A cost-aware auto-scaling approach using the workload prediction in service clouds

Service clouds are distributed infrastructures which deploys communication services in clouds. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand computing power and storage capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a novel service cloud architecture is presented, and a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The auto-scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user Service Level Agreement (SLA) while keeping scaling costs low.     

Market‐based grid resource co‐allocation and reservation for applications with hard deadlines

Grid computing technology enables the creation of large‐scale IT infrastructures that are shared across organizational boundaries. In such shared infrastructures, conflicts between user requirements are common and originate from the selfish actions that users perform when formulating their service requests. The introduction of economic principles in grid resource management offers a promising way of dealing with these conflicts. We develop and analyze both a centralized and a decentralized algorithm for economic grid resource management in the context of compute bound applications with deadline‐based quality of service requirements and non‐migratable workloads. Through the use of reservations, we co‐allocate resources across multiple providers in order to ensure that applications finish within their deadline. An evaluation of both algorithms is presented and their performance in terms of realized user value is compared with an existing market‐based resource management algorithm. We establish that our algorithms, which operate under a more realistic workload model, can closely approximate the performance of this algorithm. We also quantify the effect of allowing local workload preemption and different scheduling heuristics on the realized user value. Copyright © 2009 John Wiley & Sons, Ltd.     

A hybrid wavelet decomposer and GMDH-ELM ensemble model for Network function virtualization workload forecasting in cloud computing

Nowadays Network function virtualization (NFV) has drawn immense attention from many cloud providers because of its benefits. NFV enables networks to virtualize node functions such as firewalls, load balancers, and WAN accelerators, conventionally running on dedicated hardware, and instead implements them as virtual software components on standard servers, switches, and storages. In order to provide NFV resources and meet Service Level Agreement (SLA) conditions, minimize energy consumption and utilize physical resources efficiently, resource allocation in the cloud is an essential task. Since network traffic is changing rapidly, an optimized resource allocation strategy should consider resource auto-scaling property for NFV services. In order to scale cloud resources, we should forecast the NFV workload. Existing forecasting methods are providing poor results for highly volatile and fluctuating time series such as cloud workloads. Therefore, we propose a novel hybrid wavelet time series decomposer and GMDH-ELM ensemble method named Wavelet-GMDH-ELM (WGE) for NFV workload forecasting which predicts and ensembles workload in different time-frequency scales. We evaluate the WGE model with three real cloud workload traces to verify its prediction accuracy and compare it with state of the art methods. The results show the proposed method provides better average prediction accuracy. Especially it improves Mean Absolute Percentage Error (MAPE) at least 8% compared to the rival forecasting methods such as support vector regression (SVR) and Long short term memory (LSTM).     

An efficient resource provisioning approach for analyzing cloud workloads: a metaheuristic-based clustering approach

With the recent advancements in Internet-based computing models, the usage of cloud-based applications to facilitate daily activities is significantly increasing and is expected to grow further. Since the submitted workloads by users to use the cloud-based applications are different in terms of quality of service (QoS) metrics, it requires the analysis and identification of these heterogeneous cloud workloads to provide an efficient resource provisioning solution as one of the challenging issues to be addressed. In this study, we present an efficient resource provisioning solution using metaheuristic-based clustering mechanism to analyze cloud workloads. The proposed workload clustering approach used a combination of the genetic algorithm and fuzzy C-means technique to find similar clusters according to the user’s QoS requirements. Then, we used a gray wolf optimizer technique to make an appropriate scaling decision to provide the cloud resources for serving of cloud workloads. Besides, we design an extended framework to show interaction between users, cloud providers, and resource provisioning broker in the workload clustering process. The simulation results obtained under real workloads indicate that the proposed approach is efficient in terms of CPU utilization, elasticity, and the response time compared with the other approaches.

     

Effects of Reducing VMs Management Times on Elastic Applications

Cloud infrastructures provide computing resources to applications in the form of Virtual Machines (VMs). Many applications deployed in cloud resources have an elastic behavior, that is, they change the number of servers (VMs) dynamically, adapting the application to the workload. Scaling-out and scaling-in operations are managed by an auto-scaler module, which can be reactive (adapting the number of VMs to the current workload) or proactive (adapting to the expected future workload). The cloud infrastructure provides a management interface to create (deploy) and destroy (shutdown) server instances, operations that require some time to complete. In this work we evaluate to what extent the reduction of the time required by VM management operations, namely deployment and shutdown, impacts the performance of applications and the behavior of reactive and proactive auto-scaling policies. After establishing several ideal boundaries on the use of resources, we carry out a set of experiments that show how short management times drastically reduce the use of resources, while allowing the application to operate within the required performance bounds.     

Artificial bee colony based energy‐aware resource utilization technique for cloud computing

Cloud computing is a form of distributed computing, which promises to deliver reliable services through next‐generation data centers that are built on virtualized compute and storage technologies. It is becoming truly ubiquitous and with cloud infrastructures becoming essential components for providing Internet services, there is an increase in energy‐hungry data centers deployed by cloud providers. As cloud providers often rely on large data centers to offer the resources required by the users, the energy consumed by cloud infrastructures has become a key environmental and economical concern. Much energy is wasted in these data centers because of under‐utilized resources hence contributing to global warming. To conserve energy, these under‐utilized resources need to be efficiently utilized and to achieve this, jobs need to be allocated to the cloud resources in such a way so that the resources are used efficiently and there is a gain in performance and energy efficiency. In this paper, a model for energy‐aware resource utilization technique has been proposed to efficiently manage cloud resources and enhance their utilization. It further helps in reducing the energy consumption of clouds by using server consolidation through virtualization without degrading the performance of users’ applications. An artificial bee colony based energy‐aware resource utilization technique corresponding to the model has been designed to allocate jobs to the resources in a cloud environment. The performance of the proposed algorithm has been evaluated with the existing algorithms through the CloudSim toolkit. The experimental results demonstrate that the proposed technique outperforms the existing techniques by minimizing energy consumption and execution time of applications submitted to the cloud. Copyright © 2014 John Wiley & Sons, Ltd.     

Performance evaluation of cloud computing platforms using statistical methods

Cloud computing is a very attractive research topic. Many studies have examined the infrastructure as a service and software as a service aspects of cloud computing; however, few studies have focused on platform as a service (PaaS). According to recent reports, demand for enterprise PaaS solutions will increase continuously. However, different sectors require different types of PaaS applications and computing resources. Therefore, an evaluation and ranking framework for PaaS solutions according to application needs is required. To address this need, this study presents the most essential aspects of PaaS solutions and provides a framework for evaluating the performance of PaaS providers. It also proposes a suitable set of benchmarking algorithms that can help determine the most appropriate PaaS provider based on different resource needs and application requirements. Performance evaluations of three well-known cloud computing PaaS providers were conducted using the analytic hierarchy process and the logic scoring of preference methods.     

SLA-driven dynamic cloud resource management

As the size and complexity of Cloud systems increase, the manual management of these solutions becomes a challenging issue as more personnel, resources and expertise are needed. Service Level Agreement (SLA)-aware autonomic cloud solutions enable managing large scale infrastructure management meanwhile supporting multiple dynamic requirement from users. This paper contributes to these topics by the introduction of Cloudcompaas, a SLA-aware PaaS Cloud platform that manages the complete resource lifecycle. This platform features an extension of the SLA specification WS-Agreement, tailored to the specific needs of Cloud Computing. In particular, Cloudcompaas enables Cloud providers with a generic SLA model to deal with higher-level metrics, closer to end-user perception, and with flexible composition of the requirements of multiple actors in the computational scene. Moreover, Cloudcompaas provides a framework for general Cloud computing applications that could be dynamically adapted to correct the QoS violations by using the elasticity features of Cloud infrastructures. The effectiveness of this solution is demonstrated in this paper through a simulation that considers several realistic workload profiles, where Cloudcompaas achieves minimum cost and maximum efficiency, under highly heterogeneous utilization patterns.     

A probabilistic model for performance analysis of cloud infrastructures

Analyzing the quantitative performance plays an important role in understanding and improving the quality of cloud computing systems and cloud‐based applications. In cloud computing, service requests from users go through numerous provider‐specific steps from the instant it is submitted to when the requested service is fully delivered. Quantitative performance analysis is not an easy task because of the complexity of cloud provisioning control flows and the increasing scale and complexity of real‐world cloud infrastructures. This work proposes a probabilistic queuing network‐based model for the performance analysis of cloud infrastructures. It considers expected task completion time and rejection probability as the performance metrics. Experimental performance data suggest the correctness of the proposed model. Copyright © 2015 John Wiley & Sons, Ltd.     

Preemptive cloud resource allocation modeling of processing jobs

Cloud computing allows execution and deployment of different types of applications such as interactive databases or web-based services which require distinctive types of resources. These applications lease cloud resources for a considerably long period and usually occupy various resources to maintain a high quality of service (QoS) factor. On the other hand, general big data batch processing workloads are less QoS-sensitive and require massively parallel cloud resources for short period. Despite the elasticity feature of cloud computing, fine-scale characteristics of cloud-based applications may cause temporal low resource utilization in the cloud computing systems, while process-intensive highly utilized workload suffers from performance issues. Therefore, ability of utilization efficient scheduling of heterogeneous workload is one challenging issue for cloud owners. In this paper, addressing the heterogeneity issue impact on low utilization of cloud computing system, conjunct resource allocation scheme of cloud applications and processing jobs is presented to enhance the cloud utilization. The main idea behind this paper is to apply processing jobs and cloud applications jointly in a preemptive way. However, utilization efficient resource allocation requires exact modeling of workloads. So, first, a novel methodology to model the processing jobs and other cloud applications is proposed. Such jobs are modeled as a collection of parallel and sequential tasks in a Markovian process. This enables us to analyze and calculate the efficient resources required to serve the tasks. The next step makes use of the proposed model to develop a preemptive scheduling algorithm for the processing jobs in order to improve resource utilization and its associated costs in the cloud computing system. Accordingly, a preemption-based resource allocation architecture is proposed to effectively and efficiently utilize the idle reserved resources for the processing jobs in the cloud paradigms. Then, performance metrics such as service time for the processing jobs are investigated. The accuracy of the proposed analytical model and scheduling analysis is verified through simulations and experimental results. The simulation and experimental results also shed light on the achievable QoS level for the preemptively allocated processing jobs.     

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.     

SLA based resource allocation policies in autonomic environments

Nowadays, large service centers provide computational capacity to many customers by sharing a pool of IT resources. The service providers and their customers negotiate utility based Service Level Agreement (SLA) to determine the costs and penalties on the base of the achieved performance level. The system is often based on a multi-tier architecture to serve requests and autonomic techniques have been implemented to manage varying workload conditions. The service provider would like to maximize the SLA revenues, while minimizing its operating costs. The system we consider is based on a centralized network dispatcher which controls the allocation of applications to servers, the request volumes at various servers and the scheduling policy at each server. The dispatcher can also decide to turn ON or OFF servers depending on the system load. This paper designs a resource allocation scheduler for such multi-tier autonomic environments so as to maximize the profits associated with multiple class SLAs. The overall problem is NP-hard. We develop heuristic solutions by implementing a local-search algorithm. Experimental results are presented to demonstrate the benefits of our approach.     

A hybrid cloud controller for vertical memory elasticity: A control-theoretic approach

Web-facing applications are expected to provide certain performance guarantees despite dynamic and continuous workload changes. As a result, application owners are using cloud computing as it offers the ability to dynamically provision computing resources (e.g., memory, CPU) in response to changes in workload demands to meet performance targets and eliminates upfront costs. Horizontal, vertical, and the combination of the two are the possible dimensions that cloud application can be scaled in terms of the allocated resources. In vertical elasticity as the focus of this work, the size of virtual machines (VMs) can be adjusted in terms of allocated computing resources according to the runtime workload. A commonly used vertical resource elasticity approach is realized by deciding based on resource utilization, named capacity-based. While a new trend is to use the application performance as a decision making criterion, and such an approach is named performance-based. This paper discusses these two approaches and proposes a novel hybrid elasticity approach that takes into account both the application performance and the resource utilization to leverage the benefits of both approaches. The proposed approach is used in realizing vertical elasticity of memory (named as vertical memory elasticity), where the allocated memory of the VM is auto-scaled at runtime. To this aim, we use control theory to synthesize a feedback controller that meets the application performance constraints by auto-scaling the allocated memory, i.e., applying vertical memory elasticity. Different from the existing vertical resource elasticity approaches, the novelty of our work lies in utilizing both the memory utilization and application response time as decision making criteria. To verify the resource efficiency and the ability of the controller in handling unexpected workloads, we have implemented the controller on top of the Xen hypervisor and performed a series of experiments using the RUBBoS interactive benchmark application, under synthetic and real workloads including Wikipedia and FIFA. The results reveal that the hybrid controller meets the application performance target with better performance stability (i.e., lower standard deviation of response time), while achieving a high memory utilization (close to 83%), and allocating less memory compared to all other baseline controllers.     

Efficient network isolation and load balancing in multi-tenant HPC clusters

Multi-tenancy promises high utilization of available system resources and helps maintaining cost-effective operations for service providers. However, multi-tenant high-performance computing (HPC) infrastructures, like dynamic HPC clouds, bring unique challenges, both associated with providing performance isolation to the tenants, and achieving efficient load-balancing across the network fabric. Each tenant should experience predictable network performance, unaffected by the workload of other tenants. At the same time, it is equally important that the network links are balanced, avoiding network saturation. The network saturation can lead to unpredictable application performance, and a potential loss of profit for the cloud service providers.In this paper, we present two significant extensions to our previously proposed partition-aware fat-tree routing algorithm, pFTree, for InfiniBand-based HPC systems. First, we extend pFTree to incorporate provider defined partition-wise policies that govern how the nodes in different partitions are allowed to share network resources with each other. Second, we present a weighted version of the pFTree routing algorithm, that besides partitions, also takes node traffic characteristics into account to balance load across the network links more evenly. A comprehensive evaluation comprising both real-world experiments and simulations confirms the correctness and feasibility of the proposed extensions.     

A cloud middleware for assuring performance and high availability of soft real-time applications

Applications are increasingly being deployed in the cloud due to benefits stemming from economy of scale, scalability, flexibility and utility-based pricing model. Although most cloud-based applications have hitherto been enterprise-style, there is an emerging need for hosting real-time streaming applications in the cloud that demand both high availability and low latency. Contemporary cloud computing research has seldom focused on solutions that provide both high availability and real-time assurance to these applications in a way that also optimizes resource consumption in data centers, which is a key consideration for cloud providers. This paper makes three contributions to address this dual challenge. First, it describes an architecture for a fault-tolerant framework that can be used to automatically deploy replicas of virtual machines in data centers in a way that optimizes resources while assuring availability and responsiveness. Second, it describes the design of a pluggable framework within the fault-tolerant architecture that enables plugging in different placement algorithms for VM replica deployment. Third, it illustrates the design of a framework for real-time dissemination of resource utilization information using a real-time publish/subscribe framework, which is required by the replica selection and placement framework. Experimental results using a case study that involves a specific replica placement algorithm are presented to evaluate the effectiveness of our architecture.     

云计算弹性评测模型的研究与实现

当前,越来越多的企业开始将自己的核心业务与数据迁移到云上,其中很多业务需要相应的弹性服务来应对负载的实时变化,因此对弹性的评测正变得越来越重要,然而当前缺少一种较为全面的弹性评测方法。为解决以上问题,从资源分配、QoS、资源配置时间等多个角度,对云计算的弹性进行较为全面的分析,提出适用于供应商和用户两个角度的评测方法。在已有基础上,提出资源分配、资源配置时间两个方面的计算模型,并对现存的罚金模型进行改进。最后,在CloudStack云平台上,使用auto-scaling和scale-out两种弹性扩展策略,以TPC-W为负载验证了所提方法的有效性。     

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.     

Online machine learning for auto-scaling in the edge computing

The evolution of edge computing devices has enabled machine intelligence techniques to process data close to its producers (the sensors) and end-users. Although edge devices are usually resource-constrained, the distribution of processing services among several nodes enables a processing capacity similar to cloud environments. However, the edge computing environment is highly dynamic, impacting the availability of nodes in the distributed system. In addition, the processing workload for each node can change constantly. Thus, the scaling of processing services needs to be rapidly adjusted, avoiding bottlenecks or wasted resources while meeting the applications’ QoS requirements. This paper presents an auto-scaling subsystem for container-based processing services using online machine learning. The auto-scaling follows the MAPE-K control loop to dynamically adjust the number of containers in response to workload changes. We designed the approach for scenarios where the number of processing requests is unknown beforehand. We developed a hybrid auto-scaling mechanism that behaves reactively while a prediction online machine learning model is continuously trained. When the prediction model reaches a desirable performance, the auto-scaling acts proactively, using predictions to anticipate scaling actions. An experimental evaluation has demonstrated the feasibility of the architecture. Our solution achieved fewer service level agreement (SLA) violations and scaling operations to meet demand than purely reactive and no scaling approaches using an actual application workload. Also, our solution wasted fewer resources compared to the other techniques.     

ServiceSs: An Interoperable Programming Framework for the Cloud

The rise of virtualized and distributed infrastructures has led to new challenges to accomplish the effective use of compute resources through the design and orchestration of distributed applications. As legacy, monolithic applications are replaced with service-oriented applications, questions arise about the steps to be taken in order to maximize the usefulness of the infrastructures and to provide users with tools for the development and execution of distributed applications. One of the issues to be solved is the existence of multiple cloud solutions that are not interoperable, which forces the user to be locked to a specific provider or to continuously adapt applications. With the objective of simplifying the programmers challenges, ServiceSs provides a straightforward programming model and an execution framework that helps on abstracting applications from the actual execution environment. This paper presents how ServiceSs transparently interoperates with multiple providers implementing the appropriate interfaces to execute scientific applications on federated clouds.