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.     

Matrix based proactive resource provisioning in mobile cloud environment

Mobile cloud computing is a dynamic, virtually scalable and network based computing environment where mobile device acts as a thin client and applications run on remote cloud servers. Mobile cloud computing resources required by different users depend on their respective personalized applications. Therefore, efficient resource provisioning in mobile clouds is an important aspect that needs special attention in order to make the mobile cloud computing a highly optimized entity. This paper proposes an adaptive model for efficient resource provisioning in mobile clouds by predicting and storing resource usages in a two dimensional matrix termed as resource provisioning matrix. These resource provisioning matrices are further used by an independent authority to predict future required resources using artificial neural network. Independent authority also checks and verifies resource usage bill computed by cloud service provider using resource provisioning matrices. It provides cost computation reliability for mobile customers in mobile cloud environment. Proposed model is implemented on Hadoop using three different applications. Results indicate that proposed model provides better mobile cloud resources utilization as well as maintains quality of service for mobile customer. Proposed model increases battery life of mobile device and decreases data usage cost for mobile customer.     

Large-scale virtual machines provisioning in clouds: challenges and approaches

The scale of global data center market has been explosive in recent years. As the market grows, the demand for fast provisioning of the virtual resources to support elastic, manageable, and economical computing over the cloud becomes high. Fast provisioning of large-scale virtual machines (VMs), in particular, is critical to guarantee quality of service (QoS). In this paper, we systematically review the existing VM provisioning schemes and classify them in three main categories. We discuss the features and research status of each category, and introduce two recent solutions, VMThunder and VMThunder+, both of which can provision hundreds of VMs in seconds.     

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.     

Dynamic bandwidth provisioning using ARIMA-based traffic forecasting for Mobile WiMAX

With fast proliferation of QoS-enabled wireless packet networks, need for effective QoS control is increasing. In this paper, we focus on QoS provisioning in Mobile WiMAX access service network (ASN). We investigate a dynamic bandwidth provisioning method that can help to increase resource utilization. Our approach consists of two stages: traffic forecasting, followed by bandwidth provisioning. For the first stage, we use auto-regressive integrated moving average (ARIMA) model to forecast traffic based on online measurement. For the second stage, we use a bandwidth provisioning scheme that allocates bandwidths depending on the traffic forecasting. We modeled our problem as a Fractional Knapsack Problem for which we used a greedy algorithm in order to find an approximate solution. Through simulation studies with real-world data sets, we found that our approach could increase the bandwidth for the real-time traffic class and guarantee adequate service quality for the nonreal-time traffic class as well, while maximizing resource utilization.     

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.     

Autonomic resource provisioning for multilayer cloud applications with K-nearest neighbor resource scaling and priority-based resource allocation

Providing a pool of various resources and services to customers on the Internet in exchanging money has made cloud computing as one of the most popular technologies. Management of the provided resources and services at the lowest cost and maximum profit is a crucial issue for cloud providers. Thus, cloud providers proceed to auto-scale the computing resources according to the users' requests in order to minimize the operational costs. Therefore, the required time and costs to scale-up and down computing resources are considered as one of the major limits of scaling which has made this issue an important challenge in cloud computing. In this paper, a new approach is proposed based on MAPE-K loop to auto-scale the resources for multilayered cloud applications. K-nearest neighbor (K-NN) algorithm is used to analyze and label virtual machines and statistical methods are used to make scaling decision. In addition, a resource allocation algorithm is proposed to allocate requests on the resources. Results of the simulation revealed that the proposed approach results in operational costs reduction, as well as improving the resource utilization, response time, and profit.     

A hybrid evolutionary algorithm for task scheduling and data assignment of data-intensive scientific workflows on clouds

A growing number of data- and compute-intensive experiments have been modeled as scientific workflows in the last decade. Meanwhile, clouds have emerged as a prominent environment to execute this type of workflows. In this scenario, the investigation of workflow scheduling strategies, aiming at reducing its execution times, became a top priority and a very popular research field. However, few work consider the problem of data file assignment when solving the task scheduling problem. Usually, a workflow is represented by a graph where nodes represent tasks and the scheduling problem consists in allocating tasks to machines to be executed at a predefined time aiming at reducing the makespan of the whole workflow. In this article, we show that the scheduling of scientific workflows can be improved when both task scheduling and the data file assignment problems are treated together. Thus, we propose a new workflow representation, where nodes of the workflow graph represent either tasks or data files, and define the Task Scheduling and Data Assignment Problem (TaSDAP), considering this new model. We formulated this problem as an integer programming problem. Moreover, a hybrid evolutionary algorithm for solving it, named HEA-TaSDAP, is also introduced. To evaluate our approach we conducted two types of experiments: theoretical and practical ones. At first, we compared HEA-TaSDAP with the solutions produced by the mathematical formulation and by other works from related literature. Then, we considered real executions in Amazon EC2 cloud using a real scientific workflow use case (SciPhy for phylogenetic analyses). In all experiments, HEA-TaSDAP outperformed the other classical approaches from the related literature, such as Min–Min and HEFT.     

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.     

Semantic service provisioning for smart objects: Integrating IoT applications into the web

Internet of Things (IoT) applications residing on the Web are the next logical development of the recent effort from academia and industry to design and standardize new communication protocols for smart objects. This paper proposes the service provisioning architecture for smart objects with semantic annotation to enables the integration of IoT applications into the Web. We aim to bring smart object services to the Web and make them accessible by plenty of existing Web APIs in consideration of its constraints such as limited resources (ROM, RAM, and CPU), low-power microcontrollers, and low-bitrate communication links.     

混合云环境下基于属性的密文策略加密方案

针对现有云存储的数据和访问控制的安全性不高,从而造成用户存储的敏感信息被盗取的现象,结合现有的基于密文策略属性加密（CP-ABE）方案和数据分割的思想,提出了一个基于混合云的高效数据隐私保护模型。首先根据用户数据的敏感程度将数据合理分割成不同敏感级别的数据块,将分割后的数据存储在不同的云平台上,再根据数据的安全级别,进行不同强度的加密技术进行数据加密。同时在敏感信息解密阶段采取“先匹配后解密”的方法,并对算法进行了优化,最后用户进行一个乘法运算解密得到明文。在公有云中对1 Gb数据进行对称加密,较单节点提高了效率一倍多。实验结果表明：该方案可以有效保护云存储用户的隐私数据,同时降低了系统的开销,提高了灵活性。     

A benefit-aware on-demand provisioning approach for multi-tier applications in cloud computing

Dynamic resource provisioning is a challenging technique to meet the service level agreement (SLA) requirements of multi-tier applications in virtualization-based cloud computing. Prior efforts have addressed this challenge based on either a cost-oblivious approach or a cost-aware approach. However, both approaches may suffer frequent SLA violations under flash crowd conditions. Because they ignore the benefit gained that a multi-tier application continuously guarantees the SLA in the new configuration. In this paper, we propose a benefit-aware approach with feedback control theory to solve this problem. Experimental results based on live workload traces show that our approach can reduce resource provisioning cost by as much as 30% compared with a costoblivious approach, and can effectively reduce SLA violations compared with a cost-aware approach.     

Optimizing virtual machine allocation for parallel scientific workflows in federated clouds

Cloud computing has established itself as an interesting computational model that provides a wide range of resources such as storage, databases and computing power for several types of users. Recently, the concept of cloud computing was extended with the concept of federated clouds where several resources from different cloud providers are inter-connected to perform a common action (e.g. execute a scientific workflow). Users can benefit from both single-provider and federated cloud environment to execute their scientific workflows since they can get the necessary amount of resources on demand. In several of these workflows, there is a demand for high performance and parallelism techniques since many activities are data and computing intensive and can execute for hours, days or even weeks. There are some Scientific Workflow Management Systems (SWfMS) that already provide parallelism capabilities for scientific workflows in single-provider cloud. Most of them rely on creating a virtual cluster to execute the workflow in parallel. However, they also rely on the user to estimate the amount of virtual machines to be allocated to create this virtual cluster. Most SWfMS use this initial virtual cluster configuration made by the user for the entire workflow execution. Dimensioning the virtual cluster to execute the workflow in parallel is then a top priority task since if the virtual cluster is under or over dimensioned it can impact on the workflow performance or increase (unnecessarily) financial costs. This dimensioning is far from trivial in a single-provider cloud and specially in federated clouds due to the huge number of virtual machine types to choose in each location and provider. In this article, we propose an approach named GraspCC-fed to produce the optimal (or near-optimal) estimation of the amount of virtual machines to allocate for each workflow. GraspCC-fed extends a previously proposed heuristic based on GRASP for executing standalone applications to consider scientific workflows executed in both single-provider and federated clouds. For the experiments, GraspCC-fed was coupled to an adapted version of SciCumulus workflow engine for federated clouds. This way, we believe that GraspCC-fed can be an important decision support tool for users and it can help determining an optimal configuration for the virtual cluster for parallel cloud-based scientific workflows.     

Elastic resource provisioning in hybrid mobile cloud for computationally intensive mobile applications

The paper addresses the integration of hybrid cloud with mobile applications. The challenge about hybrid mobile cloud resource provisioning is the trade-offs between energy consumption, performance provided to users and how resources, such as processing power and network, are being utilized. The proposed elastic hybrid mobile cloud resource provisioning model is jointly optimized to improve mobile user experience within the constraints of available resources and user QoS requirement. The paper presents the system utility of hybrid cloud system involving local cloud and public cloud infrastructure. From the perspectives of both mobile applications and cloud providers, the proposed system utility is optimized to improve the performance of mobile applications and the utilization of cloud resources. The proposed elastic hybrid mobile cloud resource provisioning algorithm includes two sub-algorithms. To evaluate and validate performance of the proposed algorithm, a series of experiments are conducted. The comparison results and analyses are discussed. The experimental results show the improvement to previous works.     

Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing

Today, almost everyone is connected to the Internet and uses different Cloud solutions to store, deliver and process data. Cloud computing assembles large networks of virtualized services such as hardware and software resources. The new era in which ICT penetrated almost all domains (healthcare, aged-care, social assistance, surveillance, education, etc.) creates the need of new multimedia content-driven applications. These applications generate huge amount of data, require gathering, processing and then aggregation in a fault-tolerant, reliable and secure heterogeneous distributed system created by a mixture of Cloud systems (public/private), mobile devices networks, desktop-based clusters, etc. In this context dynamic resource provisioning for Big Data application scheduling became a challenge in modern systems. We proposed a resource-aware hybrid scheduling algorithm for different types of application: batch jobs and workflows. The proposed algorithm considers hierarchical clustering of the available resources into groups in the allocation phase. Task execution is performed in two phases: in the first, tasks are assigned to groups of resources and in the second phase, a classical scheduling algorithm is used for each group of resources. The proposed algorithm is suitable for Heterogeneous Distributed Computing, especially for modern High-Performance Computing (HPC) systems in which applications are modeled with various requirements (both IO and computational intensive), with accent on data from multimedia applications. We evaluate their performance in a realistic setting of CloudSim tool with respect to load-balancing, cost savings, dependency assurance for workflows and computational efficiency, and investigate the computing methods of these performance metrics at runtime.     

Hints to improve automatic load balancing with LeWI for hybrid applications

The DLB (Dynamic Load Balancing) library and LeWI (LEnd When Idle) algorithm provide a runtime solution to deal with the load imbalance of parallel applications independently of the source of imbalance. DLB relies on the usage of hybrid programming models and exploits the malleability of the second level of parallelism to redistribute computation power across processes.     

Profit maximization based task scheduling in hybrid clouds using whale optimization technique

ABSTRACT

In cloud computing system, task scheduling plays an important key role. The tasks provided by the user to allocate in cloud have to pay for the share of resources that are used by them. The requirement of task scheduling in the cloud environment has become more and more complex, and the amount of resources and tasks is growing rapidly. Therefore, an efficient task-scheduling algorithm is necessary for allocating the task efficiently in the cloud, which can achieve minimum resource utilization, minimum processing time, high efficiency, and maximum profit. In hybrid clouds to maximize the profit of a private cloud while guaranteeing the service delay bound of delay-tolerant tasks is studied in this article. Here, a new metaheuristic technique inspired from the bubble-net hunting technique of humpback whales, namely whale optimization algorithm (WOA), has been applied to solve the task-scheduling problem. Then WOA algorithm is compared with existing algorithms such as artificial bee colony algorithm (ABC) and Genetic algorithm (GA). The experimental result shows that the proposed WOA algorithm greatly increases the efficiency and achieves maximum profit for the private cloud.     

A multi-objective reinforcement learning algorithm for deadline constrained scientific workflow scheduling in clouds

Recently, a growing number of scientific applications have been migrated into the cloud. To deal with the problems brought by clouds, more and more researchers start to consider multiple optimization goals in workflow scheduling. However, the previous works ignore some details, which are challenging but essential. Most existing multi-objective workflow scheduling algorithms overlook weight selection, which may result in the quality degradation of solutions. Besides, we find that the famous partial critical path (PCP) strategy, which has been widely used to meet the deadline constraint, can not accurately reflect the situation of each time step. Workflow scheduling is an NP-hard problem, so self-optimizing algorithms are more suitable to solve it.In this paper, the aim is to solve a workflow scheduling problem with a deadline constraint. We design a deadline constrained scientific workflow scheduling algorithm based on multi-objective reinforcement learning (RL) called DCMORL. DCMORL uses the Chebyshev scalarization function to scalarize its Q-values. This method is good at choosing weights for objectives. We propose an improved version of the PCP strategy calledMPCP. The sub-deadlines in MPCP regularly update during the scheduling phase, so they can accurately reflect the situation of each time step. The optimization objectives in this paper include minimizing the execution cost and energy consumption within a given deadline. Finally, we use four scientific workflows to compare DCMORL and several representative scheduling algorithms. The results indicate that DCMORL outperforms the above algorithms. As far as we know, it is the first time to apply RL to a deadline constrained workflow scheduling problem.     

Affinity-aware modeling of CPU usage with communicating virtual machines

Use of virtualization in Infrastructure as a Service (IaaS) environments provides benefits to both users and providers: users can make use of resources following a pay-per-use model and negotiate performance guarantees, whereas providers can provide quick, scalable and hardware-fault tolerant service and also utilize resources efficiently and economically. With increased acceptance of virtualization-based systems, an important issue is that of virtual machine migration-enabled consolidation and dynamic resource provisioning. Effective resource provisioning can result in higher gains for users and providers alike. Most hosted applications (for example, web services) are multi-tiered and can benefit from their various tiers being hosted on different virtual machines. These mutually communicating virtual machines may get colocated on the same physical machine or placed on different machines, as part of consolidation and flexible provisioning strategies. In this work, we argue the need for network affinity-awareness in resource provisioning for virtual machines. First, we empirically quantify the change in CPU resource usage due to colocation or dispersion of communicating virtual machines for both Xen and KVM virtualization technologies. Next, we build models based on these empirical measurements to predict the change in CPU utilization when transitioning between colocated and dispersed placements. Due to the modeling process being independent of virtualization technology and specific applications, the resultant model is generic and application-agnostic. Via extensive experimentation, we evaluate the applicability of our models for synthetic and benchmark application workloads. We find that the models have high prediction accuracy — maximum prediction error within 2% absolute CPU usage.