain static and mobile sensors to support various applications. Static sensors monitor environment to find where events occur. Mobile sensors (MSs) move to event locations (ELs) to do advanced analysis. Since MSs are powered by batteries, how to dispatch them to visit ELs in an energy-efficient manner is critical. Moreover, as the technology of wireless chargers (WCs) is mature, it is feasible to use WCs to replenish energy of MSs during their working time. The paper addresses the multi-round sensor dispatch problem in a hybrid WSN with WCs, whose goal is to schedule the paths of MSs to visit ELs and WCs in each round, such that their lifetime is maximized. The problem is NP-hard and we propose a group-based mobile sensor dispatch (G-MSD) algorithm, which clusters ELs into groups by their positions. G-MSD dispatches MSs to visit each group of ELs to save and balance their moving energy costs. Besides, it schedules MSs to call at WCs to recharge batteries on demand. Simulation results verify that the G-MSD algorithm greatly extends lifetime of MSs, as comparing with other methods. [ABSTRACT FROM AUTHOR]

A Wireless Sensor Network (WSN) comprises of spatially scattered autonomous sensors to screen physical or natural conditions and to amiably go their information through the system to a Base Station. Grouping is a basic assignment in Wireless Sensor Networks for vitality effectiveness and system quality. Grouping through Central Processing Unit in remote sensor systems is outstanding and being used for quite a while. In this paper, we propose a few procedures that balance the vitality utilization of these hubs and guarantee greatest system lifetime by adjusting the activity stack as similarly as could be expected under the circumstances. Directly grouping through dispersed strategies is being produced for conveying with the issues like system lifetime and vitality. In our work, we connected both concentrated and conveyed kmeans clustering calculation in system test system. K-means is a model based algorithm that surrogates between two noteworthy advances, passing on perceptions to groups and processing cluster focuses until the point when a ceasing standard is satisfied. Improved results are accomplished and related which demonstrate that conveyed clustering is compelling than brought together grouping. [ABSTRACT FROM AUTHOR]

Cloud Computing can be defined as enabling computing resources to whoever want remotely upon demand. This necessarily requires using virtualization. A virtual machine (VM) is an emulation of a computer that runs in virtualization software. Tasks coming from different users are passed to the virtual machines to be processed. This paper studies the load balancing problem among various VMs; to ensure that the network resources are distributed in a fair way between various clients. This paper proposes solving the load balancing problem using a combinatorial optimization approach named the Hungarian method. The proposed protocol is named LBCC-Hung (Load Balancing Protocol for Cloud Computing Based on Hungarian Method). Using simulation, the performance of LBCC-Hung is measured and compared with two well-known methods; MIN-MIN and First Come First Serve FCFS methods. The simulation results proved that LBCC-Hung overperforms the others two protocols, in terms of both the Makespan and the throughput and the virtual machine utilization deviation. [ABSTRACT FROM AUTHOR]

In this paper, we presented a further development of our research on developing an optimal software-hardware mapping framework. We used the Petri Net model of the complete hardware and software High Performance Computing (HPC) system running a Computational Fluid Dynamics (CFD) application, to simulate the behaviour of the proposed diffusive two level multi-objective load-balancing algorithm. We developed an meta-heuristic algorithm for generating an approximation of the Pareto-optimal set to be used as reference. The simulations showed the advantages of this algorithm over other diffusive algorithms: reduced computational and communication overhead and robustness due to low dependence on uncertain data. The algorithm also had the capacity to handle unpredictable events as a load increase due to domain refinement or loss of a computation resource due to malfunction. [ABSTRACT FROM AUTHOR]

In many wireless sensor networks due to the limited energy of sensor nodes energy conservation is one of the most important challenges. To enhance the lifetime of the network emphasis is given to design energy efficient routing algorithms. In WSN, sensor nodes which are nearer to the base station having a task of collecting data for the entire area and send to the base station. This node has an additional load and depletes its energy faster. This paper addresses the problem of lifetime maximization by load balancing. This paper proposes energy efficient load balanced data collection algorithm considering different network parameter (e.g., density, degree). In this method, Data collection tree topology is built at the sink node. Performance of the proposed algorithm is evaluated by considering various parameters like topology, availability of resources and the energy utilization of nodes in different paths of the tree, which may vary and ultimately impacts the overall network lifetime. Sensor nodes are switched from their original path to other based on the load and it reduces communication overhead. [ABSTRACT FROM AUTHOR]

In this paper, taking the positions of pico-cell base stations (PBSs) into consideration, a scheme of cell range expansion (CRE) for maximum sum rate is addressed in heterogeneous multi-input multi-output multi-user wireless networks. The optimal CRE bias obtained numerically by the proposed CRE scheme with inter-cell interference coordination (ICIC) allows us to maximize the sum rate while successfully maintaining the load balance between the macrocell base station and PBSs. Numerical results confirm that the proposed CRE scheme with ICIC can provide higher sum rate than conventional schemes and balanced load. [ABSTRACT FROM AUTHOR]

Multimedia applications over wireless networks have been evolving to augmented reality or virtual reality services. However, a rich data size compared to conventional multimedia services causes bandwidth bottlenecks over wireless networks, which is one of the main reasons why those applications are not used widely. To overcome this limitation, bandwidth aggregation techniques, which exploit a multi-path transmission, have been considered to maximize link utilization. Currently, most of the conventional researches have been focusing on the user end problems to improve the quality of service (QoS) through optimal load distribution. In this paper, we address the joint pricing and load distribution problem for multi-homing in heterogeneous wireless access networks (ANs), considering the interests of both the users and the service providers. Specifically, we consider profit from resource allocation and cost of power consumption expenditure for operation as an utility of each service provider. Here, users decide how much to request the resource and how to split the resource over heterogeneous wireless ANs to minimize their cost while supporting the required QoS. Then, service providers compete with each other by setting the price to maximize their utilities over user reactions. We study the behaviors of users and service providers by analyzing their hierarchical decision-making process as a multileader-, multifollower Stackelberg game. We show that both the user and service provider strategies are closed form solutions. Finally, we discuss how the proposed scheme is well converged to equilibrium points. [ABSTRACT FROM AUTHOR]

This paper proposes an experimental design process for the application of energy disaggregation using multi-label classification. The classification approach has recently shown to be a suitable representative model for treating data disaggregation in which the primary task is to identify or predict multiple load usage from aggregate data. The experiments were conducted by collecting basic electrical parameters from individual circuit branches of the load distribution in a house. Sets of data were analyzed through machine learning process to select the optimal set of parameters, learning algorithm and model parameter so that the system resulted from the learning process could deliver the optimal predictive performance for appliance loads. By taking the electrical parameters of current (I), real power (P), reactive power (Q), and power factor (PF) at every one-minute and employing RAkEL (RAndom k-labELsets) with Decision Tree as the multi-label classification algorithm together with the right model parameter configuration. F-score and prediction accuracy were evaluated as the predictive performance which found to be 97% and 99%, respectively, for high power appliances (water heater, Air-conditioner); 59% and 93%, respectively for lightings; finally, 75% and 92%, respectively for plug-outlet utilities. [ABSTRACT FROM AUTHOR]

Cloud computing offers diverse services such as servers, storage and applications to the end users as per the requirement with pay per use concept. These services are provided by the cloud service provider through datacenters with high configured servers. However, Datacenters which are providing these services are geographically distributed gets overloaded if multiple requests arrive simultaneously from the same location. The complexity involved in guaranteeing these services to achieve reliable operation under peak loads is a challenging task. Many requests with variable workloads may affects the performance of data centers. Cloud Service provider has to make decisions for allocating resources for these requests by choosing appropriate Datacenter. In this regard, the Service broker act as mediator between the service provider and cloud user for choosing the best datacenter. Certain policies are used by a service broker to direct the user requests to appropriate data center. Therefore, a heuristic based approach called dynamic cost-load aware service brokering policy is proposed to reduce overall processing time, Cost of Virtual Machine and response time by assigning user requests in efficient method. [ABSTRACT FROM AUTHOR]