A novel clustering and optimal resource scheduling in vehicular cloud networks using MKMA and the CM-CSO algorithm

Various opportunities aimed at the employment of delay-sensitive applications in the vehicular environment are presented by the vehicular cloud (VC). Contrary to diverse wireless networks, VC networks possess exceptional features among others, namely, shorter transmission time along with a higher dynamic topology. Although integration with the cloud offers higher storage along with computation capabilities, it as well entails restricted resource availability. The restrictions on the number of resources serve as a challenge in servicing the applications with their necessary quality of service (QoS) guarantees as the number of service requests for applications keeps on augmenting with diverse circumstances. Thus, the need for an effective scheduling methodology arises to decide the sequence of servicing application requests and successful utilization of a broadcast medium, along with data transmission. To do efficient resource scheduling on VC networks, an optimization algorithm, namely, the crossover and mutation (CM)-centered chicken swarm optimization (CSO) is proposed and implemented with the help of a publicly available dataset. Initially, the VC infrastructure is initialized and some vehicle information is extracted as features. Next, the Brownian motion-centered bacteria foraging optimization (BM-BFO) algorithm chooses the essential features. Centered on the chosen features, the vehicles are clustered using the modified K-means algorithm. Next, as for the cloud server's virtual machines (VMs), the resource information is extracted. Lastly, the CM-CSO algorithm carries out the optimal scheduling in the VC by means of the clustered features of vehicles and features of the VM. The proposed techniques' findings are scrutinized and analogized to the other prevailing methodologies to confirm that the proposed work performs effectively and gives optimal resource allocation (RA) to the VC.     

云环境下基于属性加密体制算法加速方案

云计算为租户提供存储、计算和网络服务，数据安全保护和租户间的数据共享与访问控制是其必不可少的能力。基于属性的加密体制是一种一对多的加密体制，可以根据用户属性实现细粒度访问控制，适用于云计算环境多租户数据共享。但现有的基于属性加密体制的算法效率较低，难以在实际环境中应用。分析了基于属性的加密体制的两种类型及其应用场景，提出一个基于属性加密体制算法的加速方案。通过实验表明，提出的方案可提高基于属性加密体制的密钥生成算法、加密算法和解密算法的效率。     

A genetic‐based decision algorithm for multisite computation offloading in mobile cloud computing

Mobile cloud computing is a promising approach to improve the mobile device's efficiency in terms of energy consumption and execution time. In this context, mobile devices can offload the computation‐intensive parts of their applications to powerful cloud servers. However, they should decide what computation‐intensive parts are appropriate for offloading to be beneficial instead of local execution on the mobile device. Moreover, in the real world, different types of clouds/servers with heterogeneous processing speeds are available that should be considered for offloading. Because making offloading decision in multisite context is an NP‐complete, obtaining an optimal solution is time consuming. Hence, we use a near optimal decision algorithm to find the best‐possible partitioning for offloading to multisite clouds/servers. We use a genetic algorithm and adjust it for multisite offloading problem. Also, genetic operators are modified to reduce the ineffective solutions and hence obtain the best‐possible solutions in a reasonable time. We evaluated the efficiency of the proposed method using graphs of real mobile applications in simulation experiments. The evaluation results demonstrate that our proposal outperforms other counterparts in terms of energy consumption, execution time, and weighted cost model.     

云环境下服务信任感知的可信动态级调度方法

以社会学中的人际关系信任模型为基础,提出了一种基于服务消费者的服务满意度评价、推荐者的服务推荐和第三方服务性能反馈的可信度量模型。将用户对服务资源的信任需求和服务资源的可信度并入DLS算法得到可信动态级调度算法CTDLS,从而在计算调度级别时考虑服务资源的可信程度。模拟实验表明,该算法能有效满足任务在信任方面的服务质量需求,对提高任务调度的成功率具有实际意义。     

云计算环境中基于用户访问需求的角色查找算法

提出了一种云计算环境中基于角色的访问控制模型CARBAC,将角色分为用户角色和资源拥有者管理角色。针对管理角色对用户访问的角色指派,提出了在混杂角色层次关系中基于用户权限的角色查找算法。对于一组给定的授权,该算法能在云计算系统的角色中选择一组数量最少的角色指派给用户。仿真实验表明,针对云计算环境中的海量用户访问,本算法能显著减少系统中角色的数量,缩短用户授权时间,提高系统运行效率。     

MapReduce optimization algorithm based on machine learning in heterogeneous cloud environment

We present an approach to optimize the MapReduce architecture, which could make heterogeneous cloud environment more stable and efficient. Fundamentally different from previous methods, our approach introduces the machine learning technique into MapReduce framework, and dynamically improve MapReduce algorithm according to the statistics result of machine learning. There are three main aspects： learning machine performance, reduce task assignment algorithm based on learning result, and speculative execution optimization mechanism. Furthermore, there are two important features in our approach. First, the MapReduce framework can obtain nodes＇ performance values in the cluster through machine learning module. And machine learning module will daily calibrate nodes＇ performance values to make an accurate assessment of cluster performance. Second, with the optimization of tasks assignment algorithm, we can maximize the performance of heterogeneous clusters. According to our evaluation result, the cluster performance could have 19% improvement in current heterogeneous cloud environment, and the stability of cluster has greatly enhanced.     

A workflow task scheduling algorithm based on the resources' fuzzy clustering in cloud computing environment

Cloud computing is the key and frontier field of the current domestic and international computer technology, workflow task scheduling plays an important part of cloud computing, which is a policy that maps tasks to appropriate resources to execute. Effective task scheduling is essential for obtaining high performance in cloud environment. In this paper, we present a workflow task scheduling algorithm based on the resources' fuzzy clustering named FCBWTS. The major objective of scheduling is to minimize makespan of the precedence constrained applications, which can be modeled as a directed acyclic graph. In FCBWTS, the resource characteristics of cloud computing are considered, a group of characteristics, which describe the synthetic performance of processing units in the resource system, are defined in this paper. With these characteristics and the execution time influence of the ready task in the critical path, processing unit network is pretreated by fuzzy clustering method in order to realize the reasonable partition of processor network. Therefore, it largely reduces the cost in deciding which processor to execute the current task. Comparison on performance evaluation using both the case data in the recent literature and randomly generated directed acyclic graphs shows that this algorithm has outperformed the HEFT, DLS algorithms both in makespan and scheduling time consumed. Copyright © 2014 John Wiley & Sons, Ltd.     

Extended artificial immune system–based optimized access control for big data on a cloud environment

Integrating parallel computing and distributed computing together can be obtained by cloud computing (CC). One of the major problems faced by public CC is security regarding data access control. CC permits people to share data, documents, videos, and other types of data. Generally, the cloud data are considered as big data, because the volume of the data is huge and it has a greater number of varieties. In recent days, attribute‐based data sharing applied only for selected data is a crucial problem. One of the existing approaches encrypts data using various kinds of keys based on several types of cryptosystems. However, those kinds of methods have some weaknesses such as inability to handle the attributes effectively, storage of more unwanted copies of the same data, and policy changes. It needs a high amount of computational cost and reduces the efficiency of memory utilization and the computational speed. This paper motivated to design and implement an efficient approach for optimized access control (OAC) for data stored in the cloud to overcome these kinds of issues. The efficiency of the proposed method is proved through a simulation‐based experiment in Cloud Simulator.     

A cloud database route scheduling method using a hybrid optimization algorithm

Cloud computing has appeared as a technology allowing a company to employ computing resources such as applications, software, and hardware to calculate over the Internet. Scholars have paid great attention to cloud computing because of its cutting-edge availability, cost decrement, and boundless applications. A cloud database is a data storage site on the web where the optimal path is spotted to access the needed database. So, placing the ideal path to a database is crucial. The cloud database defined the scheduling problem to choose the perfect route. Cloud database path scheduling is a multifaceted procedure consisting of congestion control, routing list, and network flow distribution. It has a postponement in searching for the needed source route from the cloud database. Offering numerous infinite resources with the growing database workload is an NP-Hard optimization problem where the query request needs optimal schedules to respond to the required services. So, we have used a hybrid cuckoo search (CS) and genetic algorithm (GA), motivated by a social bird's phenomenon, to solve this problem. Integrating genetic operators has dramatically enhanced the balance between the capability of searching and utilization.     

UFKLDA: An unsupervised feature extraction algorithm for anomaly detection under cloud environment

In a cloud environment, performance degradation, or even downtime, of virtual machines (VMs) usually appears gradually along with anomalous states of VMs. To better characterize the state of a VM, all possible performance metrics are collected. For such high‐dimensional datasets, this article proposes a feature extraction algorithm based on unsupervised fuzzy linear discriminant analysis with kernel (UFKLDA). By introducing the kernel method, UFKLDA can not only effectively deal with non‐Gaussian datasets but also implement nonlinear feature extraction. Two sets of experiments were undertaken. In discriminability experiments, this article introduces quantitative criteria to measure discriminability among all classes of samples. The results show that UFKLDA improves discriminability compared with other popular feature extraction algorithms. In detection accuracy experiments, this article computes accuracy measures of an anomaly detection algorithm (i.e., C‐SVM) on the original performance metrics and extracted features. The results show that anomaly detection with features extracted by UFKLDA improves the accuracy of detection in terms of sensitivity and specificity.     

Data update algorithm based on secure network coding in cloud environment

In the cloud environment for data storage,the use of secure network coding technology can be a good solution to the data privacy and reliability issues.However,each coding block usually has a high correlation after network coding,very few updates to the file need to be re-encoded which is extremely easy to cause information leakage and serious consumption of system resources.To solve this problem,a network coding cloud storage data updating algorithm was proposed.Just by sending files change difference matrix,the storage node could update parts of the coding block accordingly which could complete the entire update files.Experimental results show that compared with RS coding and Tornado coding,the algorithm can not only ensure data security,but also greatly improve the efficiency of data update and data reconstruction.     

云计算环境下虚拟机在线迁移优化

在线迁移在云计算平台下已广泛使用,虚拟机内存迁移主要采用的是预拷贝算法.传统预拷贝算法在迭代过程中会将脏页反复重传导致迁移时间较长,针对这一问题文章提出了在拷贝过程中增加脏页预测算法和内存压缩算法相结合的方法.脏页预测算法是采用统计方法,依据内存页历史操作情况对改动频繁的内存页进行标记,根据标记决定是否在最后一轮内存迁移中传输,为了减少迁移传输量,先通过内存压缩算法将其压缩,然后再传输.实验结果表明,改进后的方法有效地降低了停机时间和迁移时间,提高了迁移效率,达到了更快迁移的目的.     

A bandwidth allocation and energy-optimal transmission rate scheduling scheme in multi-services wireless networks

The energy-saving of mobile devices during their application offloading process has always been the research hotspot in the field of mobile cloud computing (MCC). In this paper, we focus on the scenario where multiple mobile devices with MCC and non-MCC services coexist. A bandwidth allocation and the corresponding transmission rate scheduling schemes are proposed with the objectives of simultaneously maximizing the overall system throughput and minimizing the energy consumption of individual mobile device with MCC service. To allocate the bandwidth to all mobile devices, two different algorithms are proposed, i.e., 0–1 integer programming algorithm and Lagrange dual algorithm. The transmission rate scheduling scheme for mobile device with MCC service is presented based on reverse order iteration method. The numerical results suggest that energy consumed by individual mobile device with MCC service can be remarkably saved while the overall system throughput can also be maximized. Moreover, the results show that 0–1 integer programming algorithm can get greater system throughput but has higher computational complexity, which means the algorithm is more suitable for small-scale systems, whereas Lagrange dual algorithm can achieve a good balance between the performance and computational complexity.     

无线传输中短码长喷泉码的度分布优化算法

数字喷泉码是针对大规模网络数据分发而提出的一种新的信道编码方式。度分布是决定数字喷泉码译码性能的关键因素。为提高译码性能,针对应用于无线信道的喷泉码提出了一种度分布优化的算法。首先,根据理想孤子分布和鲁棒孤子分布产生度值序列,然后将该度值序列截短,在此基础上根据优化算法求解该序列中每个度值的最优概率,最后得到优化的度分布。仿真结果表明,本算法产生的度分布进行编译码产生的误码率低于鲁棒孤子分布和固定度分布,提高了译码性能。     

A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm

One of the most critical issues in using service‐oriented technologies is the combination of services, which has become an important challenge in the present. There are some significant challenges in the service composition, most notable is the quality of service (QoS), which is more challenging due to changing circumstances in dynamic service environments. Also, trust value in the case of selection of more reliable services is another challenge in the service composition. Due to NP‐hard complexity of service composition, many metaheuristic algorithms have been used so far. Therefore, in this paper, the honeybee mating optimization algorithm as one of the powerful metaheuristic algorithms is used for achieving the desired goals. To improve the QoS, inspirations from the mating stages of the honeybee, the interactions between honeybees and queen bee mating and the selection of the new queen from the relevant optimization algorithm have been used. To address the trust challenge, a trust‐based clustering algorithm has also been used. The simulation results using C# language have shown that the proposed method in small scale problem acts better than particle swarm optimization algorithm, genetic algorithm, and discrete gbest‐guided artificial bee colony algorithm. With the clustering and reduction of the search space, the response time is improved; also, more trusted services are selected. The results of the simulation on a large‐scale problem have indicated that the proposed method is exhibited worse performance than the average results of previous works in computation time.     

A novel membrane-inspired multiverse optimizer algorithm for quality of service-aware cloud web service composition with service level agreements

Cloud computing is an emerging technology in computing that provides different services over the Internet. It needs composite services to perform a complex task. Optimal selection of services that provides both functionality and nonfunctionality requirements is an NP-hard problem. This study uses nondeterministic parallel and distributed structures of membrane systems for the recently improved multiverse optimization algorithm to improve the quality of solutions. In the previous membrane-inspired algorithm, the population was divided into subpopulations that evolve different dynamic membranes. This study not only uses a conventional membrane-inspired approach to introduce a conventional membrane-inspired multiverse optimizer (CMIMVO) for the first time but also proposes an algorithm that divides the variables (dimension) into subgroups for different membranes called proposed membrane-inspired multiverse optimizer (PMIMVO). Thus, in PMIMVO, each membrane works on a subgroup to gain global information, which considers the best values obtained by other membranes for other variables. The PMIMVO shows promising results on benchmark function problems. Furthermore, simulation results show that the PMIMVO approach could achieve up to 38% improvement in integrated quality of service (QoS) with attributes including response time, price, availability, and reliability in comparison with the previous approaches, including genetics algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), moth–flame optimization (MFO) improved multiverse optimizer (MVO), and CMIMVO.     

A comparative study and workload distribution model for re‐encryption schemes in a mobile cloud computing environment

The researchers are using the various variations of re‐encryption schemes, which migrate the computational intensive re‐encryption jobs of mobile devices to the trusted entity/cloud. However, the messages are still encrypted and decrypted using the limited computational power of mobile devices. Our contribution in this paper is to propose a workload distribution model for re‐encryption schemes, which offloads the computational intensive operations, such as encryption and decryption on a trusted entity. Moreover, the proposed workload distribution model is compared with existing re‐encryption schemes of resource utilization on trusted entity and mobile device. The experimental results show substantial improvement in performance compared to the existing schemes.     

A secure and robust group key distribution and authentication protocol with efficient rekey mechanism for dynamic access control in secure group communications

In today's Internet era, group communications have become more and more essential for many emerging applications. Given the openness of today's networks, efficient and secure distribution of common key is an essential issue for secure communications in the group. To maintain confidentiality during communication in the group, all authorized members require a common key called the group key in advance. This paper proposes a group key distribution and authentication protocol for dynamic access control in secure group communication using Chinese remainder theorem (CRT), which is highly secure and computationally efficient. The proposed protocol (1) has drastically reduced the computation complexity of group controller ( GC ) and members, (2) has provided intense security by means of an additional secret parameter used by GC and members, (3) has minimized storage and communication overheads, (4) has been decentralized for higher scalability so that it can efficiently handle large‐scale changes in the group membership, and (5) is suitable for many practical applications due to intense security along with low computation and storage overheads. Detailed security analysis proves that our protocol can guarantee the privacy and security requirements of group communications. Moreover, performance analysis also verifies the efficiency and effectiveness of the proposed protocol. The proposed protocol has been experimented on star topology‐based key distribution system and observed that the protocol significantly reduces the computation cost and minimizes the communication and storage overheads.     

An energy‐saving algorithm for cloud resource management using a Kalman filter

Cloud computing has emerged as a promising technique to provide storage and computing component on‐demand services over a network. In this paper, we present an energy‐saving algorithm using the Kalman filter for cloud resource management to predict the workload and to further achieve high resource availability with low service level agreement. Using the proposed algorithm, one can estimate the potential future workload trend then predict the computing component workload utilizations and further retrench energy consumption and achieve load balancing in a cloud system. Experimental results show that the proposed algorithm achieves more than 92.22% accuracy in the computing component workload prediction, improves 55.11% energy in energy consumption, and has 3.71% in power prediction error rate, respectively. Copyright © 2013 John Wiley & Sons, Ltd.     

An efficient algorithm for dynamic task offloading using cloudlets in mobile cloud computing

It is a visible fact that the growth of mobile devices is enormous. More computations are required to be carried out for various applications in these mobile devices. But the drawback of the mobile devices is less computation power and low available energy. The mobile cloud computing helps in resolving these issues by integrating the mobile devices with cloud technology. Again, the issue is increased in the latency as the task and data to be offloaded to the cloud environment uses WAN. Hence, to decrease the latency, this paper proposes cloudlet‐based dynamic task offloading (CDTO) algorithm where the task can be executed in device environment, cloudlet environment, cloud server environment, and integrated environment. The proposed algorithm, CDTO, is tested in terms of energy consumption and completion time.