David Breitgand

Mobile edge computing offers ultra-low latency, high bandwidth, and high reliability. Thus, it can support a plethora of emerging services that can be placed in close proximity to the user. One of the fundamental problems in this context is maximizing the benefit from the placement of networked services, while meeting bandwidth and latency constraints. In this study, we propose an adaptive and predictive resource allocation strategy for virtual-network function placement comprising services at the mobile edge. Our study focuses on maximizing the service provider’s benefit under user mobility, i.e., uncertainty. This problem is NP-hard. Therefore, we propose a heuristic solution: we exploit local knowledge about the likely movements of users to speculatively allocate service functions. We allow the service functions to be allocated at different edge nodes, as long as latency and bandwidth constraints are met. We evaluate our proposal against a theoretically optimal algorithm as well as against recent previous work, using widely used simulation tools. Through an extensive simulation study, we demonstrate that under realistic scenarios, an adaptive and proactive strategy coupled with flexible placement can achieve close-to-optimal benefit.

Serverless computing has become very popular today since it largely simplifies cloud programming. Developers do no longer need to worry about provisioning or operating servers, and they have to pay only for the compute resources used when their code is run. This new cloud paradigm suits well for many applications, and researchers have already begun investigating the feasibility of serverless computing for data analytics. Unfortunately, today’s serverless computing presents important limitations that make it really difficult to support all sorts of analytics workloads. This chapter first starts by analyzing three fundamental trade-offs of today’s serverless computing model and their relationship with data analytics. It studies how by relaxing disaggregation, isolation, and simple scheduling, it is possible to increase the overall computing performance, but at the expense of essential aspects of the model such as elasticity, security, or sub-second activations, respectively. The consequence of these trade-offs is that analytics applications may well end up embracing hybrid systems composed of serverless and serverful components, which we call ServerMix in this chapter. We will review the existing related work to show that most applications can be actually categorized as ServerMix.

To ensure high Quality of Experience (QoE) for end users, many media applications require significant quantities of computing and network resources, making their realization challenging in resource constrained environments. In this paper, we present the approach of the 5G-MEDIA project, providing an integrated programmable service platform for the development, design and operations of media applications in 5G networks, facilitating media service management across the service life cycle. The platform offers tools to service developers for efficient development, testing and continuous correction of services. One step further, it provides a service virtualization platform offering horizontal services, such as a Media Service Catalogue and accounting services, as well as optimization mechanisms to flexibly adapt service operations to dynamic conditions with efficient use of infrastructure resources. The paper outlines three use cases where the platform was tested and validated.

Hybrid cloud multi-access edge computing (MEC) deployments have been proposed as efficient means to support Internet of Things (IoT) applications, relying on a plethora of nodes and data. In this paper, an overview on the area of hybrid clouds considering relevant research areas is given, providing technologies and mechanisms for the formation of such MEC deployments, as well as emphasizing several key issues that should be tackled by novel approaches, especially under the 5G paradigm. Furthermore, a decentralized hybrid cloud MEC architecture, resulting in a Platform-as-a-Service (PaaS) is proposed and its main building blocks and layers are thoroughly described. Aiming to offer a broad perspective on the business potential of such a platform, the stakeholder ecosystem is also analyzed. Finally, two use cases in the context of smart cities and mobile health are presented, aimed at showing how the proposed PaaS enables the development of respective IoT applications.

Self-healing capabilities allow software systems to overcome problems occurring during testing and run time, and thus improve overall system behavior. The PANACEA framework introduced in this paper provides a design methodology as well as ready-to-use healing elements aimed at enhancing software systems with self-healing capabilities both at design time and at run time. The PANACEA approach is based on inserting self- healing elements into the system at design and coding time, to be used later for healing at testing and run time. Specifically, the Panacea framework is based on inserting annotations into the system code at design and coding time, to later on serve as an interface for runtime monitoring, managing, configuring and healing of the annotated system components. The current embodiment of PANACEA includes several generic components that provide self-healing capabilities suited for a variety of application types. The PANACEA runtime environment automatically activates and invokes these components in order to optimize and heal the application. The PANACEA framework provides an innovative programming model that enables development of advanced self-healing applications. PANACEA introduces a paradigm shift in which software is made self-healing by design. This paradigm shift, however, is graceful since developers are not required to master neither new programming skills, nor languages. As our initial experiments demonstrate, PANACEA introduces a very small performance overhead, and scales well.

Design of Service Level Agreements (SLAs) emerges as an increasingly important discipline in business-oriented IT management. In this work, we study utility maximization of contractual obligations of a business service provider for a typical SLA. We demonstrate that using service usage and performance data as well as IT performance data, routinely collected by enterprises, efficient automated derivation of optimal response time Service Level Objectives (SLOs) of an SLA is possible. This paper addresses a specific facet of the SLA design problem not sufficiently addressed in previous studies. One common approach is to calculate SLOs attainable for the given IT infrastructure by means of simple percentile analysis. However, this methodology is business agnostic and may result in sub-optimal SLOs. Another widespread approach addresses IT infrastructure (re)design, where the goal is to enable the IT to meet specified target SLOs. In contrast to these approaches, our work proposes finding optimal response lime SLOs attainable for the given IT infrastructure in a business aware manner. We define the response time SLO optimization problem, propose an algorithm that efficiently solves it using linear optimization and percentile analysis, and evaluate our solution both analytically and experimentally.