Self-adaptation is a first class concern for cloud applications, which should be able to withstand diverse runtime changes. Variations are simultaneously happening both at the cloud infrastructure levelfor example hardware failures -and at the user workload levelflash crowds. However, robustly withstanding extreme variability, requires costly hardware over-provisioning.In this paper, we introduce a self-adaptation programming paradigm called brownout. Using this paradigm, applications can be designed to robustly withstand unpredictable runtime variations, without over-provisioning. The paradigm is based on optional code that can be dynamically deactivated through decisions based on control theory.We modified two popular web application prototypes -RUBiS and RUBBoS -with less than 170 lines of code, to make them brownout-compliant. Experiments show that brownout selfadaptation dramatically improves the ability to withstand flashcrowds and hardware failures.

In order to meet stringent performance requirements, system administrators must e↵ectively detect undesirable performance behaviours, identify potential root causes and take adequate corrective measures. The problem of uncovering and understanding performance anomalies and their causes (bottlenecks) in di↵erent system and application domains is well studied. In order to assess progress, research trends and identify open challenges, we have reviewed major contributions in the area and present our findings in this survey. Our approach provides an overview of anomaly detection and bottleneck identification research as it relates to the performance of computing systems. By identifying fundamental elements of the problem, we are able to categorize existing solutions based on multiple factors such as the detection goals, nature of applications and systems, system observability, and detection methods.

Abstract-Cloud applications are often subject to unexpected events like flash crowds and hardware failures. Without a predictable behaviour, users may abandon an unresponsive application. This problem has been partially solved on two separate fronts: first, by adding a self-adaptive feature called brownout inside cloud applications to bound response times by modulating user experience, and, second, by introducing replicas -copies of the applications having the same functionalities -for redundancy and adding a load-balancer to direct incoming traffic.However, existing load-balancing strategies interfere with brownout self-adaptivity. Load-balancers are often based on response times, that are already controlled by the self-adaptive features of the application, hence they are not a good indicator of how well a replica is performing.In this paper, we present novel load-balancing strategies, specifically designed to support brownout applications. They base their decision not on response time, but on user experience degradation. We implemented our strategies in a selfadaptive application simulator, together with some state-of-theart solutions. Results obtained in multiple scenarios show that the proposed strategies bring significant improvements when compared to the state-of-the-art ones.

An automated solution to horizontal vs. vertical elasticity problem is central to make cloud autoscalers truly autonomous. Today's cloud autoscalers are typically varying the capacity allocated by increasing and decreasing the number of virtual machines (VMs) of a predefined size (horizontal elasticity), not taking into account that as load varies it may be advantageous not only to vary the number but also the size of VMs (vertical elasticity). We analyze the price/performance effects achieved by different strategies for selecting VM-sizes for handling increasing load and we propose a cost-benefit based approach to determine when to (partly) replace a current set of VMs with a different set. We evaluate our repacking approach in combination with different auto-scaling strategies. Our results show a range of 7% up to 60% cost saving in total resource utilization cost of our sample applications and workloads.

We focus on improving resilience of cloud services (e.g., e-commerce website), when correlated or cascading failures lead to computing capacity shortage. We study how to extend the classical cloud service architecture composed of a loadbalancer and replicas with a recently proposed self-adaptive paradigm called brownout. Such services are able to reduce their capacity requirements by degrading user experience (e.g., disabling recommendations).Combining resilience with the brownout paradigm is to date an open practical problem. The issue is to ensure that replica self-adaptivity would not confuse the load-balancing algorithm, overloading replicas that are already struggling with capacity shortage. For example, load-balancing strategies based on response times are not able to decide which replicas should be selected, since the response times are already controlled by the brownout paradigm.In this paper we propose two novel brownout-aware loadbalancing algorithms. To test their practical applicability, we extended the popular lighttpd web server and load-balancer, thus obtaining a production-ready implementation. Experimental evaluation shows that the approach enables cloud services to remain responsive despite cascading failures. Moreover, when compared to Shortest Queue First (SQF), believed to be nearoptimal in the non-adaptive case, our algorithms improve user experience by 5%, with high statistical significance, while preserving response time predictability.

Abstract-Cloud computing is mostly allocating resources at course grain, e.g., entire CPU cores are allocated for as long as an hour. For improving resource efficiency of clouds, the Resource-as-a-Service cloud is envisioned, which allocated resources at fraction of a core and second granularity. Despite technology enabling such infrastructure, e.g., through lightweight virtualization such as LXC or vertical elasticity in the Xen hypervisor, performance models to decide how much capacity to allocate to each application are lagging behind.In this paper, we evaluate two performance models for mean response time, a previously proposed one and a novel one. The models are tested with 3 applications in both open and closed system models. Results show that the inverse model reacts fast and remains stable for targets as low as 0.5 seconds.

Abstract-Resource overbooking is an admission control technique to increase utilization in cloud environments. However, due to uncertainty about future application workloads, overbooking may result in overload situations and deteriorated performance. We mitigate this using brownout, a feedback approach to application performance steering, that ensures graceful degradation during load spikes and thus avoids overload. Additionally, brownout management information is included into the overbooking system, enabling the development of improved reactive methods to overload situations. Our combined brownout-overbooking approach is evaluated based on real-life interactive workloads and noninteractive batch applications. The results show that our approach achieves an improvement of resource utilization of 11 to 37 percentage points, while keeping response times lower than the set target of 1 second, with negligible application degradation.

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