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.     

QoS and preemption aware scheduling in federated and virtualized Grid computing environments

Resource provisioning is one of the challenges in federated Grid environments. In these environments each Grid serves requests from external users along with local users. Recently, this resource provisioning is performed in the form of Virtual Machines (VMs). The problem arises when there are insufficient resources for local users to be served. The problem gets complicated further when external requests have different QoS requirements. Serving local users could be solved by preempting VMs from external users which impose overheads on the system. Therefore, the question is how the number of VM preemptions in a Grid can be minimized. Additionally, how we can decrease the likelihood of preemption for requests with more QoS requirements. We propose a scheduling policy in InterGrid, as a federated Grid, which reduces the number of VM preemptions and dispatches external requests in a way that fewer requests with QoS constraints get affected by preemption. Extensive simulation results indicate that the number of VM preemptions is decreased at least by 60%, particularly, for requests with more QoS requirements.     

A Review of Dynamic Resource Management in Cloud Computing Environments

In a cloud environment, Virtual Machines (VMs) consolidation and resource provisioning are used to address the issues of workload fluctuations. VM consolidation aims to move the VMs from one host to another in order to reduce the number of active hosts and save power. Whereas resource provisioning attempts to provide additional resource capacity to the VMs as needed in order to meet Quality of Service (QoS) requirements. However, these techniques have a set of limitations in terms of the additional costs related to migration and scaling time, and energy overhead that need further consideration. Therefore, this paper presents a comprehensive literature review on the subject of dynamic resource management (i.e., VMs consolidation and resource provisioning) in cloud computing environments, along with an overall discussion of the closely related works. The outcomes of this research can be used to enhance the development of predictive resource management techniques, by considering the awareness of performance variation, energy consumption and cost to efficiently manage the cloud resources.     

Multi-objective Swarm Intelligence schedulers for online scientific Clouds

Cloud Computing is a promising paradigm for parallel computing. However, as Cloud-based services become more dynamic, resource provisioning in Clouds becomes more challenging. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. In a Cloud, an appropriate number of Virtual Machines (VM) is created and allocated in physical resources for executing jobs. This work focuses on the Infrastructure as a Service (IaaS) model where custom VMs are launched in appropriate hosts available in a Cloud to execute scientific experiments coming from multiple users. Finding optimal solutions to allocate VMs to physical resources is an NP-complete problem, and therefore many heuristics have been developed. In this work, we describe and evaluate two Cloud schedulers based on Swarm Intelligence (SI) techniques, particularly Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. We also perform a sensitivity analysis by varying the specific-parameter values of each algorithm to evaluate the impact on the performance of the two objective metrics. The intra-Cloud network traffic is also measured. Simulated experiments performed using CloudSim and job data from real scientific problems show that the use of SI-based techniques succeeds in balancing the studied metrics compared to Genetic Algorithms.     

Novel resource allocation algorithms to performance and energy efficiency in cloud computing

The rapid growth in demand for computational power has led to a shift to the cloud computing model established by large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy. Cloud providers must ensure that their service delivery is flexible to meet various consumer requirements. However, to support green computing, cloud providers also need to minimize the cloud infrastructure energy consumption while conducting the service delivery. In this paper, for cloud environments, a novel QoS-aware VMs consolidation approach is proposed that adopts a method based on resource utilization history of virtual machines. Proposed algorithms have been implemented and evaluated using CloudSim simulator. Simulation results show improvement in QoS metrics and energy consumption as well as demonstrate that there is a trade-off between energy consumption and quality of service in the cloud environment.     

Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)

The Cloud Computing paradigm focuses on the provisioning of reliable and scalable infrastructures (Clouds) delivering execution and storage services. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. The goal of this work is to study private Clouds to execute scientific experiments coming from multiple users, i.e., our work focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate hosts available in a Cloud. Then, correctly scheduling Cloud hosts is very important and it is necessary to develop efficient scheduling strategies to appropriately allocate VMs to physical resources. The job scheduling problem is however NP-complete, and therefore many heuristics have been developed. In this work, we describe and evaluate a Cloud scheduler based on Ant Colony Optimization (ACO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. Besides, the number of intra-Cloud network messages sent are evaluated. Simulated experiments performed using CloudSim and job data from real scientific problems show that our scheduler succeeds in balancing the studied metrics compared to schedulers based on Random assignment and Genetic Algorithms.     

URL: A unified reinforcement learning approach for autonomic cloud management

Cloud computing is emerging as an increasingly important service-oriented computing paradigm. Management is a key to providing accurate service availability and performance data, as well as enabling real-time provisioning that automatically provides the capacity needed to meet service demands. In this paper, we present a unified reinforcement learning approach, namely URL, to automate the configuration processes of virtualized machines and appliances running in the virtual machines. The approach lends itself to the application of real-time autoconfiguration of clouds. It also makes it possible to adapt the VM resource budget and appliance parameter settings to the cloud dynamics and the changing workload to provide service quality assurance. In particular, the approach has the flexibility to make a good trade-off between system-wide utilization objectives and appliance-specific SLA optimization goals. Experimental results on Xen VMs with various workloads demonstrate the effectiveness of the approach. It can drive the system into an optimal or near-optimal configuration setting in a few trial-and-error iterations.     

Resource provisioning based on preempting virtual machines in distributed systems

Resource provisioning is one of the main challenges in large‐scale distributed systems such as federated Grids. Recently, many resource management systems in these environments have started to use the lease abstraction and virtual machines (VMs) for resource provisioning. In the large‐scale distributed systems, resource providers serve requests from external users along with their own local users. The problem arises when there is not sufficient resources for local users, who have higher priority than external ones, and need resources urgently. This problem could be solved by preempting VM‐based leases from external users and allocating them to the local ones. However, preempting VM‐based leases entails side effects in terms of overhead time as well as increasing makespan of external requests. In this paper, we model the overhead of preempting VMs. Then, to reduce the impact of these side effects, we propose and compare several policies that determine the proper set of lease(s) for preemption. We evaluate the proposed policies through simulation as well as real experimentation in the context of InterGrid under different working conditions. Evaluation results demonstrate that the proposed preemption policies serve up to 72% more local requests without increasing the rejection ratio of external requests. Copyright © 2013 John Wiley & Sons, Ltd.     

Design and analysis of an efficient QoS improvement policy in cloud computing

Cloud computing offers the proficiency to use computing and storage resources on a metered basis and reduces the investments in Information Technology domain. This paper highlights a major research issue, i.e., providing good quality of service (QoS) to the cloud users. The QoS is associated with several parameters such as completion time, response time, turnaround time (TAT), waiting time (WT), bandwidth. A new cloudlet scheduling algorithm—improved round robin cloudlet scheduling algorithm—has been proposed which improves the TAT, WT and number of context switching. It enhances the resource utilization. The experimental results are obtained by CloudSim toolkit extending few base classes and compared by classical round robin algorithm.     

Toward Cloud Computing QoS Architecture: Analysis of Cloud Systems and Cloud Services

Cloud can be defined as a new computing paradigm that provides scalable, on-demand, and virtualized resources for users. In this style of computing, users can access a shared pool of computing resources which are provisioned with minimal management efforts of users. Yet there are some obstacles and concerns about the use of clouds. Guaranteeing quality of service (QoS) by service providers can be regarded as one of the main concerns for companies tending to use it. Service provisioning in clouds is based on service level agreements representing a contract negotiated between users and providers. According to this contract, if a provider cannot satisfy its agreed application requirements, it should pay penalties as compensation. In this paper, we intend to carry out a comprehensive survey on the models proposed in literature with respect to the implementation principles to address the QoS guarantee issue.      

An online valuation-based sealed winner-bid auction game for resource allocation and pricing in clouds

Cloud computing is able to allocate different resources as virtual machines (VMs) to users, who need only pay for the amount of resources used. Two of the challenges in clouds are resource allocation and pricing in such a way to satisfy both cloud providers and users. Existing allocation and pricing mechanisms cannot guarantee increased profits due to various reasons. A better solution to increase the satisfaction of both parties, which is supported by economic theory, is the employment of auction-based allocation and pricing mechanisms. In these mechanisms, cloud resources and services are awarded based on the highest bids, while winners receive the quality of services expected. However, most existing auction-based mechanisms are inefficient and cannot be used in real clouds due to high computational or communication overhead, the bid function’s time complexity, and/or its inaccurate estimates. In the present paper, a lightweight mechanism is introduced which can be utilized in the real-world application of clouds. The currently proposed mechanism is a winner-bid auction game that seals users’ bids by a multi-criteria valuation-based bid function and sends them to the auctioneer. During scheduling, the auctioneer awards VMs exclusively to users with the highest bids. The presented approach is an online auction whose main aim is to increase the profits of the provider and user from different criteria. While determining the Nash equilibrium, the current study specifies the prices to be paid by users in various cases and proves the truthfulness of the proposed method. Finally, the effectiveness of the presented mechanism is examined through extensive experiments on different simulation scenarios and actual workload data.     

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.     

A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds

Security is increasingly critical for various scientific workflows that are big data applications and typically take quite amount of time being executed on large-scale distributed infrastructures. Cloud computing platform is such an infrastructure that can enable dynamic resource scaling on demand. Nevertheless, based on pay-per-use and hourly-based pricing model, users should pay attention to the cost incurred by renting virtual machines (VMs) from cloud data centers. Meanwhile, workflow tasks are generally heterogeneous and require different instance series (i.e., computing optimized, memory optimized, storage optimized, etc.). In this paper, we propose a security and cost aware scheduling (SCAS) algorithm for heterogeneous tasks of scientific workflow in clouds. Our proposed algorithm is based on the meta-heuristic optimization technique, particle swarm optimization (PSO), the coding strategy of which is devised to minimize the total workflow execution cost while meeting the deadline and risk rate constraints. Extensive experiments using three real-world scientific workflow applications, as well as CloudSim simulation framework, demonstrate the effectiveness and practicality of our algorithm.     

Integrating QoS awareness with virtualization in cloud computing systems for delay-sensitive applications

Cloud computing provides scalable computing and storage resources over the Internet. These scalable resources can be dynamically organized as many virtual machines (VMs) to run user applications based on a pay-per-use basis. The required resources of a VM are sliced from a physical machine (PM) in the cloud computing system. A PM may hold one or more VMs. When a cloud provider would like to create a number of VMs, the main concerned issue is the VM placement problem, such that how to place these VMs at appropriate PMs to provision their required resources of VMs. However, if two or more VMs are placed at the same PM, there exists certain degree of interference between these VMs due to sharing non-sliceable resources, e.g. I/O resources. This phenomenon is called as the VM interference. The VM interference will affect the performance of applications running in VMs, especially the delay-sensitive applications. The delay-sensitive applications have quality of service (QoS) requirements in their data access delays. This paper investigates how to integrate QoS awareness with virtualization in cloud computing systems, such as the QoS-aware VM placement (QAVMP) problem. In addition to fully exploiting the resources of PMs, the QAVMP problem considers the QoS requirements of user applications and the VM interference reduction. Therefore, in the QAVMP problem, there are following three factors: resource utilization, application QoS, and VM interference. We first formulate the QAVMP problem as an Integer Linear Programming (ILP) model by integrating the three factors as the profit of cloud provider. Due to the computation complexity of the ILP model, we propose a polynomial-time heuristic algorithm to efficiently solve the QAVMP problem. In the heuristic algorithm, a bipartite graph is modeled to represent all the possible placement relationships between VMs and PMs. Then, the VMs are gradually placed at their preferable PMs to maximize the profit of cloud provider as much as possible. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed heuristic algorithm by comparing with other VM placement algorithms.     

PerficientCloudSim: a tool to simulate large-scale computation in heterogeneous clouds

The major reason for using a simulator, instead of a real test-bed, is to enable repeatable evaluation of large-scale cloud systems. CloudSim, the most widely used simulator, enables users to implement resource provisioning, and management policies. However, CloudSim does not provide support for: (i) interactive online services; (ii) platform heterogeneities; (iii) virtual machine migration modelling; and (iv) other essential models to abstract a real datacenter. This paper describes modifications needed in the classical CloudSim to support realistic experimentations that closely match experimental outcomes in a real system. We extend, and partially re-factor CloudSim to “PerficientCloudSim” in order to provide support for large-scale computation over heterogeneous resources. In the classical CloudSim, we add several classes for workload performance variations due to: (a) CPU heterogeneities; (b) resource contention; and (c) service migration. Through plausible assumptions, our empirical evaluation, using real workload traces from Google and Microsoft Azure clusters, demonstrates that “PerficientCloudSim” can reasonably simulate large-scale heterogeneous datacenters in respect of resource allocation and migration policies, resource contention, and platform heterogeneities. We discuss statistical methods to measure the accuracy of the simulated outcomes.

     

EMUSIM: an integrated emulation and simulation environment for modeling,evaluation, and validation of performance of Cloud computing applications

Cloud computing allows the deployment and delivery of application services for users worldwide. Software as a Service providers with limited upfront budget can take advantage of Cloud computing and lease the required capacity in a pay‐as‐you‐go basis, which also enables flexible and dynamic resource allocation according to service demand. One key challenge potential Cloud customers have before renting resources is to know how their services will behave in a set of resources and the costs involved when growing and shrinking their resource pool. Most of the studies in this area rely on simulation‐based experiments, which consider simplified modeling of applications and computing environment. In order to better predict service's behavior on Cloud platforms, we developed an integrated architecture that is based on both simulation and emulation. The proposed architecture, named EMUSIM, automatically extracts information from application behavior via emulation and then uses this information to generate the corresponding simulation model. We performed experiments using an image processing application as a case study and found that EMUSIM was able to accurately model such application via emulation and use the model to supply information about its potential performance in a Cloud provider. We also discuss our experience using EMUSIM for deploying applications in a real public Cloud provider. EMUSIM is based on an open source software stack and therefore it can be extended for analysis behavior of several other applications. Copyright © 2012 John Wiley & Sons, Ltd.     

Enhancing performance of failure-prone clusters by adaptive provisioning of cloud resources

In this paper, we investigate Cloud computing resource provisioning to extend the computing capacity of local clusters in the presence of failures. We consider three steps in the resource provisioning including resource brokering, dispatch sequences, and scheduling. The proposed brokering strategy is based on the stochastic analysis of routing in distributed parallel queues and takes into account the response time of the Cloud provider and the local cluster while considering computing cost of both sides. Moreover, we propose dispatching with probabilistic and deterministic sequences to redirect requests to the resource providers. We also incorporate checkpointing in some well-known scheduling algorithms to provide a fault-tolerant environment. We propose two cost-aware and failure-aware provisioning policies that can be utilized by an organization that operates a cluster managed by virtual machine technology, and seeks to use resources from a public Cloud provider. Simulation results demonstrate that the proposed policies improve the response time of users’ requests by a factor of 4.10 under a moderate load with a limited cost on a public Cloud.     

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.

     

Using Byzantine Fault-Tolerance to Improve Dependability in Federated Cloud Computing

Computing Clouds are typically characterized as large scale systems that exhibit dynamic behavior due to variance in workload. However, how exactly these characteristics affect the dependability of Cloud systems remains unclear. Furthermore provisioning reliable service within a Cloud federation, which involves the orchestration of multiple Clouds to provision service, remains an unsolved problem. This is especially true when considering the threat of Byzantine faults. Recently, the feasibility of Byzantine Fault-Tolerance within a single Cloud and federated Cloud environments has been debated. This paper investigates Cloud reliability and the applicability of Byzantine Fault-Tolerance in Cloud computing and introduces a Byzantine fault-tolerance framework that enables the deployment of applications across multiple Cloud administrations. An implementation of this framework has facilitated in-depth experiments producing results comparing the reliability of Cloud applications hosted in a federated Cloud to that of a single Cloud.     

ContainerCloudSim: An environment for modeling and simulation of containers in cloud data centers

Containers are increasingly gaining popularity and becoming one of the major deployment models in cloud environments. To evaluate the performance of scheduling and allocation policies in containerized cloud data centers, there is a need for evaluation environments that support scalable and repeatable experiments. Simulation techniques provide repeatable and controllable environments, and hence, they serve as a powerful tool for such purpose. This paper introduces ContainerCloudSim, which provides support for modeling and simulation of containerized cloud computing environments. We developed a simulation architecture for containerized clouds and implemented it as an extension of CloudSim. We described a number of use cases to demonstrate how one can plug in and compare their container scheduling and provisioning policies in terms of energy efficiency and SLA compliance. Our system is highly scalable as it supports simulation of large number of containers, given that there are more containers than virtual machines in a data center. Copyright © 2016 John Wiley & Sons, Ltd.