Allocation of Virtual Machines in Cloud Data Centers - A Survey of Problem Models and Optimization Algorithms

Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines (VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical resources incurs significant monetary costs and also environmental impact. Therefore, cloud providers must optimize the usage of physical resources by a careful allocation of VMs to hosts, continuously balancing between the conflicting requirements on performance and operational costs. In recent years, several algorithms have been proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable because of subtle differences in the used problem models. This paper surveys the used problem formulations and optimization algorithms, highlighting their strengths and limitations, also pointing out the areas that need further research in the future

Autonomous management of cost, performance, and resource uncertainty for migration of applications to infrastructure-as-a-service (IaaS) clouds

2014 Fall.Includes bibliographical references.Infrastructure-as-a-Service (IaaS) clouds abstract physical hardware to provide computing resources on demand as a software service. This abstraction leads to the simplistic view that computing resources are homogeneous and infinite scaling potential exists to easily resolve all performance challenges. Adoption of cloud computing, in practice however, presents many resource management challenges forcing practitioners to balance cost and performance tradeoffs to successfully migrate applications. These challenges can be broken down into three primary concerns that involve determining what, where, and when infrastructure should be provisioned. In this dissertation we address these challenges including: (1) performance variance from resource heterogeneity, virtualization overhead, and the plethora of vaguely defined resource types; (2) virtual machine (VM) placement, component composition, service isolation, provisioning variation, and resource contention for multitenancy; and (3) dynamic scaling and resource elasticity to alleviate performance bottlenecks. These resource management challenges are addressed through the development and evaluation of autonomous algorithms and methodologies that result in demonstrably better performance and lower monetary costs for application deployments to both public and private IaaS clouds. This dissertation makes three primary contributions to advance cloud infrastructure management for application hosting. First, it includes design of resource utilization models based on step-wise multiple linear regression and artificial neural networks that support prediction of better performing component compositions. The total number of possible compositions is governed by Bell's Number that results in a combinatorially explosive search space. Second, it includes algorithms to improve VM placements to mitigate resource heterogeneity and contention using a load-aware VM placement scheduler, and autonomous detection of under-performing VMs to spur replacement. Third, it describes a workload cost prediction methodology that harnesses regression models and heuristics to support determination of infrastructure alternatives that reduce hosting costs. Our methodology achieves infrastructure predictions with an average mean absolute error of only 0.3125 VMs for multiple workloads

CoLocateMe: Aggregation-based, energy, performance and cost aware VM placement and consolidation in heterogeneous IaaS clouds

In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users’ costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this article, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users’ costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is ∼9.61% more energy and ∼20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users’ costs

Utility-based Allocation of Resources to Virtual Machines in Cloud Computing

In recent years, cloud computing has gained a wide spread use as a new computing model that offers elastic resources on demand, in a pay-as-you-go fashion. One important goal of a cloud provider is dynamic allocation of Virtual Machines (VMs) according to workload changes in order to keep application performance to Service Level Agreement (SLA) levels, while reducing resource costs. The problem is to find an adequate trade-off between the two conflicting objectives of application performance and resource costs. In this dissertation, resource allocation solutions for this trade-off are proposed by expressing application performance and resource costs in a utility function. The proposed solutions allocate VM resources at the global data center level and at the local physical machine level by optimizing the utility function. The utility function, given as the difference between performance and costs, represents the profit of the cloud provider and offers the possibility to capture in a flexible and natural way the performance-cost trade-off. For global level resource allocation, a two-tier resource management solution is developed. In the first tier, local node controllers are located that dynamically allocate resource shares to VMs, so to maximize a local node utility function. In the second tier, there is a global controller that makes VM live migration decisions in order to maximize a global utility function. Experimental results show that optimizing the global utility function by changing the number of physical nodes according to workload maintains the performance at acceptable levels while reducing costs. To allocate multiple resources at the local physical machine level, a solution based on feed-back control theory and utility function optimization is proposed. This dynamically allocates shares to multiple resources of VMs such as CPU, memory, disk and network I/O bandwidth. In addressing the complex non-linearities that exist in shared virtualized infrastructures between VM performance and resource allocations, a solution is proposed that allocates VM resources to optimize a utility function based on application performance and power modelling. An Artificial Neural Network (ANN) is used to build an on- line model of the relationships between VM resource allocations and application performance, and another one between VM resource allocations and physical machine power. To cope with large utility optimization times in the case of an increased number of VMs, a distributed resource manager is proposed. It consists of several ANNs, each responsible for modelling and resource allocation of one VM, while exchanging information with other ANNs for coordinating resource allocations. Experiments, in simulated and realistic environments, show that the distributed ANN resource manager achieves better performance-power trade-offs than a centralized version and a distributed non-coordinated resource manager. To deal with the difficulty of building an accurate online application model and long model adaptation time, a solution that offers model-free resource management based on fuzzy control is proposed. It optimizes a utility function based on a hill-climbing search heuristic implemented as fuzzy rules. To cope with long utility optimization time in the case of an increased number of VMs, a multi-agent fuzzy controller is developed where each agent, in parallel with others, optimizes its own local utility function. The fuzzy control approach eliminates the need to build a model beforehand and provides a robust solution even for noisy measurements. Experimental results show that the multi-agent fuzzy controller performs better in terms of utility value than a centralized fuzzy control version and a state-of-the-art adaptive optimal control approach, especially for an increased number of VMs. Finally, to address some of the problems of reactive VM resource allocation approaches, a proactive resource allocation solution is proposed. This approach decides on VM resource allocations based on resource demand prediction, using a machine learning technique called Support Vector Machine (SVM). To deal with interdependencies between VMs of the same multi-tier application, cross- correlation demand prediction of multiple resource usage time series of all VMs of the multi-tier application is applied. As experiments show, this results in improved prediction accuracy and application performance

Software-Defined Cloud Computing: Architectural Elements and Open Challenges

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi, Indi

Energy and Performance: Management of Virtual Machines: Provisioning, Placement, and Consolidation

Cloud computing is a new computing paradigm that offers scalable storage and compute resources to users on demand through Internet. Public cloud providers operate large-scale data centers around the world to handle a large number of users request. However, data centers consume an immense amount of electrical energy that can lead to high operating costs and carbon emissions. One of the most common and effective method in order to reduce energy consumption is Dynamic Virtual Machines Consolidation (DVMC) enabled by the virtualization technology. DVMC dynamically consolidates Virtual Machines (VMs) into the minimum number of active servers and then switches the idle servers into a power-saving mode to save energy. However, maintaining the desired level of Quality-of-Service (QoS) between data centers and their users is critical for satisfying users’ expectations concerning performance. Therefore, the main challenge is to minimize the data center energy consumption while maintaining the required QoS. This thesis address this challenge by presenting novel DVMC approaches to reduce the energy consumption of data centers and improve resource utilization under workload independent quality of service constraints. These approaches can be divided into three main categories: heuristic, meta-heuristic and machine learning. Our first contribution is a heuristic algorithm for solving the DVMC problem. The algorithm uses a linear regression-based prediction model to detect over-loaded servers based on the historical utilization data. Then it migrates some VMs from the over-loaded servers to avoid further performance degradations. Moreover, our algorithm consolidates VMs on fewer number of server for energy saving. The second and third contributions are two novel DVMC algorithms based on the Reinforcement Learning (RL) approach. RL is interesting for highly adaptive and autonomous management in dynamic environments. For this reason, we use RL to solve two main sub-problems in VM consolidation. The first sub-problem is the server power mode detection (sleep or active). The second sub-problem is to find an effective solution for server status detection (overloaded or non-overloaded). The fourth contribution of this thesis is an online optimization meta-heuristic algorithm called Ant Colony System-based Placement Optimization (ACS-PO). ACS is a suitable approach for VM consolidation due to the ease of parallelization, that it is close to the optimal solution, and its polynomial worst-case time complexity. The simulation results show that ACS-PO provides substantial improvement over other heuristic algorithms in reducing energy consumption, the number of VM migrations, and performance degradations. Our fifth contribution is a Hierarchical VM management (HiVM) architecture based on a three-tier data center topology which is very common use in data centers. HiVM has the ability to scale across many thousands of servers with energy efficiency. Our sixth contribution is a Utilization Prediction-aware Best Fit Decreasing (UP-BFD) algorithm. UP-BFD can avoid SLA violations and needless migrations by taking into consideration the current and predicted future resource requirements for allocation, consolidation, and placement of VMs. Finally, the seventh and the last contribution is a novel Self-Adaptive Resource Management System (SARMS) in data centers. To achieve scalability, SARMS uses a hierarchical architecture that is partially inspired from HiVM. Moreover, SARMS provides self-adaptive ability for resource management by dynamically adjusting the utilization thresholds for each server in data centers.Siirretty Doriast

DEPENDABILITY IN CLOUD COMPUTING

The technological advances and success of Service-Oriented Architectures and the Cloud computing paradigm have produced a revolution in the Information and Communications Technology (ICT). Today, a wide range of services are provisioned to the users in a flexible and cost-effective manner, thanks to the encapsulation of several technologies with modern business models. These services not only offer high-level software functionalities such as social networks or e-commerce but also middleware tools that simplify application development and low-level data storage, processing, and networking resources. Hence, with the advent of the Cloud computing paradigm, today's ICT allows users to completely outsource their IT infrastructure and benefit significantly from the economies of scale. At the same time, with the widespread use of ICT, the amount of data being generated, stored and processed by private companies, public organizations and individuals is rapidly increasing. The in-house management of data and applications is proving to be highly cost intensive and Cloud computing is becoming the destination of choice for increasing number of users. As a consequence, Cloud computing services are being used to realize a wide range of applications, each having unique dependability and Quality-of-Service (Qos) requirements. For example, a small enterprise may use a Cloud storage service as a simple backup solution, requiring high data availability, while a large government organization may execute a real-time mission-critical application using the Cloud compute service, requiring high levels of dependability (e.g., reliability, availability, security) and performance. Service providers are presently able to offer sufficient resource heterogeneity, but are failing to satisfy users' dependability requirements mainly because the failures and vulnerabilities in Cloud infrastructures are a norm rather than an exception. This thesis provides a comprehensive solution for improving the dependability of Cloud computing -- so that -- users can justifiably trust Cloud computing services for building, deploying and executing their applications. A number of approaches ranging from the use of trustworthy hardware to secure application design has been proposed in the literature. The proposed solution consists of three inter-operable yet independent modules, each designed to improve dependability under different system context and/or use-case. A user can selectively apply either a single module or combine them suitably to improve the dependability of her applications both during design time and runtime. Based on the modules applied, the overall proposed solution can increase dependability at three distinct levels. In the following, we provide a brief description of each module. The first module comprises a set of assurance techniques that validates whether a given service supports a specified dependability property with a given level of assurance, and accordingly, awards it a machine-readable certificate. To achieve this, we define a hierarchy of dependability properties where a property represents the dependability characteristics of the service and its specific configuration. A model of the service is also used to verify the validity of the certificate using runtime monitoring, thus complementing the dynamic nature of the Cloud computing infrastructure and making the certificate usable both at discovery and runtime. This module also extends the service registry to allow users to select services with a set of certified dependability properties, hence offering the basic support required to implement dependable applications. We note that this module directly considers services implemented by service providers and provides awareness tools that allow users to be aware of the QoS offered by potential partner services. We denote this passive technique as the solution that offers first level of dependability in this thesis. Service providers typically implement a standard set of dependability mechanisms that satisfy the basic needs of most users. Since each application has unique dependability requirements, assurance techniques are not always effective, and a pro-active approach to dependability management is also required. The second module of our solution advocates the innovative approach of offering dependability as a service to users' applications and realizes a framework containing all the mechanisms required to achieve this. We note that this approach relieves users from implementing low-level dependability mechanisms and system management procedures during application development and satisfies specific dependability goals of each application. We denote the module offering dependability as a service as the solution that offers second level of dependability in this thesis. The third, and the last, module of our solution concerns secure application execution. This module considers complex applications and presents advanced resource management schemes that deploy applications with improved optimality when compared to the algorithms of the second module. This module improves dependability of a given application by minimizing its exposure to existing vulnerabilities, while being subject to the same dependability policies and resource allocation conditions as in the second module. Our approach to secure application deployment and execution denotes the third level of dependability offered in this thesis. The contributions of this thesis can be summarized as follows.The contributions of this thesis can be summarized as follows. \u2022 With respect to assurance techniques our contributions are: i) de finition of a hierarchy of dependability properties, an approach to service modeling, and a model transformation scheme; ii) de finition of a dependability certifi cation scheme for services; iii) an approach to service selection that considers users' dependability requirements; iv) de finition of a solution to dependability certifi cation of composite services, where the dependability properties of a composite service are calculated on the basis of the dependability certi ficates of component services. \u2022 With respect to off ering dependability as a service our contributions are: i) de finition of a delivery scheme that transparently functions on users' applications and satisfi es their dependability requirements; ii) design of a framework that encapsulates all the components necessary to o er dependability as a service to the users; iii) an approach to translate high level users' requirements to low level dependability mechanisms; iv) formulation of constraints that allow enforcement of deployment conditions inherent to dependability mechanisms and an approach to satisfy such constraints during resource allocation; v) a resource management scheme that masks the a ffect of system changes by adapting the current allocation of the application. \u2022 With respect to security management our contributions are: i) an approach that deploys users' applications in the Cloud infrastructure such that their exposure to vulnerabilities is minimized; ii) an approach to build interruptible elastic algorithms whose optimality improves as the processing time increases, eventually converging to an optimal solution

DISSECT-CF: a simulator to foster energy-aware scheduling in infrastructure clouds

Infrastructure as a service (IaaS) systems offer on demand virtual infrastructures so reliably and flexibly that users expect a high service level. Therefore, even with regards to internal IaaS behaviour, production clouds only adopt novel ideas that are proven not to hinder established service levels. To analyse their expected behaviour, new ideas are often evaluated with simulators in production IaaS system-like scenarios. For instance, new research could enable collaboration amongst several layers of schedulers or could consider new optimisation objectives such as energy consumption. Unfortunately, current cloud simulators are hard to employ and they often have performance issues when several layers of schedulers interact in them. To target these issues, a new IaaS simulation framework (called DISSECT-CF) was designed. The new simulator's foundation has the following goals: easy extensibility, support energy evaluation of IaaSs and to enable fast evaluation of many scheduling and IaaS internal behaviour related scenarios. In response to the requirements of such scenarios, the new simulator introduces concepts such as: a unified model for resource sharing and a new energy metering framework with hierarchical and indirect metering options. Then, the comparison of several simulated situations to real-life IaaS behaviour is used to validate the simulator's functionality. Finally, a performance comparison is presented between DISSECT-CF and some currently available simulators