Glauco Estacio Gonçalves

Resource allocation for distributed cloud: concepts and research challenges

In a cloud computing environment, dynamic resource allocation and reallocation are keys for accommodating unpredictable demands and, ultimately, contribute to investment return. This article discusses this process in the context of distributed clouds, which are seen as systems where application developers can selectively lease geographically distributed resources. This article highlights and categorizes the main challenges inherent to the resource allocation process particular to distributed clouds, offering a stepwise view of this process that covers the initial modeling phase through to the optimization phase.

High availability in clouds: systematic review and research challenges

Cloud Computing has been used by different types of clients because it has many advantages, including the minimization of infrastructure resources costs, and its elasticity property, which allows services to be scaled up or down according to the current demand. From the Cloud provider point-of-view, there are many challenges to be overcome in order to deliver Cloud services that meet all requirements defined in Service Level Agreements (SLAs). High availability has been one of the biggest challenges for providers, and many services can be used to improve the availability of a service, such as checkpointing, load balancing, and redundancy. Beyond services, we can also find infrastructure and middleware solutions. This systematic review has as its main goal to present and discuss high available (HA) solutions for Cloud Computing, and to introduce some research challenges in this area. We hope this work can be used as a starting point to understanding and coping with HA problems in Cloud.

D-CRAS: Distributed cloud resource allocation system

In this paper we present implementation guidelines of the Distributed Cloud Resource Allocation System (D-CRAS). D-CRAS ensures an automatic monitoring and control of resources to guarantee the optimal functioning of the Cloud while meeting developers requirements. Additionally, this work establishes the necessary technologies that meet the construction of the proposed system.

On the load balancing of virtual networks in distributed clouds

The distribution of computing resources in different geographical regions and the promotion of full integration with network resources are important issues of new architectures for Cloud computing. Such scattered Cloud deployments, called Distributed Clouds (D-Clouds), can directly reach users due to their inherently distributed infrastructure and the ownership of the network. Thus, D-Clouds can comply with geographically-based requirements and network-based quality of service. One of the challenges in this area is the resource management. In this way, a clever resource allocation algorithm is needed to satisfy service requirements and an owners management objectives. This paper proposes algorithms for allocation of computing and network resources in a D-Cloud with the objectives of balancing the load in the virtualized infrastructure and of considering constraints, such as processing power, memory, storage, and network delay. The evaluation of the algorithm shows that it is indeed adequate for link allocation across different physical networks.

Risk-based model for availability estimation of SAF redundancy models

Cloud outages, no matter how long, are responsible for large financial losses. Cloud providers look for solutions that provide high availability even in failure cases. Redundancy has been often used as an alternative; however it has a cost, and depending on the redundancy model, we can have different levels of availability. Beyond that, adding more resources also means adding more complexity and more point of failures. In this paper, we propose a risk-based model for estimating the availability of a redundancy setup. In order to analyze our model, we conduct Monte Carlo simulations and results show that for all models, the number of applications we deploy in a Cloud environment impacts on its availability.

On the Use of an ADSL2+ Testbed for Video Quality Assessment

In this paper we share our experience in building an ADSL video delivery test-bed. As a result, we are able to measure the impact of important parameters such as the loop length, the presence of background traffic and line protection against transmission errors on video quality. We show that although these access technologies promise a broadband pipe and low delay communication, a careful configuration of line parameters is paramount to achieve optimal access and is needed for the success of multimedia triple play (3P) services. We particularly analyze the delay, jitter, packet loss and bitrate consumption obtained from real ADSL measurements. These results are an important step towards understanding the adequate deployment of such services. As a special case, we study the effect of repetitive noise on SDTV.

Estimating Video Quality over ADSL2+ under Impulsive Line Disturbance

QoS Provisioning for 3P over xDSL remains a challenging task due to the effects of line impairments on such services. Differently from simple data, video and voice services have strict requirements for loss and delay tolerance. The accurate assessment of final service quality is part of this provisioning process, but its direct measurement is yet not practical. In this paper we explore the possibility of estimating service quality, with focus on video delivery, by investigating its relationship with performance data available to xDSL operators and deriving models for estimating quality from this data. Experiments using a real xDSL platform and different noise types were conducted. The derived models showed to be accurate enough to estimate video quality for the scenarios evaluated.

Collaborative Algorithm with a Green Touch

Discovery and announcement processes are regular tasks in ubiquitous scenarios, since they are important to support devices’ self-adaptation. However, devices enable a multitude of protocols by default, including many not always required by users or networks. For example, the NetBIOS protocol is rarely used in some networks, but it is continuously executed by several Desktops. Consequently, today’s networks waste energy processing protocol messages that should be turned off. In this paper, we analyze such energy cost, and we propose devices’ collaboration and sharing of traffic knowledge to save energy. Firstly, we suggest that devices should view protocols in the same way of users through a social analogy. Skype, MSN, ARP and other protocols are seen as societies with specific languages, which devices can learn and teach, similarly to societies. Finally, simulations showed that our proposal is able to reduce energy consumption for all network’s devices.

Highly Available Clouds: System Modeling, Evaluations, and Open Challenges

Cloud-based solution adoption is becoming an indispensable strategy for enterprises, since it brings many advantages, such as low cost. On the other hand, to attend this demand, cloud providers are facing a great challenge regarding their resource management: how to provide services with high availability relying on finite computational resources and limited physical infrastructure? Understanding the components and operations of cloud data center is a key point to manage resources in an optimal way and to estimate how physical and logical failures can impact on users’ perception. This book chapter aims to explore computational modeling theories in order to represent a cloud infrastructure focusing on how to estimate and model cloud availability.

Failover time evaluation between checkpoint services in multi-tier stateful applications

Cloud applications are offered to users with high availability and minimal data loss. Any (hardware or software) failure must be detected and recovered quickly, in order to maintain customer trust and avoid financial losses. When we are dealing with multi-tier and stateful applications, the failure recovery process is a big challenge because the whole state of the failed application must be retrieved and restored in a new instance. This process is named as failover; it can be performed by a checkpoint service at application-level or at system-level. Depending on the location of the checkpoint data storage, it can be classified as non-collocated, collocated warm, or collocated hot. This work presents an evaluation between these two checkpoint services in both virtualized and physical environments, considering a multi-tier and stateful application.