A survey and taxonomy of self-aware and self-adaptive cloud autoscaling systems

Autoscaling system can reconfigure cloud-based services and applications, through various configurations of cloud sofware and provisions of hardware resources, to adapt to the changing environment at runtime. Such a behavior offers the foundation for achieving elasticity in modern cloud computing paradigm. Given the dynamic and uncertain nature of the shared cloud infrastructure, cloud autoscaling system has been engineered as one of the most complex, sophisticated and intelligent artifacts created by human, aiming to achieve self-aware, self-adaptive and dependable runtime scaling. Yet, existing Self-aware and Self-adaptive Cloud Autoscaling System (SSCAS) is not mature to a state that it can be reliably exploited in the cloud. In this article, we survey the state-of-the-art research studies on SSCAS and provide a comprehensive taxonomy for this feld. We present detailed analysis of the results and provide insights on open challenges, as well as the promising directions that are worth investigated in the future work of this area of research. Our survey and taxonomy contribute to the fundamentals of engineering more intelligent autoscaling systems in the cloud

Self-aware and self-adaptive autoscaling for cloud based services

Modern Internet services are increasingly leveraging on cloud computing for flexible, elastic and on-demand provision. Typically, Quality of Service (QoS) of cloud-based services can be tuned using different underlying cloud configurations and resources, e.g., number of threads, CPU and memory etc., which are shared, leased and priced as utilities. This benefit is fundamentally grounded by autoscaling: an automatic and elastic process that adapts cloud configurations on-demand according to time-varying workloads. This thesis proposes a holistic cloud autoscaling framework to effectively and seamlessly address existing challenges related to different logical aspects of autoscaling, including architecting autoscaling system, modelling the QoS of cloudbased service, determining the granularity of control and deciding trade-off autoscaling decisions. The framework takes advantages of the principles of self-awareness and the related algorithms to adaptively handle the dynamics, uncertainties, QoS interference and trade-offs on objectives that are exhibited in the cloud. The major benefit is that, by leveraging the framework, cloud autoscaling can be effectively achieved without heavy human analysis and design time knowledge. Through conducting various experiments using RUBiS benchmark and realistic workload on real cloud setting, this thesis evaluates the effectiveness of the framework based on various quality indicators and compared with other state-of-the-art approaches

Self-adaptive trade-off decision making for autoscaling cloud-based services

Elasticity in the cloud is often achieved by on-demand autoscaling. In such context, the goal is to optimize the Quality of Service (QoS) and cost objectives for the cloud-based services. However, the difficulty lies in the facts that these objectives, e.g., throughput and cost, can be naturally conflicted; and the QoS of cloud-based services often interfere due to the shared infrastructure in cloud. Consequently, dynamic and effective trade-off decision making of autoscaling in the cloud is necessary, yet challenging. In particular, it is even harder to achieve well-compromised trade-offs, where the decision largely improves the majority of the objectives; while causing relatively small degradations to others. In this paper, we present a self-adaptive decision making approach for autoscaling in the cloud. It is capable to adaptively produce autoscaling decisions that lead to well-compromised trade-offs without heavy human intervention. We leverage on ant colony inspired multi-objective optimization for searching and optimizing the trade-offs decisions, the result is then filtered by compromise-dominance, a mechanism that extracts the decisions with balanced improvements in the trade-offs. We experimentally compare our approach to four state-of-the-arts autoscaling approaches: rule, heuristic, randomized and multi-objective genetic algorithm based solutions. The results reveal the effectiveness of our approach over the others, including better quality of trade-offs and significantly smaller violation of the requirements

The handbook of engineering self-aware and self-expressive systems

When faced with the task of designing and implementing a new self-aware and self-expressive computing system, researchers and practitioners need a set of guidelines on how to use the concepts and foundations developed in the Engineering Proprioception in Computing Systems (EPiCS) project. This report provides such guidelines on how to design self-aware and self-expressive computing systems in a principled way. We have documented different categories of self-awareness and self-expression level using architectural patterns. We have also documented common architectural primitives, their possible candidate techniques and attributes for architecting self-aware and self-expressive systems. Drawing on the knowledge obtained from the previous investigations, we proposed a pattern driven methodology for engineering self-aware and self-expressive systems to assist in utilising the patterns and primitives during design. The methodology contains detailed guidance to make decisions with respect to the possible design alternatives, providing a systematic way to build self-aware and self-expressive systems. Then, we qualitatively and quantitatively evaluated the methodology using two case studies. The results reveal that our pattern driven methodology covers the main aspects of engineering self-aware and self-expressive systems, and that the resulted systems perform significantly better than the non-self-aware systems

DeepScaler: Holistic Autoscaling for Microservices Based on Spatiotemporal GNN with Adaptive Graph Learning

Autoscaling functions provide the foundation for achieving elasticity in the modern cloud computing paradigm. It enables dynamic provisioning or de-provisioning resources for cloud software services and applications without human intervention to adapt to workload fluctuations. However, autoscaling microservice is challenging due to various factors. In particular, complex, time-varying service dependencies are difficult to quantify accurately and can lead to cascading effects when allocating resources. This paper presents DeepScaler, a deep learning-based holistic autoscaling approach for microservices that focus on coping with service dependencies to optimize service-level agreements (SLA) assurance and cost efficiency. DeepScaler employs (i) an expectation-maximization-based learning method to adaptively generate affinity matrices revealing service dependencies and (ii) an attention-based graph convolutional network to extract spatio-temporal features of microservices by aggregating neighbors' information of graph-structural data. Thus DeepScaler can capture more potential service dependencies and accurately estimate the resource requirements of all services under dynamic workloads. It allows DeepScaler to reconfigure the resources of the interacting services simultaneously in one resource provisioning operation, avoiding the cascading effect caused by service dependencies. Experimental results demonstrate that our method implements a more effective autoscaling mechanism for microservice that not only allocates resources accurately but also adapts to dependencies changes, significantly reducing SLA violations by an average of 41% at lower costs.Comment: To be published in the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

Self-adaptive trade-off decision making for autoscaling cloud-based services

Elasticity in the cloud is often achieved by on-demand autoscaling. In such context, the goal is to optimize the Quality of Service (QoS) and cost objectives for the cloud-based services. However, the difficulty lies in the facts that these objectives, e.g., throughput and cost, can be naturally conflicted; and the QoS of cloud-based services often interfere due to the shared infrastructure in cloud. Consequently, dynamic and effective trade-off decision making of autoscaling in the cloud is necessary, yet challenging. In particular, it is even harder to achieve well-compromised trade-offs, where the decision largely improves the majority of the objectives; while causing relatively small degradations to others. In this paper, we present a self-adaptive decision making approach for autoscaling in the cloud. It is capable to adaptively produce autoscaling decisions that lead to well-compromised trade-offs without heavy human intervention. We leverage on ant colony inspired multi-objective optimization for searching and optimizing the trade-offs decisions, the result is then filtered by compromise-dominance, a mechanism that extracts the decisions with balanced improvements in the trade-offs. We experimentally compare our approach to four state-of-the-arts autoscaling approaches: rule, heuristic, randomized and multi-objective genetic algorithm based solutions. The results reveal the effectiveness of our approach over the others, including better quality of trade-offs and significantly smaller violation of the requirements

A study on performance measures for auto-scaling CPU-intensive containerized applications

Autoscaling of containers can leverage performance measures from the different layers of the computational stack. This paper investigate the problem of selecting the most appropriate performance measure to activate auto-scaling actions aiming at guaranteeing QoS constraints. First, the correlation between absolute and relative usage measures and how a resource allocation decision can be influenced by them is analyzed in different workload scenarios. Absolute and relative measures could assume quite different values. The former account for the actual utilization of resources in the host system, while the latter account for the share that each container has of the resources used. Then, the performance of a variant of Kubernetes’ auto-scaling algorithm, that transparently uses the absolute usage measures to scale-in/out containers, is evaluated through a wide set of experiments. Finally, a detailed analysis of the state-of-the-art is presented