Foued Jrad

A broker-based framework for multi-cloud workflows

Computational science workflows have been successfully run on traditional HPC systems like clusters and Grids for many years. Today, users are interested to execute their workflow applications in the Cloud to exploit the economic and technical benefits of this new emerging technology. The deployment and management of workflows over the current existing heterogeneous and not yet interoperable Cloud providers, however, is still a challenging task for the workflow developers. In this paper, we present a broker-based framework for running workflows in a multi-Cloud environment. The framework allows an automatic selection of the target Clouds, a uniform access to the Clouds, and workflow data management with respect to user Service Level Agreement (SLA) requirements. Following a simulation approach, we evaluated the framework with a real scientific workflow application in different deployment scenarios. The results show that our framework offers benefits to users by executing workflows with the expected performance and service quality at lowest cost.

Load and Thermal-Aware VM Scheduling on the Cloud

Virtualization is one of the key technologies that enable Cloud Computing, a novel computing paradigm aiming at provisioning on-demand computing capacities as services. With the special features of self-service and pay-as-you-use, Cloud Computing is attracting not only personal users but also small and middle enterprises. By running applications on the Cloud, users need not maintain their own servers thus to save administration cost. Cloud Computing uses a business model meaning that the operation overhead must be a major concern of the Cloud providers. Today, the payment of a data centre on energy may be larger than the overall investment on the computing, storage and network facilities. Therefore, saving energy consumption is a hot topic not only in Cloud Computing but also for other domains. This work proposes and implements a virtual machine (VM) scheduling mechanism that targets on both load-balancing and temperature-balancing with a final goal of reducing the energy consumption in a Cloud centre. Using the strategy of VM migration it is ensured that none of the physical hosts suffers from either high temperature or over-utilization. The proposed scheduling mechanism has been evaluated on CloudSim, a well-known simulator for Cloud Computing. Initial experimental results show a significant benefit in terms of energy consumption.

SLA enactment for large-scale healthcare workflows on multi-Cloud

Computing Clouds offer a new way of using IT facilities including the hardware, storage, applications and networks. The huge resource pool on the Cloud forms an appropriate platform for running applications with both computing and data intensity, like the DNA sequencing workflows. This paper studies the topic of running scientific workflows on multiple Clouds, with the DNA sequencing workflow as a driven application. We focus on the problem of matching the workflow functional and non-functional Service Level Agreement (SLA) requirements to the compute and storage services provisioned by underlying Clouds with different service price and quality. We designed an ontological model for a semantic description of the problem and developed a novel utility-based genetic matching algorithm for selecting the Cloud services with respect to the user requirements and the properties of the Clouds. We validated the approach by comparing the performance of the proposed algorithm with other matching algorithms in executing the DNA sequencing application on a realistic simulation platform. The results show the effectiveness of our approach in reducing the total costs and fulfilling the requested service quality even with large-scale service compositions. An ontological model to semantically describe composite multi-Cloud services.A mathematical formulation of the SLA-based match-making problem on multi-Cloud.A utility-based genetic algorithm to optimize the selection of Cloud resources.A simulation-based evaluation with a real DNA sequencing healthcare workflow.The proposed matching algorithm reduces execution costs while fulfilling the SLAs.

Energy-Aware Cloud Management Through Progressive SLA Specification

Novel energy-aware cloud management methods dynamically reallocate computation across geographically distributed data centers to leverage regional electricity price and temperature differences. As a result, a managed virtual machine (VM) may suffer occasional downtimes. Current cloud providers only offer high availability VMs, without enough flexibility to apply such energy-aware management. In this paper we show how to analyse past traces of dynamic cloud management actions based on electricity prices and temperatures to estimate VM availability and price values. We propose a novel service level agreement (SLA) specification approach for offering VMs with different availability and price values guaranteed over multiple SLAs to enable flexible energy-aware cloud management. We determine the optimal number of such SLAs as well as their availability and price guaranteed values. We evaluate our approach in a user SLA selection simulation using Wikipedia and Grid’5000 workloads. The results show higher customer conversion and \(39\%\) average energy savings per VM.

Storage CloudSim

Since Cloud services are billed by the pay-as-you-go principle, organizations can save huge investment costs. Hence, they want to know, what costs will arise by the usage of those services. On the other hand, Cloud providers want to provide the best-matching hardware configurations for different use-cases. Therefore, CloudSim, a popular event-based framework, was developed to model and simulate the usage of IaaS (Infrastructure-as-a-Service) Clouds. Metrics like usage costs, resource utilization and energy consumption can be also investigated using CloudSim. But this favored simulation framework does not provide any mechanisms to simulate todays object storage based Cloud-services (STaaS, Storage-as-a-Service). In this paper, we propose a storage extension for CloudSim to enable the simulations of STaaS-components. Interactions between users and the modeled STaaS Clouds are inspired by the CDMI (Cloud Data Management Interface) standard. In order to validate our extension, we evaluated the resource utilization and costs that arise by the usage of STaaS Clouds based on different simulation scenarios.

Using a vision cognitive algorithm to schedule virtual machines

Abstract Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption