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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.

Service mesh is getting widely adopted as the cloud-native mechanism for traffic management in microservice-based applications, in particular for generic IT workloads hosted in more centralized cloud environments. Performance-demanding applications continue to drive the decentralization of modern application execution environments, as in the case of mobile edge cloud. This paper presents a systematic and qualitative analysis of state-of-the-art service mesh to evaluate how suitable its design is for addressing the traffic management needs of performance-demanding application workloads hosted in a mobile edge cloud environment. With this analysis, we argue that today's dependability-centric service mesh design fails at addressing the needs of the different types of emerging mobile edge cloud workloads and motivate further research in the directions of performance-efficient architectures, stronger QoS guarantees and higher complexity abstractions of cloud-native traffic manage-ment frameworks.

A microservice architecture features hundreds or even thousands of small loosely coupled services with multiple instances. Because microservice performance depends on many factors including the workload, inter-service traffic management is complex in such dynamic environments. Service meshes aim to handle this complexity and to facilitate management, observability, and communication between microservices. Service meshes provide various traffic management policies such as circuit breaking and retry mechanisms, which are claimed to protect microservices against overload and increase the robustness of communication between microservices. However, there have been no systematic studies on the effects of these mechanisms on microservice performance and robustness. Furthermore, the exact impact of various tuning parameters for circuit breaking and retries are poorly understood. This work presents a large set of experiments conducted to investigate these issues using a representative microservice benchmark in a Kubernetes testbed with the widely used Istio service mesh. Our experiments reveal effective configurations of circuit breakers and retries. The findings presented will be useful to engineers seeking to configure service meshes more systematically and also open up new areas of research for academics in the area of service meshes for (autonomic) microservice resource management.

Welcome to the 10th International Conference on Cloud Engineering (IC2E-2021), sponsored by IEEE and held inperson in beautiful Pacific Grove, CA (near Monterrey, CA) - returning to the Bay Area of California for this 10th anniversary - the location where IC2E started over a decade ago! We are thrilled to be returning to a safe, yet inperson, event this year after being virtual last year due to the COVID-19 pandemic. We look forward to catching up with you, hearing about your latest cloud computing research, socializing in a beautiful setting, and meeting those of you new to IC2E.

Service meshes factor out code dealing with inter-micro-service communication. The overall resilience of a cloud application is improved if constituent micro-services return stale data, instead of no data at all. This paper proposes and implements application agnostic caching for micro services. While caching is widely employed for serving web service traffic, its usage in inter-micro-service communication is lacking. Micro-services responses are highly dynamic, which requires carefully choosing adaptive time-to-life caching algorithms. Our approach is application agnostic, is cloud native, and supports gRPC. We evaluate our approach and implementation using the micro-service benchmark by Google Cloud called Hipster Shop. Our approach results in caching of about 80% of requests. Results show the feasibility and efficiency of our approach, which encourages implementing caching in service meshes. Additionally, we make the code, experiments, and data publicly available.

Site Reliability Engineers are at the center of two tensions: On one hand, they need to respond to alerts within a short time, to restore a non-functional system. On the other hand, short response times is disruptive to everyday life and lead to alert fatigue. To alleviate this tension, many resource management mechanisms are proposed handle overload and mitigate the faults. One recent such mechanism is circuit breaking in service meshes. Circuit breaking rejects incoming requests to protect latency at the expense of availability (successfully answered requests), but in many scenarios achieve neither due to the difficulty of knowing when to trigger circuit breaking in highly dynamic microservice environments.

We propose an adaptive circuit breaking mechanism, implemented through an adaptive controller, that not only avoids overload and mitigate failure, but keeps the tail response time below a given threshold while maximizing service throughput. Our proposed controller is experimentally compared with a static circuit breaker across a wide set of overload scenarios in a testbed based on Istio and Kubernetes. The results show that our controller maintains tail response time below the given threshold 98% of the time (including cold starts) on average with an availability of 70% with 29% of requests circuit broken. This compares favorably to a static circuit breaker configuration, which features a 63% availability, 30% circuit broken requests, and more than 5% of requests timing out.