The edge computing paradigm comes with a promise of lower application latency compared to the cloud. Moreover, offloading user device computations to the edge enables running demanding applications on resource-constrained mobile end devices. However, there is a lack of workload models specific to edge offloading using applications as their basis.In this work, we build upon the reconfigurable open-source mixed reality (MR) framework MR-Leo as a vehicle to study resource utilisation and quality of service for a time-critical mobile application that would have to rely on the edge to be widely deployed. We perform experiments to aid estimating the resource footprint and the generated load by MR-Leo, and propose an application model and a statistical workload model for it. The idea is that such empirically-driven models can be the basis of evaluations of edge algorithms within simulation or analytical studies.A comparison with a workload model used in a recent work shows that the computational demand of MR-Leo exhibits very different characteristics from those assumed for MR applications earlier.

The emerging field of edge computing is suffering from a lack of representative data to evaluate rapidly introduced new algorithms or techniques. That is a critical issue as this complex paradigm has numerous different use cases which translate into a highly diverse set of workload types.

In this work, within the context of the edge computing activity of SPEC RG Cloud, we continue working towards an edge benchmark by defining high-level workload classes as well as collecting and analyzing traces for three real-world edge applications, which, according to the existing literature, are the representatives of those classes. Moreover, we propose a practical and generic methodology for workload definition and gathering. The traces and gathering tool are provided open-source.

In the analysis of the collected workloads, we detect discrepancies between the literature and the traces obtained, thus highlighting the need for a continuing effort into gathering and providing data from real applications, which can be done using the proposed trace gathering methodology. Additionally, we discuss various insights and future directions that rise to the surface through our analysis.

In the emerging edge computing architecture, several types of devices have computational resources available. In order to make efficient use of those resources, deciding on which device a task should execute is of great importance. Existing works on task placement in edge computing focus on a resource supply side consisting of stationary devices only. In this paper, we consider the addition of mobile edge devices. We explore how mobile and stationary edge devices can augment the original task placement problem with a second placement problem: the placement of the mobile edge devices. We propose the ORCH framework in order to solve the joint problem in a distributed manner and evaluate it in the context of a spatially-changing load. Our implementation of the combined task and edge placement algorithms shows a normalized 83% delay-sensitive task completion rate compared to a perfect edge placement strategy.

Deciding where to handle services and tasks, as well as provisioning an adequate amount of computing resources for this handling, is a main challenge of edge computing systems. Moreover, latency-sensitive services constrain the type and location of edge devices that can provide the needed resources. When available resources are scarce there is a possibility that some resource allocation requests are denied. In this work, we propose the VioLinn system to tackle the joint problems of task placement, service placement, and edge device provisioning. Dealing with latency-sensitive services is achieved through proximityaware algorithms that ensure the tasks are handled close to the end-user. Moreover, the concept of spare edge device is introduced to handle sudden load variations in time and space without having to continuously over-provision. Several spare device selection algorithms are proposed with different cost/performance tradeoffs. Evaluations are performed both in a Kubernetes-based testbed and using simulations and show the benefit of using spare devices for handling localized load spikes with higher quality of service (QoS) and lower computing resource usage. The study of the different algorithms shows that it is possible to achieve this increase in QoS with different tradeoffs against cost and performance.

Information and communication technologies are increasingly pervasive in our everyday lives. Their use has greatly evolved from an ancillary service to a component of all our activities, anytime, anywhere. To this aim, we rely heavily on cloud computing and, more recently, on edge computing. Hence, their contribution (or obstruction) to the sustainability of our society at large is pivotal.

Unfortunately, cloud/edge provisioning has a dark side: it too often prioritizes economic gain over the cost of long-lasting sustainability. Also, sustainability is often absent from the discussions in the cloud/edge research community.

To start the discussion and highlight a number of sustainability shortcomings of the cloud and edge computing paradigms, we carry out an exploratory study involving experts-in-the-field, capture their inputs in the form of so-called unsustainable patterns, and complement them with examples of possible countermeasures.

The results of our study include: (i) the definition of a Pattern Model, (ii) a catalog of unsustainable patterns for the cloud and edge computing paradigms, and (iii) the identification of preliminary countermeasures and takeaways in order to make these two paradigms more sustainable.

Edge computing is becoming a reality and attracts an increasing interest both from academia and industry. This is driven by its promises of enabling/improving use cases thanks to, e.g., lower latency or alleviated network load. This paves the way for edge computing having a huge impact on our daily lives in the (near) future. However, except works dealing with energy efficiency, studies of the (un)sustainability of edge computing are almost nonexistent, which is worrying. In this article, we advocate the need to go beyond energy efficiency and face the resource impact of edge computing. At this point when we are still able to influence design choices, it is the responsibility of this community to ensure future systems do not become unsustainable down the line. In particular, we suggest embracing a sufficiency mindset, aiming at reducing absolute resource impact and defining what is a good enough service level. After explaining why we need to move beyond efficiency, we explore the concept of sufficiency and identify related challenges. Then, we propose a first version of an edge sufficiency toolkit as a helper for shifting toward a sufficiency mindset. Finally, we illustrate the use of this toolkit in a case study.