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

Dependencies discovery and analysis in distributed systems (short paper)

The welfare of our daily life depends, even more, on the correct functioning of complex distributed applications. Moreover, new paradigms such as Service oriented computing and Cloud computing encourage the design of application realized coupling services running on different nodes of the same data center or distributed in a geographic fashion. Dependencies discovery and analysis (DDA) is core for the identification of critical and strategical assets an application depends on, and it is valid support to risk and impact analysis [10

Autonomic orchestration of containers: Problem definition and research challenges

Today, a new technology is going to change the way cloud platforms are designed and managed. This technology is called container. A container is a software environment where to install an application or application component and all the library dependencies, the binaries, and a basic configuration needed to run the application. The container technology promises to solve many cloud application issues, for example the application portability problem and the virtual machine performance overhead problem. The cloud industry is adopting the container technology both for internal usage and as commercial offering. However, we are far away from the maturity stage and there are still many research challenges to be solved. One of them is container orchestration, that make it possible to define how to select, deploy, monitor, and dynamically control the configuration of multi-container packaged applications in the cloud. This paper presents the problem of autonomic container orchestration, analyze the state of the art solutions and discuss open challenge

An Autonomic Legal-Rule Aware Cloud Service Broker

The ICT industry, and specifically critical sectors such as healthcare, transportation, energy and government require as mandatory the compliance of the ICT systems and services with legislation and regulation, as well as with standards. In the era of cloud computing, and particularly in a public cloud scenario, this law and regulation compliance management issue is exacerbated by the distributed nature of the system and by the limited control of the customer on the infrastructure/services. Also if the cloud industry is aware of this legislation/regulation compliance issue (e.g. the compliance program of Amazon, Google and Microsoft Azure), right now, there are no mechanism/architectures capable to check and to assure that the compliance is guaranteed during the whole life cycle of a cloud service, off-line and at run-time. In this paper we outline a reference architecture for the autonomic and legislation-aware cloud service broker and we propose a run-time linear programming based model that consider legal constraints and that perform service adaptation for the assurance of QoS and legal rule compliance

A Client-Aware Dispatching Algorithm for Web Clusters Providing Multiple Services

The typical Web cluster architecture consists of replicated back-end and Web servers, and a network Web switch that routes client requests among the nodes. In this paper, we propose a new scheduling policy, namely client-awarepolicy (CAP), for Web switches operating at layer-7 of the OSI protocol stack. Its goal is to improve load sharing in Web clusters that provide multiple services such as static, dynamic and secure information. CAP classies the clientrequestson the basis of their expected impact on main server resources, that is, network interface, CPU, disk. At run-time, CAP schedules client requests reaching the Web cluster with the goal of sharing all classes of services among the server nodes. We demonstrate through a large set of simulations and some prototype experiments that dispatching policies aiming to improve locality in server caches give best results for Web publishing sites providing static information and some simple database searches. When we consider Web sites providing also dynamic and secure services, CAP is more eective than state-of-the-art layer-7 Web switch policies. The proposed client-aware algorithm is also more robust than server-aware policies whose performance depends on optimal tuning of system parameters, veryhardtoachieveina highly dynamic system suchasaWeb site. Categories and Subject Descriptors C.2.4 [######## ############# ########]: Distributed Systems; C.4 [########### ## #######]: Design studies; H.3.5 [########### ####### ### #########]: Online Information Services|Web-based services General Terms Algorithms, Design, Performance Keywords Load balancing, Dispatching algorithms, Clusters Copyright is held by the author/owner. WWW10, May 1-5, 2001, Hong Kong. Copyright 2001 ACM 1-58113-348-0/01/0005 ...$5.00. 1

Distributed subscriptions clustering with limited knowledge sharing for content-based publish/subscribe systems

One of the main issues in content-based publish/subscribe (CBPS) systems is how to dynamically determine groups of similar subscriptions to be adopted for exploiting efficient multicast techniques while guaranteeing at the same time the expressiveness of the subscription scheme. In this work, we propose a distributed mechanism which aims at satisfying important requirements of CBPS systems, that are: i) to guarantee the expressiveness of the subscription languages typical of the content-based paradigm, ii) to exploit efficient events dissemination, iii) to maintain the system scalability in terms of nodes and subscriptions, iv) to start an adaptive system reconfiguration despite new incoming subscriptions. One of the main feature of the proposed mechanism is the use of the system state knowledge sharing by system nodes, with the goal of limiting the system overhead in terms of computing, bandwidth and storage resources. Through a set of simulations we demonstrate the efficiency of the proposed solution

Mechanisms for SLA provisioning in cloud-based service providers

A challenge in cloud resource management is to design self-adaptable solutions capable to react to unpredictable workload fluctuations and changing utility principles. This paper analyzes the problem from the perspective of an Application Service Provider (ASP) that uses a cloud infrastructure to achieve scalable provisioning of its services in the respect of QoS constraints. First we draw a taxonomy of IaaS provider and use the identified features to drive the design of four autonomic service management architectures differing on the degree of control an ASP have on the system. We implemented two of this solutions and related mechanism to test five different resource provisioning policies. The implemented testbed has been evaluated under a realistic workload based on Wikipedia access traces on Amazon EC2 platform. The experimental evaluation performed confirms that: the proposed policies are capable to properly dimension the system resources making the whole system self-adaptable respect to the workload fluctuation. Moreover, having full control over the resource management plan allow to save up to the 32% of resource allocation cost always in the respect of SLA constraints

A Cloud Service Broker with Legal-Rule Compliance Checking and Quality Assurance Capabilities

The ICT industry, and specifically critical sectors such as healthcare, transportation, energy and government require as mandatory the compliance of the ICT systems and services with legislation and regulation, as well as with standards. In the era of cloud computing, and particularly in a public cloud scenario, this compliance management issue is exacerbated by the distributed nature of the system and by the limited control of the customer on the infrastructure/services. Also if the cloud industry is aware of this legislation/regulation compliance issue (e.g. the compliance program of Amazon, Google and Microsoft Azure), right now, there are nor reference architectures neither mechanisms capable to check and to assure, off-line and at run-time, that the compliance is guaranteed during the whole life cycle of a cloud service. Cloud service brokerage can play an important role in law/regulation compliance management of cloud services. In this paper we propose a broker-based solution for the management of law/regulation compliance. In the specific first we define a reference architecture for a legislation-aware cloud service broker, and second we propose an autonomic manager that integrate the MAPE-K control loop with the LegEx framework for the management of the legal compliance checking lifecycle

Auto-scaling of containers: the impact of relative and absolute metrics

Today, The cloud industry is adopting the container technology both for internal usage and as commercial offering. The use of containers as base technology for large-scale systems opens many challenges in the area of resource management at run-Time. This paper addresses the problem of selecting the more appropriate performance metrics to activate auto-scaling actions. Specifically, we investigate the use of relative and absolute metrics. Results demonstrate that, for CPU intense workload, the use of absolute metrics enables more accurate scaling decisions. We propose and evaluate the performance of a new autoscaling algorithm that could reduce the response time of a factor between 0.66 and 0.5 compared to the actual Kubernetes' horizontal auto-scaling algorithm