Advances in Dynamic Virtualized Cloud Management

Cloud computing continues to gain in popularity, with more and more applications being deployed into public and private clouds. Deploying an application in the cloud allows application owners to provision computing resources on-demand, and scale quickly to meet demand. An Infrastructure as a Service (IaaS) cloud provides low-level resources, in the form of virtual machines (VMs), to clients on a pay-per-use basis. The cloud provider (owner) can reduce costs by lowering power consumption. As a typical server can consume 50% or more of its peak power consumption when idle, this can be accomplished by consolidating client VMs onto as few hosts (servers) as possible. This, however, can lead to resource contention, and degraded VM performance. As such, VM placements must be dynamically adapted to meet changing workload demands. We refer to this process as dynamic management. Clients should also take advantage of the cloud environment by scaling their applications up and down (adding and removing VMs) to match current workload demands. This thesis proposes a number of contributions to the field of dynamic cloud management. First, we propose a method of dynamically switching between management strategies at run-time in order to achieve more than one management goal. In order to increase the scalability of dynamic management algorithms, we introduce a distributed version of our management algorithm. We then consider deploying applications which consist of multiple VMs, and automatically scale their deployment to match their workload. We present an integrated management algorithm which handles both dynamic management and application scaling. When dealing with multi-VM applications, the placement of communicating VMs within the data centre topology should be taken into account. To address this consideration, we propose a topology-aware version of our dynamic management algorithm. Finally, we describe a simulation tool, DCSim, which we have developed to help evaluate dynamic management algorithms and techniques

Dynamic Resource Management in Virtualized Data Centres

In the last decade, Cloud Computing has become a disruptive force in the computing landscape, changing the way in which software is designed, deployed and used over the world. Its adoption has been substantial and it is only expected to continue growing. The growth of this new model is supported by the proliferation of large-scale data centres, built for the express purpose of hosting cloud workloads. These data centres rely on systems virtualization to host multiple workloads per physical server, thus increasing their infrastructures\u27 utilization and decreasing their power consumption. However, the owners of the cloud workloads expect their applications\u27 demand to be satisfied at all times, and placing too many workloads in one physical server can risk meeting those service expectations. These and other management goals make the task of managing a cloud-supporting data centre a complex challenge, but one that needs to be addressed. In this work, we address a few of the management challenges associated with dynamic resource management in virtualized data centres. We investigate the application of First Fit heuristics to the Virtual Machine Relocation problem (that is, the problem of migrating VMs away from stressed or overloaded hosts) and the effect that different heuristics have, as reflected in the performance metrics of the data centre. We also investigate how to pursue multiple goals in data centre management and propose a method to achieve precisely that by dynamically switching management strategies at runtime according to data centre state. In order to improve system scalability and decrease network management overhead, we propose architecting the management system as a topology-aware hierarchy of managing elements, which limits the flow of management data across the data centre. Finally, we address the challenge of managing multi-VM applications with placement constraints in data centres, while still trying to achieve high levels of resource utilization and client satisfaction

Comparative Analysis of Cloud Simulators and Authentication Techniques in Cloud Computing

Cloud computing is the concern of computer hardware and software resources above the internet so that anyone who is connected to the internet can access it as a service or provision in a seamless way. As we are moving more and more towards the application of this newly emerging technology, it is essential to study, evaluate and analyze the performance, security and other related problems that might be encountered in cloud computing. Since, it is not a practicable way to directly examine the behavior of cloud on such problems using the real hardware and software resources due to its high costs, modeling and simulation has become an essential tool to withstand with these issues. In this paper, we retrospect, analyse and compare features of the existing cloud computing simulators and various location based authentication and simulation tools

A Vector-Based Approach to Virtual Machine Arrangement

Cloud based data centres benefit from minimizing operating costs and service level agreement violations. Vector-based data centre management policies have been shown to assist with these goals. Vector-based data centre management policies arrange virtual machines in a data centre to minimize the number of hosts being used which translates to greater power efficiency and reduced costs for the data centre overall. I propose an improved vector-based virtual machine arrangement algorithm with two novel additions, namely a technique that changes what it means for a host to be balanced and a concept that excludes undesirable target hosts, thereby improving the arrangement process. Experiments conducted with a simulated data centre demonstrate the effectiveness of this algorithm and compares it to existing algorithms

UTIL-DSS: Utilization-Based Dynamic Strategy Switching for Improvement in Data Centre Operation

Applications are shifting into large scale, virtualized data centres that provide resources on a pay-per-usage basis. With power consumption representing a major operational cost, data centres must prioritize efficiency while still providing enough resources to meet application requirements. To meet variable application demands, a dynamic approach to virtual machine (VM) management is required. This requires: (i) placing newly arrived VMs, (ii) migrating VMs from highly utilized machines to avoid performance degradation, and (iii) migrating VMs from underutilized machines so that they may be deactivated to save power. Here, a management strategy is considered to be policy-set that guides these three operations. To achieve the conflicting goals of performance and efficiency, I propose and evaluate a system of dynamically switching between two management strategies, each with a single goal, based on trends in data centre workload. Experimentation over a simulated data centre demonstrates the superiority of this approach over single-strategy techniques

A distributed approach to dynamic vm management

Abstract-Computing today is increasingly moving into largescale virtualized data centres, offering computing resources in the form of virtual machines (VMs) on a pay-per-usage basis. In order to minimize costs, VMs should be consolidated on as few physical machines (PMs) as possible, switching idle PMs into a power saving mode. It may be necessary to dynamically allocate and reallocate VMs to PMs in order to meet highly dynamic VM resource requirements. The problem of assigning VMs to PMs is known to be NP-Hard. Most solutions focus on a centralized approach, with a single management node making allocation decisions periodically. This approach suffers from poor scalability and the existence of a single point of failure. We present a fully distributed approach to dynamic VM management, and evaluate our approach using a simulation tool. Results indicate that the distributed approach can achieve similar performance to the centralized solution, while eliminating the single point of failure and reducing the network bandwidth required for management

CloudBench: an integrated evaluation of VM placement algorithms in clouds

A complex and important task in the cloud resource management is the efficient allocation of virtual machines (VMs), or containers, in physical machines (PMs). The evaluation of VM placement techniques in real-world clouds can be tedious, complex and time-consuming. This situation has motivated an increasing use of cloud simulators that facilitate this type of evaluations. However, most of the reported VM placement techniques based on simulations have been evaluated taking into account one specific cloud resource (e.g., CPU), whereas values often unrealistic are assumed for other resources (e.g., RAM, awaiting times, application workloads, etc.). This situation generates uncertainty, discouraging their implementations in real-world clouds. This paper introduces CloudBench, a methodology to facilitate the evaluation and deployment of VM placement strategies in private clouds. CloudBench considers the integration of a cloud simulator with a real-world private cloud. Two main tools were developed to support this methodology, a specialized multi-resource cloud simulator (CloudBalanSim), which is in charge of evaluating VM placement techniques, and a distributed resource manager (Balancer), which deploys and tests in a real-world private cloud the best VM placement configurations that satisfied user requirements defined in the simulator. Both tools generate feedback information, from the evaluation scenarios and their obtained results, which is used as a learning asset to carry out intelligent and faster evaluations. The experiments implemented with the CloudBench methodology showed encouraging results as a new strategy to evaluate and deploy VM placement algorithms in the cloud.This work was partially funded by the Spanish Ministry of Economy, Industry and Competitiveness under the Grant TIN2016-79637-P “Towards Unifcation of HPC and Big Data Paradigms” and by the Mexican Council of Science and Technology (CONACYT) through a Ph.D. Grant (No. 212677)

MT-EA4Cloud: A Methodology For testing and optimising energy-aware cloud systems

Currently, using conventional techniques for checking and optimising the energy consumption in cloud systems is unpractical, due to the massive computational resources required. An appropriate test suite focusing on the parts of the cloud to be tested must be efficiently synthesised and executed, while the correctness of the test results must be checked. Additionally, alternative cloud configurations that optimise the energetic consumption of the cloud must be generated and analysed accordingly, which is challenging. To solve these issues we present MT-EA4Cloud, a formal approach to check the correctness – from an energy-aware point of view – of cloud systems and optimise their energy consumption. To make the checking of energy consumption practical, MT-EA4Cloud combines metamorphic testing, evolutionary algorithms and simulation. Metamorphic testing allows to formally model the underlying cloud infrastructure in the form of metamorphic relations. We use metamorphic testing to alleviate both the reliable test set problem, generating appropriate test suites focused on the features reflected in the metamorphic relations, and the oracle problem, using the metamorphic relations to check the generated results automatically. MT-EA4Cloud uses evolutionary algorithms to efficiently guide the search for optimising the energetic consumption of cloud systems, which can be calculated using different cloud simulatorsThis work was supported by the Spanish MINECO/FEDER projects DArDOS, FAME and MASSIVE under Grants TIN2015-65845-C3-1-R, RTI2018-093608-B-C31 and RTI2018-095255- B-I00, and the Comunidad de Madrid project FORTE-CM under grant S2018/TCS-4314. The first author is also supported by the Universidad Complutense de Madrid Santander Universidades grant (CT17/17-CT18/17