A self-learning fuzzy approach for proactive resource provisioning in cloud environment

The development of a communication infrastructure has made possible the expansion of the popular massively multiplayer online games. In these games, players all over the world can interact with one another in a virtual environment. The arrival rate of new players to the game environment causes fluctuations and players always expect services to be available and offer an acceptable service-level agreement (SLA), especially in terms of response time and cost. Cloud computing emerged in the recent years as a scalable alternative to respond to the dynamic changes of the workload. In massively multiplayer online games applications, players are allowed to lease resources from a cloud provider in an on-demand basis model. Proactive management of cloud resources in the face of workload fluctuations and dynamism upon the arrival of players are challenging issues. This paper presents a self-learning fuzzy approach for proactive resource provisioning in cloud environment, where key is to predict parameters of the probability distribution of the incoming players in each period. In addition, we propose a self-learning fuzzy autoscaling decision-maker algorithm to compute the proper number of resources to be allocated to each tier in the massively multiplayer online games by applying the predicted workload and user SLA. We evaluate the effectiveness of the proposed approach under real and synthetic workloads. The experimental results indicate that the proposed approach is able to allocate resources more efficiently than other approaches.     

Empirical prediction models for adaptive resource provisioning in the cloud

Cloud computing allows dynamic resource scaling for enterprise online transaction systems, one of the key characteristics that differentiates the cloud from the traditional computing paradigm. However, initializing a new virtual instance in a cloud is not instantaneous; cloud hosting platforms introduce several minutes delay in the hardware resource allocation. In this paper, we develop prediction-based resource measurement and provisioning strategies using Neural Network and Linear Regression to satisfy upcoming resource demands.Experimental results demonstrate that the proposed technique offers more adaptive resource management for applications hosted in the cloud environment, an important mechanism to achieve on-demand resource allocation in the cloud.     

Failure-aware resource provisioning for hybrid Cloud infrastructure

Hybrid Cloud computing is receiving increasing attention in recent days. In order to realize the full potential of the hybrid Cloud platform, an architectural framework for efficiently coupling public and private Clouds is necessary. As resource failures due to the increasing functionality and complexity of hybrid Cloud computing are inevitable, a failure-aware resource provisioning algorithm that is capable of attending to the end-users quality of service (QoS) requirements is paramount. In this paper, we propose a scalable hybrid Cloud infrastructure as well as resource provisioning policies to assure QoS targets of the users. The proposed policies take into account the workload model and the failure correlations to redirect users’ requests to the appropriate Cloud providers. Using real failure traces and a workload model, we evaluate the proposed resource provisioning policies to demonstrate their performance, cost as well as performance–cost efficiency. Simulation results reveal that in a realistic working condition while adopting user estimates for the requests in the provisioning policies, we are able to improve the users’ QoS about 32% in terms of deadline violation rate and 57% in terms of slowdown with a limited cost on a public Cloud.     

Policy based resource allocation in IaaS cloud

In present scenario, most of the Infrastructure as a Service (IaaS) clouds use simple resource allocation policies like immediate and best effort. Immediate allocation policy allocates the resources if available, otherwise the request is rejected. Best-effort policy also allocates the requested resources if available otherwise the request is placed in a FIFO queue. It is not possible for a cloud provider to satisfy all the requests due to finite resources at a time. Haizea is a resource lease manager that tries to address these issues by introducing complex resource allocation policies. Haizea uses resource leases as resource allocation abstraction and implements these leases by allocating Virtual Machines (VMs). Haizea supports four kinds of resource allocation policies: immediate, best effort, advanced reservation and deadline sensitive. This work provides a better way to support deadline sensitive leases in Haizea while minimizing the total number of leases rejected by it. Proposed dynamic planning based scheduling algorithm is implemented in Haizea that can admit new leases and prepare the schedule whenever a new lease can be accommodated. Experiments results show that it maximizes resource utilization and acceptance of leases compared to the existing algorithm of Haizea.     

Prediction of mobile image saliency and quality under cloud computing environment

Recent years have witnessed the explosive growth of multimedia applications over networks and increasingly high requirements of consumers for multimedia signals in terms of quality of experience (QoE). Effective and efficient yet energy-saving saliency detection model and quality prediction method are eagerly desired, since they play critical roles in raising users' QoE and promoting the progress of green multimedia communication. Current studies of saliency detection and quality evaluation are far from ideal yet. In this paper we investigate the influence of complexity on visual saliency and quality. Complexity is an essential concept in human perception to visual stimulus, but it is substantially abstract and hard to be endowed with a clear definition. We suppose that brain systematically combines global and local features during the whole process of human perception. Global features lead a dominant position in seeking salient areas under the condition that image complexity is high, namely without obviously isolated foreground objects, whereas local features play a key role in an opposite situation. With this consideration, this paper establishes a novel framework for detecting visual saliency based on image complexity estimation before complexity-adaptive merging of global and local features. Furthermore, the concept of complexity is deployed for blind photographic image quality assessment (IQA) by means of saliency-based weighting. Features which refer to contrast, artifacts, brightness and natural scene statistics (NSS) are modified and integrated to derive a blind IQA model and predict the quality of photos. Based on the above two technologies, this paper introduces smart phones as mobile terminals, cloud platforms for speed-up and energy-saving, and wireless networks for transmission, and provides a practical mobile multimedia application. Comparative experiments validate that, within this application system, our proposed saliency detection model and blind photographic IQA method implement better than existing relevant competitors in terms of effectiveness and efficiency comparison.     

Understanding and predicting students' intention to use mobile cloud storage services

The challenges of mobile devices such as limited bandwidth, computing, and storage have led manufacturers and service providers to develop new value-added mobile services. To address these limitations, mobile cloud computing, which offers on-demand services including platforms, infrastructure, and software have been developed. This study attempts to build a significantly improved research framework based on the Technology Acceptance Model in order to identify factors that affect students' attitudes toward and intentions in using mobile cloud storage services. A structural equation model was used to assess the proposed model based on the data collected from 262 undergraduate students. Results show that perceived usefulness, subjective norm, and trust have a significantly positive effect on the attitude, which in turn is a significant predictor of behavioral intentions. The research model, which explains 82% of the variance in attitudes toward using mobile cloud storage services has a strong predictive power. The findings have both theoretical and practical implications for academics, managers, and educational institutions.     

基于SAC的多服务移动边缘计算中任务卸载和资源配置算法

多服务移动边缘计算网络环境中的不同服务的缓存要求、受欢迎程度、计算要求以及从用户传输到边缘服务器的数据量是随时间变化的。如何在资源有限的边缘服务器中调整总服务类型的缓存子集,并确定任务卸载目的地和资源分配决策,以获得最佳的系统整体性能是一个具有挑战性的问题。为了解决这一难题,首先将优化问题转换为马尔可夫决策过程,然后提出了一种基于软演员—评论家（soft actor-critic,SAC）的深度强化学习算法来同时确定服务缓存和任务卸载的离散决策以及上下带宽和计算资源的连续分配决策。算法采用了将多个连续动作输出转换为离散的动作选择的有效技巧,以应对连续—离散混合行动空间所带来的关键设计挑战,提高算法决策的准确性。此外,算法集成了一个高效的奖励函数,增加辅助奖励项来提高资源利用率。广泛的数值结果表明,与其他基线算法相比,提出的算法在有地减少任务的长期平均完成延迟的同时也具有良好的稳定性。     

An energy-efficient task scheduling for mobile devices based on cloud assistant

Mobile cloud computing is an emerging service model to extend the capability and the battery life of mobile devices. Mostly one network application can be decomposed into fine-grained tasks which consist of sequential tasks and parallel tasks. With the assistance of mobile cloud computing, some tasks could be offloaded to the cloud for speeding up executions and saving energy. However, the task offloading results in some additional cost during the communication between cloud and mobile devices. Therefore, this paper proposes an energy-efficient scheduling of tasks, in which the mobile device offloads appropriate tasks to the cloud via a Wi-Fi access point. The scheduling aims to minimize the energy consumption of mobile device for one application under the constraint of total completion time. This task scheduling problem is reconstructed into a constrained shortest path problem and the LARAC method is applied to get the approximate optimal solution. The proposed energy-efficient strategy decreases 81.93% of energy consumption and 25.70% of time at most, compared with the local strategy. Moreover, the applicability and performance of the proposed strategy are verified in different patterns of applications, where the time constraint, the workload ratio between communication and computation are various.     

Soft sets based symbiotic organisms search algorithm for resource discovery in cloud computing environment

The dynamicity, coupled with the uncertainty that occurs between advertised resources and users’ resource requirement queries, remains significant problems that hamper the discovery of candidate resources in a cloud computing environment. Network size and complexity continue to increase dynamically which makes resource discovery a complex, NP-hard problem that requires efficient algorithms for optimum resource discovery. Several algorithms have been proposed in literature but there is still room for more efficient algorithms especially as the size of the resources increases. This paper proposes a soft-set symbiotic organisms search (SSSOS) algorithm, a new hybrid resource discovery solution. Soft-set theory has been proved efficient for tackling uncertainty problems that arises in static systems while symbiotic organisms search (SOS) has shown strength for tackling dynamic relationships that occur in dynamic environments in search of optimal solutions among objects. The SSSOS algorithm innovatively combines the strengths of the underlying techniques to provide efficient management of tasks that need to be accomplished during resource discovery in the cloud. The effectiveness and efficiency of the proposed hybrid algorithm is demonstrated through empirical simulation study and benchmarking against recent techniques in literature. Results obtained reveal the promising potential of the proposed SSSOS algorithm for resource discovery in a cloud environment.     

A cost‐efficient resource provisioning algorithm for DHT‐based cloud storage systems

Personal cloud storage provides users with convenient data access services. Service providers build distributed storage systems by utilizing cloud resources with distributed hash table (DHT), so as to enhance system scalability. Efficient resource provisioning could not only guarantee service performance, but help providers to save cost. However, the interactions among servers in a DHT‐based cloud storage system depend on the routing process, which makes its execution logic more complicated than traditional multi‐tier applications. In addition, production data centers often comprise heterogeneous machines with different capacities. Few studies have fully considered the heterogeneity of cloud resources, which brings new challenges to resource provisioning. To address these challenges, this paper presents a novel resource provisioning model for service providers. The model utilizes queuing network for analysis of both service performance and cost estimation. Then, the problem is defined as a cost optimization with performance constraints. We propose a cost‐efficient algorithm to decompose the original problem into a sub‐optimization one. Furthermore, we implement a prototype system on top of an infrastructure platform built with OpenStack. It has been deployed in our campus network. Based on real‐world traces collected from our system and Dropbox, we validate the efficiency of our proposed algorithms by extensive experiments. Copyright © 2016 John Wiley & Sons, Ltd.     

A trustworthy agent-based encrypted access control method for mobile cloud computing environment

To strengthen the security of access control protocols for mobile cloud environment, dynamic attributes of mobile devices are used. The weak or disconnection issue of the mobile network is a critical task to deal with. The proposed approach provides access control as well as data confidentiality using dynamic attributes encryption. The pairs of mobile agents are used to deal with the issue of network connection. The secret key is distributed using the anonymous key-issuing protocol which preserves the anonymity of the user. The approach is implemented in a real mobile cloud environment, and the performance under various parameters is evaluated.     

云计算环境下兼顾买卖双方利益的双向拍卖资源分配算法

针对当前云计算环境下的资源分配算法不能充分考虑买卖双方利益的问题,本文提出了一种适用于云计算环境的组合双向拍卖资源分配模型。首先,初始化云经纪人列表和供应商报价列表,拍卖人通知拍卖参与者拍卖开始;然后,根据属性值按升序排序云经纪人请求和云服务供应商报价列表,从而确定投标获胜者;最后,获胜的云经纪人向相关云服务供应商发送任务并支付费用,云服务商执行任务。仿真实验使用CloudSim原型化,在基于Java的仿真云环境中从经济角度进行了效率评估。仿真结果表明,本文模型适用于云环境中的资源分配,在经济上非常有效。相比其他的现有模型,本文模型更能鼓励参与者在买卖双方公平公正的前提下根据真实估值竞购资源。     

Optimising resource costs of cloud computing for education

There is a growing interest around the utilisation of cloud computing in education. As organisations involved in the area typically face severe budget restrictions, there is a need for cost optimisation mechanisms that explore unique features of digital learning environments. In this work, we introduce a method based on Maximum Likelihood Estimation that considers heterogeneity of IT infrastructure in order to devise resource allocation plans that maximise platform utilisation for educational environments. We performed experiments using modelled datasets from real digital teaching solutions and obtained cost reductions of up to 30%, compared with conservative resource allocation strategies.     

云环境下一种基于资源分离的ATN模型

针对云环境下自动信任协商过程中存在的协商效率低、敏感信息泄露等问题,提出一种基于资源分离的自动信任协商模型（ Resource Separation Based ATN in Cloud ）CRSBATN。该模型利用云环境分离资源拥有者和资源,使访问者不能直接与拥有者建立关系,只有能解密访问控制策略的访问者才能获得拥有者的信息,从而可以过滤大部分不符合策略的访问者,减少大量非必要的计算开销;提出使用ABS-OSBE算法对资源访问者的属性真实性进行一致性校验,并给出自动信任协商模型框架和协商策略形式化描述,最后实验结果表明该模型能够降低协商开销,提升隐私保护能力。     

More than bin packing: Dynamic resource allocation strategies in cloud data centers

Resource allocation strategies in virtualized data centers have received considerable attention recently as they can have substantial impact on the energy efficiency of a data center. This led to new decision and control strategies with significant managerial impact for IT service providers. We focus on dynamic environments where virtual machines need to be allocated and deallocated to servers over time. Simple bin packing heuristics have been analyzed and used to place virtual machines upon arrival. However, these placement heuristics can lead to suboptimal server utilization, because they cannot consider virtual machines, which arrive in the future. We ran extensive lab experiments and simulations with different controllers and different workloads to understand which control strategies achieve high levels of energy efficiency in different workload environments. We found that combinations of placement controllers and periodic reallocations achieve the highest energy efficiency subject to predefined service levels. While the type of placement heuristic had little impact on the average server demand, the type of virtual machine resource demand estimator used for the placement decisions had a significant impact on the overall energy efficiency.     

An efficient dynamic decision-based task optimization and scheduling approach for microservice-based cost management in mobile cloud computing applications

The use of smartphones and mobile devices has increased significantly, as have Mobile Cloud Applications based on cloud computing. These applications are used in various fields, including Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. While existing cloud-based frameworks provide such services on Virtual Machines, they incur problems such as overhead, lengthy boot time, and high costs. To address these issues, the paper proposes a Dynamic Decision-Based Task Scheduling Approach for Microservice-based Mobile Cloud Computing Applications (MSCMCC) that can run delay-sensitive applications and mobility with less cost than existing approaches. The study focuses on Task Offloading problems on heterogeneous Mobile Cloud servers. It proposes a Task Offloading and Microservices based Computational Offloading (TSMCO) framework to solve Task Scheduling in steps such as Resource Matching, Task Sequencing, and Task Offloading. Experimental results show that the proposed MSCMCC and TSMCO enhance Mobile Server Utilization while minimizing costs and improving boot time, resource utilization, and task arrival time for various applications. Specifically, the proposed system effectively reduces the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.     

Joint optimization method for task scheduling time and energy consumption in mobile cloud computing environment

An unheard of growth in mobile data traffic has drawn attention from academia and industry. Mobile cloud computing is an emerging computing paradigm combining cloud computing and mobile networks to alleviate resource-constrained limitations of mobile devices, which can greatly improve network quality of service and efficiency to make good use of available network resource. Mobile cloud computing not only inherits the advantages of strong computing capacity and massive storage of cloud computing, but also overcomes the time and geographical restrictions, bringing benefits for mobile users to offload complex computation to powerful cloud servers for execution anytime and anywhere. To this end, an optimal task workflow scheduling scheme is proposed for the mobile devices, based on the dynamic voltage and frequency scaling technique and the whale optimization algorithm. Through considering three factors: task execution position, task execution sequence, and operating voltage and frequency of mobile devices, this study makes a tradeoff between performance and energy consumption by solving the joint optimization for task completion time and energy consumption simultaneously. Finally, a series of extensive simulation results has demonstrated and verified the scheme has distinguished performance in terms of efficiency and operational cost, providing feasible solutions to similar optimization problems of mobile cloud computing.     

A reliable recommendation and privacy-preserving based cross-layer reputation mechanism for mobile cloud computing

Mobile cloud computing (MCC) is gaining popularity due to anywhere anytime data access. However, at the same time it also introduces the new privacy and security threats that have become an obstacle to the widespread use and popularity of MCC. In this paper, we propose a reliable recommendation and privacy preserving based cross-layer reputation mechanism (RP-CRM) to provide secure and privacy-aware communication process in wireless mesh networks (WMNs) based MCC (WM-MCC). RP-CRM integrates the cross-layer design with recommendation reputation reliability evaluation mechanism and the privacy preserving scheme to identify and manage the internal malicious nodes and protect the security and privacy against internal multi-layer attack, bad mouthing attack and information disclosure attack. Simulation results and performance analysis demonstrate that RP-CRM can provide rapid and accurate malicious node identification and management, and provide security and privacy protection against aforementioned attacks more effectively and efficiently.     

Impact of user patience on auto-scaling resource capacity for cloud services

An important feature of most cloud computing solutions is auto-scaling, an operation that enables dynamic changes on resource capacity. Auto-scaling algorithms generally take into account aspects such as system load and response time to determine when and by how much a resource pool capacity should be extended or shrunk. In this article, we propose a scheduling algorithm and auto-scaling triggering strategies that explore user patience, a metric that estimates the perception end-users have from the Quality of Service (QoS) delivered by a service provider based on the ratio between expected and actual response times for each request. The proposed strategies help reduce costs with resource allocation while maintaining perceived QoS at adequate levels. Results show reductions on resource-hour consumption by up to approximately 9% compared to traditional approaches.     

Automatic offloading of mobile applications into the cloud by means of genetic programming

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.