Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> E-science is about global collaboration in key areas of science such as cognitive science and brain science, and the next generation of infrastructure such as the Wisdom Web and Knowledge Grids. As a case study, we investigate human multiperception mechanism by cooperatively using various psychological experiments, physiological measurements, and data mining techniques for developing artificial systems which match human ability in specific aspects. In particular, we observe fMRI (functional magnetic resonance imaging) and EEG (electroencephalogram) brain activations from the viewpoint of peculiarity oriented mining and propose a way of peculiarity oriented mining for knowledge discovery in multiple human brain data. Based on such experience and needs, we concentrate on the architectural aspect of a brain-informatics portal from the perspective of the Wisdom Web and Knowledge Grids. We describe how to build a data-mining grid on the Wisdom Web for multiaspect human brain data analysis. The proposed methodology attempts to change the perspective of cognitive scientists from a single type of experimental data analysis toward a holistic view at a long-term, global field of vision. <s> BIB001 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Bioinformatic tools are often used by researchers through interactive Web interfaces, resulting in a strong demand for computational resources. The tools are of different kind and range from simple, quick tasks, to complex analyses requiring minutes to hours of processing time and often longer than that. Batteries of computational nodes, such as those found in parallel clusters, provide a platform of choice for this application, especially when a relatively large number of concurrent requests is expected. Here, we describe a scheduling architecture operating at the application level, able to distribute jobs over a large number of hierarchically organized nodes. While not contrasting and peacefully living together with low-level scheduling software, the system takes advantage of tools, such as SQL servers, commonly used in Web applications, to produce low latency and performance which compares well and often surpasses that of more traditional, dedicated schedulers. The system provides the basic functionality necessary to node selection, task execution and service management and monitoring, and may combine loosely linked computational resources, such as those located in geographically distinct sites. <s> BIB002 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Cloud computing relies heavily on virtualization. Virtualization technology has developed rapidly because of the rapid decrease in hardware cost and concurrent increase in hardware computing power. A virtual machine monitor (VMM, also called a hγpervisor) between the hardware and the OS enables multiple virtual machines (VMs) to run on top of a single physical machine. The VMM manages scheduling and dispatching the physical resources to the individual VMs as needed, and the VMs appear to users as separate computers. Widely used virtualization technologies include VMWare, Xen, Denali, and the Kernel-Based Virtual Machine (KVM). In this framework, a module measures executables running in virtual machines (VMs) and transfers the values to a trusted VM. Comparing those values to a reference table containing the trusted measurement values of running executables verifies the executable/s status. <s> BIB003 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Workflows have been used to represent a variety of applications involving high processing and storage demands. As a solution to supply this necessity, the cloud computing paradigm has emerged as an on-demand resources provider. While public clouds charge users in a per-use basis, private clouds are owned by users and can be utilized with no charge. When a public cloud and a private cloud are merged, we have what we call a hybrid cloud. In a hybrid cloud, the user has elasticity provided by public cloud resources that can be aggregated to the private resources pool as necessary. One question faced by the users in such systems is: Which are the best resources to request from a public cloud based on the current demand and on resources costs? In this paper we deal with this problem, presenting HCOC: The Hybrid Cloud Optimized Cost scheduling algorithm. HCOC decides which resources should be leased from the public cloud and aggregated to the private cloud to provide sufficient processing power to execute a workflow within a given execution time. We present extensive experimental and simulation results which show that HCOC can reduce costs while achieving the established desired execution time. <s> BIB004 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Distributed denial of service attacks are a great threat to service availability in cloud computing. In recent years, DDoS attacks have increased tremendously in bandwidth and technique. In this article, we propose a novel approach to mitigate DDoS attacks using an intelligent fast-flux swarm network. An intelligent swarm network is required to ensure autonomous coordination and allocation of swarm nodes to perform its relaying operations. We adapted the Intelligent Water Drop algorithm for distributed and parallel optimization. The fast-flux technique was used to maintain connectivity between swarm nodes, clients, and servers. Fast-flux service networks also allow us to build a transparent service, which allows minimal modifications of existing cloud services (e.g. HTTP, SMTP). A software simulation consisting of 400,000 client nodes and 10,000 swarm nodes has shown that we can maintain 99.96 percent packet delivery ratio when the network is under attack from a similarly sized DDoS network of 10,000 dedicated malicious nodes. <s> BIB005 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. One of the most important aspects which differentiate a cloud workflow system from its other counterparts is the market-oriented business model. This is a significant innovation which brings many challenges to conventional workflow scheduling strategies. To investigate such an issue, this paper proposes a market-oriented hierarchical scheduling strategy in cloud workflow systems. Specifically, the service-level scheduling deals with the Task-to-Service assignment where tasks of individual workflow instances are mapped to cloud services in the global cloud markets based on their functional and non-functional QoS requirements; the task-level scheduling deals with the optimisation of the Task-to-VM (virtual machine) assignment in local cloud data centres where the overall running cost of cloud workflow systems will be minimised given the satisfaction of QoS constraints for individual tasks. Based on our hierarchical scheduling strategy, a package based random scheduling algorithm is presented as the candidate service-level scheduling algorithm and three representative metaheuristic based scheduling algorithms including genetic algorithm (GA), ant colony optimisation (ACO), and particle swarm optimisation (PSO) are adapted, implemented and analysed as the candidate task-level scheduling algorithms. The hierarchical scheduling strategy is being implemented in our SwinDeW-C cloud workflow system and demonstrating satisfactory performance. Meanwhile, the experimental results show that the overall performance of ACO based scheduling algorithm is better than others on three basic measurements: the optimisation rate on makespan, the optimisation rate on cost and the CPU time. <s> BIB006 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> In Infrastructure-as-a-Service (IaaS) cloud computing, computational resources are provided to remote users in the form of leases. For a cloud user, he/she can request multiple cloud services simultaneously. In this case, parallel processing in the cloud system can improve the performance. When applying parallel processing in cloud computing, it is necessary to implement a mechanism to allocate resource and schedule the execution order of tasks. Furthermore, a resource optimization mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose two online dynamic resource allocation algorithms for the IaaS cloud system with preemptable tasks. Our algorithms adjust the resource allocation dynamically based on the updated information of the actual task executions. And the experimental results show that our algorithms can significantly improve the performance in the situation where resource contention is fierce. <s> BIB007 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> A new meta-heuristic method, called Chemical Reaction Optimization (CRO), has been proposed very recently. The method encodes solutions as molecules and mimics the interactions of molecules in chemical reactions to search the optimal solutions. The CRO method has demonstrated its capability in solving NP-hard optimization problems. In this paper, the CRO scheme is used to formulate the scheduling of Directed Acyclic Graph (DAG) jobs in heterogeneous computing systems, and a Double Molecular Structure-based Chemical Reaction Optimization (DMSCRO) method is developed. There are two molecular structures in DMSCRO: one is used to encode the execution order of the tasks in a DAG job, and the other to encode the task-to-computing-node mapping. The DMSCRO method also designs four elementary chemical reaction operations and the fitness function suitable for the scenario of DAG scheduling. In this paper, we have also conducted the simulation experiments to verify the effectiveness and efficiency of DMSCRO over a large set of randomly generated graphs and the graphs for real-world problems. <s> BIB008 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Cloud-assisted mobile health (mHealth) monitoring, which applies the prevailing mobile communications and cloud computing technologies to provide feedback decision support, has been considered as a revolutionary approach to improving the quality of healthcare service while lowering the healthcare cost. Unfortunately, it also poses a serious risk on both clients' privacy and intellectual property of monitoring service providers, which could deter the wide adoption of mHealth technology. This paper is to address this important problem and design a cloud-assisted privacy preserving mobile health monitoring system to protect the privacy of the involved parties and their data. Moreover, the outsourcing decryption technique and a newly proposed key private proxy reencryption are adapted to shift the computational complexity of the involved parties to the cloud without compromising clients' privacy and service providers' intellectual property. Finally, our security and performance analysis demonstrates the effectiveness of our proposed design. <s> BIB009 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Cloud computing provides on-demand comput- ing services and data. The cloud infrastructure can be owned by a private organization, a group of organizations, or the public. In this work, the feasibility of using cloud computing for the optimization of machining processes is explored. A web system is developed in which a main server keeps the repository of data and carries out optimization. The main server can provide the optimized process parameters on demand to various clients. The clients can use the optimized data and fine-tune them if necessary. The clients send the feedback to the server, which is utilized with the help of a probability-based approach. The case examples show the feasibility of using the system for helping in agile manufacturing. <s> BIB010 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Cloud computing plays an increasingly important role in realizing scientific applications by offering virtualized compute and storage infrastructures that can scale on demand. This paper presents a self-configuring adaptive framework optimizing resource utilization for scientific applications on top of Cloud technologies. The proposed approach relies on the concept of utility, i.e., measuring the usefulness, and leverages the well-established principle from autonomic computing, namely the MAPE-K loop, in order to adaptively configure scientific applications. Therein, the process of maximizing the utility of specific configurations takes into account the Cloud stack: the application layer, the execution environment layer, and the resource layer, which is supported by the defined Cloud stack configuration model. The proposed framework self-configures the layers by evaluating monitored resources, analyzing their state, and generating an execution plan on a per job basis. Evaluating configurations is based on historical data and a utility function that ranks them according to the costs incurred. The proposed adaptive framework has been integrated into the Vienna Cloud Environment (VCE) and the evaluation by means of a data-intensive application is presented herein. <s> BIB011 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Cost models are broadly used in query processing to drive the query optimization process, accurately predict the query execution time, schedule database query tasks, apply admission control and derive resource requirements to name a few applications. The main role of cost models is to produce the time needed to run the query on a specific machine. In a multi-cloud environment, this is insufficient in two aspects: firstly, the machines employed are not defined a-priori, and secondly, time estimates need to be complemented with monetary cost information, because both the economic cost and the performance are of primary importance. This work addresses these two shortcomings and aims to serve as the first proposal for a bi-objective query cost model that is suitable for queries executed over resources provided by potentially multiple cloud providers. Moreover, our approach is applicable to more generic data flow graphs, the execution plans of which do not necessarily comprise relational operators. <s> BIB012 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. The energy consumption and makespan associated with the resources allocated should be taken into account. This paper proposes an improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment. We have analyzed the performance of our approach using the CloudSim toolkit. The experimental results show that our approach has immense potential as it offers significant improvement in the aspects of response time and makespan, demonstrates high potential for the improvement in energy efficiency of the data center, and can effectively meet the service level agreement requested by the users. <s> BIB013 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Graphical abstractDisplay Omitted HighlightsThis work defines a taxonomy of the main properties of a mobile application useful for the task of offloading, divided into four categories: user, network, data and application.The paper describes the design of an automatic framework that drives the process of offloading, by building and validating models for that process.The system permits the analysis of the behavior of our algorithm for different categories of mobile applications, presenting different distributions of the main properties.A GP-based tool for building the decision tree-based model, which will decide whether it is convenient to perform the offloading of a mobile application, is adopted.Experimental evaluation permitted the analysis of the behavior of our algorithm for different categories of mobile applications. The limited battery life of modern mobile devices is one of the key problems limiting their use. Even if the offloading of computation onto cloud computing platforms can considerably extend battery duration, it is really hard not only to evaluate the cases where offloading guarantees real advantages on the basis of the requirements of the application in terms of data transfer, computing power needed, etc., but also to evaluate whether user requirements (i.e. the costs of using the cloud services, a determined QoS required, etc.) are satisfied. To this aim, this paper presents a framework for generating models to make automatic decisions on the offloading of mobile applications using a genetic programming (GP) approach. The GP system is designed using a taxonomy of the properties useful to the offloading process concerning the user, the network, the data and the application. The fitness function adopted permits different weights to be given to the four categories considered during the process of building the model. Experimental results, conducted on datasets representing different categories of mobile applications, permit the analysis of the behavior of our algorithm in different applicative contexts. Finally, a comparison with the state of the art of the classification algorithm establishes the goodness of the approach in modeling the offloading process. <s> BIB014 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> The evolution of technologies of informatics and telecommunications has improved significantly the way water distribution systems can be managed. The use of quasi real-time information is becoming a desired standard in many water utilities. Nevertheless, tools for water distribution system analysis and for supporting decision making havent been integrated with the same speed. This research uses cloud computing capabilities for supporting analysis and decision making in water distribution systems. It includes the development of evolutionary algorithms ready to run in a cloud environment for searching the best possible decisions considering a set of objectives. The evaluation of solutions profits from the scalability of the cloud for making faster the solution search process. <s> BIB015 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> By leveraging and modifying ciphertext-policy attribute based encryption (CP-ABE) and OAuth, we propose a new authorization scheme, called fuzzy authorization , to facilitate an application registered with one cloud party to access data residing in another cloud party. The new proposed scheme enables the fuzziness of authorization to enhance the scalability and flexibility of file sharing by taking advantage of the one-to-one correspondence between linear secret-sharing scheme (LSSS) and generalized Reed Solomon (GRS) code. Furthermore, by conducting attribute distance checking and distance adjustment, operations like sending attribute sets and satisfying an access tree are eliminated. In addition, the automatic revocation is realized with update of $TimeSlot$ attribute when data owner modifies the data. The security of the fuzzy authorization is proved under the d-BDHE assumption. In order to measure and estimate the performance of our scheme, we have implemented the protocol flow of fuzzy authorization with OMNET $++$ $4.2.2$ and realized the cryptographic part with pairing-based cryptography (PBC) library. Experimental results show that fuzzy authorization can achieve fuzziness of authorization among heterogeneous clouds with security and efficiency. <s> BIB016 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Multiple query optimization (MQO) in the cloud has become a promising research direction due to the popularity of cloud computing, which runs massive data analysis queries (jobs) routinely. These CPU/IO intensive analysis queries are complex and time-consuming but share common components. It is challenging to detect, share and reuse the common components among thousands of SQL-like queries. Previous solutions to MQO, heuristic or genetic based, are not appropriate for the large growing query set situation. In this paper, we develop a sharing system called LSShare using our proposed Lineage-Signature approach. By LSShare, we can efficiently solve the MQO problem in a recurring query set situation in the cloud. Our system has been prototyped in a distributed system built for massive data analysis based on Alibaba's cloud computing platform ( http://www.alibaba.com/ ). Experimental results on real data sets demonstrate the efficiency and effectiveness of the proposed approach. <s> BIB017 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Many efforts have been made in optimizing cloud service resource management for efficient service provision and delivery, yet little research addresses how to consume the provisioned service resources efficiently. Meanwhile, typical existing resource scaling management approaches often rest on single monitor category statistics and are driven by certain threshold algorithms, they usually fail to function effectively in case of dealing with complicated and unpredictable workload patterns. Fundamentally, this is due to the inflexibility of using static monitor, threshold and scaling parameters. This paper presents Off-the-Cloud Service Optimization (OCSO), a novel user-side optimization solution which specifically deals with service resource consumption efficiency from the service consumer perspective. OCSO rests on an intelligent resource scaling algorithm which relies on multiple service monitor metrics plus dynamic threshold and scaling parameters. It can achieve proactive and continuous service optimizations for both real-world IaaS and PaaS services, through OCSO cloud service API. From the two series of experiments conducted over Amazon EC2 and ElasticBeanstalk using OCSO prototype, it is demonstrated that the proposed approach can make significant improvement over Amazon native automated service provision and scaling options, regardless of scaling up/down or in/out. <s> BIB018 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Abstract Cloud radio access networks (C-RANs) were proposed as promising solutions to increase both spectrum and energy efficiency performance in next generation wireless communication systems. Much works discussed the concrete implementation technology to justify the huge development potential of C-RAN. However, only a few litterateurs focused on characterizing the physical layer security in the downlink. The authors studied the physical layer security in downlink heterogeneous C-RAN systems in the article. To characterize the random deployment of remote radio units (RRUs) with single antenna configuration, the stochastic geometry is based to evaluate the proposals' secrecy transmission capacity performances, where the closed-form expressions are derived. Furthermore, two security strategies based on eavesdropper neutralization region to protect the target RRU user against eavesdropping were presented and analyzed. Simulation illustrates the secrecy transmission capacity performance limits on different system parameters. The presented security strategies show a significant enhancement on the secrecy performance. <s> BIB019 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based (host-centric) Internet infrastructure. NDN can overcome the fundamental limitations of the current Internet, in particular, Denial-of-Service (DoS) attacks. However, NDN can be subject to new type of DoS attacks namely Interest flooding attacks and content poisoning. These types of attacks exploit key architectural features of NDN. This paper presents a new intelligent hybrid algorithm for proactive detection of DoS attacks and adaptive mitigation reaction in NDN. In the detection phase, a combination of multiobjective evolutionary optimization algorithm with PSO in the context of the RBF neural network has been applied in order to improve the accuracy of DoS attack prediction. Performance of the proposed hybrid approach is also evaluated successfully by some benchmark problems. In the adaptive reaction phase, we introduced a framework for mitigating DoS attacks based on the misbehaving type of network nodes. The evaluation through simulations shows that the proposed intelligent hybrid algorithm (proactive detection and adaptive reaction) can quickly and effectively respond and mitigate DoS attacks in adverse conditions in terms of the applied performance criteria. <s> BIB020 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Introducing and implementing the Expert Cloud to find, share, exploit, manage and employ the HR.Presenting EaaS as a new service in Cloud computing.Virtualizing the HR as well as sharing his/her skill, knowledge and experiences. Human resources (HRs) as the most important asset of any organizations play a significant role in their success; but, HR virtualization and sharing her/his knowledge and skills was not studied in detail. In this paper, we present a new framework named Expert Cloud to enable the Cloud users for requesting the human expertise and skills without any knowledge of their location and to share the skill, knowledge and experiences of HRs. Also, we analyze, design and implement the Expert Cloud employing Internet infrastructures and Cloud computing concepts. The results show that the Expert Cloud improves the HR utilization, decreases customer response time and task completion time in order to achieve high level of customers' satisfaction and better performance of HR in organization. We conclude the paper with some suggestions for future researches and practices. <s> BIB021 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Server Consolidation is one of the foremost concerns associated with the effective management of a Cloud Data Center as it has the potential to accomplish significant reduction in the overall cost and energy consumption. Most of the existing works on Server Consolidation have focused only on reducing the number of active physical servers (PMs) using Virtual Machine (VM) Live Migration. But, along with reducing the number of active PMs, if a consolidation approach reduces residual resource fragmentation, the residual resources can be efficiently used for new VM allocations, or VM reallocations, and some future migrations can also be reduced. None of the existing works have explicitly focused on reducing residual resource fragmentation along with reducing the number of active PMs to the best of our knowledge. We propose RFAware Server Consolidation, a heuristics based server consolidation approach which performs residual resource defragmentation along with reducing the number of active PMs in cloud data centers. We model residual resource fragmentation in cloud data centers.We examine feasibility of residual resource defragmentation with server consolidation.Proposed approach performs defragmentation with low energy cost and SLA violations.Controlled defragmentation with consolidation reduces VM migrations.A good mix of cloud applications ensures better defragmentation. <s> BIB022 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> CSSICA is proposed to find the lowest service time for cloud composite services.PROCLUS is used to divide service providers into 3 categories based on service time.Selection probability of categories is calculated using their average service time.Utilizing achieved categories CSSICA has been able to make more conscious selections. Aiming to provide satisfying and value-added cloud composite services, suppliers put great effort into providing a large number of service providers. This goal, achieved by providing the "best" solutions, will not be guaranteed unless an efficient composite service composer is employed to choose an optimal set of required unique services (with respect to user-defined values for quality of service parameters) from the large number of provided services in the pool. Facing a wide service pool, user constraints, and a large number of required unique services in each request, the composer must solve an NP-hard problem. In this paper, CSSICA is proposed to make advances toward the lowest possible service time of composite service; in this approach, the PROCLUS classifier is used to divide cloud service providers into three categories based on total service time and assign a probability to each provider. An improved imperialist competitive algorithm is then employed to select more suitable service providers for the required unique services. Using a large real dataset, experimental and statistical studies are conducted to demonstrate that the use of clustering improved the results compared to other investigated approaches; thus, CSSICA should be considered by the composer as an efficient and scalable approach. <s> BIB023 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Abstract High quality of security service is increasingly critical for Cloud workflow applications. However, existing scheduling strategies for Cloud systems disregard security requirements of workflow applications and only consider CPU time neglecting other resources like memory, storage capacities. These resource competition could noticeably affect the computation time and monetary cost of both submitted tasks and their required security services. To address this issue, in this paper, we introduce immoveable dataset concept which constrains the movement of certain datasets due to security and cost considerations and propose a new scheduling model in the context of Cloud systems. Based on the concept, we propose a Security-Aware and Budget-Aware workflow scheduling strategy (SABA), which holds an economical distribution of tasks among the available CSPs (Cloud Service Providers) in the market, to provide customers with shorter makespan as well as security services. We conducted extensive simulation studies using six different workflows from real world applications as well as synthetic ones. Results indicate that the scheduling performance is affected by immoveable datasets in Clouds and the proposed scheduling strategy is highly effective under a wide spectrum of workflow applications. <s> BIB024 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> This paper studies the routing problem in energy-efficient networks for cloud computing. We propose a robust routing algorithm to reach the higher network energy efficiency, which is based on optimization problem. To attain the highly energy-efficient routing in energy-efficient networks for cloud computing, the link of low utilization is turned into the sleeping state to save the network energy. At the same time, the low link traffic is aggregated to the link with high utilization to enhance the link utilization and to sleep the links as many as possible. We present an optimized link sleeping method to maximize the number of the sleeping links. By targeting the network robustness, a weight adaptive strategy is brought forth to reduce the link congestion and enhance the robustness of the network. Simulation results indicate that our algorithm is effective and feasible to achieve energy-efficient networks for cloud computing. <s> BIB025 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Cloud manufacturing (CMfg) platform for group enterprises (GE) is a kind of private CMfg, which is to integrate and optimize GE’s internal resources and capacity for large complex equipment manufacturing. The platform makes a connection between distributed and heterogeneous manufacturing resources to build a virtual pooling of resources for the group. However, it is evident that uncertainties and dynamics inherently exist in the platform and it causes manufacturing service requirement conflict among multiple projects. In order to address this issue, the management process of large complex equipment manufacturing project in GE-oriented CMfg platform is described. And next, this paper analyzes the causes and characteristic of service conflict in the platform. Then, a multi-objective mathematical model of conflict resolution is proposed. The proposed model considered both global target of shortest duration and partial target of tasks change minimization. Moreover, a method based on serial schedule generation scheme (SSGS) and ant colony optimization (ACO) algorithm is put forward to solve the model. Finally, a conflict resolution case study in a cement equipment manufacturing group enterprise is provided to illustrate the application of the proposed the model and algorithm. <s> BIB026 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Abstract Workflow scheduling in scientific computing systems is one of the most challenging problems that focuses on satisfying user-defined quality of service requirements while minimizing the workflow execution cost. Several cost optimization approaches have been proposed to improve the economic aspect of Scientific Workflow Scheduling (SWFS) in cloud and grid computing. To date, the literature has not yet seen a comprehensive review that focuses on approaches for supporting cost optimization in the context of SWFS in cloud and grid computing. Furthermore, providing valuable guidelines and analysis to understand the cost optimization of SWFS approaches is not well-explored in the current literature. This paper aims to analyze the problem of cost optimization in SWFS by extensively surveying existing SWFS approaches in cloud and grid computing and provide a classification of cost optimization aspects and parameters of SWFS. Moreover, it provides a classification of cost based metrics that are categorized into monetary and temporal cost parameters based on various scheduling stages. We believe that our findings would help researchers and practitioners in selecting the most appropriate cost optimization approach considering identified aspects and parameters. In addition, we highlight potential future research directions in this on-going area of research. <s> BIB027 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Abstract H1N1 is an infectious virus which, when spread affects a large volume of the population. It is an airborne disease that spreads easily and has a high death rate. Development of healthcare support systems using cloud computing is emerging as an effective solution with the benefits of better quality of service, reduced costs and flexibility. In this paper, an effective cloud computing architecture is proposed which predicts H1N1 infected patients and provides preventions to control infection rate. It consists of four processing components along with secure cloud storage medical database. The random decision tree is used to initially assess the infection in any patient depending on his/her symptoms. Social Network Analysis (SNA) is used to present the state of the outbreak. The proposed architecture is tested on synthetic data generated for two million users. The system provided 94% accuracy for the classification and around 81% of the resource utilization on Amazon EC2 cloud. The key point of the paper is the use of SNA graphs to calculate role of an infected user in spreading the outbreak known as Outbreak Role Index (ORI). It will help government agencies and healthcare departments to present, analyze and prevent outbreak effectively. <s> BIB028 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> Efficient resource allocation of computational resources to services is one of the predominant challenges in a cloud computing environment. Furthermore, the advent of cloud brokerage and federated cloud computing systems increases the complexity of cloud resource management. Cloud brokers are considered third party organizations that work as intermediaries between the service providers and the cloud providers. Cloud brokers rent different types of cloud resources from a number of cloud providers and sublet these resources to the requesting service providers. In this paper, an autonomic performance management approach is introduced that provides dynamic resource allocation capabilities for deploying a set of services over a federated cloud computing infrastructure by considering the availability as well as the demand of the cloud computing resources. A distributed control based approach is used for providing autonomic computing features to the proposed framework via a feedback-based control loop. This distributed control based approach is developed using one of the decomposition-coordination methodologies, named interaction balance, for interactive bidding of cloud computing resources. The primary goals of the proposed approach are to maintain the service level agreements, maximize the profit, and minimize the operating cost for the service providers and the cloud broker. The application of interaction balance methodology and prioritization of profit maximization for the cloud broker and the service providers during resource allocation are novel contributions of the proposed approach. We develop a dynamic resource allocation approach in cloud broker environment with limited resources.The developed approach can also be applied to federated cloud computing environment.The interaction balance principles are utilized in the developed approach.The developed approach maximizes the profitability of the cloud broker and service provider simultaneously. <s> BIB029 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Introduction <s> A cloud computing environment offers a simplified, centralized platform or resources for use when needed at a low cost. One of the key functionalities of this type of computing is to allocate the resources on an individual demand. However, with the expanding requirements of cloud user, the need of efficient resource allocation is also emerging. The main role of service provider is to effectively distribute and share the resources which otherwise would result into resource wastage. In addition to the user getting the appropriate service according to request, the cost of respective resource is also optimized. In order to surmount the mentioned shortcomings and perform optimized resource allocation, this research proposes a new Agent based Automated Service Composition (A2SC) algorithm comprising of request processing and automated service composition phases and is not only responsible for searching comprehensive services but also considers reducing the cost of virtual machines which are consumed by on-demand services only. <s> BIB030
Cloud computing has emerged from the web-based technologies. It includes the idea of providing computing services and data on demand. The developments in cloud computing platforms help in solving the computationally complex problems in chemistry, meteorology, high-energy physics, astronomy, biology, human brain planning and sensor networks [71]  BIB001 BIB002  .The National Institute of Standards and Technology [52] defines cloud computing as "a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be 2. Literature review 2.1. Chronological review In 2010, Liu et al. BIB003 had done the module measurement in a virtual environment, and it is compared with the trusted environment. They verified the executables status and added memory watcher module to the virtual machine measurement. In 2011, B i t t e n c o u r t and M a d e i r a BIB004 proposed the Hybrid Cloud Optimized Cost scheduling algorithm to identify the resources that have to be leased from the public cloud and added to the private cloud for the execution of workflow within the scheduled time. Huu et al.  developed an automating cloud resources allocation based on the cost estimation model for optimizing cloud computing infrastructures. L u a and Y o w BIB005 exploited the intelligent water drop algorithm for mitigating the distributed denial of service attacks. For optimization in cloud computing, an intelligent fast-flux swarm network is proposed by them. In 2012, L i et al. BIB007 proposed two online dynamic resource allocation algorithms for resource optimization in the Infrastructure-as-a-Service cloud system. Liu et al.  presented a novel security constraint model for formulating the scheduling problem in distributed data-intensive computing environments. Various algorithms are-multistart PSO, multi-start genetic algorithm and variable neighbourhood PSO are used for the study. In 2013, X u et al. BIB008 developed a novel a Double Molecular Structure-based Chemical Reaction Optimization (CRO) method and exploited the CRO to formulate the scheduling of Directed Acyclic Graph (DAG) jobs in heterogeneous computing systems. They also performed simulation experiments to measure its efficiency. W u et al. BIB006 have worked on cloud workflow systems and developed a strategy in market-oriented hierarchical scheduling. Based on this strategy, they proposed a package based random scheduling algorithm. They also used other metaheuristic algorithm and analyzed the scheduling algorithms. Lin et al. BIB009 exploited the cloud computing technologies and designed a privacy-preserving mobile health monitoring system. They also developed a new private proxy reencryption to shift the computational complexity of the involved parties.  developed a model to find out the optimal sampling size based on the cloud data and the capacity of the network. C h a n d r a s e k a r a n, Mur a l i d h a r and D i x i t BIB010 have worked on optimization of machine process using the cloud computing techniques which provides on-demand computing services and data. In 2014, K a r a m p a g l i s, G o u n a r i s and M a n o l o p o u l o s BIB012 developed a bi-objective query cost model which can be applied for queries executed over resources with the multiple crowd providers. Chen et al.  proposed a co-evolutionary negotiation process between service consumer and service provider over the quality of service requirements in cloud computing. K o e h l e r BIB011 introduced the optimization of resource utilization using the selffiguring adaptive framework and integrated into the Vienna Cloud Environment for scientific applications on top of Cloud technologies. Shu, W. W a n g and Y. W a n g BIB013 proposed an improved clonal selection algorithm for meeting the service level agreement requested by the users and using Cloud Sim toolkit; they analysed its performance. F o l i n o and P i s a n i BIB014 implemented a new generating model framework using the genetic programming for making automatic decisions on the offloading of the mobile applications. A r a n g o et al. BIB015 exploited the cloud computing capabilities and developed the evolutionary algorithms for analyzing and making decisions in water distribution systems. Z h u and G o n g BIB016 developed a new fuzzy authorization scheme by exploiting the Ciphertext-Policy Attribute-Based Encryption and OAuth to enhance the scalability and flexibility of the file sharing. G e et al. BIB017 proposed a sharing system known as Lineage-Signature to find a solution to the Multiple Query optimization problem in the cloud computing. F a n g et al. BIB018 proposed a novel user-side optimization process-Off-the-Cloud Service Optimization method for efficient service provision. S h i and B o BIB019 focused on studying the physical layer security in downlink heterogeneous Cloud radio access network systems. In 2015, C h e n et al.  had worked on cloud security and proposed a cloud computing-based network monitoring and threat detection system using Hadoop, MapReduce, and Spark. A. K o n a k, S. K o n a k and S n y d e r  developed the Game-Theoretic Genetic Algorithm and compared its performance with the nested genetic algorithms for maximizing the network reliability and simultaneously reducing the reliable server assignment problem. B h a r a t h i and S h a r i f i a n  used hybrid genetic algorithm and PSO to develop a newly tuned support vector regression method for cloud computing elasticity. K a r a m i and S a p a t a BIB020 detected the denial of service attacks and adaptive mitigation reaction in Named data networking by using a multiobjective evolutionary optimization algorithm with PSO. B a m a k a n et al.  improved the performance of Intrusion detection system by implementing the simple optimization technique-PSO and multiple criteria linear programming. S i n g h, J u n e j a and M a l h o t r a BIB030 developed a new Agentbased Automated Service Composition Algorithm which includes request processing and automated service composition phases, thereby optimizing the resource allocation and reducing the cost of virtual machines. J i a n g et al. BIB025 have worked on high energy efficient routing in energy-efficient networks for cloud computing and optimized the link sleeping method to increase the number of sleeping links.  introduced the Discrete Gbest-guided Artificial Bee Colony algorithm for optimizing the cloud service composition. S u g u n a and S u h a s i n i  performed the analysis of massive data storage process using the Enriched Multi-Objective Optimization Model for Efficient Disaster Recovery in a cloud environment. N a v i m i p o u r et al. BIB021 proposed a new Expert Cloud framework for the cloud users to share the human resource skills, knowledge and visualization. They also introduced the Expert Cloud employing Internet infrastructures and Cloud computing concepts. R a o and T h i l a k a m BIB022 have worked on server consolidation in managing cloud data centre. They developed RFAware Server Consolidation approach for reducing the number of physical servers. J u l a, O t h m a n and S u n d a r a r a j a n BIB023 proposed CSSICA approach with PROCLUS classifier to make advances toward the lowest possible service time and developed an improved imperialist competitive algorithm. W a n g et al.  introduced a security-aware model and applied meta-heuristic which is based on the supercomputer hybrid architecture for heterogenous cloud scheduling. Sun et al.  optimized the cloud resource allocation for offering web applications using the software as a service model in the cloud and developed a modelling framework-Resource optimization, Allocation and Recommendation System for optimization. Z e n g, V e e r a v a l l i and Li BIB024 used immovable dataset concept and developed a Security-Aware and Budget-Aware workflow scheduling strategy, to provide customers with shorter makespan and high-quality security services. H u a m g et al. BIB026 have worked on Cloud Manufacturing (CMfg) platform for group enterprises (GE) and proposed conflict resolution mathematical model based on the Ant colony optimization algorithm and serial schedule generation scheme. Z i n n e d i n e  proposed a cloud security framework for mitigating the vulnerabilities and had done cost optimization using Cuckoo search algorithm with Levy flight as random walks and thereby balanced the IT security coverage. In 2016, Han et al.  introduced a defence mechanism to get away from the security risks and eliminated various types of side channels and preventing co-residence by using cluster analysis and semi-supervised learning techniques. A l k h a n a k et al. BIB027 analyzed the problem of cost optimization in Scientific Workflow Scheduling (SWFS) by studying the traditional SWFS in cloud and grid computing and provided the cost optimization classification of SWFS. A h m a d et al.  proposed a Hybrid genetic algorithm for solving the work flow scheduling problem and also used for optimizing the load balance for maximum resource utilization. P a l m i e r i et al.  used the greedy randomized adaptive search procedure, augmented by path re-linking to re-optimize the communication paths between virtual machines and big data sources, thereby increasing the performance of service quality. S i n d h u, G i l l and S o o d BIB028 have worked on H1N1 infected patients and proposed a cloud computing architecture for predicting the infected patients and the prevention measures to control the infection rate and to stop the outbreak of H1N1. A b d u l l a h i, N g a d i and A b d u l h a m i d  developed a meta-heuristic optimization technique-Discrete Symbiotic Organism Search algorithm for optimal, efficient task scheduling on cloud resources. M e h r o t r a et al. BIB029 introduced a framework of autonomic performance management and developed a distributed control based approach for interactive bidding of cloud computing resources and to maintain the service level agreements. Fig. 1 shows the year-wise percentage of contributions in the cloud security. About 44% of the works have been done in the year 2015, and it is in the highest range. A lot of works are also done in 2016. Only 1% work is done in 2010 corresponding to cloud security. The figured data predicts the recent advancements and research in the area of cloud computing.