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Mobile Edge Clouds (MECs) are emerging as a key complement to centralized cloud infrastructures by bringing computing and storage resources closer to the network edge, thereby reducing network bandwidth, latency, and jitter. A critical challenge in leveraging MECs effectively is the application placement problem, which seeks to minimize operational costs while ensuring end-user Quality of Service (QoS). This problem is further complicated by user mobility, as applications must migrate to maintain optimal QoS, yet frequent migrations can lead to unnecessary bandwidth consumption due to state transfer. In this paper, we tackle the application placement problem for stateful applications in MEC environments. We model the dynamic workloads, applications, and infrastructure typical of MECs and define the associated costs: resource utilization, migration, and QoS degradation. Based on this model, we propose two online placement algorithms – Gale-Shapley-based and Follow-me – designed to minimize the total cost of operating applications. These algorithms are compared against an offline benchmark that has complete future knowledge. Experimental results demonstrate that both proposed algorithms efficiently place applications in MECs, achieving operating costs within 8% of the global optimum approximated by the offline algorithm. Furthermore, the Gale-Shapley-based algorithm outperforms the Follow-me algorithm, reducing operating costs by up to 17% and improving load balancing across MECs to mitigate resource scarcity.

Recent advances in multi-core chip technology have enabled the dynamic tuning of shared memory resources, such as last-level cache and memory bus bandwidth. However, despite proven performance benefits, the complexity of effectively utilizing these hardware-level QoS enforcement features has limited their adoption in real-world cloud computing environments. In this paper, we introduce ESTHER, a novel approach to autonomously fine-tune QoS enforcement features in cloud environments using extremum seeking control, focusing on applications needs and operator ease-of-use. We demonstrate that ESTHER effectively maintains latency-critical workload SLOs and rapidly resolves any infringements by prioritizing shared memory resources. Such fast node-level resolution of SLO violations ensures that costly cluster-level scaling events may be avoided. Furthermore, ESTHER improves best-effort job throughput without impacting latency-critical workloads, achieving performance gains without utilizing workload profiling or prior knowledge of system dynamics.

Mission-critical applications, such as real-time emergency response, healthcare, and transport systems, depend heavily on the low latency and reliability provided by Mobile Edge Computing (MEC). The failure of such applications can lead to high latency and severe consequences, including loss of life, financial catastrophe, or operational disruption. However, the dependability of edge clusters is often overlooked, particularly in terms of fault awareness and recovery strategies, which are crucial to these applications. In this work, we focus on loosely coupled IoT applications and propose a Failure and Latency-aware Scheduling approach for Edge (FaLSE) that balances the trade-off between the availability of edge clusters and the latency of containerized mission-critical applications. We used a decentralized network coordinate system to estimate latency between IoT devices/users and nodes. To validate the proposed approach, we compare it with the standard Kubernetes scheduler, which is currently among the most widely used workload orchestration platforms. The results indicate that FaLSE reduced the failure request rate by 87.9% while maintaining a 71.97% lower 95th percentile latency for mission-critical applications and a 10.63% lower latency for normal applications compared to the standard Kubernetes scheduler.

Recent advancements in commodity server processors have enabled dynamic hardware-based quality-of-service (QoS) enforcement. These features have gathered increasing interest in research communities due to their versatility and wide range of applications. Thus, there exists a need to understand how scholars leverage hardware QoS enforcement in research, understand strengths and shortcomings, and identify gaps in current state-of-the-art research. This paper observes relevant publications, presents a novel taxonomy, discusses the approaches used, and identifies trends. Furthermore, an opportunity is recognized for QoS enforcement utilization in service-based cloud computing environments, and open challenges are presented.

In the realm of edge computing, the optimization of latency, energy, bandwidth, and local computation is critical, especially for mission-critical applications in sectors like disaster management and healthcare. Such applications, exemplified by deploying UAVs and autonomous robots, demand instantaneous data processing. Given the inherent constraints of edge servers—characterized by their limited capacity—meticulous resource management becomes paramount. This entails judicious resource allocation, astute provisioning, strategic task offloading, and judicious application placement, all pivotal for both fixed and mobile resource service delivery. This survey delves deep into the nuances of deploying mission-critical applications in an edge environment, dissecting their technological prerequisites. Our exploration employs a systematic literature review grounded in a conventional review methodology. We analyze the cornerstone quality of service metrics pivotal for such critical applications in edge contexts, aiming for efficient service delivery. Moreover, we identified some major gaps in current resource management strategies. Our overarching ambition is to pave the way for robust edge computing paradigms tailored for mission-critical applications.

Disaster management, such as early warnings for earthquakes, hurricanes, and fires, requires IoT sensors and cameras, which produce tremendous amounts of data.To avoid network bandwidth congestion, much of this data needs to be processed close to where it is produced, as enabled by Mobile Edge Clouds (MEC). However, for such use cases, the disaster itself may take out the MEC, hence hindering disaster management efforts. We present a fault tolerance infrastructure tailored specifically for MEC systems to address various types of failures as part of a holistic disaster recovery solution. Our research investigates using current technologies, such as Kubernetes, to effectively handle fault tolerance in situations involving the failure of one or several edge nodes and RabbitMQ as a resilient message broker in our proposed infrastructure to ensure dependable message transmission, even during network outages. To evaluate our framework, we conduct a case study using weather stations as mission-critical assets within an urban setting next to forests where edge nodes are placed as safely as possible. The experiments demonstrate that the infrastructure can handle two node failures simultaneously. The proposed infrastructure ensures 99.966\% availability for both the system and mission-critical applications.

Placing applications within Mobile Edge Clouds (MEC) poses challenges due to dynamic user mobility. Maintaining optimal Quality of Service may require frequent application migration in response to changing user locations, potentially leading to bandwidth wastage. This paper addresses application placement challenges in MEC environments by developing a comprehensive model covering workloads, applications, and MEC infrastructures. Following this, various costs associated with application operation, including resource utilization, migration overhead, and potential service quality degradation, are systematically formulated. An online application placement algorithm, App EDC Match, inspired by the Gale-Shapley matching algorithm, is introduced to optimize application placement considering these cost factors. Through experiments that employ real mobility traces to simulate workload dynamics, the results demonstrate that the proposed algorithm efficiently determines near-optimal application placements within Edge Data Centers. It achieves total operating costs within a narrow margin of 8% higher than the approximate global optimum attained by the offline precognition algorithm, which assumes access to future user locations. Additionally, the proposed placement algorithm effectively mitigates resource scarcity in MEC.

Microservice-based architectures consist of numerous, loosely coupled services with multiple instances. Service meshes aim to simplify traffic management and prevent microservice overload through circuit breaking and request retry mechanisms. Previous studies have demonstrated that the static configuration of these mechanisms is unfit for the dynamic environment of microservices. We conduct a sensitivity analysis to understand the impact of retrying across a wide range of scenarios. Based on the findings, we propose a retry controller that can also work with dynamically configured circuit breakers. We have empirically assessed our proposed controller in various scenarios, including transient overload and noisy neighbors while enforcing adaptive circuit breaking. The results show that our proposed controller does not deviate from a well-tuned configuration while maintaining carried response time and adapting to the changes. In comparison to the default static retry configuration that is mostly used in practice, our approach improves the carried throughput up to 12x and 32x respectively in the cases of transient overload and noisy neighbors.

Microservice-based architectures have become ubiq-uitous in large-scale software systems. Experimental cloud re-searchers constantly propose enhanced resource management mechanisms for such systems. These mechanisms need to be eval-uated using both realistic and flexible microservice benchmarks to study in which ways diverse application characteristics can affect their performance and scalability. However, current mi-croservice benchmarks have limitations including static compu-tational complexity, limited architectural scale, and fixed topology (i.e., number of tiers, fan-in, and fan-out characteristics).

We therefore propose HydraGen, a tool that enables re-searchers to systematically generate benchmarks with different computational complexities and topologies, to tackle experimental evaluation of performance at scale for web-serving applications, with a focus on inter-service communication. To illustrate the potential of our open-source tool, we demonstrate how it can reproduce an existing microservice benchmark with preserved architectural properties. We also demonstrate how HydraGen can enrich the evaluation of cloud management systems based on a case study related to traffic engineering.

One of the promises of container orchestration technologies is that they enable effective utilization of hardware resources and thus reduce infrastructure costs. However, as applications scale up and down over time in a Kubernetes-managed environment, the cluster may enter a state of resource fragmentation. In such a state, the cluster's hardware cannot be used to its full potential because resources become locked by poor Pod to Node mappings. This paper shows that such problems are common as workload requirements approach a cluster's resource capacity. Additionally, we present an experimental analysis of directed Pod eviction as a technique to combat the issue of resource fragmentation. Our findings show that directed Pod eviction can reduce resource fragmentation and allow clusters to run a given workload using 16.7% fewer Nodes than is consistently possible using standard Kubernetes scheduling. These findings are unaffected by applying input shaking to the experimental analysis, strengthening confidence in the generality of these resource utilization improvements.