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A Break in the Clouds: Towards a Cloud Definition

Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties Finally, this paper overviews the current standardization activities and elaborates further on open research challenges

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On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration

Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system's self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-a properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how, what, when and where, towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.

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Self-adaptive software: Landscape and research challenges

Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

A multi-tenant virtual machine infrastructure (MTVMI) allows multiple tenants to independently access and use a plurality of virtual computing resources via the Internet. Within the MTVMI, different tenants may define unique configurations of virtual computing resources and unique rules to govern the use of the virtual computing resources. The MTVMI may be configured to provide valuable services for tenants and users associated with the tenants.

Multitenant hosted virtual machine infrastructure

Scalability is critical to the success of many enterprises currently involved in doing business on the web and in providing information that may vary drastically from one time to another. Maintaining sufficient resources just to meet peak requirements can be costly. Cloud computing provides a powerful computing model that allows users to access resources on-demand. In this paper, we will describe a novel architecture for the dynamic scaling of web applications based on thresholds in a virtualized Cloud Computing environment. We will illustrate our scaling approach with a front-end load-balancer for routing and balancing user requests to web applications deployed on web servers installed in virtual machine instances. A dynamic scaling algorithm for automated provisioning of virtual machine resources based on threshold number of active sessions will be introduced. The on-demand capability of the Cloud to rapidly provision and dynamically allocate resources to users will be discussed. Our work has demonstrated the compelling benefits of the Cloud which is capable of handling sudden load surges, delivering IT resources on-demands to users, and maintaining higher resource utilization, thus reducing infrastructure and management costs.

Dynamic Scaling of Web Applications in a Virtualized Cloud Computing Environment

The goal of autonomic computing is to create computing systems capable of managing themselves to a far greater extent than they do today. This paper presents Unity, a decentralized architecture for autonomic computing based on multiple interacting agents called autonomic elements. We illustrate how the Unity architecture realizes a number of desired autonomic system behaviors including goal-driven self-assembly, self-healing, and real-time self-optimization. We then present a realistic prototype implementation, showing how a collection of Unity elements self-assembles, recovers from certain classes of faults, and manages the use of computational resources (e.g. servers) in a dynamic multi-application environment. In Unity, an autonomic element within each application environment computes a resource-level utility function based on information specified in that applicationýs service-level utility function. Resource-level utility functions from multiple application environments are sent to a Resource Arbiter element, which computes a globally optimal allocation of servers across the applications. We present illustrative empirical data showing the behavior of our implemented system in handling realistic Web-based transactional workloads running on a Linux cluster.

A Multi-Agent Systems Approach to Autonomic Computing

A system and a method are disclosed for dynamically analyzing software, some of whose potentially-important behaviors (such as worm-like behavior) may only be displayed when the software is executed in an environment where it has, or appears to have, access to a production network and/or to the global Internet. The software can be executed in a real or an emulated network environment that includes a monitoring component and an emulation component. The monitoring component serves to capture and/or record the behaviors displayed by the software and/or other components of the system, and the emulation component gives the software being analyzed the impression that it is executing with access to a production network and/or to the global Internet. The software being analyzed is effectively confined to the analysis network environment, and cannot in fact read information from, or alter any information on, any production network or the global Internet.

Method and apparatus for replicating and analyzing worm programs

A method and system for the automatic determination of the behavioural profile of a program suspected of having worm-like characteristics includes analyzing data processing system resources required by the program and, if the required resources are not indicative of the program having worm-like characteristics, running the program in a controlled non-network environment while monitoring and logging accesses to system resources to determine the behaviour of the program in the non-network environment. A logged record of the observed behaviour is analyzed to determine if the behaviour is indicative of the program having worm-like characteristics. The non-network environment may simulate the appearance of a network to the program, without emulating the operation of the network.

Method and apparatus for the automatic determination of potentially worm-like behaviour of a program

A method, apparatus, and computer instructions for updating a software component. A determination is made as to whether to update the software component (602). The software component is updated to form an updated software component in response to a determination to update the software component (606). The updated software component is automatically tested (612). A corrective action is performed in response to a failure in testing of the updated component (620).

Alla Segal

Papers

Dynamic Scaling of Web Applications in a Virtualized Cloud Computing Environment

A Multi-Agent Systems Approach to Autonomic Computing

Method and apparatus for replicating and analyzing worm programs

Method and apparatus for the automatic determination of potentially worm-like behaviour of a program

Method and apparatus for automatic updating and testing of software