Patricia Takako Endo

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.

A Summary Description of the A2RD Project.

This paper describes the Autonomous Architecture Over Restricted Domains project. It begins with the description of the context upon which the project is focused, and in the sequence describes the project and implementation models. It finish by presenting the environment conceptual model, showing where stand the components, inputs and facilities required to interact among the intelligent agents of the various implementations in their respective and restricted, routing domains (Autonomous Systems) which together make the Internet work.

Self-management of Live Streaming Application in Distributed Cloud Infrastructure

Currently, live streaming traffic is responsible for more than half of aggregated traffic from fixed access networks in North America. But, due to traffic redundancy, it does not suitably utilize bandwidth and network resources. To cope with this problem in the context of Distributed Clouds (DClouds) we present RBSA4LS, an autonomic strategy that manages the dynamic creation of reflectors for reducing redundant traffic in live streaming applications. Under this strategy, nodes continually assess the utilization level by live streaming flows. When necessary, the network nodes communicate and self-appoint a new reflector node, which switches to multicasting video flows hence alleviating network links. We evaluated RBSA4LS through extensive simulations and the results showed that such a simple strategy can provide as much as 40 % of reduction in redundant traffic even for random topologies and reaches 85 % of bandwidth gain in a scenario with a large ISP topology.