Design and evaluation of elastic media resource allocation algorithms using CloudSim extensions

With the maturity of Cloud computing comes research into converting a range of traditionally best effort programs into cloud-enabled services. One such service currently under investigation in the Elastic Media Distribution (EMD) project, is how to enable qualitative, reliable and scalable real-time media collaboration services using proven Cloud technology. While existing best-effort solutions provide plenty of features, they do not provide the quality guarantees and reliability required for critical services in globally distributed corporations. On the other hand, some pricey dedicated solutions do offer these low-delay, reliable cooperation services, but without the benefits that clouds can bring in terms of scalability. In this paper we describe results attained in the EMD project on novel resource provisioning algorithms for a mixture of end-to-end Audio/Video streams with file-based transfers, allowing for configurable trade-offs between service response time and cost. We extended the CloudSim simulator with models allowing us to simulate collaborative interactive sessions (more specifically educational real-time collaboration), and evaluated the performance of our proposed provisioning heuristics. The results show that the proposed dynamic algorithm allows for automated cost-performance trade-off by reducing average total Virtual Machine (VM) cost by a maximum of 58% compared to more naive approaches, while keeping average time for clients to join a meeting in line

Design and evaluation of automatic workflow scaling algorithms for multi-tenant SaaS

Current Cloud software development efforts to come up with novel Software-as-a-Service (SaaS) applications are, just like traditional software development, usually no longer built from scratch. Instead more and more Cloud developers are opting to use multiple existing components and integrate them in their application workflow. Scaling the resulting application up or down, depending on user/tenant load, in order to keep the SLA, no longer becomes an issue of scaling resources for a single service, rather results in a complex problem of scaling all individual service endpoints in the workflow, depending on their monitored runtime behavior. In this paper, we propose and evaluate algorithms through CloudSim for automatic and runtime scaling of such multi-tenant SaaS workflows. Our results on time-varying workloads show that the proposed algorithms are effective and produce the best cost-quality trade-off while keeping Service Level Agreements (SLAs) in line. Empirically, the proactive algorithm with careful parameter tuning always meets the SLAs while only suffering a marginal increase in average cost per service component of approximate to 5-8% over our baseline passive algorithm, which, although provides the least cost, suffers from prolonged violation of service component SLAs

Model driven simulation of elastic OCCI cloud resources

International audienceDeploying a cloud configuration in a real cloud platform is mostly cost-and time-consuming, as large number of cloud resources have to be rent for the time needed to run the configuration. Thereafter, cloud simulation tools are used as a cheap alternative to test Cloud configuration. However, most of existing cloud simulation tools require extensive technical skills and does not support simulation of any kind of cloud resources. In this context, using a model-driven approach can be helpful as it allows developers to efficiently describe their needs at a high level of abstraction. To do, we propose, in this article, a Model-Driven Engineering (MDE) approach based on the OCCI (Open Cloud Computing Interface) standard metamodel and CloudSim toolkit. We firstly extend OCCI metamodel for supporting simulation of any kind of cloud resources. Afterward, to illustrate the extensibility of our approach, we enrich the proposed metamodel by new simulation capabilities. As proof of concept, we study the elasticity and pricing strategies of Amazon Web Services (AWS). This article benefits from OCCIware Studio to design an OCCI simulation extension and to provide a simulation designer for designing cloud configurations to be simulated. We detail the approach process from defining an OCCI simulation extension until the generation and the simulation of the OCCI cloud configurations. Finally, we validate the proposed approach by providing a realistic experimentation to study its usability, the resources coverage rate and the cost. The results is compared with the ones computed from AWS

MACHS: Mitigating the Achilles Heel of the Cloud through High Availability and Performance-aware Solutions

Cloud computing is continuously growing as a business model for hosting information and communication technology applications. However, many concerns arise regarding the quality of service (QoS) offered by the cloud. One major challenge is the high availability (HA) of cloud-based applications. The key to achieving availability requirements is to develop an approach that is immune to cloud failures while minimizing the service level agreement (SLA) violations. To this end, this thesis addresses the HA of cloud-based applications from different perspectives. First, the thesis proposes a component’s HA-ware scheduler (CHASE) to manage the deployments of carrier-grade cloud applications while maximizing their HA and satisfying the QoS requirements. Second, a Stochastic Petri Net (SPN) model is proposed to capture the stochastic characteristics of cloud services and quantify the expected availability offered by an application deployment. The SPN model is then associated with an extensible policy-driven cloud scoring system that integrates other cloud challenges (i.e. green and cost concerns) with HA objectives. The proposed HA-aware solutions are extended to include a live virtual machine migration model that provides a trade-off between the migration time and the downtime while maintaining HA objective. Furthermore, the thesis proposes a generic input template for cloud simulators, GITS, to facilitate the creation of cloud scenarios while ensuring reusability, simplicity, and portability. Finally, an availability-aware CloudSim extension, ACE, is proposed. ACE extends CloudSim simulator with failure injection, computational paths, repair, failover, load balancing, and other availability-based modules

A service broker for Intercloud computing

This thesis aims at assisting users in finding the most suitable Cloud resources taking into account their functional and non-functional SLA requirements. A key feature of the work is a Cloud service broker acting as mediator between consumers and Clouds. The research involves the implementation and evaluation of two SLA-aware match-making algorithms by use of a simulation environment. The work investigates also the optimal deployment of Multi-Cloud workflows on Intercloud environments

Simulating fog and edge computing scenarios: an overview and research challenges

The fourth industrial revolution heralds a paradigm shift in how people, processes, things, data and networks communicate and connect with each other. Conventional computing infrastructures are struggling to satisfy dramatic growth in demand from a deluge of connected heterogeneous endpoints located at the edge of networks while, at the same time, meeting quality of service levels. The complexity of computing at the edge makes it increasingly difficult for infrastructure providers to plan for and provision resources to meet this demand. While simulation frameworks are used extensively in the modelling of cloud computing environments in order to test and validate technical solutions, they are at a nascent stage of development and adoption for fog and edge computing. This paper provides an overview of challenges posed by fog and edge computing in relation to simulation

Evaluation of cloud computing modelling tools: simulators and predictive models

Experimenting with novel algorithms and configurations for the automatic management of Cloud Computing infrastructures is expensive and time consuming on real systems. Cloud computing delivers the benefits of using virtualisation techniques to data centers instead of physical servers for customers. However, it is still complex for researchers to test and run their experiments on data center due to the cost for repeating the experiments. To address this, various tools are available to enable simulators, emulators, mathematical models, statistical models and benchmarking. Despite this, there are different methods used by researchers to avoid the difficulty of conducting Cloud Computing research on actual large data centre infrastructure. However, it is still difficult to chose the best tool to evaluate the proposed research. This research focuses on investigating the level of accuracy of existing known simulators in the field of cloud computing. Simulation tools are generally developed for particular experiments, so there is little assurance that using them with different workloads will be reliable. Moreover, a predictive model based on a data set from a realistic data center is delivered as an alternative model of simulators as there is a lack of their sufficient accuracy. So, this work addresses the problem of investigating the accuracy of different modelling tools by developing and validating a procedure based on the performance of a target micro data centre. Key insights and contributions are: Involving three alternative models for Cloud Computing real infrastructure showing the level of accuracy of selected simulation tools. Developing and validating a predictive model based on a Raspberry Pi small scale data centre. The use of predictive model based on Linear Regression and Artificial Neural Net- works models based on training data set drawn from a Raspberry Pi Cloud infrastructure provides better accuracy