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This paper presents a new opportunistic scheduling and resource consolidation system based on an economic model related to different service level agreements (SLAs) classes. The goal is to address the problem of companies that manage a private infrastructure of machines, i.e., a cloud platform and would like to optimize the scheduling of several requests submitted online by users. For the sake of simplicity of the presentation, the proposed economic model has two SLAs classes (qualitative and quantitative) with three Quality of Service for each SLA class (Premium, Advanced and Best effort). The consequence of this choice as well as the need to serve requests as they come have an impact on the algorithmic ways to consolidate an infrastructure. Indeed, our system proposes a new allocation heuristic that adapts the number of active machines in the cloud according to the global resources usage of all machines inside the infrastructure. This heuristic can be examined as a consolidation heuristic, based on the idea that the system can make reasonable choices, based on the SLAs, for the placement and the allocation of resources for each request. Experimentation with our system is conducted on Prezi (Web workload) and Google Cloud Data (HPC-oriented workload) traces, and they demonstrate the potential of our approach under different scenarios. From a methodological point of view, we propose a general framework which is limited in scope, for the sake of simplicity in reading the paper, with a small number of SLAs, but the idea can be extended to many more SLAs and performance metrics. In this way, the user or the provider operating the cloud have more latitude, thanks to our multi-criteria approach, to control the workload without a sacrifice on performance.

Infrastructure-as-a-service container-based virtualization is gaining interest as a platform for running distributed applications. With increasing scale of cloud architectures, faults are becoming a frequent occurrence, which makes availability true challenge. Replication is a method to survive failures whether of checkpoints, containers or data to increase their availability. In fact, following a node failure, fault-tolerant cloud systems restart failed containers on a new node from distributed images of containers (or checkpoints). With a high failure rate, we can lose some replicas. It is interesting to increase the replication factor in some cases and finding the trade-off between restarting all failed containers and storage overhead. This paper addresses the issue of adapting the replication factor and contributes with a novel replication factor modeling approach, which is able to predict the right replication factor using prediction techniques. These techniques are based on experimental modeling, which analyze collected data related to different executions. We have used regression technique to find the relation between availability and replicas number. Experiments on the Grid’5000 testbed demonstrate the benefits of our proposal to satisfy the availability requirement, using a real fault-tolerant cloud system.

Nowadays, the adoption of Cloud Computing platforms and Service Computing technologies are almost natural for the different e-Science communities. Cost benefits for data-intensive applications, ease of access, rich and varied offers for services are examples of positive returns by users. However, beyond this favorable welcome for the technology, some research problems remain and are still challenging. In this paper, we focus on the problems of automatically deploying IaaS for computing and for data management, using the SlapOS Cloud. The core of the system is a distributed protocol for orchestrating data and compute nodes. Using this interaction scheme, users are able to deploy, without any system administrator intervention, a PaaS inside the IaaS basically a Desktop Grid middleware. The aim of this paper is to demonstrate that the Desktop Grid and Cloud paradigms may merge and may be widely used by non-experts in the different areas of e-Science. We propose a fully self-organized volunteer Cloud for researchers where they can carry out e-Science experiments and process large amounts of data in a coherent way.

After a decade of visible success stories in building clusters, then cluster of clusters, what we call “Grid systems” are expected to connect large numbers of heterogeneous resources (PC, databases, HPC clusters, instruments, sensors, visualization tools, etc.), to be accessed by many users, to execute a large variety of applications (number crunching, data access, multimedia, etc.) and to deal with many scientific fields (health, economy, computing, etc.). Such context forces applications to work in a dynamically changing computing environment, which gives rise to approaches to master complexity that are radically different from those found in traditional computing environments. An architecture (in the sense of ‘hardware’) alone is not sufficient to ensure an efficient use of resources and as with other fields in computing research, we need also to focus on middleware, languages, to cite but a few of the challenges. We considered first that large-scale systems such as Grids are devoted to exploit parallelism in applications, may entail the coordination of large numbers of processing elements, the synchronization and communication in the larger scale, the programming models and