Mohamed

rSLA: Monitoring SLAs in Dynamic Service Environments

Today’s application environments combine Cloud and on-premise infrastructure, as well as platforms and services from different providers to enable quick development and delivery of solutions to their intended users. The ability to use Cloud platforms to stand up applications in a short time frame, the wide availability of Web services, and the application of a continuous deployment model has led to very dynamic application environments. In those application environments, managing quality of service has become more important. The more external service vendors are involved the less control an application owner has and must rely on Service Level Agreements (SLAs). However, SLA management is becoming more difficult. Services from different vendors expose different instrumentation. In addition, the increasing dynamism of application environments entails that the speed of SLA monitoring set up must match the speed of changes to the application environment.

Foggy: A Framework for Continuous Automated IoT Application Deployment in Fog Computing

Traditional Cloud model is not designed to handle latency-sensitive Internet of Things applications. The new trend consists on moving data to be processed close to where it was generated. To this end, Fog Computing paradigm suggests using the compute and storage power of network elements. In such environments, intelligent and scalable orchestration of thousands of heterogeneous devices in complex environments is critical for IoT Service providers. In this vision paper, we present a framework, called Foggy, that facilitates dynamic resource provisioning and automated application deployment in Fog Computing architectures. We analyze several applications and identify their requirements that need to be taken intoconsideration in our design of the Foggy framework. We implemented a proof of concept of a simple IoT application continuous deployment using Raspberry Pi boards.

Extending OCCI for autonomic management in the cloud

We propose an autonomic management model for Cloud resources.We extend Open Cloud Computing Interface to describe our autonomic model.We illustrate our autonomic model with a real use case that spans over different layers.We detail the implementation and experiments details.We show the efficiency of our approach on a realistic cloud environment. Cloud Computing is an emerging paradigm involving different kinds of Information Technologies (IT) services. One of the major advantages of this paradigm resides on its pay-as-you-go economic model. To remain efficient, it becomes necessary to couple this model with autonomic computing. By autonomic computing we mean the ability of the system to automatically and dynamically manage its resources to respond to the requirements of the business based on Service Level Agreement (SLA). In this paper, we propose an extension for Open Cloud Computing Interface (OCCI) to support the different aspects of autonomic computing. This OCCI extension describes new Resources and Links that are generic Kinds and are specialized using OCCI Mixins. We introduce the Autonomic Manager as a special Resource that starting from a SLA instantiates all needed entities to automatically establish an infrastructure to enable an autonomic management of Cloud resources. The other introduced OCCI Resources are: Analyzer to analyze monitoring data based on specific analysis rules and Reconfiguration Manager to generate reconfiguration actions based on reconfiguration strategies. These Resources are linked using new defined Link entities. We describe herein, a real use case to show that we can apply our approach to the different levels of the Cloud (i.e., IaaS, PaaS and SaaS) at the same time. We present also the implementation details as well as evaluation preliminary results that are encouraging.

Optimal assignment of autonomic managers to cloud resources

There has been an increasing number of companies moving towards cloud computing due to its economic model based on the so-called pay-as-you-go. The cloud is known as a dynamic and scalable environment. These characteristics make the management of this environment a complex task. Using autonomic management potentially helps to solve the complexity of managing large number of provisioned cloud resources. Since using one autonomic manager (AM) might result on inefficiency in the management of the system, we propose in this paper to use a decentralized approach for autonomic management. The problem that we are solving herein is to determine how many AMs to use in order to maximize the performance of the management and minimize the cost of the used AMs. We propose a mathematical model that allows to determine the optimal assignment of resources and AMs in different availability zones taking into account the different costs of the involved AMs as well as the communication overhead. We also give an overview of the implementation of the proposed mathematical model.

The rSLA Framework: Monitoring and Enforcement of Service Level Agreements for Cloud Services

Managing service quality in heterogeneous Cloud environments is complex: different Cloud providers expose different management interfaces. To manage Service Level Agreements (SLAs) in this context, we have developed the rSLA framework that enables fast setup of SLA monitoring in dynamic and heterogeneous Cloud environments. The rSLA framework is made up of three main components: the rSLA language to formally represent SLAs, the rSLA Service, which interprets the SLAs and implements the behavior specified in them, and a set of Xlets - lightweight, dynamically bound adapters to monitoring and controlling interfaces. In this paper, we present the rSLA framework, and describe how it enables the monitoring and enforcement of service level agreements for heterogeneous Cloud services.

Toward locality-aware scheduling for containerized cloud services

The state-of-the-art scheduler of containerized cloud services considers load-balance as the only criterion and neglects many others such as application performance. In the era of Big Data, however, applications have evolved to be highly data-intensive thus perform poorly in existing systems. This particularly holds for Platform-as-a-Service environments that encourage an application model of stateless application instances in containers reading and writing data to services storing states, e.g., key-value stores. To this end, this work strives to improve todays cloud services by incorporating sensitivity to both load-balance and application performance. We built and analyzed theoretical models that respect both dimensions, and unlike prior studies, our model abstracts the dilemma between load-balance and application performance into an optimization problem and employs a statistical method to meet the discrepant requirements. Using heuristic algorithms and approaches we try to solve the abstracted problems. We implemented the proposed approach in Diego (an open-source cloud service scheduler) and demonstrate that it can significantly boost the performance of containerized applications while preserving a relatively high load-balance.

Collaborative Autonomic Management of Distributed Component-Based Applications

Executing component-based applications in dynamic distributed environments requires autonomic management to cope with the changes of these environments. However, using a centralized Autonomic Manager AM for monitoring and adaptation of a large number of distributed components is a non trivial task. Therefore, we argue for a distributed management by using an AM for each component. These distributed managers should collaborate to avoid conflicting decisions that may entail the applications failure. Towards this objective, we propose a collaborative autonomic management of component-based applications in distributed environments. An application is considered as a composite of atomic or composite components. Each component or composite is managed by its AM that holds local strategies for its reconfiguration. An AM is able to collaborate with other managers in different hierarchical levels for the self-management of the whole application. We show the utility of our approach through a use case in the context of Cloud computing.

In Search of the Ideal Storage Configuration for Docker Containers

Containers are a widely successful technology today popularized by Docker. Containers improve system utilization by increasing workload density. Docker containers enable seamless deployment of workloads across development, test, and production environments. Dockers unique approach to data management, which involves frequent snapshot creation and removal, presents a new set of exciting challenges for storage systems. At the same time, storage management for Docker containers has remained largely unexplored with a dizzying array of solution choices and configuration options. In this paper we unravel the multi-faceted nature of Docker storage and demonstrate its impact on system and workload performance. As we uncover new properties of the popular Docker storage drivers, this is a sobering reminder that widespread use of new technologies can often precede their careful evaluation.

Wharf: Sharing Docker Images in a Distributed File System.

Container management frameworks, such as Docker, package diverse applications and their complex dependencies in self-contained images, which facilitates application deployment, distribution, and sharing. Currently, Docker employs a shared-nothing storage architecture, i.e. every Docker-enabled host requires its own copy of an image on local storage to create and run containers. This greatly inflates storage utilization, network load, and job completion times in the cluster. In this paper, we investigate the option of storing container images in and serving them from a distributed file system. By sharing images in a distributed storage layer, storage utilization can be reduced and redundant image retrievals from a Docker registry become unnecessary. We introduce Wharf, a middleware to transparently add distributed storage support to Docker. Wharf partitions Dockers runtime state into local and global parts and efficiently synchronizes accesses to the global state. By exploiting the layered structure of Docker images, Wharf minimizes the synchronization overhead. Our experiments show that compared to Docker on local storage, Wharf can speed up image retrievals by up to 12x, has more stable performance, and introduces only a minor overhead when accessing data on distributed storage.

Coop-DAAB: Cooperative Attribute Based Data Aggregation for Internet of Things Applications

The deployment of IoT devices is gaining an expanding interest in our daily life. Indeed, IoT networks consist in interconnecting several smart and resource constrained devices to enable advanced services. Security management in IoT is a big challenge as personal data are shared by a huge number of distributed services and devices. In this paper, we propose a Cooperative Data Aggregation solution based on a novel use of Attribute Based signcryption scheme ((mathsf {Coop})-(mathsf {DAAB})). (mathsf {Coop})-(mathsf {DAAB}) consists in distributing data signcryption operation between different participating entities (i.e., IoT devices). Indeed, each IoT device encrypts and signs in only one step the collected data with respect to a selected sub-predicate of a general access predicate before forwarding to an aggregating entity. This latter is able to aggregate and decrypt collected data if a sufficient number of IoT devices cooperates without learning any personal information about each participating device. Thanks to the use of an attribute based signcryption scheme, authenticity of data collected by IoT devices is proved while protecting them from any unauthorized access.