Cloud Scheduling

Fault tolerance in cloud data centers is a critical mechanism for handling the escalating frequency of failures. As the size and complexity of large-scale systems grow, the challenge of predicting and mitigating failures becomes increasingly exponential, rendering previous solutions inadequate for meeting the high-performance demands of both cloud users and providers. This paper aims to address the growing need for improved fault tolerance by developing a management computational intelligence scheme tailored for real-time, on-demand cloud data center environments. In this study, a fault-tolerant computational intelligence scheme was elicited for the parameters necessary for the design. A computational system was also designed to determine league winners based on scheduling checkpoints. Also, the designed system was simulated using java programming language in CloudSim simulation toolkit (3.0.3) with a customized Cloud Analyst Graphic User Interface (GUI) on the Eclipse Integrated Development Environment (IDE) Luna release 4.4. The developed system (Checkpointed League Winner Algorithm) was compared with existing scheme such as Ant Colony Optimization (ACO), Genetic Algorithm (GA) and League Championship Algorithm (LCA) in real time. Checkpointed League Winner Algorithm was also evaluated to check the scheme's resistance to faults and the improvement percentage of the cloud data centres. The parameters used to evaluate the scheme are: failure to perform Ratio (FPR), Failure that causes a Delay in Performance (FDP), and the Rate at which Performance improves (RPI). The result indicates that when the whole average life of each scheme is considered, Checkpointed League Winner Algorithm (CPLWA) results in a 38.2%, 29.9%, and 20.5% improvement over ACO, GA, and LCA, respectively. The average makespan of the scheme indicates that the Checkpointed League Winner Algorithm exhibits a significant improvement, outperforming the ACO, GA, and LCA with 41%, 33%, and 23%, respectively; the response time of the scheme indicates that the Checkpointed League Winner Algorithm outperformed the ACO, GA, and LCA with 54.3%, 56.6%, and 30.2%, respectively; and the failure ratio of the scheme indicates that the Checkpointed League Winner Algorithm performs better than existing meta-heuristics methods (ACO, GA, and LCA) with a lower failure ratio. This improvement can be attributed to the iterative structure, the migration, and the checkpointing approaches employed in the scheme. This study developed a fault tolerance computational intelligence scheme for an on-demand real-time data centre cloud environment in which the Checkpointed League Winner Algorithm outperformed the existing scheme in terms of response time, average makepan and failure ratio. It is then suggested to explore the application of the Checkpointed League Winner Algorithm scheme to address resource management, provisioning, and virtual machine placement challenges within distributed systems.

Based on pay-as-per-usage policy, there is a tremendous use of cloud computing in scientific society like bio-medical, healthcare and online financial applications. Fault tolerance is one of the biggest challenges to guarantee the reliability and availability of critical services. We must make the system to avail by minimizing the impact of failure. In this paper, we conducted a comparative analysis of various approaches for tolerating faults through scheduling in cloud computing environment based on their policies. The goal of this paper is not only used to analyze the existing methods, but also to identify the areas needed for future research.

System integration of an operation center has become more complex and heterogeneous due to rapid innovations in the IT industry. Problems arise when a new integration requirement emerges due to complexity of the system integration architecture. To simplify the system integration, interoperability has to be emphasized at the design stage. Architectural design is the stage wherein the process to identify the interfaces involved in the system integration needs to be considered. Approaches to simplifying system integration via service interface design have been the subject of many research studies. In this paper, case study on two (2) traffic operation centers in Malaysia i.e. Transport Management Centre of Kuala Lumpur City Hall and Traffic Monitoring Centre of Malaysian Highway Authority were conducted to identify gaps amongst the system integration approaches used in both operation centers. Three (3) prominent approaches of system integration used by both operation centers were chosen, explored and discussed, namely, methodoriented interface, message-oriented interface and resource-oriented interface. The result of a systematic comparison of the approaches mentioned is also presented. Six (6) criteria were established to make comparison on the approaches: interoperability, uniformity, scalability, reusability, heterogeneity, and compatibility. The objective was to determine the best contemporary approach to service interface design in centralized system integration. The outcome of the evaluation was examined and improvement to the service interface design is proposed. The study is very significant as an attempt to establish a practice reference for enhancement of the current system integration as well as serve as a guide for future deployment of operation centers.

In cloud computing, resources are dynamically provisioned and delivered to users in a transparent manner automatically on-demand. Task execution failure is no longer accidental but a common characteristic of cloud computing environment. In recent times, a number of intelligent scheduling techniques have been used to address task scheduling issues in cloud without much attention to fault tolerance. In this research article, we proposed a dynamic clustering league championship algorithm (DCLCA) scheduling technique for fault tolerance awareness to address cloud task execution which would reflect on the current available resources and reduce the untimely failure of autonomous tasks. Experimental results show that our proposed technique produces remarkable fault reduction in task failure as measured in terms of failure rate. It also shows that the DCLCA outperformed the MTCT, MAXMIN, ant colony optimization and genetic algorithm-based NSGA-II by producing lower makespan with improvement of 57.8, 53.6, 24.3 and 13.4 % in the first scenario and 60.0, 38.9, 31.5 and 31.2 % in the second scenario, respectively. Considering the experimental results, DCLCA provides better quality fault tolerance aware scheduling that will help to improve the overall performance of the cloud environment.

In this paper, we propose a novel architecture to allow the implementation of a cyber environment composed of different High Performance Computing (HPC) infrastructures (i.e., clusters, grids and clouds). To access this cyber environment, scientific researchers do not have to become computer experts. In particular, we assume that scientific researchers provide a description of the problem as an input to the cyber environment and then get their results without being responsible for managing the computational resources. We provide a prototype of the architecture and introduce an evaluation which studies a real workload of scientific applications executions. The results show the advantages of the proposed architecture. Besides, we highlight this work provides guidelines for developing cyber environments focused on e-Science.

Fault tolerance is one of the major challenges during fault occurrence in any computational environment. Since, computational grids provide access to huge pool of shared processing power, the chances of fault occurrence are high. The proposed system is focused on performing job scheduling using Two Choices Scheduling Algorithm (TCSA) with tolerating the faults that occur during the process. Job allocation is done with the help of grid system consisting of resource broker and GIS. Once, the faulty resources are identified, scheduler analyses the load of remaining idle resources and reschedules the faulty jobs to the new set of resources which currently has the least load. A comparison is done between Round Robin algorithm and TCSA before and after incorporating fault tolerance. It is observed that the time taken for the completion of allocated tasks has been minimized after the implementation of improvised TCSA incorporating fault tolerance. The work focuses on tolerating the faults ...

Today, the IT becomes the key pillar for effective structural and organizational management. All the scenarios related to the activity, the communication and the management of current businesses are made of information systems (IS). Besides, the need of companies cooperation converge them to exchange and communicate information about their information systems. This interconnection faces many complex challenges especially when it comes to real-time exchange or mergence of all or parts of IS connected. The aim of the model driven engineering (MDE) and the model driven architecture (MDA) is to bring good solutions to interoperability challenges between companies and organizations IS. They offer several significant improvements in the development of complex systems and enable to focus on abstract goals independent of technical platform. Moreover, service-oriented architecture (SOA) has established for some time as a good methodology for encapsulation of the complexities of the various c...

The need for storage and instant access to personal data (patient or professional) in Moroccan healthcare is exponential. Thus, the access needs of health professionals to different health information necessitate continuity of care, hence an architecture based on the concept of the cloud proves necessary. Considering the increasing demands placed on healthcare providers for infrastructure, systems and support, cloud computing will be the appropriate solution to meet the needs for care coordination, collaboration and communication between the different actors in the field Of health. Cloud Computing represents an evolution of information systems. Cloud computing brings a new approach to computing, a different way of using computer resources. This development has given existence to several providers offering multiple services. Then, Cloud end users are faced with the choice of the appropriate provider in terms of supported technologies, security and access rules. So, the sector aims to...

IoT solutions are formulated and accomplished to cope with distinct challenges influenced by domainspecific necessities, thus not considering issues of visibility, scalability, interoperability along with use-case independence. Consequently, they can be less principled, the vendor focused and barely replicable as any generalized design available. The conventional SOA model address the architecture design issue, as core issue with conventional SOA is, it does not look into whether existing application platforms can provide the solutions to help with the implementation of IoT as a generalized approach for numerous tasks. To upgrade the framework of existing IoT options, present research developed new hybrid execution service oriented architecture model as a “middleware” which identifies new principles for developing IoT architectures and supporting the interoperation of IoT solutions. In this particular work, we seek to fill this gap and additionally we suggest an architectural strate...

In this paper, we propose a novel architecture to allow the implementation of a cyber environment composed of different High Performance Computing (HPC) infrastructures (i.e., clusters, grids and clouds). To access this cyber environment, scientific researchers do not have to become computer experts. In particular, we assume that scientific researchers provide a description of the problem as an input to the cyber environment and then get their results without being responsible for managing the computational resources. We provide a prototype of the architecture and introduce an evaluation which studies a real workload of scientific applications executions. The results show the advantages of the proposed architecture. Besides, we highlight this work provides guidelines for developing cyber environments focused on e-Science.

The Implications of big data analytics of current trends in nanotechnology theory, model and simulation are becoming impressive issues. The potential applications of nanotechnology in the industrial sector, identifying and prioritizing research across the emerging technology are motivating to perform the integration between the conceptual framework of nanotechnology and a big data system development. This paper presents six variations to meet the contexts of a conceptual framework for modeling the complex systems involve nanotechnology theory, modeling, large scale numerical simulation in the real world problem. Integrated mathematical modeling and large scale numerical simulations are the tools to solve the complex systems. The conceptual framework is a comprehensive concept in theory, ordinary differential equation (ODE) or partial differential equation (PDE) modeling and simulation based on high performance computing (HPC). The main objective is to improve the process of huge com...

This overview gives the basic concept, defines the terms used in the industry, and outlines the general architecture and applications of Cloud computing. It gives a summary of Cloud Computing and provides a good foundation for understanding.

A grid-computing paradigm delivers the processing power of massively parallel computation to all subscribed users. Current trends, research, and developments in grid computing show that the available grid resources exist as non-storable compute commodities and are distributed geographically-the grid problem. To solve the grid problem, several initiatives have developed frameworks for grid economy and have proposed several algorithms towards an optimized resources scheduling in a grid environment. However, since the grid resources availability depends on the time of usage and are transient, such generic approaches lack the ability to capture the realistic valuation of the resources and fail to guarantee the certainty in their availability measured as Quality of Service (QoS). Uncertainties in grid resource availability do not guarantee a user expected QoS without over committing (e.g., storing the non-storable resources) resources to the users. To guarantee QoS (satisfy a users' computing needs), we propose a price-based and quality-aware model that captures the realistic value of the grid compute commodities. We use the financial option theory from a real option perspective to value grid resources by treating them as real assets. We discuss a set of results on pricing grid compute cycles. Our results are based on the compute cycle usage obtained from the WestGrid node at the University of Manitoba. We extend and generalize our study to any grid in general but with specific reference to the WestGrid.

A grid-computing paradigm delivers the processing power of massively parallel computation to all subscribed users. Current trends, research, and developments in grid computing show that the available grid resources exist as non-storable compute commodities and are distributed geographically-the grid problem. To solve the grid problem, several initiatives have developed frameworks for grid economy and have proposed several algorithms towards an optimized resources scheduling in a grid environment. However, since the grid resources availability depends on the time of usage and are transient, such generic approaches lack the ability to capture the realistic valuation of the resources and fail to guarantee the certainty in their availability measured as Quality of Service (QoS). Uncertainties in grid resource availability do not guarantee a user expected QoS without over committing (e.g., storing the non-storable resources) resources to the users. To guarantee QoS (satisfy a users' computing needs), we propose a price-based and quality-aware model that captures the realistic value of the grid compute commodities. We use the financial option theory from a real option perspective to value grid resources by treating them as real assets. We discuss a set of results on pricing grid compute cycles. Our results are based on the compute cycle usage obtained from the WestGrid node at the University of Manitoba. We extend and generalize our study to any grid in general but with specific reference to the WestGrid.

In the era of digital space, the enterprise applications should be accessible from mobile and different channels like SMS, IVR, email and other smart devices. Modernizing traditional existing applications to meet the demand of the increasing enterprise growth requires major shift in the focus of adopting modern technologies. Enterprise are looking for most cost effective way of modernizing their existing portfolio of application and subsequently looking for reducing the enterprise costs. Cloud and big data adoption is the must for any enterprise to do business insight modeling and by keeping their enterprise innovation strategy at par with their competitors. In order to mitigate the risk involved and to improve on the Return on Investment (ROI) it needs a systematic approach and methodology to mitigate the key risks and challenges involved. The modernization strategy should not be big bang and it should be a phase wise approach and should bring out all the relevance aspects upfront ...

Data mining, known as knowledge discovery is a computer-assisted, analytical process of digging through and analyzing large amount of data and extracting knowledge from the data. Data mining technologies has made many industries like marketing, sales, healthcare organization, financial institutions etc. quite successful. It has a lot of benefits in various fields. It helps in the quick analysis of data and has improved the quality of decision making process and is used to turn information into actionable knowledge. In this paper, data mining process and knowledge discovery process is discussed. Three well known data mining classifier algorithms namely ID3, J48 and Naive Bayes are discussed and their performance has been evaluated using different parameters to find the best algorithm. Further, Naive Bayes classifier algorithm is used for classification of workloads based on different Quality of Service (QoS) parameters.

Cloud computing has been gaining popularity in the recent years. Several studies are being proceeded to build cloud applications with exquisite quality based on user's demands. In achieving the same, one of the applied criteria is checkpoint based load balancing in real time scheduling through which suitable cloud service is chosen from a group of cloud services candidates. Valuable information can be collected to rank the services within this checkpoint based load balancing. In order to attain ranking, different services are needed to be invoked in the cloud, which is time-consuming and wastage of services invocation. To avoid the same, this paper proposes an algorithm for predicting the ranks of different cloud services by using the values from previously offered services.

The Implications of big data analytics of current trends in nanotechnology theory, model and simulation are becoming impressive issues. The potential applications of nanotechnology in the industrial sector, identifying and prioritizing research across the emerging technology are motivating to perform the integration between the conceptual framework of nanotechnology and a big data system development. This paper presents six variations to meet the contexts of a conceptual framework for modeling the complex systems involve nanotechnology theory, modeling, large scale numerical simulation in the real world problem. Integrated mathematical modeling and large scale numerical simulations are the tools to solve the complex systems. The conceptual framework is a comprehensive concept in theory, ordinary differential equation (ODE) or partial differential equation (PDE) modeling and simulation based on high performance computing (HPC). The main objective is to improve the process of huge com...

Cloud computing offers on-demand access to large-scale computing resources in a pay-as-you go manner. Market-based resource allocation mechanisms are gaining popularity among commercial cloud providers to deal with dynamically fluctuating resource demands. For example, the recently introduced Amazon EC2 spot instances allow users to bid for computing resources and thus control the cost vs. reliability trade-offs of their workloads. Although this promises significant cost reduction, it comes at an additional risk of price fluctuation. This will get worse as cloud computing gradually moves towards a free market system. We propose a novel approach that utilizes financial option theory to simultaneously mitigate risk and minimize cost for cloud users. We formulate the cloud user optimization problem and mathematically characterize the cost of using European style options for clouds. We also propose a novel on-line policy using American options that outperforms base-line spot policies in terms of price variance reduction against high risk factors. We present trace-driven simulation experiments to support our results.

Context: Developments in cloud computing have made it attractive for consumers to migrate their applications to the cloud environment. However, in the crowded cloud market, application customers and providers face the problem of how to assess and cho ose appropriate service providers for migrating the ir applications to the cloud. Objective: The main goal of this systematic review to identify and classify the current cloud migration optimization approaches. The ancillary goal is to p resent a comparative evaluation of the existing optimization approaches to application migration in cloud computing, in order to clarify the gaps in the existing approaches and identify promising directions in future research. Method: We performed a systematic review of the software engineering literature between 2010 and 2014 to accomplish the objectives of the study. Results: The use of the search string in databases resulted in the identification of 1548 studies. After applying the inclusion and excl...

Cloud computing consumes a huge amount of electrical power which penalizes both the cloud service providers and environmental standards. Energyperformance tradeoff is still an open issue in cloud computing that targets minimizing energy consumption by the datacenters' infrastructure while meeting the user QoS as well as defined by the Service Level Agreement. Scientific distributed applications generate complex workflows that may contain thousands of tasks deployed over the cloud datacenters for execution. These applications are high demanding in terms of computation capabilities and may lead to hosts' overloading which may increase the energy consumption. In this study, we analyze the contribution of task clustering techniques on energy consumption by the cloud's infrastructure. The most relevant scheduling polices will be setup according to horizontal and vertical task clustering schemes with the Montage scientific workflow to demonstrate the impact on energy saving. For this purpose, we used Work-flowSim: an open source cloud simulator providing a workflow-level support, task scheduling and clustering techniques. The simulations' results highlight the impact of horizontal scheme in increasing the energy consumption.

Grid computing makes large-scale computations easier to handle. In heterogeneous systems like grid computing, failure is inevitable. Because of the volume and diversity of the resources, scheduling algorithm is among the most difficult challenges to overcome in grid computing. To reduce the make-span of the job to be executed a thorough understanding of scheduling in grid is important. Say there are two computing nodes that aren't being used right now. The scheduler may choose the node that has higher computing strength (for example, higher CPU speed, higher free memory), even though this node may also have high potential of failure. High potential of failure refers to the possibility of the failure occurring at execution time, resulting in the decrease of system performance. Therefore, awareness of failure is also very important in scheduling. This work proposes and implements a failure-aware scheduling method to schedule the tasks which uses both performance factors and failure factors of resources while making scheduling decision. The proposed algorithm is analyzed over various performance matrices and it shows considerably improved performance over existing algorithm

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.

Workload and resource management are two essential functions provided in the service level of a Grid software infrastructure. Consistently, efficient load balancing algorithms are fundamentally important to improve the global throughput of these environments. Although previous works show that, ant colony algorithm works well for load balancing, the cost is a very important factor in this subject. In this paper, a grid load balancing algorithm is proposed by using an ant colony optimization which is able to consider shortest path, type of resource, and running speed of resource. The experimental results show that the proposed algorithm by using this ant colony optimization can reduce the cost of load balancing in comparison with standard algorithm DASUD.

Data mining, known as knowledge discovery is a computer-assisted, analytical process of digging through and analyzing large amount of data and extracting knowledge from the data. Data mining technologies has made many industries like marketing, sales, healthcare organization, financial institutions etc. quite successful. It has a lot of benefits in various fields. It helps in the quick analysis of data and has improved the quality of decision making process and is used to turn information into actionable knowledge. In this paper, data mining process and knowledge discovery process is discussed. Three well known data mining classifier algorithms namely ID3, J48 and Naive Bayes are discussed and their performance has been evaluated using different parameters to find the best algorithm. Further, Naive Bayes classifier algorithm is used for classification of workloads based on different Quality of Service (QoS) parameters.

Based on pay-as-per-usage policy, there is a tremendous use of cloud computing in scientific society like bio-medical, healthcare and online financial applications. Fault tolerance is one of the biggest challenges to guarantee the reliability and availability of critical services. We must make the system to avail by minimizing the impact of failure. In this paper, we conducted a comparative analysis of various approaches for tolerating faults through scheduling in cloud computing environment based on their policies. The goal of this paper is not only used to analyze the existing methods, but also to identify the areas needed for future research.

Data mining, known as knowledge discovery is a computer-assisted, analytical process of digging through and analyzing large amount of data and extracting knowledge from the data. Data mining technologies has made many industries like marketing, sales, healthcare organization, financial institutions etc. quite successful. It has a lot of benefits in various fields. It helps in the quick analysis of data and has improved the quality of decision making process and is used to turn information into actionable knowledge. In this paper, data mining process and knowledge discovery process is discussed. Three well known data mining classifier algorithms namely ID3, J48 and Naive Bayes are discussed and their performance has been evaluated using different parameters to find the best algorithm. Further, Naive Bayes classifier algorithm is used for classification of workloads based on different Quality of Service (QoS) parameters.

QoS (Quality of Service) aware task scheduling in cloud computing is a continuous practice due to the divergent scope of user needs. Henceforth the current research is moving in a direction to find optimal solutions for efficient task scheduling towards QoS aware resource utilization in cloud workflow management. Much of the existing solutions are specific to one or two QoS factors mainly task completion and bandwidth. According to the real-time practices, the QoS assessment by one or two factors is impractical. Moreover much of the existing approaches are delivering the computational complexity as O(n 2 ), which is due to the magnification of the increment in number of tasks due to overwhelmed users and their requirements. In this context here we devised an explorative statistical approach, which is based on metrics called resource optimal value ( ropt ) and coupling between tasks ( cbt ), which enables to assess the optimal order of tasks to utilize desired cloud resource. The oth...

The fundamental problems with cloud computing environment are resource allocation and cloudlets scheduling. When scheduling cloudlets in cloud environment, different cloudlets needs to be executed simultaneously by the available resources in order to meet consumers’ expectations and to achieve better performances by minimizing makespan and balancing load effectively. To achieve this, we proposed a new noble mechanism called Modified Max-Min (MMax-Min) algorithm, inspired from Max-Min algorithm. The proposed algorithm finds a cloudlet with maximum completion time and minimum completion time and assigns either of the cloudlets for execution according to the specifications for the purpose of boosting up cloud scheduling processes and increasing throughput. From the results of the simulation using CloudSim, it shows that our proposed approach is able to produce good quality solutions, producing good values of makespan and balancing load effectively as compared to the standard Max-Min, a...

The fundamental problems with cloud computing environment are resource allocation and cloudlets scheduling. When scheduling cloudlets in cloud environment, different cloudlets needs to be executed simultaneously by the available resources in order to meet consumers’ expectations and to achieve better performances by minimizing makespan and balancing load effectively. To achieve this, we proposed a new noble mechanism called Modified Max-Min (MMax-Min) algorithm, inspired from Max-Min algorithm. The proposed algorithm finds a cloudlet with maximum completion time and minimum completion time and assigns either of the cloudlets for execution according to the specifications for the purpose of boosting up cloud scheduling processes and increasing throughput. From the results of the simulation using CloudSim, it shows that our proposed approach is able to produce good quality solutions, producing good values of makespan and balancing load effectively as compared to the standard Max-Min, a...

In this paper we present a balanced scheduling algorithm with considering the fault tolerance and task migration of allocating independent tasks in grid systems. Resource scheduling and its management are great challenges in heterogeneous environment. Hence load balancing is one of the best solutions to achieve the above purposes. The scheduling algorithm which we will present in follow, with taking the fault tolerance, checkpointing method, task migration and priority for mapping independent tasks on heterogeneous computing environment, creates the specific situation to ensure high performance in grid systems. So by implementing these parameters we can achieve more efficient and dependable performance than similar previous algorithms. It will be done with better condition and achieve high performance in computational grids in compare with Min-min algorithm. Finally the experiment and simulated results show that proposed balanced scheduling algorithm performs significantly to ensure high throughput, reduced makespan, reliability and more efficiency in the grid environment.

System integration of an operation center has become more complex and heterogeneous due to rapid innovations in the IT industry. Problems arise when a new integration requirement emerges due to complexity of the system integration architecture. To simplify the system integration, interoperability has to be emphasized at the design stage. Architectural de sign is the stage wherein the process to identify the interfaces involved in the system integration needs to be considered. Approaches to simplifying system integration via service interface design have been the subject of many research studies. In this paper, case study on two (2) traffic operation centers in Malaysia i.e. Transport Management Centre of Kuala Lumpur City Hall and Traffic Monitoring Centre of Malaysian Highway Auth ority were conducted to identify gaps amongst the system integration approaches used in both operation centers. Three (3) prominent approaches of system integration used by both operation center s were ch...

Virtual machine (VM) migration is a methodology used for attaining the system load balancing in a cloud environment by transferring the one VM from one physical host to another host. In this paper, we plan to migrate the extra tasks from overloaded VM to suitable VM instead of migrating the entire overloaded VM. In order to select the host VMs, a FCM clustering algorithm has been used to group the similar kind of host VMs. Once the VMs identified as overloaded, then the corresponding candidate VMs are found using the FCM clustering algorithm. Binary Particle Swarm Optimization (BPSO) methodology has been used for selecting the host VMs from the set of candidate VMs based on multi-objective fitness function, which includes task transferring time, task execution time and energy consumption. By allocating the extra task from the overloaded VMs to the proper VMs, we achieved the load balancing in the cloud environment. The implementation of proposed methodology FCM-BPSO has been done us...

Workload and resource management are two essential functions provided in the service level of a Grid software infrastructure. Consistently, efficient load balancing algorithms are fundamentally important to improve the global throughput of these environments. Although previous works show that, ant colony algorithm works well for load balancing, the cost is a very important factor in this subject. In this paper, a grid load balancing algorithm is proposed by using an ant colony optimization which is able to consider shortest path, type of resource, and running speed of resource. The experimental results show that the proposed algorithm by using this ant colony optimization can reduce the cost of load balancing in comparison with standard algorithm DASUD.

Cloud brokering service is an intermediate service which enables the producer-consumer business model enforcing the easy access to cloud services from Cloud Service Providers (CSPs). Cloud broker is to provide a platform where broker collects the information from the user, analyze the data, and sends those data to the CSPs. Cloud broker also provides data integration services and modeling the data across all the components or units of the cloud services. This paper deals with designing criteria and issues of cloud broker, system activity of broker, and sequence diagram of system design with implementation procedure.

Vehicular Ad hoc Network (VANET)is a kind of mobile Ad Hoc networks (MANETs)that is applied for progress of intelligent transportation system (ITS).Vehicles get various types of data from roadside units (RSUs)including voice, video and emergency data. Since the number of vehicles as well as the number and size of files are potentially large and with regard to the types of files can be various, QOS-based scheduling is a challenge. We inquire the scheduling algorithm problems of file distribution from RSUs to vehicles in an urban environment and propose a new scheduling scheme which considers the types of data and improved the performance of network. Request of vehicles are inserted in to 4 queues: 1) Data-queue 2) voice-queue 3) video-queue 4) emergency-queue. Some scheduling policies are used to choose one queue and serve the vehicles. Furthermore, we gave high priority to some requests such as video and emergency data to achieve high quality. In this regard, Simulation results indi...

IoT solutions are formulated and accomplished to cope with distinct challenges influenced by domain-specific necessities, thus not considering issues of visibility, scalability, interoperability along with use-case independence. Consequently, they can be less principled, the vendor focused and barely replicable as any generalized design available. The conventional SOA model address the architecture design issue, as core issue with conventional SOA is, it does not look into whether existing application platforms can provide the solutions to help with the implementation of IoT as a generalized approach for numerous tasks. To upgrade the framework of existing IoT options, present research developed new hybrid execution service oriented architecture model as a "middleware" which identifies new principles for developing IoT architectures and supporting the interoperation of IoT solutions. In this particular work, we seek to fill this gap and additionally we suggest an architect...

Demand for web content continues to increase at exponential rates and this has intensified the challenges of satisfying customer’s Quality of Service. Several techniques for Web content delivery vis-à-vis resource allocation have been proposed, one of which is the use of Content Distribution Networks. However, in recent times, cloud computing has become a driving force in Information Technology where Service Providers’ limited resources are shared among numerous users with different QoS requirements. In this work, focus is on developing a model for allocation of resources on cloud computing Infrastructure in order to improve delivery of Web content and optimize service cost. An analytical approach was adopted and expressed as an optimization problem subject to QoS metrics: delay, throughput, and bandwidth. The optimization problem was formulated as an Integer Linear Programming problem in which the decision variable takes the value of 0 or 1. A single Infrastructure-as-a-Service clo...

Vehicular Ad hoc Network (VANET)is a kind of mobile Ad Hoc networks (MANETs)that is applied for progress of intelligent transportation system (ITS).Vehicles get various types of data from roadside units (RSUs)including voice, video and emergency data. Since the number of vehicles as well as the number and size of files are potentially large and with regard to the types of files can be various, QOS-based scheduling is a challenge. We inquire the scheduling algorithm problems of file distribution from RSUs to vehicles in an urban environment and propose a new scheduling scheme which considers the types of data and improved the performance of network. Request of vehicles are inserted in to 4 queues: 1) Data-queue 2) voice-queue 3) video-queue 4) emergency-queue. Some scheduling policies are used to choose one queue and serve the vehicles. Furthermore, we gave high priority to some requests such as video and emergency data to achieve high quality. In this regard, Simulation results indi...

The Implications of big data analytics of current trends in nanotechnology theory, model and simulation are becoming impressive issues. The potential applications of nanotechnology in the industrial sector, identifying and prioritizing research across the emerging technology are motivating to perform the integration between the conceptual framework of nanotechnology and a big data system development. This paper presents six variations to meet the contexts of a conceptual framework for modeling the complex systems involve nanotechnology theory, modeling, large scale numerical simulation in the real world problem. Integrated mathematical modeling and large scale numerical simulations are the tools to solve the complex systems. The conceptual framework is a comprehensive concept in theory, ordinary differential equation (ODE) or partial differential equation (PDE) modeling and simulation based on high performance computing (HPC). The main objective is to improve the process of huge com...

Virtual machine (VM) migration is a methodology used for attaining the system load balancing in a cloud environment by transferring the one VM from one physical host to another host. In this paper, we plan to migrate the extra tasks from overloaded VM to suitable VM instead of migrating the entire overloaded VM. In order to select the host VMs, a FCM clustering algorithm has been used to group the similar kind of host VMs. Once the VMs identified as overloaded, then the corresponding candidate VMs are found using the FCM clustering algorithm. Binary Particle Swarm Optimization (BPSO) methodology has been used for selecting the host VMs from the set of candidate VMs based on multi-objective fitness function, which includes task transferring time, task execution time and energy consumption. By allocating the extra task from the overloaded VMs to the proper VMs, we achieved the load balancing in the cloud environment. The implementation of proposed methodology FCM-BPSO has been done us...

During the participation in the 2018 International Conference on Air Transport Research, "ICRAT" the scientific community of air transport stressed the importance of traffic control within airport taxiways. Scheduling aircraft (ASCP) is addressed in this document for taxiways (single-track road). For different aircraft categories, a detailed examination of the variable response to aircraft delay is provided. A heuristic method based on the prediction of the global conflict distribution (CDPG) is the first approach presented. In the CDPG, two problems that restricted the implementation of the system are fixed: the impasse situation and an optimal travel strategy. The second part details a "first come, first served"(FCFS) planner to develop an integrated departure and arrival management system at Mohamed V Airport. Improved traffic flow management has been implemented to take into account directional constraints on traffic lane links as well as crossing constraints...

Collaborative Virtual Environment (CVE) systems allow the sharing of virtual space, and each participant is represented by an entity in the CVE. Resources are consumed when interaction among several users occurs. The resources are consumed as a result of updating its own state, and the communication resources required to distribute the update of counterpart users in the CVE. The Peer-to-Peer (p2p) and Client Server (CS) architecture in CVE has the limitation of scaling to large number of users while maintaining consistency in the virtual world. This paper identifies the basic requirements of CVE systems, proposed a model based on cloud computing to improve the performance of the traditional CVE systems to evade the limitations of the p2p and CS. The design of the cloud computing-based CVE architecture proposes in our research consist of three layers namely, infrastructure, platform, and application. The potential benefits of the CVE architecture presented in this paper indicated a performance improvement over the traditional CVE. The proposed architecture with little modification can easily be implemented in real life applications to improve the performance of collaborative virtual applications.

Load balancing is an important metric for enhancing system performance. The distributed nature and heterogeneity of cloud resources makes scheduling and load balancing a challenging task. Scheduling of jobs to the appropriate virtual machines (VMs) can be done using different mechanisms, but balancing the load is the major problem that occurs due to fluctuation of load, and different VM specifications. This leads to imbalanced resource utilization and performance degradation of the system. This paper proposes a scheme for maximizing resource utilization by balancing the load across cloud system by distributing jobs to reliable VMs using firefly approach for better performance. CloudSim tool is used to simulate the scheme and results show that the scheme performs better than existing work.

Task resubmission and checkpoint are among several popular techniques used in providing fault tolerance in grid computing. However, due to the lack of side-by-side comparison, it is not certain of the best technique that would not degrade the system performance in addition to providing fault tolerance capability. This study proposed Dynamic ACS-based Fault Tolerance in grid computing using resubmission to new resource, checkpoint technique and utilization of resource execution history with the aim to reduce execution and task processing time and to increase the success rate in grid environment. The proposed algorithm is compared with other relevant algorithms to measure the performance in terms of execution time, success rate and average processing time. The results suggest that the proposed algorithm with improved task resubmission, checkpoint and extended pheromone update formula gives better performance in managing execution failure as well as resource selection during task assig...

Grid computing provides a virtual framework that integrates resources and services distributed across multiple controlled domains. Grouping based job scheduling is an important issue in grid computing in aspects of minimizing the processing time and maximizing the resource utilization. In particular, reliable scheduling is very important for efficient job execution. In general the failure of resources affects job execution during runtime fatally due to dynamic nature of grid environment. Our proposed system, grouping based scheduling with resource failure handling handles the resource failure before and after allocating the grouped fine-grained jobs with the suitable resources in computational grid. In this system, the reliability of sites is monitored and the historical data is used for predicting resource failures. This information is taken into account when jobs groups are created and dispatched to resources. Hence, it minimizes the probability of job failure during execution. An...

IoT solutions are formulated and accomplished to cope with distinct challenges influenced by domainspecific necessities, thus not considering issues of visibility, scalability, interoperability along with use-case independence. Consequently, they can be less principled, the vendor focused and barely replicable as any generalized design available. The conventional SOA model address the architecture design issue, as core issue with conventional SOA is, it does not look into whether existing application platforms can provide the solutions to help with the implementation of IoT as a generalized approach for numerous tasks. To upgrade the framework of existing IoT options, present research developed new hybrid execution service oriented architecture model as a “middleware” which identifies new principles for developing IoT architectures and supporting the interoperation of IoT solutions. In this particular work, we seek to fill this gap and additionally we suggest an architectural strate...

The utilization of information technology in health care information systems tends to be slower compared with the other areas. Some of the reasons are because of these areas have special characteristics which are: 1) the data and information in the medical information systems consist of many data types, such as alphanumeric, graphics, images, and sounds; 2) health workers are always mobile in performing their duties; 3) the source data is not only coming from the physician or health worker but also could be obtained from high-tech medical equipment; 4) when an error occurs in any health care data processing could caused death to the patient. The main function of the medical information system is not only used for management purposes but it also used for managing clinical data that will be useful for the development of medical science. Radiology department is one of the hospital departments that use many computerized systems. Therefore, the implementation of information systems in ra...

System integration of an operation center has become more complex and heterogeneous due to rapid innovations in the IT industry. Problems arise when a new integration requirement emerges due to complexity of the system integration architecture. To simplify the system integration, interoperability has to be emphasized at the design stage. Architectural de sign is the stage wherein the process to identify the interfaces involved in the system integration needs to be considered. Approaches to simplifying system integration via service interface design have been the subject of many research studies. In this paper, case study on two (2) traffic operation centers in Malaysia i.e. Transport Management Centre of Kuala Lumpur City Hall and Traffic Monitoring Centre of Malaysian Highway Auth ority were conducted to identify gaps amongst the system integration approaches used in both operation centers. Three (3) prominent approaches of system integration used by both operation center s were ch...