Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB001 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB002 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB003 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB004 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB005 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB006 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB007 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB008 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB009 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB010 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB011 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB012 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB013 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB014 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB015 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB016 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB017 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB018 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <s> In order to cope with steadily increasing healthcare costs, hospitals introduce a new type of employee taking over logistics tasks from specialized nurses, namely logistics assistants. In the light of the introduction, hospitals are faced with the question of dimensioning their number. We present a mixed-integer program that allows defining the optimal number of logistics assistants, given predefined task requirements. We combine flexible shift scheduling with a task scheduling problem. We incorporate flexibility both in terms of shift scheduling as well as task scheduling in order to define the minimum number of workers. We present a column generation based solution approach that finds optimal solutions, and compare decomposition approaches with one and two subproblems. Neither the general model nor the solution approach are limited to logistics assistants but can also be applied to other problem settings in the healthcare industry and beyond. The approach is tested with 48 problem instances in total and compared to benchmarks. As part of our solution approach, we present a lower bound for staff minimization problems with an unknown number of available workers. We show that flexibility in shift scheduling and task scheduling leads to a decrease of 40–49% of the required workforce, compared to the non-flexible case. <s> BIB019 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Shi and B o [42] <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> BIB020
Characterization of physical layer security in the downlink. Z e n g, V e e r a v a l l i and Li BIB013  -Liu et al. BIB001  -C h e n et al.   -Lin et al. BIB005  -H u a n g et al. [ The Equations (1)-(5) represents the task scheduling problem constraints referred from BIB019 . Equation (1) makes sure that each task is performed and Equation (2) ensures the successive tasks, all predecessors are completed before the successor initiates. Equation (3) illustrates the add up demand of all the tasks a at a time period t , and it is indicated as  latest latest earliest earliest start start indicates the latest start time of the task a. In quality of service, the response time measures the delay time in seconds. The probability that the service is accessible is known as the availability, and the probability that a request is responded correctly is termed as reliability. The execution fee per request is known as the Price (Cost). The average user rating for the service is called as the Rating. The Equation (6) illustrates the energy efficient resource allocation. Here, assume the Idlei indicates the time of the resource ri. L(j) represents a set of DVFS levels used for the tasks assigned to resource ri, vmin and fmin represents the voltage and frequency while the resource ri transition to the sleep mode in the idle time, and  indicates the load factor, subsequently the cumulative energy used by the resource ri for the completion of all tasks assigned to the resource is illustrated in Equation (6). The energy consumed for the offloading process and waiting for the end of the computation on the cloud sever is illustrated as Equation (7). Equation (8) illustrates the service attack throughput  . Here, RTO  BIB006  BIB016  BIB004  . Optimization has been done in    BIB002 to minimize the operational cost and to ensure the performance and quality of service. For designing the chemical reaction operation, various optimization processes has been used in BIB006 . Cloud load prediction optimization has been done in  . The mitigation of DOS attacks in Named data networking has been optimized for better performance in BIB014 . Improvising the performance of MCLP classifier has been enhanced with the optimization techniques in  . The resource provisioning for cloud computing BIB020 and cloud service resource management for efficient service delivery BIB009 has been optimized. Optimization techniques have been used in cloud manufacturing platform for group enterprise BIB010 and multi-pass machining BIB007 . Running massive data analysis queries BIB011 , cloud computing service composition BIB015 and scientific application on top of cloud technologies BIB008 has been optimized. The estimation capacity of the network  and ensuring autonomous coordination BIB003 have been optimized. Optimization has been used to improve energy efficient networks for cloud computing BIB017 BIB012 , disaster recovery in cloud environment  and balancing throughput and response time in online scientific clouds  . Table 1 illustrates the various challenges in the cloud environment. Among the problems addressed, Task scheduling problem seems to be the major problem focused in cloud computing research, and it was found to be 22% among the literature reviewed. The energy efficient allocation resource has been taken as a challenge and addressed in 18% of the works in cloud computing. Other problems considered include Quality service, load balancing, service attack and co-resident attack. Quality service problem is shown in 8% of the reviewed papers. About 4% of the works have been done to solve the problem of load balancing. The service attack problem is considered in about 6% of the papers reviewed. Also, the co-resident attack is discussed in 4% of the collected reviews. The analysis of the contributions to solving the objective models is illustrated in Fig. 2 . Table 2 shows the various performance analyses for cloud optimization. Makespan has been used as the performance parameter for cloud optimization in most of the works, and it was about 31% of the 29 cloud optimization papers. B a m a k a n et al.  Accuracy, False Alarm Rate S i n g h, J u n e j a and M a l h o t r a BIB020  Cost of Resources Shu, W. W a n g and Y. W a n g BIB012    -K o e h l e r BIB008 Resource utility ratio J i a n g et al. Size selection error rate W a n g et al.   Guarantee ratio F a n g et al. BIB009 Resource capacity ratio L i et al. BIB004  C h a n d r a s e k a r a n, M u r a l i d h a r and D i x i t BIB007 Machining cost Sun et al.    H u a n g et al. BIB018 Total delay time, Service capacity Liu et al.    -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    Degree of imbalance Z i n e d d i n e  Cost of loss