A Participatory Cloud Infrastructure (PCI) is a collaborative framework designed to host services for a virtual community thanks to residential devices contributed by its members. In this work, we deploy on these devices a photo-sharing application based on microservices. The placement of services on devices significantly influences the overall performance of the application. To address this concern, we present a heuristic able to find a placement that approximates the ideal application response-time in constant time. Our proposed approach has undergone validation within a real infrastructure, revealing the necessity for dynamic adjustments of service placements. Consequently, we introduce a comprehensive adaptation process for placement and an associated specialized strategy.

International audience

IaaS clouds allow customers (called tenants) to deploy their IT as virtualized infrastructures. However IaaS clouds features, such as multi-tenancy and elasticity, generate new security vulnerabilities for which the security monitoring must be partly run by the cloud provider to give visibility at the virtualization infrastructure level. Unfortunately the same IaaS clouds features make the virtualized infrastructures frequently reconfigurable and thus affect the ability of a provider-run security monitoring system to detect attacks.

Infrastructure as a Service (IaaS) clouds are very dynamic with at runtime frequent changes at different levels of the virtual infrastructure. For cloud tenants, this affects the ability of a security monitoring framework to successfully detect attacks. In this paper, we propose SAIDS, a self-adaptable intrusion detection system for IaaS clouds that is able to adapt its components based on dynamic events that occur in a cloud infrastructure. We implemented and experimentally evaluated SAIDS, and show that it is a scalable solution that successfully detects attacks even during the adaptation process while imposing negligible overhead to cloud operations and tenant applications.

It is now feasible for consumers to buy inexpensive devices that can be installed at home and accessed remotely thanks to an Internet connection. Such a simple "self-hosting" can be an alternative to traditional cloud providers, especially for privacy-conscious users. We discuss how a community of users can pool their devices in order to host microservices-based applications, where each microservice is deployed on a different device. The performance of such an application depends heavily on the computing and network resources that are available and on the placement of each microservice. Finding the placement that minimizes the application response time is an NP-hard problem. We show that, thanks to well known optimization techniques (Particle Swarm Optimization), it is possible to quickly find a service placement resulting in a response time close to the optimal one. Thanks to an emulation platform, we evaluate the robustness of this solution to changes in the Quality of Service under conditions typical of a residential access network.

Summary Cloud computing has become the infrastructure of choice for building and delivering software services. A key challenge for service providers is effectively managing cloud resources in order to increase profit while maintaining service‐level agreements (SLAs) with customers. To address this challenge, this paper proposes a combination of automated mechanisms for resource and execution management. The resource management mechanisms, namely, under‐provisioning and contract rescission, reduce resource allocation costs and minimize penalties incurred when performance objectives are violated. The execution management mechanisms, namely, crash recovery and delay recovery, minimize penalties incurred when reliability objectives are violated. The mechanisms are integrated in the Qu4DS framework and evaluated in the Grid'5000 testbed. The results show that the under‐provisioning mechanism increases profit by 20–50%, the contract rescission mechanism increases profit up to four times, and the execution management mechanisms increase profit by up to 60%. Copyright © 2016 John Wiley & Sons, Ltd.

While the proliferation of cloud services have greatly impacted our society, how green are these services is yet to be answered. Although, demand escalation for green services has grown due to societal awareness, the approaches to provide green services and establish Green SLAs remain oblivious for cloud or infrastructure providers. The main challenge for cloud provider is to manage Green SLAs with their customers while satisfying their business objectives, such as maximizing profits by lowering expenditure for green energy. Since, Green SLA needs to be proposed based on the presence of green energy, the intermittent nature of renewable sources makes it difficult to be achieved. In response, this paper presents a scheme for green energy management in the presence of explicit and implicit integration of renewable energy in data center. More specifically we propose three contributions: i) we introduce the concept of virtualization of green energy to address the uncertainty of green energy availability, ii) we extend the Cloud Service Level Agreement (CSLA) language to support Green SLA by introducing two new threshold parameters and iii) we introduce greenSLA algorithm which leverages the concept of virtualization of green energy to provide per interval specific Green SLA. Experiments were conducted with real workload profile from PlanetLab and server power model from SPECpower to demonstrate that, Green SLA can be successfully established and satisfied without incurring higher cost.

With the ever growing demand and popularity of cloud based services, data centers have to urgently face energy consumption issue. Similar to other large consumers of power, data centers find themselves increasingly pressured to reduce their carbon footprint. In response, cloud providers have started to set sustainability goals to reduce carbon emissions by using renewable sources to their services. Traditionally, batch processing cloud applications are deadline oriented, hence they can be easily adapted with the different green energy profile. Whereas, interactive cloud applications are imposed with several performance criteria. This paper, the first of its kind, investigates a thorough analysis of energy consumption and performance trade-off by allowing smart usage of green energy for interactive cloud application. Moreover, we propose an auto-scaler, named SaaScaler, that implements several control loop based application controllers to satisfy different performance (i.e., response time, availability, and user experience) and resource aware metrics (i.e., quality of energy). Based on extensive experiments with RUBiS benchmark and real workload traces using single compute node in Openstack/Grid'5000, results suggest that 13 percent brown energy consumption can be reduced without deprovisioning any physical or virtual resources at IaaS layer while 29 percent more users can access the application by dynamically adjusting capacity requirements. Furthermore, our investigation verifies that the energy consumption deviates as little as .07 percent when our approach is scaled using several physical nodes.

As cloud computing is being more and more used, datacenters play a large role in the overall energy consumption. We propose to tackle this problem, by continuously and autonomously optimizing the cloud datacenters energy efficiency. To this end, modeling the energy consumption for these infrastructures is crucial to drive the optimization process, anticipate the effects of aggressive optimization policies, and to determine precisely the gains brought with the planned optimization. Yet, it is very complex to model with accuracy the energy consumption of a physical device as it depends on several factors. Do we need a detailed and fine-grained energy model to perform good optimizations in the datacenter? Or is a simple and naive energy model good enough to propose viable energy-efficient optimizations? Through experiments, our results show that we don't get energy savings compared to classical bin-packing strategies but there are some gains inusing precise modeling: better utilization of the network and the VM migration processes.

Application-level firewalls filter traffic based on a white list of processes that are allowed to access the network. Although they have a complete overview of the system in which they are executed, they can be easily bypassed by knowledgable attackers. In this paper we present AL-SAFE, a cloud-tailored application-level self-adaptable firewall which combines the high degree of visibility of an application-level firewall with the isolation of a traditional standalone firewall. AL-SAFE is able to filter traffic at two distinct points in the virtual infrastructure and adapt the enforced rulesets based on changes in the virtual infrastructure topology and the list of services running inside the virtual machines. Our performance analysis shows that AL-SAFE imposes a tolerable delay to legitimate network connections while it is able to filter out all unauthorised packets.

With the proliferation of Cloud computing, data centers have to urgently face energy consumption issues. Although recent efforts such as the integration of renewable energy to data centers or energy efficient techniques in (virtual) machines contribute to the reduction of carbon footprint, creating green energy awareness around Interactive Cloud Applications by smartly using the presence of green energy has not been yet addressed. By awareness, we mean the inherited capability of Software-as-a-Service applications to dynamically adapt with the availability of green energy and to reduce energy consumption while green energy is scarce or absent. In this paper, we present two application controllers based on different metrics (e.g., availability of green energy, response time, user experience level). Based on %and we have done extensive experiments with a real application benchmark and workloads in Grid'5000, results suggest that providers revenue can be increased as high as 64%, while 13% brown energy can be reduced without deprovisioning any physical or virtual resources at IaaS layer and 17 fold increment of performance can be guaranteed.

Traditional intrusion detection systems are not adaptive enough to cope with the dynamic characteristics of cloud-hosted virtual infrastructures. This makes them unable to address new cloud-oriented security issues. In this paper we introduce SAIDS, a self-adaptable intrusion detection system tailored for cloud environments. SAIDS is designed to re-configure its components based on environmental changes. A prototype of SAIDS is described.

Orchestration and choreography are two perspectives to model service interactions in executing a service composition. Compared to orchestration model, choreography can improve the performances in scalability, throughput and execution time by eliminating centralized point of coordination. However, it also brings additional complexities and challenges such as inter-organizational service coordination, fault-tolerance and etc. In this paper, we propose two self-managed and self-adaptive choreography models, namely semi-choreography and auto-choreography. Both models as well as a centralized orchestration model have been implemented in a chemistry-inspired middleware. With the metaphor from chemical reactions, the middleware is modeled as an autonomic distributed chemical system, where services are described as complex molecules and service coordination and adaptation is performed by a series of pervasive chemical reactions controlled by a number of rules. By using different sets of rules, providers are able to specify their preferred execution models to run service compositions, in either centralized or collaborative way. The middleware is implemented in Higher-Order Chemical Language (HOCL) and running on distributed infrastructures (Grid'5000). Finally, by executing two experimental workflows in the middleware, a number of experiments have been conducted to evaluate and compare different models in terms of complexity and efficiency.

Cloud computing has evolved from the provisioning of virtual machines to the provisioning of complex services, delivered to customers under the terms of Service-Level Agreements (SLAs). SLAs specify the Quality of Service (QoS) that should be provided to customers as well as the billing model. A main concern for cloud service providers is to maintain the agreed SLA terms in order to avoid losses and penalties. Maintaining the SLA in turn requires translating the QoS to configurations of low-level mechanisms, able to enforce the agreed terms. Current systems provide no integrated support for SLA specification, translation, and enforcement. In this paper, we propose an approach for specifying and enforcing SLAs for cloud service providers. The approach covers the creation of SLA templates under a billing model, the design of performance and fault-tolerance QoS assurance mechanisms as well as the translation of QoS to appropriate configurations of those mechanisms. We demonstrate the feasibility of our approach by using the Qu4DS framework for PaaS cloud providers. Moreover, we evaluate the impact of failures on the provider profit. The experiments were carried out on the Grid5000 testbed and demonstrate the effectiveness of ensuring fault tolerance in different scenarios.

Service-Oriented Architectures address the development of distributed and dynamic service-based applications. Due to the dynamics of their environments, services should be self-adaptable in order to maintain agreed resource-level qualities. To support building such services, this work proposes the Self-Adaptable Service Execution Manager (SASEM), responsible for monitoring and controlling the service execution on grid resources in order to prevent SLA violations. SASEM builds on the Dynaco adaptation model and on an enhanced grid monitoring and actuation system, implemented using the XtreemOS grid operating system.

Distributed computing platforms contribute for a large part to some of the most powerful computers.Such architectures raise new challenges, typically in terms of scheduling, adaptability and security. This paper addresses the issue of result-checking in distributed environments, where tasks or their results could have been corrupted due to benign or malicious acts.Using a macro-data flow representation of the program execution, this article presents a novel approach based on work-stealing scheduling to dynamically adapt the execution to sabotage while keeping a reasonable slowdown rate.Unlike static adaptation or adaptation at the source code level, a dynamic adaptation at the middleware level is proposed, enforcing separation of concepts and programming transparency.This article contains both conceptual and experimental results that show the interest, feasibility and limits of the concept.

Grid applications have been prone to encountering problems such as failures or malicious attacks during execution, due to their distributed and large-scale features. The application itself, however, has limited power to address these problems. This paper presents the design, and implementation of an adaptive framework - Dynasa, which strives to handle security problems using adaptive fault-tolerance (i.e., checkpointing and replication) during the execution of applications according to the status of the grid environments.

Since the concept of grid computing has emerged; one of the major challenges consists in making applications able to benefit efficiently from the huge amount of resources with out burdening the developer. Observing that grid computing environments are dynamic, adaptability has been proposed as a way to tackle this problem. Adaptability is in deed the ability of applications to modify themselves according to their actual environment. It guarantees that they continuously behave in the best possible way despite environmental changes. In the context of grid computing, environmental changes concern mostly processor and network availability because of resource sharing between applications, administrative tasks and failures

As grid architectures provide execution environments that are distributed, parallel and dynamic, applications require to be not only parallel and distributed, but also able to adapt themselves to their execution environment. This article presents a model for designing self-adaptable parallel components that can be assembled to build applications for grid. This model includes the definition of a consistency criterion for the dynamic adaptation of SPMD components. We propose a solution to implement this criterion. It has been evaluated on both synthetic and real codes to exhibit the behavior of the several proposed strategies

The goal of the Pandore system is to allow the execution of parallel algorithms on DMPCs (Distributed Memory Parallel Computers) without having to take into account the low-level characteristics of the target distributed computer to program the algorithm. No explicit process definition and interprocess communications are needed. Parallelization is achieved through logical data organization. The Pandore system provides the user with a mean to specify data partitioning and data distribution over a domain of virtual processors for each parallel step of his algorithm.